resources: - authors: - Elisabeth Kugler name: 'Sharing Your Poster on Figshare: A Community Guide to How-To and Why' proficiency_level: novice tags: - Sharing - Research Data Management - include in DALIA type: - Blog Post url: https://focalplane.biologists.com/2023/07/26/sharing-your-poster-on-figshare/ uuid: 63067620-fda2-407d-97f4-2f7c78bd5f9c - authors: - Marcelo Zoccoler license: CC-BY-4.0 name: Running Deep-Learning Scripts in the BiA-PoL Omero Server proficiency_level: proficient tags: - Python - Artificial Intelligence - Bioimage Analysis - include in DALIA type: - Blog Post url: https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html uuid: 9d158030-2d9c-41f5-b119-2878a9186e60 - authors: - Robert Haase license: CC-BY-4.0 name: Browsing the Open Microscopy Image Data Resource with Python proficiency_level: competent tags: - OMERO - Python - include in DALIA type: - Blog Post url: https://biapol.github.io/blog/robert_haase/browsing_idr/readme.html uuid: 7871fe01-c733-4785-89c8-0a8021fbbe96 - authors: - Mara Lampert license: CC-BY-4.0 name: Getting started with Mambaforge and Python proficiency_level: novice tags: - Python - Conda - Mamba - include in DALIA type: - Blog Post url: https://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html uuid: 46c85dfd-9324-47ae-b2d8-87a6efd55e7b - authors: - Jennifer Waters name: Promoting Data Management at the Nikon Imaging Center and Cell Biology Microscopy Facility proficiency_level: novice tags: - Research Data Management - include in DALIA type: - Blog Post url: https://datamanagement.hms.harvard.edu/news/promoting-data-management-nikon-imaging-center-and-cell-biology-microscopy-facility uuid: a55de52a-b50c-4a9a-8e60-fd952a637bfe - authors: - Job Fermie name: Data handling in large-scale electron microscopy proficiency_level: novice tags: - Research Data Management - include in DALIA type: - Blog Post url: https://blog.delmic.com/data-handling-in-large-scale-electron-microscopy uuid: 7f976f41-06d4-4a44-8b4c-ef0fb9f542df - authors: - Mara Lampert name: Tracking in napari proficiency_level: advanced beginner tags: - Python - Napari - Bioimage Analysis - include in DALIA type: - Blog Post url: https://focalplane.biologists.com/2023/06/01/tracking-in-napari/ uuid: 70b60e86-ee10-4340-93f8-35f5e99c43bf - authors: - Mara Lampert name: Feature extraction in napari proficiency_level: advanced beginner tags: - Python - Napari - Bioimage Analysis - include in DALIA type: - Blog Post url: https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/ uuid: e8ec2083-7a24-4fea-97f2-20dfa600a943 - authors: - Mara Lampert name: Annotating 3D images in napari proficiency_level: advanced beginner tags: - Python - Napari - Bioimage Analysis - include in DALIA type: - Blog Post url: https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/ uuid: 75af3616-5048-48bc-ad0e-363b12207905 - authors: - Robert Haase license: CC-BY-4.0 name: Managing Scientific Python environments using Conda, Mamba and friends proficiency_level: novice tags: - Python - Conda - Mamba - include in DALIA type: - Blog Post url: https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/ uuid: 5ece191e-3202-4983-a655-28ce75f8574b - authors: - Mara Lampert name: Quality assurance of segmentation results proficiency_level: advanced beginner tags: - Python - Napari - Bioimage Analysis - include in DALIA type: - Blog Post url: https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/ uuid: 02965bee-7ce6-4969-ad6d-f85f76b61f05 - authors: - Mara Lampert name: Rescaling images and pixel (an)isotropy proficiency_level: advanced beginner tags: - Python - Napari - Bioimage Analysis - include in DALIA type: - Blog Post url: https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/ uuid: 2437b060-c216-4990-ae05-38a91b4730ff - authors: - Jens Wendt name: User friendly Image metadata annotation tool/workflow for OMERO proficiency_level: competent tags: - Metadata - Workflow - OMERO - include in DALIA type: - Forum Post url: https://forum.image.sc/t/user-friendly-image-metadata-annotation-tool-workflow-for-omero/87925/1 uuid: fa95f541-7c96-4e49-8e82-e496e5e9bc54 - authors: - Mara Lampert name: How to write a bug report proficiency_level: novice tags: - Github - Python - Science Communication - include in DALIA type: - Blog Post url: https://focalplane.biologists.com/2024/04/03/how-to-write-a-bug-report/ uuid: 3fd00ec8-90f0-40f9-8270-edbce0df0da7 - authors: - Mara Lampert name: Prompt Engineering in Bio-image Analysis proficiency_level: advanced beginner tags: - Python - Jupyter - Bioimage Analysis - Prompt Engineering - Biabob - include in DALIA type: - Blog Post url: https://focalplane.biologists.com/2024/07/18/prompt-engineering-in-bio-image-analysis/ uuid: 07956f26-ffe7-4fda-96b0-e78079a1c7e9 - name: TESS Event database tags: - Bioinformatics - exclude from DALIA type: - Collection - Event url: https://tess.elixir-europe.org/events uuid: a6b3ac56-043f-4920-a9c8-bd45111d115b - name: Global BioImaging Training Database tags: - Bioimage Analysis - exclude from DALIA type: - Collection - Event url: https://globalbioimaging.org/international-training-courses uuid: 8ef028a2-2b99-44f2-bcd4-3460c07f684a - name: OME Event Database tags: - OMERO - Research Data Management - exclude from DALIA type: - Collection - Event url: https://www.openmicroscopy.org/events/ uuid: 0faf043c-4b80-4d31-a14a-6733c5612d44 - event_date: September 10-15 2023 event_location: Heidelberg, Germany name: EMBO Practical Course Advanced methods in bioimage analysis proficiency_level: competent tags: - Bioimage Analysis - exclude from DALIA type: - Event url: https://www.embl.org/about/info/course-and-conference-office/events/bia23-01/ uuid: 8019473d-ecd7-4050-b81a-eec8853acb37 - name: PoL Bio-Image Analysis GPU Accelerated Image Analysis Training School proficiency_level: competent tags: - Bioimage Analysis - Python - include in DALIA type: - Event - Notebook url: https://biapol.github.io/PoL-BioImage-Analysis-TS-GPU-Accelerated-Image-Analysis/intro.html uuid: fa89910a-1d90-47ae-907f-1a446dfccc27 - name: PoL Bio-Image Analysis Early Career Bio-image Analysis Training School proficiency_level: advanced beginner tags: - Bioimage Analysis - Python - include in DALIA type: - Event - Notebook url: https://biapol.github.io/PoL-BioImage-Analysis-TS-Early-Career-Track/intro.html uuid: 96076993-1e61-4d60-8096-3e83f8fd5eba - name: Bring your own data workshops tags: - Bioimage Analysis - Research Data Management - exclude from DALIA type: - Workshop url: https://www.dtls.nl/fair-data/byod/ uuid: b1acb006-b359-4e73-abfb-c4471e5f238b - license: MIT name: Workshop-June2024-Madrid tags: - Bioimage Analysis - exclude from DALIA type: - Workshop - Collection url: https://github.com/bioimage-io/Workshop-June2024-Madrid uuid: 2a8f2eed-0cd9-4b70-8ded-535f1569f3f1 - name: Bio-image Analysis Workshop Taipei proficiency_level: novice tags: - Bioimage Analysis - include in DALIA type: - Workshop - Collection url: https://github.com/Koushouu/Bioimage-Analysis-Workshop-Taipei/ uuid: 958f7db0-3e37-4365-bec9-c9f4203908e8 - name: Bio-image Analysis Workshop Kioto and Taipei 23/24 proficiency_level: novice tags: - Bioimage Analysis - include in DALIA type: - Workshop - Collection url: https://github.com/Koushouu/Bioimage-Analysis-Workshop-23-24 uuid: 8c13a888-1b73-407c-9a7f-44071d30e508 - name: Bio-image Analysis ICOB 2023 proficiency_level: novice tags: - Bioimage Analysis - exclude from DALIA type: - Workshop - Collection url: https://github.com/WeiChenChu/Bioimage_Analysis_ICOB_2023 uuid: 281a3756-56a0-4d0e-9b04-d3937a92c336 - description: Starts Oct 16, 2024, 9:00 AM, Ends Oct 17, 2024, 5:00 PM name: 'Fit for OMERO: How imaging facilities and IT departments work together to enable RDM for bioimaging' proficiency_level: advanced beginner tags: - Bioimage Analysis - OMERO - Research Data Management - exclude from DALIA type: - Workshop url: https://doi.org/10.5281/zenodo.14013025 uuid: 3d360956-7e11-455e-9894-65817b7faf4c - license: CC-BY-4.0 name: RDM4Mic Presentations proficiency_level: advanced beginner tags: - Research Data Management - exclude from DALIA type: - Collection url: https://github.com/German-BioImaging/RDM4mic/tree/master/presentations uuid: 9a65470a-560c-46f8-88f5-6568714c9ea9 - license: ODC-BY-1.0 name: BioImage Informatics Index Training Materials tags: - Bioimage Analysis - exclude from DALIA type: - Collection url: https://biii.eu/training-material uuid: 260eb5bf-11c5-4d96-a234-9cc79041f1c7 - authors: - Robert Haase et al. license: - CC-BY-4.0 - BSD-3-CLAUSE name: BioImage Analysis Notebooks proficiency_level: advanced beginner tags: - Python - Bioimage Analysis - include in DALIA type: - Book - Notebook url: https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/intro.html uuid: c44c9890-0804-4430-90f4-2dca4317262b - authors: - Pete Bankhead license: CC-BY-4.0 name: Introduction to Bioimage Analysis proficiency_level: novice tags: - Python - Imagej - Bioimage Analysis - include in DALIA type: - Book - Notebook url: https://bioimagebook.github.io/index.html uuid: 77964697-4063-4d5c-a39c-27fd3e207017 - authors: - Robert Haase license: CC-BY-4.0 name: Generative artificial intelligence for bio-image analysis proficiency_level: competent tags: - Python - Bioimage Analysis - Artificial Intelligence - include in DALIA type: - Slides url: https://f1000research.com/slides/12-971 uuid: 2dc12287-0ad3-482e-b71f-a29c0557bb9a - license: ALL RIGHTS RESERVED name: MicroscopyDB type: - Collection url: https://microscopydb.io/ uuid: c513235e-203d-4521-b0fe-f3ca66db53e3 tags: - exclude from DALIA - license: CC-BY-4.0 name: OME Documentation proficiency_level: competent tags: - OMERO - include in DALIA type: - Documentation url: https://www.openmicroscopy.org/docs/ uuid: f3d7a01a-aabd-4ee3-8949-3410a68d902e - license: UNKNOWN name: OMERO documentation proficiency_level: competent tags: - OMERO - include in DALIA type: - Documentation url: https://omero.readthedocs.io/en/stable/ uuid: 5d1a63af-02e3-49f3-ae08-33b67eec9f2d - license: BSD-2-CLAUSE name: OMERO Guide proficiency_level: competent tags: - OMERO - include in DALIA type: - Collection url: https://omero-guides.readthedocs.io/en/latest/ uuid: 802c51a5-5249-40ae-99ba-fefd02627833 - license: BSD-2-CLAUSE name: OMERO walkthrough for facility managers proficiency_level: competent tags: - OMERO - include in DALIA type: - Document url: https://omero-guides.readthedocs.io/en/latest/example_facility_manager.html uuid: a629c2fb-c6fb-47ef-ae9c-0ea60c3e0b54 - license: BSD-2-CLAUSE name: OMERO walkthrough example proficiency_level: advanced beginner tags: - OMERO - include in DALIA type: - Document url: https://omero-guides.readthedocs.io/en/latest/example.html uuid: c96de2a2-7a2d-4859-98d4-19aaa3d1cb33 - license: CC-BY-4.0 name: Bio.tools database tags: - Bioinformatics - exclude from DALIA type: - Collection url: https://bio.tools/ uuid: ff1045b4-6ee3-43a7-a386-e7bbe8ca5a21 - description: List of training materials by the German BioImaging community provided by facilities, the scientific community, and companies. license: UNKNOWN name: GerBI Teaching Resources Link List type: - Collection url: https://gerbi-gmb.de/resources/teaching-resources/ uuid: afdee7a7-edaf-4aef-9cef-f7ea92874d82 tags: - exclude from DALIA - description: Research Data Management Information Portal in German license: PUBLIC DOMAIN name: Forschungsdaten.info tags: - Research Data Management - include in DALIA type: - Collection url: https://forschungsdaten.info/ uuid: b5b33e56-46b5-488b-ab76-0863a55fbba4 - description: Research Data Management Wiki in German license: CC-BY-4.0 name: Forschungsdaten.org tags: - Research Data Management - include in DALIA type: - Collection url: https://www.forschungsdaten.org/ uuid: 06254785-a83d-4c69-bc76-68335c2900be - authors: - Katarzyna Biernacka - Maik Bierwirth - Petra Buchholz - Dominika Dolzycka - Kerstin Helbig - Janna Neumann - Carolin Odebrecht - Cord Wiljes - Ulrike Wuttke description: 'Within the project FDMentor, a German Train-the-Trainer Programme on Research Data Management (RDM) was developed and piloted in a series of workshops. The topics cover many aspects of research data management, such as data management plans and the publication of research data, as well as didactic units on learning concepts, workshop design and a range of didactic methods. After the end of the project, the concept was supplemented and updated by members of the Sub-Working Group Training/Further Education (UAG Schulungen/Fortbildungen) of the DINI/nestor Working Group Research Data (DINI/nestor-AG Forschungsdaten). The newly published English version of the Train-the-Trainer Concept contains the translated concept, the materials and all methods of the Train-the-Trainer Programme. Furthermore, additional English references and materials complement this version.' license: CC-BY-4.0 name: Train-the-Trainer Concept on Research Data Management num_downloads: 4568 publication_date: '2020-11-04' proficiency_level: competent tags: - Research Data Management - include in DALIA type: - Book url: - https://zenodo.org/record/4071471 - https://doi.org/10.5281/zenodo.4071471 uuid: 7c95a128-92f8-47b0-86e7-725f0301c44d language: en authors_with_orcid: - Katarzyna Biernacka https://orcid.org/0000-0002-6363-0064 - Maik Bierwirth https://orcid.org/0000-0003-1042-6702 - Petra Buchholz https://orcid.org/0000-0002-2401-1543 - Dominika Dolzycka https://orcid.org/0000-0002-6177-8815 - Kerstin Helbig https://orcid.org/0000-0002-2775-6751 - Janna Neumann https://orcid.org/0000-0002-0161-1888 - Carolin Odebrecht https://orcid.org/0000-0003-4887-7701 - Cord Wiljes https://orcid.org/0000-0003-2528-5391 - Ulrike Wuttke https://orcid.org/0000-0002-8217-4025 file_formats: .pdf * .zip - authors: - Katarzyna Biernacka - Katrin Cortez - Kerstin Helbig description: 'Researchers are increasingly often confronted with research data management (RDM) topics during their work. Higher education institutions therefore begin to offer services for RDM at some point to give support and advice. However, many groundbreaking decisions have to be made at the very beginning of RDM services. Priorities must be set and policies formulated. Likewise, the staff must first be qualified in order to provide advice and adequately deal with the manifold problems awaiting. The FDMentor project has therefore bundled the expertise of five German universities with different experiences and levels of RDM knowledge to jointly develop strategies, roadmaps, guidelines, and open access training material. Humboldt-Universität zu Berlin, Freie Universität Berlin, Technische Universität Berlin, University of Potsdam, and European University Viadrina Frankfurt (Oder) have worked together on common solutions that are easy to adapt. With funding of the German Federal Ministry of Education and Research, the collaborative project addressed four problem areas: strategy development, legal issues, policy development, and competence enhancement. The aim of the project outcomes is to provide other higher education institutions with the best possible support for the efficient introduction of research data management. Therefore, all project results are freely accessible under the CC-BY 4.0 international license. The early involvement of the community in the form of workshops and the collection of feedback has proven its worth: the FDMentor strategies, roadmaps, guidelines, and training materials are applied and adapted beyond the partner universities.' license: CC-BY-4.0 name: Efficiently starting institutional research data management num_downloads: 110 publication_date: '2019-10-15' proficiency_level: proficient tags: - Research Data Management - include in DALIA type: - Document url: - https://zenodo.org/record/3490058 - https://doi.org/10.5281/zenodo.3490058 uuid: ca9f9ec2-2ec6-421e-86b1-2b165a875d37 language: en authors_with_orcid: - Katarzyna Biernacka https://orcid.org/0000-0002-6363-0064 - Katrin Cortez - Kerstin Helbig https://orcid.org/0000-0002-2775-6751 file_formats: .pdf - license: CC-BY-4.0 name: RDMKit Training Resources tags: - Research Data Management - include in DALIA type: - Collection url: https://rdmkit.elixir-europe.org/all_training_resources uuid: 51f39451-ebee-414f-9db6-cfca415a7e27 - license: UNKNOWN name: TESS training materials tags: - Bioinformatics - exclude from DALIA type: - Collection url: https://tess.elixir-europe.org/materials uuid: 932c958c-2695-495a-9485-9d31966cacf5 - description: List of links to training materials by the I3D:bio community. license: UNKNOWN name: I3D:bio list of online training material tags: - Research Data Management - exclude from DALIA type: - Collection url: https://gerbi-gmb.de/i3dbio/i3dbio-teaching/train-mat/bioimagelist/ uuid: f0a4d52a-e64f-4a0a-80da-6e20eb848ee0 - description: CellProfiler tutorials and guided exercises about translocation, segmentation, pixel-based classification and quality control license: BSD-3-CLAUSE name: CellProfiler tutorials proficiency_level: advanced beginner tags: - Cellprofiler - Bioimage Analysis - include in DALIA type: - Notebook url: https://github.com/CellProfiler/tutorials uuid: 014895d2-b7d1-49c5-acb4-6a43f4228d10 - authors: - Mark Jenkinson - Jens Rittscher - Dominic Waithe description: This repository contains the materials for the University of Oxford DTC ONBI Image Analysis course. license: GPL-2.0 name: ONBI Image Analysis Course proficiency_level: advanced beginner tags: - Python - Bioimage Analysis - include in DALIA type: - Notebook url: https://github.com/dwaithe/ONBI_image_analysis uuid: b2dafe83-bb42-4476-9aa3-e30ff6ae5307 - authors: - Albert Cardona license: CC0-1.0 name: A Fiji Scripting Tutorial proficiency_level: competent tags: - Imagej - Bioimage Analysis - include in DALIA type: - Notebook url: https://syn.mrc-lmb.cam.ac.uk/acardona/fiji-tutorial/ uuid: 5ea2eb5d-55bc-4e16-a94d-6f89875d5bc1 - authors: - Robert Haase description: Slides, scripts, data and other exercise materials of the BioImage Analysis lecture at CMCB TU Dresden 2020 name: Lecture Applied Bioimage Analysis 2020 proficiency_level: advanced beginner tags: - Imagej - Bioimage Analysis - include in DALIA type: - Slides url: https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis uuid: 880bb639-4f2b-42a8-bc37-eaff433d4128 - authors: - Robert Haase license: BSD-3-CLAUSE name: ImageJ2 API-beating proficiency_level: competent tags: - Neubias - Imagej - Bioimage Analysis - include in DALIA type: - Slides url: https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev uuid: 02e325a3-a6e6-40c6-9f89-1a7aece80657 - authors: - Robert Haase description: Lecture slides of a session on Multiview Fusion in Fiji license: BSD-3-CLAUSE name: Multi-view fusion proficiency_level: advanced beginner tags: - Neubias - Imagej - Bioimage Analysis - include in DALIA type: - Slides url: https://git.mpi-cbg.de/rhaase/lecture_multiview_registration uuid: ad8e4b08-6d80-48b3-a0a3-0536b2ea9023 - authors: - Robert Haase description: Lecture slides of a session on Cell Tracking in Fiji license: BSD-3-CLAUSE name: Tracking Theory, TrackMate, and Mastodon proficiency_level: advanced beginner tags: - Neubias - Imagej - Bioimage Analysis - include in DALIA type: - Slides url: https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate uuid: 692a2590-5441-4f96-b1fc-313cfcdc1497 - authors: - Robert Haase license: BSD-3-CLAUSE name: Working with pixels proficiency_level: novice tags: - Neubias - Imagej - Bioimage Analysis - include in DALIA type: - Slides url: https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels uuid: e65a750c-e625-4ccf-9caf-7eed1daffbb5 - authors: - Robert Haase license: BSD-3-CLAUSE name: Working with objects in 2D and 3D proficiency_level: novice tags: - Neubias - Imagej - Bioimage Analysis - include in DALIA type: - Slides url: https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d uuid: 2e0663e0-8ccf-4245-9f41-1cf287a37432 - authors: - Guillaume Witz description: Series of Notebooks exposing how to do mostly basic and some advanced image processing using Python. It uses standard packages (Numpy, Maplotlib) and for the image processing parts is heavily based on the scikit-image package. license: MIT name: Image processing with Python proficiency_level: advanced beginner tags: - Python - include in DALIA type: - Notebook url: https://github.com/guiwitz/Python_image_processing uuid: eacf6731-f6d2-41cb-961d-d0b0d75e1b52 language: en - license: AGPL-3.0 name: Deep Learning Based Segmentation For Biologists proficiency_level: competent tags: - Python - R - Artificial Intelligence - include in DALIA type: - Notebook url: https://github.com/tpecot/DeepLearningBasedSegmentationForBiologists/ uuid: c1d233ac-00fe-42f4-a076-ebef684d9252 - authors: - Fabrizio Musacchio license: CC-BY-ND-SA-4.0 name: Bioimage analysis with Napari proficiency_level: advanced beginner tags: - Python - Napari - Bioimage Analysis - include in DALIA type: - Collection url: https://www.fabriziomusacchio.com/teaching/teaching_bioimage_analysis/ uuid: cb043ec9-b800-4c35-b721-940a911edabb - license: GPL-3.0 name: ZEN & Python workshop proficiency_level: competent tags: - Python - Napari - Bioimage Analysis - include in DALIA type: - Collection - Notebook url: https://github.com/zeissmicroscopy/ZEN_Python_OAD_workshop uuid: 933da51a-f479-4619-a872-2fef5e0d67b3 - authors: - Stefano Della Chiesa description: "This Research Data Management (RDM) Slides introduce to the multidisciplinary\ \ knowledge and competencies required to address policy compliance and research\ \ data management best practices throughout a project lifecycle, and beyond it.\n\ \n\n\tModule 1 - Introduces the RDM giving its context in the Research Data Governance\n\ \tModule 2 - Illustrates the most important RDM policies and principles\n\tModule\ \ 3 - Provides the most relevant RDM knowledge bricks\n\tModule 4 - Discuss the\ \ Data Management Plans (DMPs), examples, templates and guidance\n\n\n " license: CC-BY-4.0 name: Research Data Management Seminar - Slides num_downloads: 1094 publication_date: '2022-05-18' proficiency_level: advanced beginner tags: - Research Data Management - include in DALIA type: - Slides url: - https://zenodo.org/record/6602101 - https://doi.org/10.5281/zenodo.6602101 uuid: 514b16b2-10bc-435d-9343-a372137934dc language: en authors_with_orcid: - Stefano Della Chiesa https://orcid.org/0000-0002-6693-2199 file_formats: .pdf - name: Lund Declaration on Maximising the Benefits of Research Data tags: - Research Data Management - exclude from DALIA type: - Document url: https://www.regeringen.se/contentassets/55e7d8fbf6df4a54ac56942b98d94e4f/lund-declaration-on-maximising-the-benefits-of-research-data-pa-engelska.pdf uuid: 38f9b348-7061-4eb7-842b-92b5d6c29b7c - authors: - Rebecca A. Senft - Barbara Diaz-Rohrer - Pina Colarusso - Lucy Swift - Nasim Jamali - Helena Jambor - Thomas Pengo - Craig Brideau - Paula Montero Llopis - Virginie Uhlmann - Jason Kirk - Kevin Andrew Gonzales - Peter Bankhead - Edward L. Evans III - Kevin W. Eliceiri - Beth A. Cimini license: BSD-3-CLAUSE name: A biologist’s guide to planning and performing quantitative bioimaging experiments proficiency_level: novice type: - Collection - Publication url: - https://doi.org/10.1371/journal.pbio.3002167 - https://www.bioimagingguide.org/ uuid: 58d5dfa5-45a7-40e8-ae79-d791dee8af98 tags: - include in DALIA - authors: - Vebjorn Ljosa - Katherine L Sokolnicki - Anne E Carpenter description: Broad Bioimage Benchmark Collection (BBBC) name: Annotated high-throughput microscopy image sets for validation type: - Collection - Data url: - https://www.nature.com/articles/nmeth.2083 - https://bbbc.broadinstitute.org/ uuid: 588a21e1-d159-4abb-92ad-6c492128c5e6 tags: - exclude from DALIA - description: A tutorial explaining how to make Github repositories citable by automatically creating DOIs using the Github-Zenodo integration. name: Making your project citable proficiency_level: advanced beginner tags: - Sharing - Citing - Research Data Management - include in DALIA type: - Tutorial url: https://coderefinery.github.io/github-without-command-line/doi/ uuid: 6861c629-6c45-4a15-9043-efcb5f261ba3 - description: How to make your Github repository citable by adding a citation.cff file. name: Software Citation with CITATION.cff proficiency_level: advanced beginner tags: - Sharing - Citing - Research Data Management - include in DALIA type: - Tutorial url: https://the-turing-way.netlify.app/communication/citable/citable-cff.html uuid: 07c3d63d-b99a-42e7-a8a7-236e02d30623 - description: A guide which covers topics related to skills, tools and best practices for research reproducibility. license: - CC-BY-4.0 - MIT name: 'The Turing Way: Guide for reproducible research' proficiency_level: competent type: - Book url: https://the-turing-way.netlify.app/reproducible-research/reproducible-research uuid: 6d58f50d-dbeb-4b51-9bac-9fa77475866b tags: - include in DALIA - authors: - Kota Miura license: ALL RIGHTS RESERVED name: Basics of Image Processing and Analysis proficiency_level: novice tags: - Bioimage Analysis - include in DALIA type: - Book url: https://github.com/miura/ij_textbook1/raw/76b51338e1f006c580b6f0f5cfc48fe02fba38d7/CMCIBasicCourse201102Bib.pdf uuid: 492433ba-7ea5-454c-913a-15c98539a7aa - license: CC-BY-4.0 name: Practical Guide to the International Alignment of Research Data Management - Extended Edition type: - Book url: - https://www.scienceeurope.org/our-resources/practical-guide-to-the-international-alignment-of-research-data-management/ - https://doi.org/10.5281/zenodo.4915861 uuid: 9d22ed68-d37b-46db-8e17-95ac37b04f36 tags: - include in DALIA - license: UNKNOWN name: Cell Tracking Challenge - 2D Datasets tags: - Bioimage Analysis - exclude from DALIA type: - Collection - Data url: http://celltrackingchallenge.net/2d-datasets/ uuid: 2d2f99b2-fbf9-494e-8e02-aa19e3b430b0 - license: UNKNOWN name: Cell Tracking Challenge - 3D Datasets tags: - Bioimage Analysis - exclude from DALIA type: - Collection - Data url: http://celltrackingchallenge.net/3d-datasets/ uuid: acd13bc4-3a1a-45e2-b457-679c058a3ed6 - description: A searchable database of resources for light and electron microscopists license: ALL RIGHTS RESERVED name: microlist type: - Collection url: https://www.microlist.org/ uuid: 0a02b3bf-2b99-4db1-8567-eb2be1087e4c tags: - exclude from DALIA - authors: - Robert Haase - Benoit Lombardot license: UNKNOWN name: Introduction to ImageJ macro programming, Scientific Computing Facility, MPI CBG Dresden proficiency_level: novice tags: - Imagej - Bioimage Analysis - include in DALIA type: - Slides url: https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01 uuid: 691c13b8-9ce1-434e-955d-d88e7bf87874 - authors: - Robert Haase - Benoit Lombardot license: CC-BY-NC-4.0 name: Introduction to Image Analysis with Fiji proficiency_level: novice tags: - Imagej - Fiji - Bioimage Analysis - include in DALIA url: https://github.com/mpicbg-scicomp/CourseIntroToIA uuid: 36fac73a-7148-4523-8163-490fecd9f9b8 - license: CC-BY-4.0 name: Image Analysis Training Resources proficiency_level: advanced beginner tags: - Neubias - Bioimage Analysis - include in DALIA type: - Book url: https://neubias.github.io/training-resources/ uuid: 83d02c21-b7f3-4689-80e8-4457e462078a - license: UNKNOWN name: Bioimage Model Zoo tags: - Bioimage Analysis - Artificial Intelligence - exclude from DALIA type: - Collection url: https://bioimage.io/ uuid: 79474541-c3e2-4be9-8f57-873477da271e - name: Bioimage Archive type: - Collection - Data - Publication url: - https://www.ebi.ac.uk/bioimage-archive/ - https://www.sciencedirect.com/science/article/abs/pii/S0022283622000791 uuid: eabb170a-0bbd-40f1-b5fa-1ad296d738c5 tags: - exclude from DALIA - name: Image Data Resources type: - Collection - Data - Publication url: - https://idr.openmicroscopy.org/ - https://www.nature.com/articles/nmeth.4326 uuid: 4d75a751-3c04-455f-aeff-16301adb434e tags: - exclude from DALIA - license: BSD-2-CLAUSE name: Omero Deployment examples proficiency_level: competent tags: - OMERO - include in DALIA type: - Collection url: https://github.com/ome/omero-deployment-examples uuid: a1a79a77-02df-4f6b-ac74-49cf1cd7ea24 - authors: - Jeremy Metz - Beatriz Serrano-Solano - Wei Ouyang description: BioEngine is a cloud infrastructure to run BioImage model zoo based workflows in the cloud. license: UNKNOWN name: BioEngine tags: - Artificial Intelligence - Workflow Engine - exclude from DALIA type: - Publication url: https://ai4life.eurobioimaging.eu/announcing-bioengine/ uuid: 6434cb60-b88b-4722-8cea-c95644c8fd9b - authors: - Kevin Yamauchi license: CC-BY-4.0 name: Making your package available on conda-forge proficiency_level: competent tags: - Deployment - Python - include in DALIA type: - Documentation url: https://kevinyamauchi.github.io/open-image-data/how_tos/conda_forge_packaging.html uuid: 03d8e770-6671-4408-8b7a-7eae1cc937b8 - authors: - Caterina Fuster-Barceló license: UNKNOWN name: AI4Life teams up with Galaxy Training Network (GTN) to enhance training resources tags: - Artificial Intelligence - Workflow Engine - Bioimage Analysis - exclude from DALIA type: - Documentation url: https://ai4life.eurobioimaging.eu/ai4life-teams-up-with-galaxy-training-network-gtn-to-enhance-training-resources/ uuid: ebdd6ada-ee19-4178-8569-90c65a4a386a - authors: - Christian Schmidt - Michele Bortolomeazzi - Tom Boissonnet - Carsten Fortmann-Grote - Julia Dohle - Peter Zentis - Niraj Kandpal - Susanne Kunis - Thomas Zobel - Stefanie Weidtkamp-Peters - Elisa Ferrando-May description: The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers' daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community. license: CC-BY-4.0 name: 'I3D:bio''s OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training' num_downloads: 3717 publication_date: '2023-11-13' proficiency_level: advanced beginner tags: - OMERO - Research Data Management - Nfdi4Bioimage - I3Dbio - include in DALIA type: - Slides - Video url: - https://zenodo.org/records/8323588 - https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU - https://doi.org/10.5281/zenodo.8323588 uuid: b484d14c-2b7a-4bf8-a229-4a4a69973e14 language: en authors_with_orcid: - Christian Schmidt https://orcid.org/0000-0001-9568-895X - Michele Bortolomeazzi https://orcid.org/0000-0001-5805-5774 - Tom Boissonnet https://orcid.org/0000-0002-3328-9467 - Carsten Fortmann-Grote https://orcid.org/0000-0002-2579-5546 - Julia Dohle - Peter Zentis https://orcid.org/0000-0002-6999-132X - Niraj Kandpal https://orcid.org/0009-0007-5101-4786 - Susanne Kunis https://orcid.org/0000-0001-6523-7496 - Thomas Zobel https://orcid.org/0000-0002-2101-8416 - Stefanie Weidtkamp-Peters https://orcid.org/0000-0001-7734-3771 - Elisa Ferrando-May https://orcid.org/0000-0002-5567-8690 file_formats: .odp * .pdf * .pptx - license: APACHE-2.0 name: ITKElastix Examples proficiency_level: competent tags: - Bioimage Analysis - exclude from DALIA url: https://github.com/InsightSoftwareConsortium/ITKElastix/tree/main/examples uuid: a545a3a1-9b11-4a69-9722-d1ff4ef92f88 - license: ALL RIGHTS RESERVED name: 'EPFLx: Image Processing and Analysis for Life Scientists' proficiency_level: competent tags: - Bioimage Analysis - exclude from DALIA type: - Online Tutorial url: https://www.edx.org/learn/image-analysis/ecole-polytechnique-federale-de-lausanne-image-processing-and-analysis-for-life-scientists uuid: 7b6940c5-dd63-4316-96e0-7d5851c78603 - authors: - Ann Wheeler (Editor) - Ricardo Henriques (Editor) name: 'Standard and Super-Resolution Bioimaging Data Analysis: A Primer' type: - Book url: https://www.wiley.com/en-us/Standard+and+Super+Resolution+Bioimaging+Data+Analysis%3A+A+Primer-p-9781119096900 uuid: e79b7717-a832-45cb-81fd-9613fd24105e tags: - exclude from DALIA - name: MorphoLibJ documentation proficiency_level: advanced beginner tags: - Bioimage Analysis - exclude from DALIA type: - Document url: https://imagej.net/plugins/morpholibj uuid: a46a7d93-ad61-45f0-abab-436e21dc0bb3 - authors: - Aurelien Barbotin - Chas Nelson - Dominic Waithe - Ola (Alexandra) Tarkowska - Mikolaj Kundegorski - Stephen Cross - Todd Fallesen license: GPL-3.0 name: IAFIG-RMS Python for Bioimage Analysis Course proficiency_level: advanced beginner tags: - Bioimage Analysis - include in DALIA type: - Notebook url: https://github.com/RMS-DAIM/Python-for-Bioimage-Analysis uuid: 31f8874f-6c53-4868-aef4-f9f07a041bf2 - name: Center for Microscopy and Image Analysis How-to Guides type: - Collection url: https://zmb.dozuki.com/c/Image_Analysis uuid: 996b1a9a-d440-4539-b08a-697f78598fa3 tags: - include in DALIA - authors: - Guillaume Witz license: UNKNOWN name: Fundamentals in digital image processing proficiency_level: advanced beginner tags: - Bioimage Analysis - include in DALIA type: - Notebook url: https://github.com/guiwitz/Fundamentals_image_processing uuid: 0f97350b-1251-466b-840b-2ae82b789bb2 - authors: - Guillaume Witz license: BSD-3-CLAUSE name: numpy pandas course proficiency_level: advanced beginner tags: - Python - include in DALIA type: - Notebook url: https://github.com/guiwitz/NumpyPandas_course uuid: 0bb87e8f-6990-4bb2-8aeb-cb2f0ced37e0 - license: BSD-3-CLAUSE name: 'NEUBIAS Academy @HOME: Interactive Bioimage Analysis with Python and Jupyter' proficiency_level: advanced beginner tags: - Python - Neubias - Bioimage Analysis - include in DALIA type: - Notebook url: https://github.com/guiwitz/neubias_academy_biapy uuid: 991174dc-ceae-4add-baee-83924ec8894f - authors: - Sreenivas Bhattiprolu license: MIT name: Python for Microscopists proficiency_level: novice tags: - Python - Bioimage Analysis - include in DALIA type: - Notebook - Collection url: https://github.com/bnsreenu/python_for_microscopists uuid: 5209a5d3-7c91-4919-b687-f96f7603ee87 - authors: - Volker Hilsenstein license: UNKNOWN name: Setting up a remote desktop to use Napari in a browser proficiency_level: competent type: - Tutorial url: https://github.com/VolkerH/Jupyter-Napari-Desktop uuid: 311cefa6-ab1b-46cc-88ff-8940601aac1e tags: - include in DALIA - authors: - Nicolas P. Rougier license: CC-BY-ND-SA-4.0 name: 'Scientific Visualization: Python + Matplotlib' proficiency_level: advanced beginner tags: - Python - include in DALIA type: - Book url: - https://github.com/rougier/scientific-visualization-book - https://inria.hal.science/hal-03427242/document uuid: dd2fd9c2-1af9-46e3-82b3-04e73c2d1fac - authors: - Rafael Camacho license: MIT name: Teaching Bioimage Analysis with Python proficiency_level: advanced beginner tags: - Python - Bioimage Analysis - include in DALIA type: - Tutorial url: https://github.com/CamachoDejay/teaching-bioimage-analysis-python uuid: 3e106024-1024-4bd2-9f7b-b22f2801124f - authors: - Rafael Camacho license: MIT name: Teaching ImageJ FIJI proficiency_level: advanced beginner tags: - Fiji - Bioimage Analysis - include in DALIA type: - Tutorial url: https://github.com/CamachoDejay/Teaching-ImageJ-FIJI uuid: 5277ef00-aa5d-4a91-9b8a-c7a1ff93df4c - authors: - Juan Nunez-Iglesias license: BSD-3-CLAUSE name: Fundamentals of image analysis in Python with scikit-image, napari, and friends proficiency_level: advanced beginner tags: - Python - Bioimage Analysis - include in DALIA type: - Notebook url: https://github.com/jni/halfway-to-i2k-skimage-napari uuid: 36b37b3c-9e09-455a-9233-ea79d1b7aeee - authors: - Juan Nunez-Iglesias license: BSD-3-CLAUSE name: Image analysis and visualization in Python with scikit-image, napari, and friends proficiency_level: advanced beginner tags: - Python - Bioimage Analysis - include in DALIA type: - Notebook url: https://github.com/scipy-2023-image-analysis/tutorial uuid: a9f682e1-2300-4c67-b1ec-453b2cf55e4b - authors: - Guillaume Witz license: UNKNOWN name: Dask Course proficiency_level: competent tags: - Python - Bioimage Analysis - Big Data - exclude from DALIA type: - Notebook url: https://github.com/guiwitz/DaskCourse uuid: 469e1270-d1d2-4332-b1f9-fca3d2397080 - authors: - Guillaume Witz license: UNKNOWN name: Course on Deep Learning for imaging using PyTorch proficiency_level: competent tags: - Python - Bioimage Analysis - Artificial Intelligence - include in DALIA type: - Notebook url: https://github.com/guiwitz/DLImaging uuid: 03783b99-e02f-4504-8933-e541819ff351 - authors: - Guillaume Witz license: UNKNOWN name: 2022 MIC Workshop on Bioimage processing with Python proficiency_level: advanced beginner tags: - Python - Bioimage Analysis - include in DALIA type: - Notebook url: https://github.com/guiwitz/MICPy_Workshop_2022 uuid: e93d9abf-9b53-4ad1-819e-69753d3b778c - authors: - Curtis Rueden - Florian Levet - J.B. Sibarta - Alexandre Dafour - Daniel Sage - Sebastien Tosi - Michal Kozubek - Jean-Yves Tinevez - Kota Miura - et al. license: UNKNOWN name: NEUBIAS Bioimage Analyst Course 2017 proficiency_level: competent tags: - Neubias - Bioimage Analysis - include in DALIA type: - Slides - Tutorial url: https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017 uuid: 9b197c90-5ab9-4a8c-a6bd-fb36a1739a59 - authors: - Stephen Royle description: First complete code set for The Digital Cell book. license: GPL-3.0 name: 'quantixed/TheDigitalCell: First complete code set' num_downloads: 104 publication_date: '2019-04-17' proficiency_level: advanced beginner tags: - Bioimage Analysis - exclude from DALIA type: - Code url: - https://github.com/quantixed/TheDigitalCell - https://zenodo.org/records/2643411 - https://doi.org/10.5281/zenodo.2643411 uuid: 3d68b98a-86f3-43fd-a67c-cfcee8d5c209 authors_with_orcid: - Stephen Royle file_formats: .zip - authors: - Stephen Royle name: 'The Digital Cell: Cell Biology as a Data Science' proficiency_level: advanced beginner tags: - Bioimage Analysis - include in DALIA type: - Book url: https://cshlpress.com/default.tpl?cart=1700309488232283050&fromlink=T&linkaction=full&linksortby=oop_title&--eqSKUdatarq=1282 uuid: c230f98b-8e62-4785-902f-2eafbe149483 - authors: - Marion Louveaux - Stéphane Verger - Arianne Bercowsky Rama - Ignacio Arganda-Carreras - Estibaliz Gómez-de-Mariscal - Kota Miura - et al. license: UNKNOWN name: NEUBIAS Bioimage Analyst School 2020 proficiency_level: competent tags: - Neubias - Bioimage Analysis - include in DALIA type: - Slides - Code - Notebook url: https://github.com/miura/NEUBIAS_AnalystSchool2020 uuid: cb081465-ed90-47c6-8178-662332183a06 - authors: - Kota Miura - Chong Zhang - Jean-Yves Tinevez - Robert Haase - Julius Hossein - Pejamn Rasti - David Rousseau - Ignacio Arganda-Carreras - Siân Culley - et al. license: UNKNOWN name: NEUBIAS Bioimage Analyst School 2019 proficiency_level: competent tags: - Neubias - Bioimage Analysis - include in DALIA type: - Slides - Code - Notebook url: https://github.com/miura/NEUBIAS_AnalystSchool2019 uuid: 703d77c3-0221-4a60-9c3d-1e4e7cf073aa - authors: - Assaf Zaritsky - Csaba Molnar - Vasja Urbancic - Richard Butler - Anna Kreshuk - Vannary Meas-Yedid license: UNKNOWN name: NEUBIAS Analyst School 2018 proficiency_level: competent tags: - Neubias - Bioimage Analysis - include in DALIA type: - Slides - Code - Notebook url: https://github.com/miura/NEUBIAS_AnalystSchool2018 uuid: 4008f880-16d8-43bf-9678-bc31eab779b0 - authors: - Marion Louveaux license: UNKNOWN name: Scripts_FilopodyanR - a case study for the NEUBIAS TS7 in Szeged proficiency_level: competent tags: - Neubias - Bioimage Analysis - exclude from DALIA type: - Code url: https://github.com/marionlouveaux/NEUBIAS2018_TS7/ uuid: 31a46fab-6dc8-426c-b2bd-90211d769168 - authors: - Kota Miura license: UNKNOWN name: 'EuBIAS course 2013: Intensity Dynamics at the Periphery of Nucleus' publication_date: 2013 proficiency_level: advanced beginner tags: - Neubias - Bioimage Analysis - include in DALIA type: - Tutorial - Book url: https://github.com/miura/BIAS_Nucleus_Segmentation/blob/master/module9.pdf uuid: 18268fdb-c64f-4512-a360-d485b8163505 - authors: - Kota Miura - Christoph Schiklenk - Clemens Lakner - Christian Tischer - Aliaksandr Halavatyi license: UNKNOWN name: Analysis of High-Throughput Microscopy Image Data proficiency_level: novice publication_date: 2014 tags: - Bioimage Analysis - include in DALIA url: https://github.com/miura/HTManalysisCourse/blob/master/CentreCourseProtocol.md uuid: d389d6ec-2a3e-4743-9b4d-470e30a5e175 - authors: - Christian Tischer license: UNKNOWN name: Bio Image Analysis proficiency_level: novice type: - Slides url: https://github.com/tischi/presentation-image-analysis uuid: 1d327e7d-3a00-465c-81f2-8567fa78beb6 tags: - exclude from DALIA - authors: - Jonas Hartmann license: MIT name: Python BioImage Analysis Tutorial proficiency_level: advanced beginner tags: - Python - Bioimage Analysis - include in DALIA url: - https://github.com/WhoIsJack/python-bioimage-analysis-tutorial uuid: c5f91915-684c-4017-8717-a551a386a163 - authors: - Jonas Hartmann - et al. license: UNKNOWN name: Materials for EMBL Coding Club Mini-Tutorials proficiency_level: advanced beginner tags: - Python - exclude from DALIA type: - Code - Notebook url: - https://github.com/WhoIsJack/EMBL-CodingClub - https://bio-it.embl.de/Coding%20Club/Curated%20Tutorials/ uuid: 881ee5b6-9125-4ce1-ba2d-cf96b5fea5a1 - authors: - Karin Sasaki - Jonas Hartmann license: UNKNOWN name: Python Workshop - Image Processing proficiency_level: advanced beginner tags: - Python - include in DALIA type: - Code - Notebook url: https://github.com/karinsasaki/python-workshop-image-processing uuid: a734aebd-4e97-482e-940f-fe8c7e1c0f8c - authors: - Karin Sasaki - Aleksej Zelezniak license: UNKNOWN name: Metabolic networks modelling with COBRApy proficiency_level: competent tags: - Python - include in DALIA type: - Notebook url: https://github.com/karinsasaki/metabolic-networks-modelling uuid: 0b2c2d33-03c8-4188-8c64-2f3df3adb77d - authors: - Carolina Wählby - Maxime Bombrun - Christian Tischer license: UNKNOWN name: CellProfiler Practical NeuBIAS Lisbon 2017 proficiency_level: advanced beginner tags: - Neubias - Cellprofiler - Bioimage Analysis - include in DALIA type: - Tutorial url: https://github.com/tischi/cellprofiler-practical-NeuBIAS-Lisbon-2017 uuid: c5b25edf-186e-43ba-9139-38a6433dbadb - authors: - Constantin Pape - Christian Tischer license: UNKNOWN name: i2k-2020-s3-zarr-workshop proficiency_level: competent tags: - Python - Big Data - exclude from DALIA type: - Github repository url: https://github.com/tischi/i2k-2020-s3-zarr-workshop uuid: 3696a250-6faf-4ec0-a863-b6a5b62ae884 - authors: - Jacob Deppen - Kimberly Meechan - David Palmquist - Ulf Schiller - Robert Turner - Marianne Corvellec - Toby Hodges - et al. license: CC-BY-4.0 name: Image Processing with Python proficiency_level: advanced beginner tags: - Python - Bioimage Analysis - include in DALIA type: - Collection url: - https://datacarpentry.org/image-processing/ - https://github.com/datacarpentry/image-processing uuid: 13a24272-de3a-4a61-89af-0263b806ca45 - license: - CC-BY-4.0 - MIT name: Data Carpentry for Biologists proficiency_level: advanced beginner type: - Tutorial - Code url: https://datacarpentry.org/semester-biology/ uuid: ed803904-1475-44ac-8baf-f70f020bdfd8 tags: - include in DALIA - authors: - Valentyna Zinchenko - Pejman Rasti - Martin Weigert - Szymon Stoma license: UNKNOWN name: EMBL Deep Learning course 2019 exercises and materials proficiency_level: competent tags: - Python - Artificial Intelligence - include in DALIA type: - Notebook url: https://github.com/kreshuklab/teaching-dl-course-2019 uuid: 99013c30-2cd0-460b-80d4-e2a06191bfb2 - authors: - Martin Weigert - Uwe Schmidt - Benjamin Gallusser - Albert Dominguez Mantes - Buglakova Alyona license: UNKNOWN name: EMBL Deep Learning course 2023 exercises and materials proficiency_level: competent tags: - Python - Artificial Intelligence - include in DALIA type: - Notebook url: https://github.com/kreshuklab/teaching-dl-course-2023 uuid: 64924ce2-d22a-4acd-b997-1559259efc15 - authors: - Martin Weigert - Constantin Pape license: UNKNOWN name: EMBL Deep Learning course 2021/22 exercises and materials proficiency_level: competent tags: - Python - Artificial Intelligence - include in DALIA type: - Notebook url: https://github.com/kreshuklab/teaching-dl-course-2022 uuid: 0261fb9f-f9b0-42cd-9e6c-d058190d7bb8 - authors: - Victor Yurchenko - Fedor Ratnikov - Viktoriia Checkalina license: MIT name: Deep Vision and Graphics proficiency_level: competent tags: - Python - Artificial Intelligence - include in DALIA type: - Notebook url: https://github.com/yandexdataschool/deep_vision_and_graphics uuid: f533cf40-e2e0-4733-b287-053f2d459fa9 - authors: - Adrian Wolny - Johannes Hugger - Qin Yu - Buglakova Alyona license: UNKNOWN name: Kreshuk Lab's EMBL EIPP predoc course teaching material proficiency_level: advanced beginner tags: - Artificial Intelligence - include in DALIA type: - Tutorial url: https://github.com/kreshuklab/predoc-course uuid: e4201532-5a30-4c42-8954-53a94ae90471 - authors: - Wei Ouyang - et al. license: UNKNOWN name: Models and Applications for BioImage.IO url: https://github.com/imjoy-team/bioimage-io-resources uuid: 41ee7274-8f36-47b0-a10d-756958e8533d tags: - exclude from DALIA - authors: - Constantin Pape - Adrian Wolny license: UNKNOWN name: Training Deep Learning Models for Vision - Compact Course proficiency_level: competent tags: - Artificial Intelligence - Bioimage Analysis - include in DALIA url: https://github.com/constantinpape/training-deep-learning-models-for-vison uuid: a1083eed-99cd-4b4e-a5b2-1d8a1c8f919c - authors: - Constantin Pape license: MIT name: Collection of teaching material for deep learning for (biomedical) image analysis proficiency_level: competent tags: - Artificial Intelligence - Bioimage Analysis - include in DALIA url: https://github.com/constantinpape/dl-teaching-resources uuid: 054a206c-4d5a-4880-88f4-8f9a04c3b6a3 - license: UNKNOWN name: Nextflow Demo Pipelines for Image Processing type: - Code url: https://github.com/JaneliaSciComp/nf-demos uuid: b618291a-453d-4428-bf09-9b2c65e4f317 tags: - include in DALIA - license: UNKNOWN name: Source Control Using Git and GitHub proficiency_level: advanced beginner type: - Tutorial url: https://github.com/JaneliaSciComp/2020AprilGitCourse uuid: 874845c6-486b-4f0e-8e05-b259c4650d4f tags: - include in DALIA - authors: - Lucas von Chamier - Romain F. Laine - Johanna Jukkala - Christoph Spahn - Daniel Krentzel - Elias Nehme - Martina Lerche - Sara Hernández-pérez - Pieta Mattila - Eleni Karinou - Séamus Holden - Ahmet Can Solak - Alexander Krull - Tim-Oliver Buchholz - Martin L Jones - Loic Alain Royer - Christophe Leterrier - Yoav Shechtman - Florian Jug - Mike Heilemann - Guillaume Jacquemet - Ricardo Henriques license: MIT name: 'ZeroCostDL4Mic: exploiting Google Colab to develop a free and open-source toolbox for Deep-Learning in microscopy' proficiency_level: competent tags: - Bioimage Analysis - exclude from DALIA type: - Notebook - Collection url: - https://github.com/HenriquesLab/ZeroCostDL4Mic - https://www.nature.com/articles/s41467-021-22518-0 - https://doi.org/10.1038/s41467-021-22518-0 uuid: 9689fb33-5e3d-45d2-83be-ca2f6584942a - authors: - Iván Hidalgo - et al. license: CC-BY-4.0 name: DL4MicEverywhere proficiency_level: novice tags: - Bioimage Analysis - exclude from DALIA type: - Notebook - Collection url: https://github.com/HenriquesLab/DL4MicEverywhere uuid: 15192c60-b8fc-4059-9cb7-5c123ee550e2 - authors: - Guillaume Jacquemet license: MIT name: CellTrackColab proficiency_level: advanced beginner type: - Notebook - Collection url: - https://www.biorxiv.org/content/10.1101/2023.10.20.563252v2 - https://github.com/guijacquemet/CellTracksColab uuid: 8782d088-e5fe-44f6-9e21-6c437c32b6ef tags: - exclude from DALIA - authors: - Christian Tischer description: Training materials about image registration, big warp and elastix license: MIT name: Image analysis course material url: https://github.com/tischi/image-analysis-course-material uuid: 7edb6581-954d-4c70-80c9-fe027b4975ae tags: - include in DALIA - authors: - Guillaume Witz license: BSD-3-CLAUSE name: Image processing for beginners proficiency_level: advanced beginner tags: - Python - Bioimage Analysis - include in DALIA type: - Notebook url: https://github.com/guiwitz/PyImageCourse_beginner uuid: 7b1cf15d-8440-4b0e-aafd-d0ea2c0ca63c - authors: - Anna Klemm license: UNKNOWN name: ImageJ Macro Introduction proficiency_level: advanced beginner tags: - Neubias - Imagej Macro - Bioimage Analysis - include in DALIA type: - Slides - Code url: https://github.com/ahklemm/ImageJMacro_Introduction uuid: d052918e-e9f8-475f-b6b6-2a7fc4447024 - authors: - Anna Klemm license: UNKNOWN name: CellProfiler Introduction proficiency_level: novice tags: - Neubias - Cellprofiler - Bioimage Analysis - include in DALIA type: - Slides url: https://github.com/ahklemm/CellProfiler_Introduction uuid: e286ba5b-b087-4f35-9cb7-fcb0f993d208 - authors: - Martin Weigert license: UNKNOWN name: CARE/Stardist tutorials for EMBO Practical Course — Computational optical biology 2022 proficiency_level: competent tags: - Python - Artificial Intelligence - Bioimage Analysis - include in DALIA type: - Notebook url: https://github.com/maweigert/embo_2022 uuid: a3101dfa-acc4-487c-9c10-96995275867b - authors: - Martin Weigert - Olivier Burri - Siân Culley - Uwe Schmidt license: UNKNOWN name: 'Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDist' proficiency_level: advanced beginner tags: - Python - Neubias - Artificial Intelligence - Bioimage Analysis - include in DALIA type: - Slides - Notebook url: https://github.com/maweigert/neubias_academy_stardist uuid: 8ec01d2f-468d-4c5c-bf89-410fd75d8f92 - authors: - Martin Weigert - Uwe Schmidt license: UNKNOWN name: CSBDeep and StarDist @ I2K 2020 proficiency_level: advanced beginner tags: - Python - Artificial Intelligence - Bioimage Analysis - include in DALIA type: - Notebook url: https://github.com/maweigert/stardist-i2k uuid: 7960bd11-dea2-49cf-b00e-8e5a51af776d - authors: - Martin Weigert license: UNKNOWN name: Deep Learning for image analysis - Exercises proficiency_level: competent tags: - Fiji - Artificial Intelligence - Bioimage Analysis - include in DALIA type: - Notebook url: https://github.com/maweigert/zidas_2020_DL_intro_Part_2 uuid: 92e07e7e-df7c-4add-8a2e-f7d47e21f2e7 - license: UNKNOWN name: Machine Learning Workflow for Imaging Flow Cytometry (IFC) proficiency_level: competent tags: - Cellprofiler - Bioimage Analysis - include in DALIA type: - Tutorial url: https://github.com/holgerhennig/machine-learning-IFC uuid: f94d6762-62f1-4875-9789-eede5aa7ed2f - authors: - Beth Cimini - Tim Becker - Shantanu Singh - Gregory Way - Hamdah Abbasi - Callum Tromans-Coia license: CC0-1.0 name: Image-based Profiling Handbook proficiency_level: advanced beginner tags: - Bioimage Analysis - include in DALIA type: - Book url: - https://github.com/cytomining/profiling-handbook - https://cytomining.github.io/profiling-handbook/ uuid: a2ae9f82-f4b6-44e7-835b-49d666d5f182 - authors: - Michael Bornholdt - Juan Caicedo - Yu Han - Nikita Moshkov - Rebecca Senft license: UNKNOWN name: DeepProfiler Handbook proficiency_level: competent tags: - Artificial Intelligence - Bioimage Analysis - include in DALIA type: - Book url: - https://github.com/cytomining/DeepProfiler-handbook - https://cytomining.github.io/DeepProfiler-handbook/docs/00-welcome.html uuid: efc74311-32cd-4a49-a9dc-4b056ca11854 - authors: - Christian Tischer license: CC-BY-4.0 name: Methods in bioimage analysis proficiency_level: advanced beginner tags: - Bioimage Analysis - include in DALIA type: - Online Tutorial - Video - Slides url: - https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/ - https://doi.org/10.6019/TOL.BioImageAnalysis22-w.2022.00001.1 - https://drive.google.com/file/d/1MhuqfKhZcYu3bchWMqogIybKjamU5Msg/view uuid: 833fc420-c36d-40d5-ae54-109baf296816 - authors: - Estibaliz Gómez-de-Mariscal license: UNKNOWN name: Building a Bioimage Analysis Workflow using Deep Learning proficiency_level: competent tags: - Artificial Intelligence - Bioimage Analysis - include in DALIA type: - Slides url: https://github.com/esgomezm/NEUBIAS_chapter_DL_2020 uuid: c8662f47-c21f-4763-a8f0-6f2cfe753c09 - authors: - Estibaliz Gómez-de-Mariscal license: UNKNOWN name: ZIDAS 2020 Introduction to Deep Learning proficiency_level: advanced beginner tags: - Artificial Intelligence - Bioimage Analysis - include in DALIA type: - Slides url: https://github.com/esgomezm/zidas2020_intro_DL uuid: cb12e085-933a-4e9e-adff-16de114b6d77 - authors: - Estibaliz Gómez-de-Mariscal license: UNKNOWN name: Machine Learning - Deep Learning. Applications to Bioimage Analysis proficiency_level: competent tags: - Artificial Intelligence - Bioimage Analysis - include in DALIA type: - Slides url: https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf uuid: 18fa109b-798b-4ed7-a16c-41e00404c044 - authors: - Jan Funke - Constantin Pape - Morgan Schwartz - Xiaoyan license: UNKNOWN name: DL@MBL 2021 Exercises proficiency_level: competent tags: - Artificial Intelligence - Bioimage Analysis - exclude from DALIA type: - Slides - Notebook url: https://github.com/JLrumberger/DL-MBL-2021 uuid: 22cef898-4121-4fec-a4f9-ea482073ab20 - license: MIT name: Galaxy Training Material type: - Slides - Tutorial url: https://github.com/galaxyproject/training-material uuid: e683a2ff-a17d-4a56-96a9-2665ab6214a9 tags: - exclude from DALIA - authors: - Anna Kreshuk - Dominik Kutra license: CC-BY-4.0 name: 'ilastik: interactive machine learning for (bio)image analysis' proficiency_level: advanced beginner tags: - Artificial Intelligence - Bioimage Analysis - exclude from DALIA type: - Slides url: https://zenodo.org/doi/10.5281/zenodo.4330625 uuid: 7e9ae5a4-4591-4aa5-ae14-88606c00735f authors_with_orcid: - Anna Kreshuk - Dominik Kutra file_formats: .pptx - name: 'CZI: Open Science Program Collection' type: - Collection url: https://zenodo.org/communities/eoss uuid: e4ef53ff-b622-4aec-9420-47889d7ca7fd tags: - exclude from DALIA - authors: - Floden Evan - Di Tommaso Paolo description: Nextflow is an open-source workflow management system that prioritizes portability and reproducibility. It enables users to develop and seamlessly scale genomics workflows locally, on HPC clusters, or in major cloud providers’ infrastructures. Developed since 2014 and backed by a fast-growing community, the Nextflow ecosystem is made up of users and developers across academia, government and industry. It counts over 1M downloads and over 10K users worldwide. license: CC-BY-4.0 name: 'Nextflow: Scalable and reproducible scientific workflows' num_downloads: 72 publication_date: '2020-12-17' proficiency_level: competent tags: - Workflow Engine - exclude from DALIA type: - Slides url: - https://zenodo.org/records/4334697 - https://doi.org/10.5281/zenodo.4334697 uuid: 953f68b3-ce51-4053-a5f0-07c4a500e4a1 language: en authors_with_orcid: - Evan Floden https://orcid.org/0000-0002-5431-190X - Paolo Di Tommaso file_formats: .pdf - authors: - Peter Bankhead description: Slides from the CZI/EOSS online meeting in December 2020. license: CC-BY-4.0 name: 'QuPath: Open source software for analysing (awkward) images' num_downloads: 172 publication_date: '2020-12-16' tags: - Bioimage Analysis - include in DALIA type: - Slides url: - https://zenodo.org/records/4328911 - https://doi.org/10.5281/zenodo.4328911 uuid: 7bcacaba-18d1-4f67-a907-29d74f5205ce authors_with_orcid: - Peter Bankhead file_formats: .pdf - authors: - Kari Jordan - Zhian Kamvar - Toby Hodges description: In this interactive session, Carpentries team members will guide attendees through three stages of the backward design process to create a lesson development plan for the open source tool of their choosing. Attendees will leave having identified what practical skills they aim to teach (learning objectives), an approach for designing challenge questions (formative assessment), and mechanisms to give and receive feedback. license: CC-BY-4.0 name: Creating open computational curricula num_downloads: 31 publication_date: '2020-12-11' proficiency_level: competent type: - Slides url: - https://zenodo.org/records/4317149 - https://doi.org/10.5281/zenodo.4317149 uuid: e91bb0ba-5bcd-48c1-8f20-5537065a735e language: en authors_with_orcid: - Kari Jordan https://orcid.org/0000-0003-4121-2432 - Zhian Kamvar https://orcid.org/0000-0003-1458-7108 - Toby Hodges https://orcid.org/0000-0003-1766-456X file_formats: .pdf tags: - include in DALIA - authors: - Guillaume Witz license: UNKNOWN name: Jupyter for interactive cloud computing proficiency_level: advanced beginner tags: - Neubias - Bioimage Analysis - exclude from DALIA type: - Slides url: https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53 uuid: 274a3076-54fe-4700-826f-2b88e4527c8b - authors: - Aastha Mathur license: UNKNOWN name: 'Image Data Services at Euro-BioImaging: Community efforts towards FAIR Image Data and Analysis Services' type: - Slides url: https://docs.google.com/presentation/d/1henPIDTpHT3bc1Y26AltItAHJ2C5xCOl/edit#slide=id.p1 uuid: aac59f89-f407-43ec-9a94-aba1449fa447 tags: - exclude from DALIA - authors: - Kota Miura license: UNKNOWN name: What is Bioimage Analysis? An Introduction proficiency_level: novice tags: - Neubias - Bioimage Analysis - exclude from DALIA type: - Slides url: https://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0 uuid: 266548e6-c44c-41af-9a0b-1b506f474e35 - authors: - Bugra Oezdemir - Christian Tischer license: UNKNOWN name: OME-Zarr course proficiency_level: competent type: - Tutorial url: https://git.embl.de/oezdemir/course_scripts#ome-zarr-course uuid: e998d09e-06dd-431e-b907-478952aba42f tags: - exclude from DALIA - authors: - Ignacio Arganda-Carreras license: UNKNOWN name: 'Machine and Deep Learning on the cloud: Segmentation' proficiency_level: competent tags: - Neubias - Artificial Intelligence - Bioimage Analysis - exclude from DALIA type: - Slides url: https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54 uuid: 9b66855d-377e-4471-9f9b-923c4d724c79 - authors: - Robert Haase license: CC-BY-4.0 name: 'Parallelization and heterogeneous computing: from pure CPU to GPU-accelerated image processing' proficiency_level: competent type: - Slides url: - https://f1000research.com/slides/11-1171 - https://doi.org/10.7490/f1000research.1119154.1 uuid: aa32c73b-4e59-4a86-96e4-668011813392 tags: - include in DALIA - authors: - Beatriz Serrano-Solano - Björn Grüning license: UNKNOWN name: Image analysis in Galaxy proficiency_level: advanced beginner tags: - Bioimage Analysis - exclude from DALIA type: - Slides url: https://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p uuid: 96c61c90-5394-4b32-b6d1-db67979967e2 - authors: - Sébastien Tosi - Volker Baecker - Benjamin Pavie license: BSD-2-CLAUSE name: Adding a Workflow to BIAFLOWS proficiency_level: competent tags: - Neubias - Bioimage Analysis - exclude from DALIA type: - Slides url: https://github.com/RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022/blob/main/Slides/Adding_a_workflow_to_BIAFLOWS.pdf uuid: 205cebc7-f838-460c-ac8c-7e2f631fb5fa - authors: - Kota Miura license: ALL RIGHTS RESERVED name: BioImage Data Analysis proficiency_level: competent tags: - Neubias - Bioimage Analysis - include in DALIA type: - Book url: https://analyticalscience.wiley.com/do/10.1002/was.00050003/full/bioimagedataanalysis.pdf uuid: 48311b90-2c15-4295-9448-a2503bb2a47f - authors: - Herearii Metuarea - David Rousseau - Pejman Rasti - Valentin Gilet license: UNKNOWN name: 'DEEP NAPARI : Napari as a tool for deep learning project management' proficiency_level: competent tags: - Artificial Intelligence - Bioimage Analysis - exclude from DALIA type: - Notebook url: https://github.com/hereariim/DEEP-NAPARI uuid: e4cac440-cacc-40b1-8d25-f3e705a894f4 - authors: - Kevin Yamauchi license: CC-BY-4.0 name: Open Image Data Handbook proficiency_level: advanced beginner tags: - Neubias - Research Data Management - Napari - Python - Bioimage Analysis - include in DALIA type: - Book - Notebook url: https://kevinyamauchi.github.io/open-image-data/intro.html uuid: f29a9557-d93f-465a-bc4e-6593a21e7b7b - authors: - Anna Poetsch - Biotec Dresden - Marcelo Leomil Zoccoler - Johannes Richard Müller - Robert Haase license: CC-BY-4.0 name: Bio-image analysis, biostatistics, programming and machine learning for computational biology proficiency_level: advanced beginner tags: - Python - Bioimage Analysis - Napari - include in DALIA type: - Notebook url: https://github.com/BiAPoL/Bio-image_Analysis_with_Python uuid: b8b8d4b8-0985-4c11-87f1-758d4546095f - authors: - Cornelia Wetzker description: This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German 'Nationale Forschungsdateninfrastruktur' (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.   license: CC-BY-4.0 name: Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities num_downloads: 78 publication_date: '2023-11-08' proficiency_level: advanced beginner tags: - Research Data Management - Nfdi4Bioimage - include in DALIA type: - Slides url: - https://zenodo.org/records/10083555 - https://doi.org/10.5281/zenodo.10083555 uuid: aaedb56f-af30-4212-8e3e-256c4d39efbb language: en authors_with_orcid: - Cornelia Wetzker https://orcid.org/0000-0002-8367-5163 file_formats: .pdf * .pptx - authors: - Robert Haase description: Large Language Models (LLMs) change the way how we use computers. This also has impact on the bio-image analysis community. We can generate code that analyzes biomedical image data if we ask the right prompts. This talk outlines introduces basic principles, explains prompt engineering and how to apply it to bio-image analysis. We also compare how different LLM vendors perform on code generation tasks and which challenges are ahead for the bio-image analysis community. license: CC-BY-4.0 name: Bio-image Analysis with the Help of Large Language Models num_downloads: 306 publication_date: '2024-03-13' proficiency_level: advanced beginner tags: - artificial intelligence - Python - include in DALIA type: - Slides url: - https://zenodo.org/records/10815329 - https://doi.org/10.5281/zenodo.10815329 uuid: 884b9483-6292-4118-94b5-859319e4403f language: en authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .odp * .pdf * .pptx - authors: - Stephane Rigaud - Brian Northan - Till Korten - Neringa Jurenaite - Apurv Deepak Kulkarni - Peter Steinbach - Sebastian Starke - Johannes Soltwedel - Marvin Albert - Robert Haase description: This repository hosts notebooks, information and data for the GPU-Accelerated Image Analysis Track of the PoL Bio-Image Analysis Symposium. license: CC-BY-4.0 name: PoL Bio-Image Analysis Training School on GPU-Accelerated Image Analysis proficiency_level: competent tags: - Gpu - Clesperanto - Dask - Python - include in DALIA type: - Notebook url: https://github.com/BiAPoL/PoL-BioImage-Analysis-TS-GPU-Accelerated-Image-Analysis/ uuid: 8bce7f29-ee9f-4f5c-a631-cc55c67c92b2 - authors: - Robert Haase description: This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. license: CC-BY-4.0 name: Bio-image Data Science proficiency_level: advanced beginner tags: - Research Data Management - artificial intelligence - Bio-Image Analysis - Python - include in DALIA type: - Notebook url: https://github.com/ScaDS/BIDS-lecture-2024 uuid: 49ce8445-c356-4810-855f-cf53f5db59e3 - authors: - Costantin Pape description: This course consists of lectures and exercises that teach the background of deep learning for image analysis and show applications to classification and segmentation analysis problems. license: MIT name: Introduction to Deep Learning for Microscopy proficiency_level: competent tags: - artificial intelligence - Python - include in DALIA type: - Notebook url: https://github.com/computational-cell-analytics/dl-for-micro uuid: 89ef565d-f686-46cb-bed1-9f31bb04c351 - authors: - Marcelo Leomil Zoccoler description: In these lectures, we will explore ways to analyze microscopy images with Python and visualize them with napari, an nD viewer open-source software. The analysis will be done in Python mostly using the scikit-image, pyclesperanto and apoc libraries, via Jupyter notebooks. We will also explore some napari plugins as an interactive and convenient alternative way of performing these analysis, especially the napari-assistant, napari-apoc and napari-flim-phasor-plotter plugins. license: CC-BY-4.0 name: QM Course Lectures on Bio-Image Analysis with napari 2024 proficiency_level: advanced beginner tags: - Napari - Python - include in DALIA type: - Notebook url: https://zoccoler.github.io/QM_Course_Bio_Image_Analysis_with_napari_2024 uuid: 4dcff839-92fb-4670-a70a-0a1e428d3411 language: en - authors: - Beth Cimini - Florian Jug - QI 2024 description: This book contains the quantitative analysis labs for the QI CSHL course, 2024 license: CC-BY-4.0 name: QI 2024 Analysis Lab Manual proficiency_level: advanced beginner tags: - Python - include in DALIA type: - Notebook url: https://bethac07.github.io/qi_2024_analysis_lab_manual/intro.html uuid: 46116bbe-482d-4703-a6c5-943463dbc95b - authors: - Elnaz Fazeli description: In these slides I introducemy journey through teaching bioimage analysis courses in different formats, from in person courses to online material. I have an overview of different training formats and comparing these for different audiences.  license: CC-BY-4.0 name: My Journey Through Bioimage Analysis Teaching Methods From Classroom to Cloud num_downloads: 158 publication_date: '2024-02-19' proficiency_level: competent tags: - Teaching - include in DALIA type: - Slides url: - https://zenodo.org/records/10679054 - https://doi.org/10.5281/zenodo.10679054 uuid: 10a61037-89b7-415d-bd15-2e45a12538f9 language: en authors_with_orcid: - Elnaz Fazeli https://orcid.org/0000-0002-0770-0777 file_formats: .pdf - authors: - Robert Haase description: In these slides introduce current challenges and potential solutions for openly sharing training materials, softly focusing on bio-image analysis. In this field a lot of training materials circulate in private channels, but openly shared, reusable materials, according to the FAIR-principles, are still rare. Using the CC-BY license and publicly acessible repositories are proposed to fill this gap. license: CC-BY-4.0 name: Cultivating Open Training num_downloads: 185 publication_date: '2024-02-14' proficiency_level: advanced beginner tags: - Teaching - include in DALIA type: - Slides url: - https://zenodo.org/records/10654775 - https://doi.org/10.5281/zenodo.10654775 uuid: f912e555-fb94-497f-834b-ac06512fd13a language: en authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .pptx - authors: - Josh Moore description: Keynote at the NFDI4BIOIMAGE All-Hands Meeting in Düsseldorf, Germany, October 16, 2023. license: CC-BY-4.0 name: '[N4BI AHM] Welcome to BioImage Town' num_downloads: 96 publication_date: '2023-10-16' tags: - Research Data Management - exclude from DALIA type: - Slides url: - https://zenodo.org/records/10008465 - https://doi.org/10.5281/zenodo.10008465 uuid: df5a4693-6382-4a1b-b1b5-855423861b0d authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X file_formats: .pdf - authors: - Marvin Albert description: Tutorial material for teaching the basics of (itk-)elastix for image registration in microscopy images. license: BSD-3-CLAUSE name: Elastix tutorial proficiency_level: advanced beginner tags: - Image Registration - Itk - Elastix - include in DALIA type: - Notebook - Collection url: https://m-albert.github.io/elastix_tutorial/intro.html uuid: 2afdf6ac-474a-4161-a2b6-18cdc8896b01 - authors: - Peter Sobolewski description: Introduction to napari workshop run at JAX (Spring 2024). license: MIT name: Intro napari slides proficiency_level: advanced beginner tags: - Napari - include in DALIA type: - Slides url: https://thejacksonlaboratory.github.io/intro-napari-slides/#/section uuid: 8fa5a183-2be2-4ea1-a624-ed98538b4fa6 - authors: - Gaelle Letort description: Tutorial for running CellPose advanced functions license: BSD-3-CLAUSE name: NeubiasPasteur2023_AdvancedCellPose proficiency_level: competent tags: - bioimage analysis - artificial intelligence - include in DALIA type: - Github repository url: https://github.com/gletort/NeubiasPasteur2023_AdvancedCellPose uuid: a90f84a9-65c3-4b21-855c-14d89d0e5460 - authors: - Andrii Iudin - Anna Foix-Romero - Anna Kreshuk - Awais Athar - Beth Cimini - Dominik Kutra - Estibalis Gomez de Mariscal - Frances Wong - Guillaume Jacquemet - Kedar Narayan - Martin Weigert - Nodar Gogoberidze - Osman Salih - Petr Walczysko - Ryan Conrad - Simone Weyend - Sriram Sundar Somasundharam - Suganya Sivagurunathan - Ugis Sarkans description: 'The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023.' license: CC-BY-4.0 name: 'Microscopy data analysis: machine learning and the BioImage Archive' proficiency_level: competent tags: - BioImage Analysis - Python - artificial intelligence - include in DALIA type: - Video - Slides url: https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/ uuid: 084549a1-f7fb-4634-8b67-40ea1c10a432 language: en - authors: - Robert Haase description: 'Gemeinsames Arbeiten im Internet stellt uns vor neue Herausforderungen: Wer hat eine Datei wann hochgeladen? Wer hat zum Inhalt beigetragen? Wie kann man Inhalte zusammenfuehren, wenn mehrere Mitarbeiter gleichzeitig Aenderungen gemacht haben? Das Versionskontrollwerkzeug git stellt eine umfassende Loesung fuer solche Fragen bereit. Die Onlineplatform github.com stellt nicht nur Softwareentwicklern weltweit eine git-getriebene Platform zur Verfuegung und erlaubt ihnen effektiv zusammen zu arbeiten. In diesem Workshop lernen wir: Infuerung in FAIR-Prinzipien im Softwarecontext Arbeiten mit git: Pull-requests Aufloesen von Merge-Konflikten Automatisiertes Archivieren von Inhalten nach Zenodo.org Eigene Webseiten auf github.io publizieren ' license: CC-BY-4.0 name: Kollaboratives Arbeiten und Versionskontrolle mit Git num_downloads: 168 publication_date: '2024-04-15' proficiency_level: advanced beginner tags: - Research Data Management - FAIR-Principles - Git - Zenodo - include in DALIA type: - Slides url: - https://zenodo.org/records/10972692 - https://doi.org/10.5281/zenodo.10972692 uuid: 44b912cb-840e-4a72-8380-aef245c23c33 language: de authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .pdf * .pptx - authors: - Robert Haase description: Wir tauchen ein in die Welt der Open Science und definieren Begriffe wie Open Source, Open Access und die FAIR-Prinzipien (Findable, Accessible, Interoperable and Reuasable). Wir diskutieren, wie diese Methoden der [wissenschaftlichen] Kommunikation und des Datenmanagements die Welt verändern und wie wir sie praktisch in unsere Arbeit integrieren können. Dabei spielen Aspekte wie Copyright und Lizenzierung eine wichtige Rolle. license: CC-BY-4.0 name: Open Science, Sharing & Licensing num_downloads: 142 publication_date: '2024-04-18' proficiency_level: novice tags: - Research Data Management - Open Access - FAIR-Principles - Licensing - include in DALIA type: - Slides url: - https://zenodo.org/records/10990107 - https://doi.org/10.5281/zenodo.10990107 uuid: ce5881a4-a19c-4fb2-a72d-281cd50fd148 language: de authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .pdf * .pptx - authors: - Robert Haase description: In dieser Data Management Session wird der Lebenszyklus von Daten näher beleuchtet. Wie entstehen Daten, was passiert mit ihnen, wenn sie verarbeitet werden? Wem gehören die Daten und wer ist dafür verantwortlich, sie zu veröffentlichen, zu archivieren und gegebenenfalls wiederzuverwenden? Wir werden einen Datenmanagementplan in Gruppenarbeit entwerfen, ggf. mit Hilfe von ChatGPT. license: CC-BY-4.0 name: Datenmanagement num_downloads: 91 publication_date: '2024-04-14' proficiency_level: novice tags: - Research Data Management - include in DALIA type: - Slides url: - https://zenodo.org/records/10970869 - https://doi.org/10.5281/zenodo.10970869 uuid: 5f7e6519-a97d-4bca-8837-a67b531797f5 language: de authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .docx * .pdf * .pptx - authors: - Robert Haase description: ' These slides introduce current challenges and potential solutions for openly sharing training materials, focusing on bio-image analysis. In this field a lot of training materials circulate in private channels, but openly shared, reusable materials, according to the FAIR-principles, are still rare. Using the CC-BY license and publicly acessible repositories are proposed to fill this gap. ' license: CC-BY-4.0 name: Cultivating Open Training to advance Bio-image Analysis num_downloads: 88 publication_date: '2024-04-25' proficiency_level: advanced beginner tags: - Research Data Management - Licensing - FAIR-Principles - include in DALIA type: - Slides url: - https://zenodo.org/records/11066250 - https://doi.org/10.5281/zenodo.11066250 uuid: 3098ea69-e657-455c-ae07-f6130b0c5663 language: en authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .odp * .pdf * .pptx - authors: - Estibaliz Gómez de Mariscal description: Introduction to FAIR deep learning. Furthermore, tools to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo) are introduced. license: CC-BY-4.0 name: FAIRy deep-learning for bioImage analysis proficiency_level: competent tags: - artificial intelligence - FAIR-Principles - BioImage Analysis - include in DALIA type: - Slides url: https://f1000research.com/slides/13-147 uuid: c9b0b3fa-d2cc-4df8-9c2a-53af9c53ffde language: en - license: CC0-1.0 name: BioImage Archive AI Gallery tags: - Bioimage Analysis - artificial intelligence - exclude from DALIA type: - Collection - Data url: https://www.ebi.ac.uk/bioimage-archive/galleries/AI.html uuid: e48b9ff9-19a9-4c35-bf6a-7dba61ebb8ac - license: CC0-1.0 name: BioImage Archive Visual Gallery tags: - Bioimage Analysis - exclude from DALIA type: - Collection - Data url: https://www.ebi.ac.uk/bioimage-archive/galleries/visualisation.html uuid: 0bff795f-e05a-420b-8172-2d1684e6dcac - license: CC0-1.0 name: BioImage Archive Volume EM Gallery tags: - Bioimage Analysis - exclude from DALIA type: - Collection - Data url: https://www.ebi.ac.uk/bioimage-archive/galleries/vem.html uuid: 16fb6fa3-db69-44a8-a137-63ce8d75a0ea - authors: - Riccardo Massei description: 'Material and solutions for the course ''Bioimage data management and analysis with OMERO'' held in Heidelberg (13th May 2024) - Module 3 (1.45 pm - 3.45 pm): OMERO and Jupyter Notebooks. Main goal of the workflow is to show the potential of JN to perform reproducible image analysis in connection with an OMERO instance. In this specific example, we are performing a simple nuclei segmentation from raw images uploaded in OMERO.' license: MIT name: OMERO - HCS analysis pipeline using Jupyter Notebooks proficiency_level: advanced beginner tags: - Teaching - Bioimage Analysis - Notebooks - Python - OMERO - include in DALIA type: - Github repository url: https://github.com/rmassei/2024-jn-omero-pipeline uuid: c0ff9aca-cc00-4c5b-af52-ebc2a71f2c77 language: en - authors: - Anirban Ray description: Presentation given at the EMBO-DL4MIA 2024, Advanced Topic Seminar, May-11-2024 license: UNKNOWN name: Diffusion Models for Image Restoration - An Introduction proficiency_level: competent tags: - Bioimage Analysis - Diffusion Models - Tu Dresden - exclude from DALIA type: - Presentation url: https://drive.google.com/file/d/1pPVUUMi5w2Ojw_SaBzSQVaXUuIKtQ7Ma/view uuid: dfca9c56-e632-4b5e-b933-8d88123013ab - authors: - Isabel Kemmer - Euro-BioImaging ERIC description: 'Euro-BioImaging has developed a Data Management Plan (DMP) template with questions tailored to bioimaging research projects. Outlining data management practices in this way ensures traceability of project data, allowing for a continuous and unambiguous flow of information throughout the research project. This template can be used to satisfy the requirement to submit a DMP to certain funders. Regardless of the funder, Euro-BioImaging users are encouraged to provide a DMP and can use this template accordingly.  This DMP template is available as a fillable PDF with further instructions and sample responses available by hovering over the fillable fields. ' license: CC-BY-4.0 name: Euro-BioImaging's Template for Research Data Management Plans num_downloads: 131 publication_date: '2024-06-04' tags: - Bioimage Analysis - FAIR-Principles - Research Data Management - exclude from DALIA type: - Collection - Tutorial url: - https://zenodo.org/records/11473803 - https://doi.org/10.5281/zenodo.11473803 uuid: eb2f5660-3512-4885-953a-d482b37b2f6d language: en authors_with_orcid: - Isabel Kemmer https://orcid.org/0000-0002-8799-4671 - Euro-BioImaging ERIC file_formats: .pdf - authors: - Isabel Kemmer - Euro-BioImaging ERIC description: 'Hands-on exercises on FAIR Bioimage Data from the interactive online workshop "Euro-BioImaging''s Guide to FAIR BioImage Data 2024" (https://www.eurobioimaging.eu/news/a-guide-to-fair-bioimage-data-2024/).  Types of tasks included: FAIR characteristics of a real world dataset Data Management Plan (DMP) Journal Policies on FAIR data sharing Ontology search Metadata according to REMBI scheme (Image from: Sarkans, U., Chiu, W., Collinson, L. et al. REMBI: Recommended Metadata for Biological Images—enabling reuse of microscopy data in biology. Nat Methods 18, 1418–1422 (2021). https://doi.org/10.1038/s41592-021-01166-8) Matching datasets to bioimage repositories Browsing bioimage repositories' license: CC-BY-4.0 name: Euro-BioImaging's Guide to FAIR BioImage Data - Practical Tasks num_downloads: 104 publication_date: '2024-06-04' proficiency_level: advanced beginner tags: - Bioimage Analysis - FAIR-Principles - Research Data Management - include in DALIA type: - Slides - Tutorial url: - https://zenodo.org/records/11474407 - https://doi.org/10.5281/zenodo.11474407 uuid: f3192992-e891-46df-baf1-cb1de1769bf4 language: en authors_with_orcid: - Isabel Kemmer https://orcid.org/0000-0002-8799-4671 - Euro-BioImaging ERIC file_formats: .pdf - authors: - Marcelo Zoccoler - Simon Bekemeier - Tom Boissonnet - Simon Parker - Luca Bertinetti - Marc Gentzel - Riccardo Massei - Cornelia Wetzker description: 'The workshop introduced key topics of research data management (RDM) and the implementation thereof on a life science campus. Internal and external experts of RDM including scientists that apply chosen software tools presented the basic concepts and their implementation to a broad audience.  Talks covered general aspects of data handling and sorting, naming conventions, data storage repositories and archives, licensing of material, data and code management using git, data protection particularly regarding patient data and in genome sequencing and more. Two data management concepts and exemplary tools were highlighted in particular, being electronic lab notebooks with eLabFTW and the bio-image management software OMERO. Those were chosen because of three aspects: the large benefit of these management tools for a life science campus, their free availability as open source tools with the option of contribution of required functionalities and first existing use cases on campus already supported by CMCB/PoL IT. Two talks by Robert Haase (ScaDS.AI/ Uni Leipzig) and Robert Müller (Kontaktstelle Forschungsdaten, TU Dresden with contributions from Denise Dörfel) that opened the symposium were shared independently: https://zenodo.org/records/11382341 https://zenodo.org/records/11261115 The workshop organization was funded by the CMCB/PoL Networking Grant and supported by the consortium NFDI4BIOIMAGE (funded by DFG grant number NFDI 46/1, project number 501864659).' fingerprint: md5:907f0466d9ef6977dff5c271c5d8aaf0 license: CC-BY-4.0 name: 'From Paper to Pixels: Navigation through your Research Data - presentations of speakers' num_downloads: 544 publication_date: '2024-06-10' proficiency_level: advanced beginner tags: - Research Data Management - include in DALIA type: - Slides url: - https://zenodo.org/records/11548617 - https://doi.org/10.5281/zenodo.11548617 uuid: 5033c46d-a160-4471-96da-b22e6c72d17b language: en authors_with_orcid: - Marcelo Zoccoler https://orcid.org/0000-0002-6165-4679 - Simon Bekemeier https://orcid.org/0000-0001-8736-8796 - Tom Boissonnet https://orcid.org/0000-0002-3328-9467 - Simon Parker https://orcid.org/0000-0001-9993-533X - Luca Bertinetti https://orcid.org/0000-0002-4666-9610 - Marc Gentzel https://orcid.org/0000-0002-4482-6010 - Riccardo Massei https://orcid.org/0000-0003-2104-9519 - Cornelia Wetzker https://orcid.org/0000-0002-8367-5163 file_formats: .pdf * .pptx - authors: - Josh Moore - Andra Waagmeester - Kristina Hettne - Katherine Wolstencroft - Susanne Kunis description: In 2005, the first version of OMERO stored RDF natively. However, just a year after the 1.0 release of RDF, performance considerations led to the development of a more traditional SQL approach for OMERO. A binary protocol makes it possible to query and retrieve metadata but the resulting information cannot immediately be combined with other sources. This is the adventure of rediscovering the benefit of RDF triples as a -- if not the -- common exchange mechanism. license: CC-BY-4.0 name: RDF as a bridge to domain-platforms like OMERO, or There and back again. tags: - Research Data Management - FAIR-Principles - Bioimage Analysis - exclude from DALIA type: - Slides url: https://zenodo.org/doi/10.5281/zenodo.10687658 uuid: 1233807d-7249-4744-8968-51bb16b16c56 language: en authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X - Andra Waagmeester https://orcid.org/0000-0001-9773-4008 - Kristina Hettne https://orcid.org/0000-0002-4182-7560 - Katherine Wolstencroft https://orcid.org/0000-0002-1279-5133 - Susanne Kunis https://orcid.org/0000-0001-6523-7496 file_formats: .pdf - authors: - Riccardo Massei - Stefan Scholz - Wibke Busch - Thomas Schnike - Hannes Bohring - Jan Bumberger description: High-content screening (HCS) bioimaging automates the imaging and analysis of numerous biological samples, generating extensive metadata that is crucial for effective image management and interpretation. Efficiently handling this complex data is essential to implementing FAIR principles and realizing HCS's full potential for scientific discoveries. license: CC-BY-4.0 name: Developing (semi)automatic analysis pipelines and technological solutions for metadata annotation and management in high-content screening (HCS) bioimaging tags: - Bioimage Analysis - exclude from DALIA type: - Poster url: https://doi.org/10.5281/zenodo.8434325 uuid: 20e4daa4-3793-46b2-ae25-d02b2d303d71 language: en - authors: - Torsten Stöter - Tobias Gottschall - Andrea Schrader - Peter Zentis - Monica Valencia-Schneider - Niraj Kandpal - Werner Zuschratter - Astrid Schauss - Timo Dickscheid - Timo Mühlhaus - Dirk von Suchodoletz description: Interdisciplinary collaboration and integrating large, diverse datasets are crucial for answering complex research questions, requiring multimodal data analysis and adherence to FAIR principles. To address challenges in capturing the full research cycle and contextualizing data, DataPLANT developed the Annotated Research Context (ARC), while the neuroimaging community extended the Brain Imaging Data Structure (BIDS) for microscopic image data, both providing standardized, file system-based storage structures for organizing and sharing data with metadata. license: CC-BY-4.0 name: Combining the BIDS and ARC Directory Structures for Multimodal Research Data Organization proficiency_level: competent tags: - Research Data Management - FAIR-Principles - exclude from DALIA type: - Poster url: https://zenodo.org/doi/10.5281/zenodo.8349562 uuid: a1f88801-2dda-4ab9-a1d8-211f7c12d8cc language: en authors_with_orcid: - Torsten Stöter - Tobias Gottschall https://orcid.org/0000-0003-3001-1491 - Andrea Schrader https://orcid.org/0000-0002-3879-7057 - Peter Zentis https://orcid.org/0000-0002-6999-132X - Monica Valencia-Schneider https://orcid.org/0000-0003-3430-2683 - Niraj Kandpal https://orcid.org/0009-0007-5101-4786 - Werner Zuschratter https://orcid.org/0000-0002-9845-6393 - Astrid Schauss https://orcid.org/0000-0002-6658-2192 - Timo Dickscheid https://orcid.org/0000-0002-9051-3701 - Timo Mühlhaus https://orcid.org/0000-0003-3925-6778 - Dirk von Suchodoletz https://orcid.org/0000-0002-4382-5104 file_formats: .pdf - authors: - Josh Moore description: For decades, the sharing of large N-dimensional datasets has posed issues across multiple domains. Interactively accessing terabyte-scale data has previously required significant server resources to properly prepare cropped or down-sampled representations on the fly. Now, a cloud-native chunked format easing this burden has been adopted in the bioimaging domain for standardization. The format — Zarr — is potentially of interest for other consortia and sections of NFDI. license: CC-BY-4.0 name: '[CORDI 2023] Zarr: A Cloud-Optimized Storage for Interactive Access of Large Arrays' proficiency_level: competent tags: - Research Data Management - Bioimage Analysis - Data Science - exclude from DALIA type: - Poster url: https://zenodo.org/doi/10.5281/zenodo.8340247 uuid: 35a7e9a6-14f0-40a9-ac8c-f1194ea2f1b1 language: en authors_with_orcid: - Joshua Allen Moore https://orcid.org/0000-0003-4028-811X file_formats: .pdf - authors: - Sarah Weischer - Jens Wendt - Thomas Zobel description: In this workshop a fully integrated data analysis solutions employing OMERO and commonly applied image analysis tools (e.g., CellProfiler, Fiji) using existing python interfaces (OMERO Python language bindings, ezOmero, Cellprofiler Python API) is presented. license: CC-BY-4.0 name: High throughput & automated data analysis and data management workflow with Cellprofiler and OMERO proficiency_level: competent tags: - OMERO - Data Analysis - Bioimage Analysis - include in DALIA type: - Collection url: https://zenodo.org/doi/10.5281/zenodo.8139353 uuid: 71149557-83a7-4005-996a-e3bfda299066 language: en authors_with_orcid: - Sarah Weischer https://orcid.org/0000-0001-7292-8308 - Jens Wendt - Thomas Zobel https://orcid.org/0000-0002-2101-8416 file_formats: .zip - authors: - Anca Margineanu - Chiara Stringari - Marcelo Zoccoler - Cornelia Wetzker description: 'The presentations introduce open-source software to read in, visualize and analyse fluorescence lifetime imaging microscopy (FLIM) raw data developed for life scientists. The slides were presented at German Bioimaging (GerBI) FLIM Workshop held February 26 to 29 2024 at the Biomedical Center of LMU München by Anca Margineanu, Chiara Stringari and Conni Wetzker. ' license: CC-BY-4.0 name: A Glimpse of the Open-Source FLIM Analysis Software Tools FLIMfit, FLUTE and napari-flim-phasor-plotter proficiency_level: advanced beginner tags: - Bioimage Analysis - Flim - include in DALIA type: - Slides url: https://zenodo.org/doi/10.5281/zenodo.10886749 uuid: 27a998d0-a946-41df-9775-12941574693e language: en authors_with_orcid: - Anca Margineanu https://orcid.org/0000-0002-6634-9729 - Chiara Stringari https://orcid.org/0000-0002-0550-7463 - Marcelo Zoccoler https://orcid.org/0000-0002-6165-4679 - Cornelia Wetzker https://orcid.org/0000-0002-8367-5163 file_formats: .pdf * .pptx - authors: - Riccardo Massei description: Results of the project 'Conversion of KNIME image analysis workflows to Galaxy' during the Hackathon 'Image Analysis in Galaxy' (Freiburg 26 Feb - 01 Mar 2024) license: CC-BY-4.0 name: Hackaton Results - Conversion of KNIME image analysis workflows to Galaxy tags: - Research Data Management - exclude from DALIA type: - Slides url: https://zenodo.org/doi/10.5281/zenodo.10793699 uuid: 69717b33-15d5-4ca1-8b66-11ffbfcb594d authors_with_orcid: - Riccardo Massei https://orcid.org/0000-0003-2104-9519 file_formats: .pptx - authors: - Constantin Pape description: 'Talks about Segment Anything for Microscopy: https://github.com/computational-cell-analytics/micro-sam. Currently contains slides for two talks: Overview of Segment Anythign for Microscopy given at the SWISSBIAS online meeting in April 2024 Talk about vision foundation models and Segment Anything for Microscopy given at Human Technopole as part of the EMBO Deep Learning Course in May 2024 ' license: CC-BY-4.0 name: MicroSam-Talks num_downloads: 34 publication_date: '2024-05-23' proficiency_level: competent tags: - Bioimage Analysis - artificial intelligence - exclude from DALIA type: - Slides url: - https://zenodo.org/records/11265038 - https://doi.org/10.5281/zenodo.11265038 uuid: d796f869-0939-4c08-8b17-303d9b4cad20 language: en authors_with_orcid: - Constantin Pape https://orcid.org/0000-0001-6562-7187 file_formats: .pdf * .pptx - authors: - Pia Voigt - Carolin Hundt description: ' Workshop zum Thema „Datenmanagement im Fokus: Organisation, Speicherstrategien und Datenschutz“ auf der Data Week Leipzig Der Umgang mit Daten ist im Alltag nicht immer leicht: Wie und wo speichert man Daten idealerweise? Welche Strategien helfen, den Überblick zu behalten und wie geht man mit personenbezogenen Daten um? Diese Fragen möchten wir gemeinsam mit Ihnen anhand individueller Datenprobleme besprechen und Ihnen Lösungen aufzeigen, wie Sie ihr Datenmanagement effizient gestalten können.' license: CC-BY-4.0 name: 'Datenmanagement im Fokus: Organisation, Speicherstrategien und Datenschutz' num_downloads: 48 publication_date: '2024-04-19' proficiency_level: advanced beginner tags: - Research Data Management - include in DALIA type: - Slides url: - https://zenodo.org/records/11107798 - https://doi.org/10.5281/zenodo.11107798 uuid: 813ff28c-bb80-4c9f-a2e5-03e58a7a47a5 language: de authors_with_orcid: - Pia Voigt https://orcid.org/0000-0001-9627-6252 - Carolin Hundt https://orcid.org/0000-0002-7237-965X file_formats: .pdf - authors: - Stefano Della Chiesa description: ' These slides were presented at the 2. SaxFDM-Beratungsstammtisch and delve into the strategic integration of Research Data Management (RDM) within research organizations. The Leibniz IOER presented an insightful overview of RDM activities and approaches, emphasizing the criticality of embedding RDM strategically within research institutions. The presentation showcases some best practices in RDM implementation through practical examples, offering valuable insights for optimizing data stewardship processes.' license: CC-BY-4.0 name: Sustainable Data Stewardship num_downloads: 38 publication_date: '2024-03-25' proficiency_level: advanced beginner tags: - Research Data Management - Data Stewardship - include in DALIA type: - Slides url: - https://zenodo.org/records/10942559 - https://doi.org/10.5281/zenodo.10942559 uuid: db291fd5-dd30-4c2a-b1a8-1d42f0986ed8 language: en authors_with_orcid: - Stefano Della Chiesa https://orcid.org/0000-0002-6693-2199 file_formats: .pdf - authors: - Robert Haase description: In this SaxFDM Digital Kitchen, I introduced current challenges and potential solutions for openly sharing training materials, softly focusing on bio-image analysis. In this field a lot of training materials circulate in private channels, but openly shared, reusable materials, according to the FAIR-principles, are still rare. Using the CC-BY license and uploading materials to publicly acessible repositories are proposed to fill this gap. license: CC-BY-4.0 name: Cultivating Open Training num_downloads: 62 publication_date: '2024-03-14' proficiency_level: advanced beginner tags: - Open Science - Research Data Management - FAIR-Principles - Bioimage Analysis - Licensing - include in DALIA type: - Slides url: - https://zenodo.org/records/10816895 - https://doi.org/10.5281/zenodo.10816895 uuid: f21196b0-c992-4e89-9121-29411702530e language: en authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .odp * .pdf * .pptx - authors: - Elfi Hesse - Jan-Christoph Deinert - Christian Löschen description: Die Online-Veranstaltung fand am 21.01.2021 im Rahmen der SaxFDM-Veranstaltungsreihe "Digital Kitchen - Küchengespräche mit SaxFDM" statt. SaxFDM-Sprecherin Elfi Hesse (HTW Dresden) erläuterte zunächst Grundsätzliches zum Thema Repositorien. Anschließend teilten Nutzer (Jan Deinert – HZDR) und Anbieter (Christian Löschen – TU Dresden/ZIH) lokaler Repositorien ihre Erfahrungen mit uns. license: CC-BY-4.0 name: '"ZENODO und Co." Was bringt und wer braucht ein Repositorium?' num_downloads: 280 publication_date: '2021-01-25' proficiency_level: advanced beginner tags: - Research Data Management - include in DALIA type: - Slides url: - https://zenodo.org/records/4461261 - https://doi.org/10.5281/zenodo.4461261 uuid: 55345762-a7c0-4c0b-838a-8e1fa6befa84 language: de authors_with_orcid: - Elfi Hesse https://orcid.org/0000-0001-5085-5048 - Jan-Christoph Deinert https://orcid.org/0000-0001-6211-0158 - Christian Löschen file_formats: .pdf - authors: - Stephan Wünsche description: 'Wem gehören Forschungsdaten? Diese Frage stellt sich bei Daten, an deren Entstehung mehrere Personen beteiligt waren, und besonders bei Textdaten, Bildern und Videos. Hier lernen Sie, für Ihr eigenes Forschungsvorhaben zu erkennen, wessen Urheber- und Leistungsschutzrechte zu berücksichtigen sind. Sie erfahren, wie Sie mit Hilfe von Vereinbarungen frühzeitig Rechtssicherheit herstellen, etwa um Daten weitergeben oder publizieren zu können.    ' license: CC-BY-4.0 name: Alles meins – oder!? Urheberrechte klären für Forschungsdaten num_downloads: 35 publication_date: '2024-06-04' proficiency_level: advanced beginner tags: - Research Data Management - Licensing - include in DALIA type: - Slides url: - https://zenodo.org/records/11472148 - https://doi.org/10.5281/zenodo.11472148 uuid: 5111b374-e337-434c-9801-e41f34e32821 language: de authors_with_orcid: - Stephan Wünsche https://orcid.org/0000-0001-9552-4402 file_formats: .pdf - authors: - Pia Voigt description: 'Der Umgang mit personenbezogenen Daten stellt Forschende oft vor rechtliche Herausforderungen: Unter welchen Bedingungen dürfen personenbezogene Daten verarbeitet werden? Welche Voraussetzungen müssen erfüllt sein und welche Strategien können angewendet werden, um Daten sicher speichern, verarbeiten, teilen und aufbewahren zu können? Mit Hilfe dieses Foliensatzes erhalten Sie Einblicke in datenschutzrechtliche Aspekte beim Umgang mit Ihren Forschungsdaten. ' license: CC-BY-4.0 name: So geschlossen wie nötig, so offen wie möglich - Datenschutz beim Umgang mit Forschungsdaten num_downloads: 30 publication_date: '2024-05-30' proficiency_level: advanced beginner tags: - Research Data Management - Data Protection - FAIR-Principles - include in DALIA type: - Slides url: - https://zenodo.org/records/11396199 - https://doi.org/10.5281/zenodo.11396199 uuid: 76dfd5a8-102d-4ec6-b9d5-c8f8936e768a language: de authors_with_orcid: - Pia Voigt https://orcid.org/0000-0001-9627-6252 file_formats: .pdf * .pptx - authors: - Stephan Wünsche - Pia Voigt description: 'Diese Präsentation wurde im Zuge der digitalen Veranstaltungsreihe "Einblicke ins Forschungsdatenmanagement" erstellt. Diese findet seit dem SS 2020 an der Universität Leipzig für alle Interessierten zu verschiedenen Themen des Forschungsdatenmanagements statt. Dieser Teil der Reihe dreht sich um Rechtsfragen im Umgang mit Forschungsdaten und deren Bedeutung für die wissenschaftliche Praxis. Sie finden in der vorliegenden Präsentation einen Überblick über relevante Rechtsbereiche sowie Erläuterungen zum Datenschutz, Urheberrecht und den Grundsätzen der guten wissenschaftlichen Praxis mit Fokus auf deren Bedeutung im Forschungsdatenmanagement.' license: CC-BY-4.0 name: Einblicke ins Forschungsdatenmanagement - Darf ich das veröffentlichen? Rechtsfragen im Umgang mit Forschungsdaten num_downloads: 243 publication_date: '2021-05-11' proficiency_level: advanced beginner tags: - Research Data Management - Data Protection - include in DALIA type: - Slides url: - https://zenodo.org/records/4748510 - https://doi.org/10.5281/zenodo.4748510 uuid: 9a4d8575-ba5e-4198-b6ed-9a9a10dfded0 language: de authors_with_orcid: - Stephan Wünsche https://orcid.org/0000-0001-9552-4402 - Pia Voigt https://orcid.org/0000-0001-9627-6252 file_formats: .pdf - authors: - Pia Voigt - Barbara Weiner description: 'Was ist ein Datenmanagementplan? Welche Vorgaben sollte ich beachten? Wie erstelle ich einen solchen für mein Forschungsprojekt und welche nützlichen Tools kann ich hierfür verwenden? Die Anforderungen der Forschungsförderer zum Datenmanagement steigen stetig. Damit verbunden ist häufig auch das Erstellen eines Datenmanagementplans. Dabei erwarten DFG, BMBF oder die EU jeweils unterschiedliche Angaben zur Erhebung, Speicherung und Veröffentlichung von projektbezogenen Forschungsdaten. Zudem bietet das Erstellen eines Datenmanagementplans viele Vorteile und hilft Ihnen nicht zuletzt, die Anforderungen der guten wissenschaftlichen Praxis strukturiert umzusetzen. Was im ersten Moment unübersichtlich und überfordernd wirkt, soll in diesem Kurs anhand einer grundlegenden theoretischen Einführung im ersten und praxisorientierter Beispiele im zweiten Teil der Veranstaltung handhabbar gemacht werden. Sie lernen, was hinter den Anforderungen der Forschungsförderer steckt, welche Elemente ein Datenmanagementplan enthalten sollte und wie sie einen solchen mithilfe interaktiver Tools selbst erstellen können.' license: CC-BY-4.0 name: Datenmanagementpläne erstellen - Teil 1 num_downloads: 340 publication_date: '2021-03-23' proficiency_level: advanced beginner tags: - Research Data Management - include in DALIA type: - Slides url: - https://zenodo.org/records/4630788 - https://doi.org/10.5281/zenodo.4630788 uuid: 0dcdd418-0c68-4464-8b7f-ed391118f632 language: de authors_with_orcid: - Pia Voigt https://orcid.org/0000-0001-9627-6252 - Barbara Weiner https://orcid.org/0000-0003-2747-8648 file_formats: .pdf - authors: - Pia Voigt - Barbara Weiner description: 'Was ist ein Datenmanagementplan? Welche Vorgaben sollte ich beachten? Wie erstelle ich einen solchen für mein Forschungsprojekt und welche nützlichen Tools kann ich hierfür verwenden? Die Anforderungen der Forschungsförderer zum Datenmanagement steigen stetig. Damit verbunden ist häufig auch das Erstellen eines Datenmanagementplans. Dabei erwarten DFG, BMBF oder die EU jeweils unterschiedliche Angaben zur Erhebung, Speicherung und Veröffentlichung von projektbezogenen Forschungsdaten. Zudem bietet das Erstellen eines Datenmanagementplans viele Vorteile und hilft Ihnen nicht zuletzt, die Anforderungen der guten wissenschaftlichen Praxis strukturiert umzusetzen. Was im ersten Moment unübersichtlich und überfordernd wirkt, soll in diesem Kurs anhand einer grundlegenden theoretischen Einführung im ersten und praxisorientierter Beispiele im zweiten Teil der Veranstaltung handhabbar gemacht werden. Sie lernen, was hinter den Anforderungen der Forschungsförderer steckt, welche Elemente ein Datenmanagementplan enthalten sollte und wie sie einen solchen mithilfe interaktiver Tools selbst erstellen können. Version 2 enthält aktuelle Links und weiterführende Hinweise zu einzelnen Aspekten eines Datenmanagementplans. Version 3 ist die überarbeitete und aktualisierte Version der ersten beiden und enthält u.a. Hinweise zur Lizenzierung und zu Nutzungsrechten an Forschungsdaten.' license: CC-BY-4.0 name: Datenmanagementpläne erstellen - Teil 2 num_downloads: 271 publication_date: '2021-03-30' proficiency_level: advanced beginner tags: - Research Data Management - include in DALIA type: - Slides url: - https://zenodo.org/records/4748534 - https://doi.org/10.5281/zenodo.4748534 uuid: cc70607c-e951-4da4-a535-15e19a21e543 language: de authors_with_orcid: - Pia Voigt https://orcid.org/0000-0001-9627-6252 - Barbara Weiner https://orcid.org/0000-0003-2747-8648 file_formats: .pdf - authors: - Barbara Weiner - Stephan Wünsche - Stefan Kühne - Pia Voigt - Sebastian Frericks - Clemens Hoffmann - Romy Elze - Ronny Gey description: 'Diese Präsentation bietet einen Einstieg in alle relevanten Bereiche des Forschungsdatenmanagements an der Universität Leipzig. Behandelt werden Grundlagen des Forschungsdatenmanagements, technische, ethische und rechtliche Aspekte sowie die Archivierung und Publikation von Forschungsdaten. Die Präsentation enthält zahlreiche weiterführende Links (rot) und Literaturhinweise. Ergänzend hierzu wird eine Präsentation mit Übungsaufgaben angeboten, die helfen soll, das Gelernte zu festigen und in der eigenen Forschungspraxis umzusetzen. Den Aufgaben folgen jeweils eine Antwortfolie sowie deren Auflösung.' license: CC-BY-4.0 name: Crashkurs Forschungsdatenmanagement num_downloads: 1733 publication_date: '2020-04-30' proficiency_level: advanced beginner tags: - Research Data Management - include in DALIA type: - Slides url: - https://zenodo.org/records/3778431 - https://doi.org/10.5281/zenodo.3778431 uuid: 8b9270cf-6005-4b03-8866-5bd17f0b9bc5 language: de authors_with_orcid: - Barbara Weiner https://orcid.org/0000-0003-2747-8648 - Stephan Wünsche https://orcid.org/0000-0001-9552-4402 - Stefan Kühne https://orcid.org/0000-0001-9492-2548 - Pia Voigt https://orcid.org/0000-0001-9627-6252 - Sebastian Frericks https://orcid.org/0000-0002-6644-2181 - Clemens Hoffmann - Romy Elze - Ronny Gey https://orcid.org/0000-0003-1028-1670 file_formats: .pdf - authors: - Robert Haase description: These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. license: CC-BY-4.0 name: Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI proficiency_level: advanced beginner tags: - Bioimage Analysis - artificial intelligence - Python - exclude from DALIA type: - Slides url: - https://zenodo.org/records/12623730 - https://doi.org/10.5281/zenodo.12623730 uuid: 7e738276-8d5b-41d1-98f2-3bdbb84e516f authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .pdf * .pptx - authors: - Robert Haase description: Training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. license: CC-BY-4.0 name: BIDS-lecture-2024 proficiency_level: advanced beginner tags: - Bioimage Analysis - artificial intelligence - Python - exclude from DALIA type: - Github repository url: https://github.com/ScaDS/BIDS-lecture-2024/ uuid: 1200a9b0-287c-4aa8-9c1b-6d12f42f90b8 - authors: - Kate Hertweck - Carly Strasser - Dario Taraborelli description: Open source software (OSS) is essential for advancing scientific discovery, particularly in biomedical research, yet funding to support these vital tools has been limited. The Chan Zuckerberg Initiative's Essential Open Source Software for Science (EOSS) program has significantly contributed to this field by providing $51.8 million in funding over five years to support the maintenance, growth, and community engagement of critical OSS tools. The program has impacted scientific OSS projects by improving their technical outputs, community building, and sustainability practices, and fostering collaborations within the OSS community. Additionally, EOSS funding has enhanced diversity, equity, and inclusion within the OSS community, although changes in principal investigator demographics were not observed. The funded projects have had a substantial impact on biomedical research by improving the usability and accessibility of scientific software, which has led to increased adoption and advancements in various biomedical fields. license: CC-BY-4.0 name: Insights and Impact From Five Cycles of Essential Open Source Software for Science tags: - Open Source Software - Funding - Sustainability - exclude from DALIA type: - Publication url: - https://zenodo.org/records/11201216 - https://doi.org/10.5281/zenodo.11201216 uuid: adf2178b-a4de-4670-9050-4077346fbddd language: en authors_with_orcid: - Kate Hertweck https://orcid.org/0000-0002-4026-4612 - Carly Strasser https://orcid.org/0000-0001-9592-2339 - Dario Taraborelli https://orcid.org/0000-0002-0082-8508 file_formats: .csv * .md * .pdf - authors: - Heidi Seibold description: A short book with 6 steps that get you closer to making your work reproducible. license: CC-BY-4.0 name: 6 Steps Towards Reproducible Research proficiency_level: novice tags: - Reproducibility - Research Data Management - include in DALIA type: - Book url: - https://zenodo.org/records/12744715 - https://doi.org/10.5281/zenodo.12744715 uuid: f7090051-8760-4b05-bab0-0b48f7478f38 authors_with_orcid: - Heidi Seibold https://orcid.org/0000-0002-8960-9642 file_formats: .epub * .jpg * .pdf * .png - authors: - Christian Schmidt - Elisa Ferrando-May license: CC-BY-SA-4.0 name: NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data tags: - Nfdi4Bioimage - Research Data Management - exclude from DALIA type: - Poster - Publication url: https://archiv.ub.uni-heidelberg.de/volltextserver/29489/ uuid: 4adb17df-1780-4932-85a0-9c1d91e5949d - authors: - Christian Schmidt - Janina Hanne - Josh Moore - Christian Meesters - Elisa Ferrando-May - Stefanie Weidtkamp-Peters - members of the NFDI4BIOIMAGE initiative license: CC-BY-4.0 name: 'Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey' tags: - Nfdi4Bioimage - Research Data Management - exclude from DALIA type: - Publication url: https://f1000research.com/articles/11-638 uuid: 78a599bf-8d0c-4ee9-b72c-5db1fc5b8e57 - authors: - Robert Haase description: Introduction to sharing resources online and licensing license: CC-BY-4.0 name: Sharing and licensing material tags: - Sharing - Research Data Management - include in DALIA type: - Slides url: https://f1000research.com/slides/10-519 uuid: c0ba9586-ade9-4953-9596-f37846f84c31 - authors: - Robert Haase description: Blog post about why we should license our work and what is important when choosing a license. license: CC-BY-4.0 name: If you license it, it’ll be harder to steal it. Why we should license our work proficiency_level: novice tags: - Licensing - Research Data Management - include in DALIA type: - Blog Post url: https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/ uuid: 535f3a5c-42b9-4814-8e75-0495163b1baf - authors: - Robert Haase description: Blog post about how to share data using zenodo.org license: CC-BY-4.0 name: Sharing research data with Zenodo proficiency_level: novice tags: - Sharing - Research Data Management - include in DALIA type: - Blog Post url: https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/ uuid: 9d2dbd73-5b52-4fe1-b6e2-3f57db3f41e7 - authors: - Robert Haase description: Introduction to version control using git for collaborative, reproducible script editing. license: CC-BY-4.0 name: Collaborative bio-image analysis script editing with git proficiency_level: advanced beginner tags: - Sharing - Research Data Management - include in DALIA type: - Blog Post url: https://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/ uuid: 588a48be-2d49-4dcd-a846-1c21155971f3 - authors: - Thomas Zobel - Sarah Weischner - Jens Wendt description: A use case example from the Münster Imaging Network license: ALL RIGHTS RESERVED name: OMERO for microscopy research data management tags: - Nfdi4Bioimage - OMERO - Research Data Management - exclude from DALIA type: - Publication url: https://analyticalscience.wiley.com/do/10.1002/was.0004000267/ uuid: cdcf6152-967e-4916-9501-e1e44e47f1ea - authors: - Susanne Kunis - Karen Bernhardt - Michael Hensel license: UNKNOWN name: Setting up a data management infrastructure for bioimaging tags: - Nfdi4Bioimage - Research Data Management - include in DALIA type: - Publication url: https://doi.org/10.1515/hsz-2022-0304 uuid: ada75a09-de30-4cb1-8aef-9482d6c1056d - authors: - Josh Moore - Susanne Kunis license: CC-BY-4.0 name: A Cloud-Optimized Storage for Interactive Access of Large Arrays tags: - Nfdi4Bioimage - Research Data Management - exclude from DALIA type: - Publication - Conference Abstract url: https://doi.org/10.52825/cordi.v1i.285 uuid: 103929ac-e9f0-4871-b4b1-181f2ecfa542 - authors: - Susanne Kunis description: 'guided walkthrough of poster at https://doi.org/10.5281/zenodo.6821815 which provides an overview of contexts, frameworks, and models from the world of bioimage data as well as metadata and the techniques for structuring this data as Linked Data. You can also watch the video in the browser on the I3D:bio website.' license: CC-BY-4.0 name: 'Structuring of Data and Metadata in Bioimaging: Concepts and technical Solutions in the Context of Linked Data' num_downloads: 26 publication_date: '2022-08-24' proficiency_level: advanced beginner tags: - Nfdi4Bioimage - Research Data Management - include in DALIA type: - Video url: - https://zenodo.org/record/7018929 - https://doi.org/10.5281/zenodo.7018929 uuid: 684081e5-eb9a-4a3d-ab82-a9abd226d979 language: en authors_with_orcid: - Susanne Kunis https://orcid.org/0000-0001-6523-7496 file_formats: .mp4 - authors: - Robert Haase description: Article about the Fiji Updater explaining how it works in the background. license: ALL RIGHTS RESERVED name: The Fiji Updater tags: - Imagej - exclude from DALIA type: - Publication url: https://analyticalscience.wiley.com/do/10.1002/was.0004000112/ uuid: 2656d512-f3b7-40e6-b639-5cc17dc19557 - authors: - Robert Haase - Loic Royer - et al. description: CLIJ is a collection of image processing functions that use graphics processing units for accelerated processing. license: ALL RIGHTS RESERVED name: 'CLIJ: GPU-accelerated image processing for everyone' publication_date: 2020 tags: - Imagej - Bioimage Analysis - exclude from DALIA type: - Publication url: https://doi.org/10.1038/s41592-019-0650-1 uuid: 5f95ceb8-3672-4314-9f89-e590bd1af92f - authors: - Robert Haase - Elnaz Fazeli - David Legland - Michael Doube - Siân Culley - Ilya Belevich - Eija Jokitalo - Martin Schorb - Anna Klemm - Christian Tischer description: This article gives an overview about commonly used bioimage analysis software and which aspects to consider when choosing a software for a specific project. license: CC-BY-4.0 name: A Hitchhiker's guide through the bio-image analysis software universe proficiency_level: advanced beginner tags: - Bioimage Analysis - include in DALIA type: - Publication url: https://febs.onlinelibrary.wiley.com/doi/full/10.1002/1873-3468.14451 uuid: fca35a6e-60c4-42bf-bd8e-3ff9a7345c00 - authors: - Johannes Richard Soltwedel - Robert Haase description: This article outlines common reasons for founding bioimage analysis core-facilities, services they can provide to fulfill certain need and conflicts of interest that arise from these services. license: CC-BY-4.0 name: Challenges and opportunities for bioimage analysis core-facilities proficiency_level: competent tags: - Bioimage Analysis - Research Data Management - include in DALIA type: - Publication url: https://onlinelibrary.wiley.com/doi/full/10.1111/jmi.13192 uuid: fea8697f-da2b-4c41-b2ef-e5283749dbe4 - authors: - Johannes Hohlbein - Benedict Diederich - Barbora Marsikova - Emmanuel G. Reynaud - Séamus Holden - Wiebke Jahr - Robert Haase - Kirti Prakash description: This comment article outlines the current state of the art in open hardware publishing in the context of microscopy. license: ALL RIGHTS RESERVED name: 'Open microscopy in the life sciences: quo vadis?' publication_date: 2022 type: - Publication url: https://doi.org/10.1038/s41592-022-01602-3 uuid: 1b9fce5b-f39f-4ef1-b479-3a5fa3ef51b7 tags: - exclude from DALIA - authors: - Florian Levet - Anne E. Carpenter - Kevin W. Eliceiri - Anna Kreshuk - Peter Bankhead - Robert Haase description: This article outlines common challenges and practices when developing open-source software for bio-image analysis. license: CC-BY-4.0 name: 'Developing open-source software for bioimage analysis: opportunities and challenges' proficiency_level: advanced beginner tags: - Neubias - include in DALIA type: - Publication url: https://f1000research.com/articles/10-302 uuid: cd969204-841c-437a-b13e-f35fe3ac4802 - authors: - Anjalie Schlaeppi - Wilson Adams - Robert Haase - Jan Huisken - Ryan B. MacDonald - Kevin W. Eliceiri - Elisabeth C. Kugler license: CC-BY-4.0 name: 'Meeting in the Middle: Towards Successful Multidisciplinary Bioimage Analysis Collaboration' tags: - Bioimage Analysis - include in DALIA type: - Publication url: https://www.frontiersin.org/articles/10.3389/fbinf.2022.889755/full uuid: 13a96001-417a-49e7-9bc1-419cd2f8c4fe - authors: - Gabriel G. Martins - Fabrice P. Cordelières - Julien Colombelli - Rocco D’Antuono - Ofra Golani - Romain Guiet - Robert Haase - Anna H. Klemm - Marion Louveaux - Perrine Paul-Gilloteaux - Jean-Yves Tinevez - Kota Miura license: CC-BY-4.0 name: 'Highlights from the 2016-2020 NEUBIAS training schools for Bioimage Analysts: a success story and key asset for analysts and life scientists' publication_date: 2021 tags: - Bioimage Analysis - Neubias - include in DALIA type: - Publication url: https://f1000research.com/articles/10-334/v1 uuid: bbae9837-1ef2-4c3f-9b76-7235fa422361 - authors: - Susanne Kunis - Sebastian Hänsch - Christian Schmidt - Frances Wong - Caterina Strambio-De-Castillia - Stefanie Weidtkamp-Peters license: ALL RIGHTS RESERVED name: 'MDEmic: a metadata annotation tool to facilitate management of FAIR image data in the bioimaging community' tags: - Research Data Management - Metadata - exclude from DALIA type: - Publication url: https://www.nature.com/articles/s41592-021-01288-z uuid: fc52a81c-7313-47e7-b852-31582cfe7bb6 - authors: - Glyn Nelson - Ulrike Boehme - et al. license: CC-BY-4.0 name: 'QUAREP-LiMi: A community-driven initiative to establish guidelines for quality assessment and reproducibility for instruments and images in light microscopy' tags: - Quareo-Limi - include in DALIA type: - Publication url: https://onlinelibrary.wiley.com/doi/10.1111/jmi.13041 uuid: c3273b24-b3f3-4468-9766-1cdcb319002b - authors: - Josh Moore - Chris Allan - Sébastien Besson - Jean-Marie Burel - Erin Diel - David Gault - Kevin Kozlowski - Dominik Lindner - Melissa Linkert - Trevor Manz - Will Moore - Constantin Pape - Christian Tischer - Jason R. Swedlow license: CC-BY-4.0 name: 'OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies' tags: - Nfdi4Bioimage - Research Data Management - exclude from DALIA type: - Publication url: https://www.nature.com/articles/s41592-021-01326-w uuid: a6b0962f-26b3-4b5e-8304-cbb3968f044e - authors: - Josh Moore - Susanne Kunis license: CC-BY-4.0 name: 'NFDI4BIOIMAGE: Perspective for a national bioimaging standard' tags: - Nfdi4Bioimage - exclude from DALIA type: - Publication url: https://ceur-ws.org/Vol-3415/paper-27.pdf uuid: a51eeb26-beef-4537-941f-1bde5661493e - authors: - Luca Marconato - Giovanni Palla - Kevin A Yamauchi - Isaac Virshup - Elyas Heidari - Tim Treis - Marcella Toth - Rahul Shrestha - Harald Vöhringer - Wolfgang Huber - Moritz Gerstung - Josh Moore - Fabian J Theis - Oliver Stegle license: CC-BY-4.0 name: 'SpatialData: an open and universal data framework for spatial omics' tags: - Python - exclude from DALIA type: - Publication - Preprint url: https://www.biorxiv.org/content/10.1101/2023.05.05.539647v1.abstract uuid: a90cb0c9-0f18-4131-9bdf-bb2e1ecb7810 - authors: - Christopher Schmied - Michael S Nelson - Sergiy Avilov - Gert-Jan Bakker - Cristina Bertocchi - Johanna Bischof - Ulrike Boehm - Jan Brocher - Mariana T Carvalho - Catalin Chiritescu - Jana Christopher - Beth A Cimini - Eduardo Conde-Sousa - Michael Ebner - Rupert Ecker - Kevin Eliceiri - Julia Fernandez-Rodriguez - Nathalie Gaudreault - Laurent Gelman - David Grunwald - Tingting Gu - Nadia Halidi - Mathias Hammer - Matthew Hartley - Marie Held - Florian Jug - Varun Kapoor - Ayse Aslihan Koksoy - Judith Lacoste - Sylvia Le Dévédec - Sylvie Le Guyader - Penghuan Liu - Gabriel G Martins - Aastha Mathur - Kota Miura - Paula Montero Llopis - Roland Nitschke - Alison North - Adam C Parslow - Alex Payne-Dwyer - Laure Plantard - Rizwan Ali - Britta Schroth-Diez - Lucas Schütz - Ryan T Scott - Arne Seitz - Olaf Selchow - Ved P Sharma - Martin Spitaler - Sathya Srinivasan - Caterina Strambio-De-Castillia - Douglas Taatjes - Christian Tischer - Helena Klara Jambor license: ALL RIGHTS RESERVED name: Community-developed checklists for publishing images and image analyses tags: - Bioimage Analysis - include in DALIA type: - Publication url: https://www.nature.com/articles/s41592-023-01987-9 uuid: 594fae1d-9050-4832-bd6e-6aba7164ef22 - authors: - Christian Tischer - Ashis Ravindran - Sabine Reither - Nicolas Chiaruttini - Rainer Pepperkok - Nils Norlin license: CC-BY-4.0 name: 'BigDataProcessor2: A free and open-source Fiji plugin for inspection and processing of TB sized image data' tags: - Research Data Management - Bioimage Analysis - exclude from DALIA type: - Publication url: https://doi.org/10.1093/bioinformatics/btab106 uuid: 13d23d56-b40d-465c-8dc5-fecf351c7899 - authors: - Matúš Kalaš - Laure Plantard - Joakim Lindblad - Martin Jones - Nataša Sladoje - Moritz A Kirschmann - Anatole Chessel - Leandro Scholz - Fabianne Rössler - Laura Nicolás Sáenz - Estibaliz Gómez de Mariscal - John Bogovic - Alexandre Dufour - Xavier Heiligenstein - Dominic Waithe - Marie-Charlotte Domart - Matthia Karreman - Raf Van de Plas - Robert Haase - David Hörl - Lassi Paavolainen - Ivana Vrhovac Madunić - Dean Karaica - Arrate Muñoz-Barrutia - Paula Sampaio - Daniel Sage - Sebastian Munck - Ofra Golani - Josh Moore - Florian Levet - Jon Ison - Alban Gaignard - Hervé Ménager - Chong Zhang - Kota Miura - Julien Colombelli - Perrine Paul-Gilloteaux license: CC-BY-4.0 name: 'EDAM-bioimaging: The ontology of bioimage informatics operations, topics, data, and formats (update 2020)' tags: - Metadata - include in DALIA type: - Publication - Poster url: https://f1000research.com/posters/9-162 uuid: ba7be5a4-7697-4293-bf03-f24da1475d5f - authors: - Susanne Kunis description: Presentation given at PoL BioImage Analysis Symposium Dresden 2023 license: CC-BY-4.0 name: Thinking data management on different scales num_downloads: 59 publication_date: '2023-08-31' tags: - Research Data Management - Nfdi4Bioimage - include in DALIA type: - Slides url: - https://zenodo.org/records/8329306 - https://doi.org/10.5281/zenodo.8329306 uuid: 846f52ff-124a-4283-904d-724d651877a2 authors_with_orcid: - Susanne Kunis https://orcid.org/0000-0001-6523-7496 file_formats: .pdf * .pptx - authors: - Robert Haase license: CC-BY-4.0 name: Challenges and opportunities for bio-image analysis core-facilities tags: - Research Data Management - Bioimage Analysis - Nfdi4Bioimage - include in DALIA type: - Slides url: https://f1000research.com/slides/12-1054 uuid: d007a237-0ee2-47d2-b672-7b868b2e13f5 - authors: - Mohamed M. Abdrabbou - Mehrnaz Babaki - Tom Boissonnet - Michele Bortolomeazzi - Eik Dahms - Vanessa A. F. Fuchs - Moritz Hoevels - Niraj Kandpal - Christoph Möhl - Joshua A. Moore - Astrid Schauss - Andrea Schrader - Torsten Stöter - Julia Thönnißen - Monica Valencia-S. - H. Lukas Weil - Jens Wendt and Peter Zentis event_date: November 29 - December 01 2023 event_location: CECAD, University of Cologne, Cologne, Germany license: CC-BY-4.0 name: NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon) tags: - Arc - Dataplant - Hackathon - Nfdi4Bioimage - OMERO - Python - Research Data Management - exclude from DALIA type: - Event - Publication - Documentation url: - https://github.com/NFDI4BIOIMAGE/Cologne-Hackathon-2023 - https://doi.org/10.5281/zenodo.10609770 uuid: 6ef64a20-6ba1-4feb-80d6-05d6249b59e5 - authors: - Josh Moore description: Welcome at NFDI4BIOIMAGE All-Hands Meeting in Düsseldorf, Germany, October 16, 2023 license: CC-BY-4.0 name: Welcome to BioImage Town tags: - OMERO - Bioimage Analysis - Nfdi4Bioimage - exclude from DALIA type: - Slides url: https://zenodo.org/doi/10.5281/zenodo.10008464 uuid: ec7c4adc-cbd0-4014-af89-d628f79130bd authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X file_formats: .pdf * .pptx - authors: - Stefanie Weidtkamp-Peters description: NFDI4BIOIMAGE core mission, bioimage data challenge, task areas, FAIR bioimage workflows. license: CC-BY-4.0 name: NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis - Online Kick-Off 2023 tags: - Research Data Management - FAIR-Principles - Bioimage Analysis - Nfdi4Bioimage - include in DALIA type: - Slides url: - https://doi.org/10.5281/zenodo.8070038 - https://zenodo.org/records/8070038 uuid: 3088e8b7-ace8-48c0-a5ec-07c3fa497ca1 authors_with_orcid: - Stefanie Weidtkamp-Peters https://orcid.org/0000-0001-7734-3771 file_formats: .pdf * .pptx - authors: - Mohamed Abdrabbou - Mehrnaz Babaki - Tom Boissonnet - Michele Bortolomeazzi - Eik Dahms - Vanessa Fuchs - A. F. Moritz Hoevels - Niraj Kandpal - Christoph Möhl - Joshua A. Moore - Astrid Schauss - Andrea Schrader - Torsten Stöter - Julia Thönnißen - Monica Valencia-S. - H. Lukas Weil - Jens Wendt - Peter Zentis description: This repository documents the first NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon), where topics like 'Interoperability', 'REMBI / Mapping', and 'Neuroglancer (OMERO / zarr)' were explored through collaborative discussions and workflow sessions, culminating in reports that bridge NFDI4Bioimage to DataPLANT. Funded by various DFG initiatives, this event emphasized documentation and use cases, contributing preparatory work for future interoperability projects at the 2nd de.NBI BioHackathon in Bielefeld. license: CC-BY-4.0 name: NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne-Hackathon-2023, GitHub repository) tags: - Research Data Management - FAIR-Principles - Bioimage Analysis - Nfdi4Bioimage - exclude from DALIA type: - Github repository url: https://zenodo.org/doi/10.5281/zenodo.10609770 uuid: 5958ff49-1cad-4de2-b158-1106e2ba45c3 language: en authors_with_orcid: - Mohamed M. Abdrabbou https://orcid.org/0000-0003-2778-7483 - Mehrnaz Babaki https://orcid.org/0000-0003-3099-618X - Tom Boissonnet https://orcid.org/0000-0002-3328-9467 - Michele Bortolomeazzi https://orcid.org/0000-0001-5805-5774 - Eik Dahms https://orcid.org/0000-0002-7452-1146 - Vanessa A. F. Fuchs https://orcid.org/0000-0002-4101-6987 - Moritz Hoevels - Niraj Kandpal https://orcid.org/0009-0007-5101-4786 - Christoph Möhl https://orcid.org/0000-0002-0829-5101 - Joshua A. Moore https://orcid.org/0000-0003-4028-811X - Astrid Schauss https://orcid.org/0000-0002-6658-2192 - Andrea Schrader https://orcid.org/0000-0002-3879-7057 - Torsten Stöter - Julia Thönnißen https://orcid.org/0000-0002-5467-871X - Monica Valencia-S. https://orcid.org/0000-0003-3430-2683 - H. Lukas Weil https://orcid.org/0000-0003-1945-6342 - Jens Wendt https://orcid.org/0009-0002-1826-7099 - Peter Zentis https://orcid.org/0000-0002-6999-132X file_formats: .zip - authors: - Josh Moore - Susanne Kunis description: Poster presented at the European Light Microscopy Initiative meeting in Liverpool (https://www.elmi2024.org/) license: CC-BY-4.0 name: '[ELMI 2024] AI''s Dirty Little Secret: Without FAIR Data, It''s Just Fancy Math' tags: - Research Data Management - FAIR-Principles - Bioimage Analysis - Nfdi4Bioimage - include in DALIA type: - Poster url: https://zenodo.org/doi/10.5281/zenodo.11235512 uuid: 27f7b02d-b759-4d22-9d71-7dde4d3f92cb authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X - Susanne Kunis https://orcid.org/0000-0001-6523-7496 file_formats: .pdf - authors: - Susanne Kunis description: Presentation given at PoL BioImage Analysis Symposium Dresden 2023 license: CC-BY-4.0 name: Thinking data management on different scales proficiency_level: advanced beginner tags: - Research Data Management - Nfdi4Bioimage - include in DALIA type: - Slides url: https://zenodo.org/doi/10.5281/zenodo.8329305 uuid: eed6bba8-e74d-4f1e-a02c-739f5e643246 authors_with_orcid: - Susanne Kunis https://orcid.org/0000-0001-6523-7496 file_formats: .pdf * .pptx - authors: - Josh Moore - Susanne Kunis description: Poster presented at Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4HCLS 2023), Feb 13--16, 2023, Basel, Switzerland. NFDI4BIOIMAGE is a newly established German consortium dedicated to the FAIR representation of biological imaging data. A key deliverable is the definition of a semantically-compatible FAIR image object integrating RDF metadata with web-compatible storage of large n-dimensional binary data in OME-Zarr. We invite feedback from and collaboration with other endeavors during the soon-to-begin 5 year funding period. license: CC-BY-4.0 name: '[SWAT4HCLS 2023] NFDI4BIOIMAGE: Perspective for a national bioimage standard' tags: - Research Data Management - FAIR-Principles - Nfdi4Bioimage - include in DALIA type: - Poster url: https://zenodo.org/doi/10.5281/zenodo.7928332 uuid: 28bcf7b5-60d7-4549-9472-1a04699ba3c3 language: en authors_with_orcid: - Joshua Allenm Moore https://orcid.org/0000-0003-4028-811X - Susanne Kunis https://orcid.org/0000-0001-6523-7496 file_formats: .pdf - authors: - Riccardo Massei description: NFDI4BIOIMAGE is a consortium within the framework of the National Research Data Infrastructure (NFDI) in Germany. In this talk, the consortium and the contribution to the work programme by the Helmholtz Centre for Environmental Research (UFZ) in Leipzig are outlined. license: CC-BY-4.0 name: 'NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis [conference talk: The Pelagic Imaging Consortium meets Helmholtz Imaging, 5.10.2023, Hamburg]' tags: - Research Data Management - Bioimage Analysis - Nfdi4Bioimage - exclude from DALIA type: - Slides url: https://zenodo.org/doi/10.5281/zenodo.8414318 uuid: 56685938-832f-46ee-ac7b-b8b69aa5cbf8 language: en authors_with_orcid: - Riccardo Massei https://orcid.org/0000-0003-2104-9519 file_formats: .pdf - authors: - Vanessa Aphaia Fiona Fuchs - Jens Wendt - Maximilian Müller - Mohsen Ahmadi - Riccardo Massei - Cornelia Wetzker description: The Data Steward Team of the NFDI4BIOIMAGE consortium presents themselves and the services (including the Helpdesk) that we offer. license: CC-BY-4.0 name: Who you gonna call? - Data Stewards to the rescue tags: - Research Data Management - Bioimage Analysis - Data Stewardship - Nfdi4Bioimage - include in DALIA type: - Poster url: https://zenodo.org/doi/10.5281/zenodo.10730423 uuid: 4762a4de-b044-4534-a268-f5d5833d2a47 authors_with_orcid: - Vanessa Aphaia Fiona Fuchs https://orcid.org/0000-0002-4101-6987 - Jens Wendt - Maximilian Müller https://orcid.org/0000-0003-2237-1147 - Mohsen Ahmadi https://orcid.org/0000-0002-7018-0460 - Riccardo Massei https://orcid.org/0000-0003-2104-9519 - Cornelia Wetzker https://orcid.org/0000-0002-8367-5163 file_formats: .pdf - authors: - Christian Schmidt description: Short Talk about the NFDI4BIOIMAGE consortium presented at the RDM in (Bio-)Medicine Information Event on April 10th, 2024, organized C³RDM & ZB MED. license: CC-BY-4.0 name: '[Short Talk] NFDI4BIOIMAGE - A consortium in the National Research Data Infrastructure' tags: - Research Data Management - Bioimage Analysis - Nfdi4Bioimage - include in DALIA type: - Slides url: https://zenodo.org/doi/10.5281/zenodo.10939519 uuid: 6b5cd720-8954-4862-ad2f-5f46697bfe49 authors_with_orcid: - Christian Schmidt https://orcid.org/0000-0001-9568-895X file_formats: .pdf - authors: - Carsten Fortmann-Grote description: Presentation was given at the 2nd MPG-NFDI Workshop on April 18th about e NFDI4BIOIMAGE Consortium, FAIRification of Image (meta)data, Zarr, RFC, Training (TA5), contributing. license: CC-BY-4.0 name: NFDI4BIOIMAGE tags: - Research Data Management - Bioimage Analysis - FAIR-Principles - Zarr - Nfdi4Bioimage - include in DALIA type: - Slides url: https://zenodo.org/doi/10.5281/zenodo.11031746 uuid: 8fb3de32-915a-4e3a-84c4-1072becd9e64 authors_with_orcid: - Carsten Fortmann-Grote https://orcid.org/0000-0002-2579-5546 file_formats: .pdf - authors: - NFDI4BIOIMAGE Consortium description: 'Bioimaging refers to a collection of methods to visualize the internal structures and mechanisms of living organisms. The fundamental tool, the microscope, has enabled seminal discoveries like that of the cell as the smallest unit of life, and continues to expand our understanding of biological processes. Today, we can follow the interaction of single molecules within nanoseconds in a living cell, and the development of complete small organisms like fish and flies over several days starting from the fertilized egg. Each image pixel encodes multiple spatiotemporal and spectral dimensions, compounding the massive volume and complexity of bioimage data. Proper handling of this data is indispensable for analysis and its lack has become a growing hindrance for the many disciplines of the life and biomedical sciences relying on bioimaging. No single domain has the expertise to tackle this bottleneck alone. As a method-specific consortium, NFDI4BIOMAGE seeks to address these issues, enabling bioimaging data to be shared and re-used like they are acquired, i.e., independently of disciplinary boundaries. We will provide solutions for exploiting the full information content of bioimage data and enable new discoveries through sharing and re-analysis. Our RDM strategy is based on a robust needs analysis that derives not only from a community survey but also from over a decade of experience in German BioImaging, the German Society for Microscopy and Image Analysis. It considers the entire lifecycle of bioimaging data, from acquisition to archiving, including analysis and enabling re-use. A foundational element of this strategy is the definition of a common, cloud-compatible, and interoperable digital object that bundles binary images with their descriptive and provenance metadata. With members from plant biology to neuroscience, NFDI4BIOIMAGE will champion the standardization of bioimage data to create a framework that answers discipline-specific needs while ensuring communication and interoperability with data types and RDM systems across domains. Integration of bioimage data with, e.g., omics data as the basis for spatial omics, holds great promise for fields such as cancer medicine. Unlocking the full potential of bioimage data will rely on the development and broad availability of exceptional analysis tools and training sets. NFDI4BIOIMAGE will make these accessible and usable including cutting-edge AI-based methods in scalable cloud environments. NFDI4BIOIMAGE intersects with multiple NFDI consortia, most prominently with GHGA for linking image and genomics data and with DataPLANT on the definition of FAIR data objects. Last but not least, NFDI4BIOIMAGE is internationally well connected and represents the opportunity for German scientists to keep path with and have a voice in several international initiatives focusing on the FAIRification of bioimage data as one of the main challenges for the advancement of knowledge in the life and biomedical sciences.' license: CC-BY-4.0 name: NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and Bioimage Analysis num_downloads: 212 publication_date: '2024-08-07' url: - https://zenodo.org/records/13168693 - https://doi.org/10.5281/zenodo.13168693 uuid: 0bdd51f9-a13c-4410-a71d-de8401f057ef language: en authors_with_orcid: - Joshua Moore https://orcid.org/0000-0003-4028-811X - Susanne Kunis https://orcid.org/0000-0001-6523-7496 - Björn Grüning https://orcid.org/0000-0002-3079-6586 - Markus Blank-Burian https://orcid.org/0000-0002-9134-5295 - Jan-Philipp Mallm https://orcid.org/0000-0002-7059-4030 - Torsten Stöter https://orcid.org/0009-0003-6932-023X - Werner Zuschratter https://orcid.org/0000-0002-9845-6393 - Marc Thilo Figge https://orcid.org/0000-0002-4044-9166 - Anna Kreshuk https://orcid.org/0000-0003-1334-6388 - Christian Tischer https://orcid.org/0000-0003-4105-1990 - Robert Haase https://orcid.org/0000-0001-5949-2327 - Thomas Zobel https://orcid.org/0000-0002-2101-8416 - Pavol Bauer https://orcid.org/0000-0003-4328-7171 - Carl-Magnus Svensson https://orcid.org/0000-0002-9723-9063 - Ruman Gerst https://orcid.org/0000-0002-0723-6038 - Janina Hanne https://orcid.org/0000-0002-5332-3589 - Christian Schmidt https://orcid.org/0000-0001-9568-895X - Markus M. Becker https://orcid.org/0000-0001-9324-3236 - Thomas Bocklitz https://orcid.org/0000-0003-2778-6624 - Jan Bumberger https://orcid.org/0000-0003-3780-8663 - Claire Chalopin https://orcid.org/0000-0001-9309-7531 - Jianxu Chen https://orcid.org/0000-0002-8500-1357 - Paul Czodrowski https://orcid.org/0000-0002-7390-8795 - Timo Dickscheid https://orcid.org/0000-0002-9051-3701 - Carsten Fortmann-Grote https://orcid.org/0000-0002-2579-5546 - Jan Huisken https://orcid.org/0000-0001-7250-3756 - Jan Lohmann https://orcid.org/0000-0003-3667-187X - Astrid Schauss https://orcid.org/0000-0002-6658-2192 - Martin Baumann https://orcid.org/0000-0002-9071-2356 - Carlo Beretta https://orcid.org/0000-0002-6027-0796 - Jean-Marie Burel https://orcid.org/0000-0002-1789-1861 - Vincent Heuveline https://orcid.org/0000-0002-2217-7558 - Rohini Kuner https://orcid.org/0000-0002-3333-9129 - Thomas Kuner https://orcid.org/0000-0003-1896-9031 - Matthias Landwehr https://orcid.org/0000-0001-9274-2578 - Andrea Leibfried https://orcid.org/0000-0001-7713-024X - Roland Nitschke https://orcid.org/0000-0002-9397-8475 - Deepti Mittal - Dirk von Suchodoletz https://orcid.org/0000-0002-4382-5104 - Monica Valencia-Schneider https://orcid.org/0000-0003-3430-2683 - Peter Zentis https://orcid.org/0000-0002-6999-132X - Dominik Brilhaus https://orcid.org/0000-0001-9021-3197 - Matthew Hartley https://orcid.org/0000-0001-6178-2884 - Bastian Hülsmann https://orcid.org/0009-0004-5318-5966 - Susanne Dunker https://orcid.org/0000-0001-7276-776X - Antje Keppler https://orcid.org/0000-0003-4358-2269 - Aastha Mathur https://orcid.org/0000-0001-9734-9767 - Christian Meesters https://orcid.org/0000-0003-2408-7588 - Wiebke Möbius https://orcid.org/0000-0002-2902-7165 - Sven Nahnsen https://orcid.org/0000-0002-4375-0691 - Claudia Pfander https://orcid.org/0000-0002-9574-9553 - Stephanie Rehwald https://orcid.org/0000-0002-5884-4471 - Beatriz Serrano-Solano https://orcid.org/0000-0002-5862-6132 - Laura Vilardell Scholten https://orcid.org/0000-0003-4025-1712 - Raimund Vogl https://orcid.org/0000-0002-6681-1530 - Lutz Becks https://orcid.org/0000-0002-3885-5253 - Elisa Ferrando-May https://orcid.org/0000-0002-5567-8690 - Stefanie Weidtkamp-Peters https://orcid.org/0000-0001-7734-3771 file_formats: .pdf tags: - include in DALIA - authors: - Josh Moore description: Presentation made to the GBI Image Data Management Working Group during the 7th Exchange of Experience in Uruguay. license: CC-BY-4.0 name: '[GBI EOE VII] Five (or ten) must-have items for making IT infrastructure for managing bioimage data' num_downloads: 19 publication_date: '2024-05-26' url: - https://zenodo.org/records/11318151 - https://doi.org/10.5281/zenodo.11318151 uuid: e194a8c5-2355-4961-8728-4a25a91b620b authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X file_formats: .pdf tags: - include in DALIA - authors: - Jonathan Geiger - Petra Steiner - Abdelmoneim Amer Desouki - Frank Lange description: 'The DALIA (Data Literacy Alliance) project aims to develop a knowledge graph for FAIR teaching and learning materials on data literacy, data competencies and research data management (RDM) skills within the National Research Data Infrastructure (NFDI) and the RDM landscape. Such a platform thrives on the participation of users who want to search, create, manage or use teaching and learning materials. A schematization of the metadata is necessary for the interoperability of teaching and learning materials. This is done by the DALIA Interchange Format (DIF), which provides a framework for making the metadata of teaching and learning materials transparent, comparable and smooth to integrate into the DALIA platform. It includes the description and explanation of the data fields for the online publication of educational resources. The DIF was developed in close consultation with the scientific community. This development process included several feedback rounds in which valuable feedback was provided and subsequently incorporated into the DIF. This not only contributed to the clear, transparent and user-oriented definitions of the data fields, and to a clear structure, but also to the integration of many existing data standards and to the (special) requirements of the scientific community. The selection of elements is based on the Dublin Core Application Profile. The DIF is provided as a PDF document and in table form (ODS) to convey the attributes of the teaching and learning materials and their definitions in an easily understandable form and to facilitate communication. It also includes a legend and an example in tabular form. In addition, a template (CSV) with the attributes as column headers is provided, which can be used for recording the metadata of the teaching and learning materials. The tables can also be transferred to technical application profiles. We would like to thank all the commentators of the previous versions, especially Susanne Arndt, Sophie Boße, Sonja Felder, Marc Fuhrmans, Jan-Michael Haugwitz, Marina Lemaire, Karoline Lemke, Birte Lindstädt, Juliane Röder, and Jakob Voß. Without their feedback and advice, the DIF would be less transparent.' license: CC-BY-SA-4.0 name: DALIA Interchange Format num_downloads: 461 publication_date: '2024-06-07' url: - https://zenodo.org/records/11521029 - https://doi.org/10.5281/zenodo.11521029 uuid: 1dd382ee-63a9-447f-aa50-8765c7a40fa4 language: en authors_with_orcid: - Jonathan Geiger https://orcid.org/0000-0002-0452-7075 - Petra Steiner https://orcid.org/0000-0001-8997-2620 - Abdelmoneim Amer Desouki https://orcid.org/0000-0003-2083-1277 - Frank Lange https://orcid.org/0000-0002-9346-6031 file_formats: .csv * .ods * .pdf tags: - include in DALIA - authors: - Joel Ryan - Thomas Pengo - Alex Rigano - Paula Montero Llopis - Michelle S. Itano - Lisa A. Cameron - Guillermo Marqués - Caterina Strambio-De-Castillia - Mark A. Sanders - Claire M. Brown name: 'MethodsJ2: a software tool to capture metadata and generate comprehensive microscopy methods text' tags: - Metadata - exclude from DALIA type: - Publication url: https://www.nature.com/articles/s41592-021-01290-5 uuid: bcc7348e-cc52-4551-83dd-e1d2c081bd5f - authors: - Alessandro Rigano - et al. name: 'Micro-Meta App: an interactive tool for collecting microscopy metadata based on community specifications' tags: - Metadata - exclude from DALIA type: - Publication url: https://doi.org/10.1038/s41592-021-01315-z uuid: cc0be79f-66c4-4527-87b3-5ea14f0bf7e0 - authors: - Melissa Linkert - et al. name: 'Metadata matters: access to image data in the real world' publication_date: 2010 tags: - Metadata - include in DALIA type: - Publication url: https://rupress.org/jcb/article/189/5/777/35828/Metadata-matters-access-to-image-data-in-the-real uuid: 52053584-d4c5-432a-9a6d-f9284a8640d6 - authors: - Jan Ellenberg - Jason R. Swedlow - Mary Barlow - Charles E. Cook - Ugis Sarkans - Ardan Patwardhan - Alvis Brazma - Ewan Birney name: A call for public archives for biological image data tags: - Research Data Management - exclude from DALIA type: - Publication url: https://www.nature.com/articles/s41592-018-0195-8 uuid: 03dbe3a6-6f2d-428c-850e-f8ab897fb2d3 - authors: - Nasim Jamali - Ellen T. A. Dobson - Kevin W. Eliceiri - Anne E. Carpenter - Beth A. Cimini license: BSD-3-CLAUSE name: '2020 BioImage Analysis Survey: Community experiences and needs for the future' publication_date: 2021 tags: - Bioimage Analysis - exclude from DALIA type: - Publication url: - https://doi.org/10.1017/s2633903x21000039 - https://github.com/ciminilab/2021_Jamali_BiologicalImaging uuid: da5a0212-4c91-4bd6-bc53-1873c8e8302c - authors: - Suganya Sivagurunathan - Stefania Marcotti - Carl J Nelson - Martin L Jones - David J Barry - Thomas J A Slater - Kevin W Eliceiri - Beth A Cimini license: BSD-3-CLAUSE name: Bridging Imaging Users to Imaging Analysis - A community survey publication_date: 2023 tags: - Bioimage Analysis - exclude from DALIA type: - Publication - Preprint url: - https://www.biorxiv.org/content/10.1101/2023.06.05.543701v1 - https://github.com/COBA-NIH/2023_ImageAnalysisSurvey uuid: 1b83facb-fa4c-47d0-b869-82c873d7d7e7 - authors: - Annika Reinke - et al. description: This article gives a detailed overview about pitfalls when using metric for image analysis algorithm validation. name: Understanding metric-related pitfalls in image analysis validation publication_date: 2023 tags: - Bioimage Analysis - exclude from DALIA type: - Publication - Preprint url: https://arxiv.org/abs/2302.01790v3 uuid: 3b59037f-dc91-46df-9475-eeda5fab1087 - authors: - Isabel Kemmer - Antje Keppler - Beatriz Serrano-Solano - Arina Rybina - Buğra Özdemir - Johanna Bischof - Ayoub El Ghadraoui - John E. Eriksson - Aastha Mathur name: Building a FAIR image data ecosystem for microscopy communities tags: - Research Data Management - exclude from DALIA type: - Publication url: https://link.springer.com/article/10.1007/s00418-023-02203-7 uuid: 681bf3f7-8ae9-4df2-b468-d0e2e1e1dbaa - authors: - Robert Haase - Deborah Schmidt - Wayne Rasband - Curtis Rueden - Florian Jug - Pavel Tomancak - Eugene W. Myers name: A study on long-term reproducibility of image analysis results on ImageJ and Fiji tags: - Imagej - exclude from DALIA type: - Publication - Poster url: https://figshare.com/articles/poster/I2K_Poster_Haase_V6_ImageJ_repro_pdf/7409525 uuid: fc92186d-3ec4-492e-82e3-baeb02b247ef - authors: - Joachim Goedhart name: Studentsourcing - aggregating and re-using data from a practical cell biology course tags: - Sharing - exclude from DALIA type: - Preprint url: https://www.biorxiv.org/content/10.1101/2023.10.09.561479v1 uuid: e15e55e0-e43f-4127-802f-7de587656b90 - authors: - Dan Chitwood - Sourabh Palande name: Plants & Python - A series of lessons in coding, plant biology, computation, and bioinformatics proficiency_level: advanced beginner tags: - Notebook - include in DALIA type: - Publication url: - https://academic.oup.com/plcell/article/34/7/e1/6628764 - https://plantsandpython.github.io/PlantsAndPython/00_Opening_page.html uuid: b62563fd-06ed-4081-9a81-294956cf8660 - authors: - Christian Schmidt - Tom Boissonnet - Julia Dohle - Karen Bernhardt - Elisa Ferrando-May - Tobias Wernet - Roland Nitschke - Susanne Kunis - Stefanie Weidtkamp-Peters name: A practical guide to bioimaging research data management in core facilities proficiency_level: advanced beginner tags: - Research Data Management - include in DALIA type: - Publication url: - https://onlinelibrary.wiley.com/doi/10.1111/jmi.13317 uuid: ca33a361-8b0f-4433-9fff-3d991949caa4 - authors: - Oleg Ryabchykov - Shuxia Guo - Thomas Bocklitz description: Photonic data analysis, combining imaging, spectroscopy, machine learning, and computer science, requires flexible methods and interdisciplinary collaborations to advance. Essential developments include standardizing data infrastructure for comparability, optimizing data-driven models for complex investigations, and creating techniques to handle limited or unbalanced data and device variations. license: CC-BY-4.0 name: Photonic data analysis in 2050 tags: - FAIR-Principles - Machine Learning - Research Data Management - include in DALIA type: - Publication url: https://doi.org/10.1016/j.vibspec.2024.103685 uuid: 38095569-6d47-41a5-9870-890552badf35 language: en - authors: - Ruman Gerst - Zoltán Cseresnyés - Marc Thilo Figge description: JIPipe is an open-source visual programming language for easy-access pipeline development name: 'JIPipe: visual batch processing for ImageJ' tags: - Workflow Engine - Imagej - exclude from DALIA type: - Publication - Documentation url: - https://www.nature.com/articles/s41592-022-01744-4 - https://jipipe.hki-jena.de/ uuid: 369f528d-3c43-4296-a24a-959af2854c3e - authors: - Birgit Möller - Markus Glaß - Danny Misiak - Stefan Posch description: The Microscope Image Analysis Toolbox is a toolbox with a collection of algorithms for processing and analyzing digital images. name: MiToBo - A Toolbox for Image Processing and Analysis tags: - Workflow Engine - Imagej - exclude from DALIA type: - Publication - Documentation url: - https://openresearchsoftware.metajnl.com/articles/10.5334/jors.103 - https://mitobo.informatik.uni-halle.de/ uuid: a4f4e74c-758b-4951-b397-2f8dc617a241 - authors: - Daniel Franco-Barranco - et al. description: BiaPy is an open source Python library for building bioimage analysis pipelines, also called workflows. name: 'BiaPy: Bioimage analysis pipelines in Python' tags: - Workflow Engine - Python - exclude from DALIA type: - Documentation url: https://biapy.readthedocs.io/ uuid: ce349cf8-9180-4a18-a168-2cc55290b1d2 - description: Galaxy is an open source, web-based platform for data intensive biomedical research. name: Galaxy Documentation tags: - Workflow Engine - exclude from DALIA type: - Documentation url: https://usegalaxy.org/ uuid: 936903ce-81e0-4d46-adce-e71d98137759 - description: Fractal is a framework to process high-content imaging data at scale and prepare it for interactive visualization. name: Fractal Documentation tags: - Workflow Engine - Python - exclude from DALIA type: - Documentation url: https://fractal-analytics-platform.github.io/ uuid: ea95dcff-00dc-4812-bd00-88bfe6a0b4a2 - description: The Snakemake workflow management system is a tool to create reproducible and scalable data analyses. name: Snakemake Documentation tags: - Workflow Engine - Python - exclude from DALIA type: - Documentation url: - https://snakemake.readthedocs.io/en/stable/ - https://academic.oup.com/bioinformatics/article/28/19/2520/290322 uuid: a3cc3aaa-0d10-4911-8836-bd7bbe0b1f53 - authors: - Stephen J. Cross - Jordan D. J. R. Fisher - Mark A. Jepson description: ModularImageAnalysis is a Fiji plugin providing a modular framework for assembling image and object analysis workflows name: 'ModularImageAnalysis (MIA): Assembly of modularisedimage and object analysis workflows in ImageJ' tags: - Workflow Engine - Imagej - exclude from DALIA type: - Publication - Documentation url: - https://doi.org/10.1111/jmi.13227 - https://mianalysis.github.io/ uuid: 2cc94ffa-91a2-4a8e-9478-7807eb15187c - description: Nextflow enables scalable and reproducible scientific workflows using software containers. name: NextFlow documentation tags: - Workflow Engine - exclude from DALIA type: - Documentation url: https://www.nextflow.io/ uuid: c7ef1727-7dfe-43a8-9cae-abebdc4192e3 - description: nf-core is a community effort to collect a curated set of analysis pipelines built using Nextflow name: NextFlow core tags: - Python - exclude from DALIA type: - Collection url: https://nf-co.re/ uuid: b0f9771c-259b-42fb-8a24-85370a00921e - authors: - Wei Ouyang - Nanguage - Jeremy Metz - Craig Russell description: BioEngine, a Python package designed for flexible deployment and execution of bioimage analysis models and workflows using AI, accessible via HTTP API and RPC. license: MIT name: BioEngine Documentation tags: - Workflow Engine - artificial intelligence - Python - exclude from DALIA type: - Documentation url: https://bioimage-io.github.io/bioengine/#/ uuid: 630fa559-1d1d-4c45-aeb1-6b50711ea5a1 - description: A collection of bio-image analysis webinars where commonly authors of open-source bio-image analysis software explain how to use their tools. name: NEUBIAS YouTube Channel tags: - Neubias - exclude from DALIA type: - Collection - Video url: https://www.youtube.com/neubias uuid: 36501c0d-b5fa-4d62-aefd-1ce7484e4d69 - authors: - Sreeni Bhattiprolu description: A collection tutorial videos for using Python in general and for processing images using Python, machine learning and deep learning name: DigitalSreeni YouTube Channel proficiency_level: novice tags: - Python - exclude from DALIA type: - Collection - Video url: - https://www.youtube.com/digitalsreeni - https://www.youtube.com/watch?v=A4po9z61TME uuid: 81a9b7b2-96bf-4011-9f7d-9bf2451ca40d - name: Global BioImaging YouTube channel type: - Collection - Video url: https://www.youtube.com/GlobalBioImaging uuid: 8a9b4487-b9cd-4e53-bc86-7c017ce47b40 tags: - exclude from DALIA - name: Bio Image Analysis Lecture 2020 proficiency_level: advanced beginner tags: - Bioimage Analysis - exclude from DALIA type: - Collection - Video url: https://www.youtube.com/watch?v=e-2DbkUwKk4&list=PL5ESQNfM5lc7SAMstEu082ivW4BDMvd0U uuid: d933aa8b-2266-470f-8fc1-5be840b9f3be - description: YouTube channel collecting videos and webinar recordings about the Open Microscopy Environment (OME), the Next Generation File Format OME-NGFF, the Image Data Resource (IDR), the Omero platform and Omero plugins. name: Open Microscopy Environment YouTube channel proficiency_level: advanced beginner tags: - OMERO - exclude from DALIA type: - Collection - Video url: https://www.youtube.com/OpenMicroscopyEnvironment uuid: 57b1e5c9-cefa-4bc4-8579-1a8644883e4f language: en - name: Euro-BioImaging Communication YouTube Channel type: - Collection - Video url: https://www.youtube.com/c/eurobioimagingcommunication uuid: 94c3cde7-d369-4d9d-9a98-c0735ec9bee5 tags: - exclude from DALIA - license: CC BY-NC-ND 3.0 DEED name: Ibiology. Bioimage Analysis Course. The Life Cycle of an Image Data Set proficiency_level: novice tags: - Bioimage Analysis - exclude from DALIA type: - Collection - Video url: https://www.ibiology.org/online-biology-courses/bioimage-analysis-course/ uuid: c50d1fac-76e9-4ee3-9c66-6ae75fa257db - name: 'COBA: Center for Open Bioimage Analysis YouTube Channel' tags: - Bioimage Analysis - exclude from DALIA type: - Collection - Video url: https://www.youtube.com/@cobacenterforopenbioimagea1864 uuid: 3fa79fb1-159d-49fb-9a59-5ecbcc186924 - description: Collection of tutorial videos for Fiji users name: Melbourne Advanced Microscopy Facility proficiency_level: novice tags: - Bioimage Analysis - exclude from DALIA type: - Collection - Video url: https://www.youtube.com/@melbourneadvancedmicroscop2617 uuid: 867574ca-b5a5-495e-8722-738bca361b9d - name: Fiji Is Just ImageJ Tutorials proficiency_level: novice tags: - Bioimage Analysis - exclude from DALIA type: - Collection - Video url: https://www.youtube.com/playlist?list=PL5Edc1v41fyCLFZbBCLo41zFO-_cXBfAb uuid: 6d3c6930-fb39-4763-ac77-c7a7630ca84a - license: ALL RIGHTS RESERVED name: Imaris Tutorials proficiency_level: advanced beginner tags: - Bioimage Analysis - exclude from DALIA type: - Collection - Video url: https://imaris.oxinst.com/tutorials uuid: 025be580-6d6f-44e7-9f1b-907b6fa246ec - license: ALL RIGHTS RESERVED name: arivis Vision4D Tutorials proficiency_level: advanced beginner tags: - Bioimage Analysis - exclude from DALIA type: - Collection - Video url: https://www.youtube.com/playlist?list=PLRc9dt9lEZh_SVRC4G5moOvgHuvjPwmv0 uuid: f697db9c-b2a1-45e5-ac76-7abacb1122a6 - name: RDMBites BioImage metadata type: - Collection - Video url: https://www.youtube.com/watch?v=aRHNHk07t3Q&list=PLyCNTVs-UBvuJF7WausQ5q7v7pI1vEpI1 uuid: c29935a8-902a-4c0c-876f-c4ab113c171a tags: - exclude from DALIA - name: How to get started with Jupyter and Colab proficiency_level: advanced beginner type: - Video url: https://www.youtube.com/watch?v=OH3VKI7ErAE uuid: 0bb670fb-a51b-4091-86f8-d062b209784a tags: - exclude from DALIA - license: UNKNOWN name: Chris Halvin YouTube channel proficiency_level: advanced beginner tags: - Napari - Python - Bioimage Analysis - exclude from DALIA type: - Collection - Video url: - https://www.youtube.com/@chrishavlin - https://www.youtube.com/playlist?list=PLqbhAmYZU5KxuAcnNBIxyBkivUEiKswq1 uuid: da259d51-63a9-4e6f-bb12-8bfe5d631a6d - license: UNKNOWN name: RDM4mic proficiency_level: advanced beginner tags: - Research Data Management - OMERO - exclude from DALIA type: - Collection - Video url: - https://www.youtube.com/@RDM4mic uuid: 15dc27ae-526d-4236-837e-7d1dde6cdf13 - license: CC-BY-4.0 name: FAIR BioImage Data proficiency_level: advanced beginner tags: - Research Data Management - Fair - Bioimage Analysis - exclude from DALIA type: - Collection - Video url: - https://www.youtube.com/watch?v=8zd4KTy-oYI&list=PLW-oxncaXRqU4XqduJzwFHvWLF06PvdVm uuid: 86ceefa0-cc79-4e60-8f4e-08e3d3273fc1 - authors: - Robert Haase description: Large Language Models (LLMs) are changing the way how humans interact with computers. This has impact on all scientific fields by enabling new ways to achieve for example data analysis goals. In this talk we will go through an introduction to LLMs with respect to applications in the life sciences, focusing on bio-image analysis. We will see how to generate text and images using LLMs and how LLMs can extract information from reproducibly images through code-generation. We will go through selected prompt engineering techniques enabling scientists to tune the output of LLMs towards their scientific goal and how to do quality assurance in this context. license: CC-BY-4.0 name: 'Large Language Models: An Introduction for Life Scientists' num_downloads: 259 publication_date: '2024-08-27' proficiency_level: advanced beginner tags: - Artificial Intelligence - include in DALIA url: - https://zenodo.org/records/13379394 - https://doi.org/10.5281/zenodo.13379394 uuid: 67cacf14-d9ff-4b32-8e94-1fde07f824dd language: en authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .pdf * .pptx - authors: - Robert Haase description: 'Large Language Models (LLMs) such as ChatGPT are changing the way we interact with computers, including how we analye microscopy imaging data. In this talk I introduce basic concepts of asking LLMs to write code and how to modify the questions to get the best out of it. For trying out these prompt engineering basics there are additional online resources available: https://scads.github.io/prompt-engineering-basics-2024/intro.html' license: CC-BY-4.0 name: ChatGPT for Image Analysis num_downloads: 434 proficiency_level: advanced beginner publication_date: '2024-08-25' url: - https://zenodo.org/records/13371196 - https://doi.org/10.5281/zenodo.13371196 uuid: ff5d01b2-2b87-4973-a519-02bb9eace69a language: en authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .pdf * .pptx * .zip tags: - include in DALIA - authors: - Robert Haase description: This talk will present the initiatives of the NFDI4BioImage consortium aimed at the long-term preservation of life science data. We will discuss our efforts to establish metadata standards, which are crucial for ensuring data reusability and integrity. The development of sustainable infrastructure is another key focus, enabling seamless data integration and analysis in the cloud. We will take a look at how we manage training materials and communicate with our community. Through these actions, NFDI4BioImage seeks to enable FAIR bioimage data management for German researchers, across disciplines and embedded in the international framework. license: CC-BY-4.0 name: Towards Preservation of Life Science Data with NFDI4BIOIMAGE num_downloads: 0 publication_date: '2024-08-29' url: - https://zenodo.org/records/13506641 - https://doi.org/10.5281/zenodo.13506641 uuid: efe78686-6a71-4977-bc9b-8a725394a11d language: en authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .pdf * .pptx tags: - exclude from DALIA - authors: - Matúš Kalaš et al. description: EDAM-bioimaging is an extension of the EDAM ontology, dedicated to bioimage analysis, bioimage informatics, and bioimaging. license: CC-BY-4.0 name: EDAM-bioimaging - The ontology of bioimage informatics operations, topics, data, and formats tags: - Ontology - Bioimage Analysis - exclude from DALIA type: - Poster url: https://hal.science/hal-02267597/document uuid: db34c2cd-b569-4dbc-abab-7dbf12aa7219 - authors: - Geert van Geest - Yann Haefliger - Monique Zahn-Zabal - Patricia M. Palagi description: Glittr.org is a platform that aggregates and indexes training materials on computational life sciences from public git repositories, making it easier for users to find, compare, and analyze these resources based on various metrics. By providing insights into the availability of materials, collaboration patterns, and licensing practices, Glittr.org supports adherence to the FAIR principles, benefiting the broader life sciences community. license: CC-BY-4.0 name: Using Glittr.org to find, compare and re-use online training materials tags: - Bioimage Analysis - Research Data Management - exclude from DALIA type: - Publication - Preprint url: https://www.biorxiv.org/content/10.1101/2024.08.20.608021v1 uuid: bb461422-c378-4d8d-b792-aa920b888346 language: en - authors: - Johannes Soltwedel description: This repository contains a collection of tools for working with OMERO. Such tools can be working with the OMERO command line interface to transfer datasets between repositories, etc. license: CC-BY-4.0 name: Omero-tools tags: - OMERO - Bioimage Analysis - exclude from DALIA type: - Github repository url: https://biapol.github.io/omero-tools/ uuid: 6785c881-99fd-476a-98c4-d90a482403b1 - authors: - Manan Lalit - Joran Deschamps - Florian Jug - Ajinkya Kulkarni description: Code Implementation for EmbedSeg, an Instance Segmentation Method for Microscopy Images license: CC-BY-NC-4.0 name: EmbedSeg Repository tags: - Bioimage Analysis - Instance Segmentation - exclude from DALIA type: - Github repository url: https://github.com/juglab/EmbedSeg uuid: f95076e0-2a5c-474c-95f7-b466536d7e3b - authors: - Pauli Virtanen et al. description: This is a conversion and second life of SciPy Cookbook as a bunch of Ipython notebooks. license: BSD-3-CLAUSE name: Scipy Cookbook tags: - Bioimage Analysis - include in DALIA type: - Github repository url: https://github.com/scipy/scipy-cookbook uuid: 765be7d9-be5a-4ea4-b704-347a846a9857 - authors: - Ziv Yaniv et al. description: Jupyter notebooks for learning how to use SimpleITK license: APACHE-2.0 name: SimpleITK-Notebooks proficiency_level: competent tags: - Bioimage Analysis - Simpleitk - include in DALIA type: - Github repository url: https://github.com/InsightSoftwareConsortium/SimpleITK-Notebooks uuid: 2e9607a5-feb6-454e-8037-17c6fa58ee79 - authors: - Juan Nunez-Iglesias et al. description: skimage-tutorials - a collection of tutorials for the scikit-image package. license: CC0 1.0 UNIVERSAL name: skimage-tutorials proficiency_level: advanced beginner tags: - Bioimage Analysis - Scikit-Image - include in DALIA type: - Github repository url: https://github.com/scikit-image/skimage-tutorials uuid: e11846ee-c527-4518-866b-d3c89b415de5 - authors: - Loïc Estève et al. description: Machine learning in Python with scikit-learn MOOC license: CC-BY-4.0 name: scikit-learn MOOC proficiency_level: advanced beginner tags: - Bioimage Analysis - Machine Learning - include in DALIA type: - Github repository url: https://github.com/INRIA/scikit-learn-mooc uuid: 8ffa8c80-9262-4de2-bda6-4035cbf28fab - authors: - Yandex School of Data Analysis description: YSDA course in Natural Language Processing license: MIT name: NLP Course proficiency_level: advanced beginner tags: - Natural Language Processing - include in DALIA type: - Github repository url: https://github.com/yandexdataschool/nlp_course uuid: e6fddd47-e3f5-4436-9cd9-4accb29cfa01 - authors: - Beth Cimini et al. description: This book is a companion to the Nature Methods publication Community-developed checklists for publishing images and image analyses. In this paper, members of QUAREP-LiMi have proposed 3 sets of standards for publishing image figures and image analysis - minimal requirements, recommended additions, and ideal comprehensive goals. By following this guidance, we hope to remove some of the stress non-experts may face in determining what they need to do, and we also believe that researchers will find their science more interpretable and more reproducible. license: BSD-3-CLAUSE name: Community-developed checklists for publishing images and image analyses proficiency_level: advanced beginner tags: - Bioimage Analysis - Research Data Management - include in DALIA type: - Notebook - Collection url: https://quarep-limi.github.io/WG12_checklists_for_image_publishing/intro.html uuid: 46634ae1-c6a4-4b71-81e4-9862140659af language: en - authors: - EMBL-EBI description: Online tutorial and webinar library, designed and delivered by EMBL-EBI experts license: CC0 (MOSTLY, BUT CAN DIFFER DEPENDING ON RESOURCE) name: EMBL-EBI material collection proficiency_level: advanced beginner tags: - Bioinformatics - exclude from DALIA type: - Collection url: https://www.ebi.ac.uk/training/on-demand?facets=type:Course%20materials&query= uuid: c354a37f-9589-4f4d-8a50-a69efe3c11db - authors: - Vanessa Fiona Aphaia Fuchs - Christian Schmidt - Tom Boissonnet description: 'Description Microscopy experiments generate information-rich, multi-dimensional data, allowing us to investigate biological processes at high spatial and temporal resolution. Image processing and analysis is a standard procedure to retrieve quantitative information from biological imaging. Due to the complex nature of bioimaging files that often come in proprietary formats, it can be challenging to organize, structure, and annotate bioimaging data throughout a project. Data often needs to be moved between collaboration partners, transformed into open formats, processed with a variety of software tools, and exported to smaller-sized images for presentation. The path from image acquisition to final publication figures with quantitative results must be documented and reproducible. In this workshop, participants learn how to use structured metadata annotations in the image data management platform OMERO (OME Remote Objects) to optimize their data handling. This strategy helps both with organizing data for easier processing and analysis and for the preparation of data publication in journal manuscripts and in public repositories such as the BioImage Archive. Participants learn the principles of leveraging object-oriented data organization in OMERO to enhance findability and usability of their data, also in collaborative settings. The integration of OMERO with image analysis tools, in particular ImageJ/Fiji, will be trained. Moreover, users learn about community-accepted metadata checklists (REMBI) to enrich the value of their data toward reproducibility and reusability. In this workshop, we will provide hands-on training and recommendations on: Structured metadata annotation features in OMERO and how to use them Types of metadata in bioimaging: Technical metadata, sample metadata, analysis metadata The use of ontologies and terminologies for metadata annotation REMBI, the recommended metadata for biological images Metadata-assisted image analysis streamlining Tools for metadata annotation in OMERO The target group for this workshop This workshop is directed at researchers at all career levels who have started using OMERO for their microscopy research data management. We encourage the workshop participants to bring example data from their research to discuss suitable metadata annotation for their everyday practice. Who are the trainers (see trainer description below for more details) Dr. Vanessa Fuchs (NFDI4BIOIMAGE Data Steward, Center for Advanced Imaging, Heinrich-Heine University of Düsseldorf) Dr. Tom Boissonnet (OMERO admin and image metadata specialist, Center for Advanced Imaging, Heinrich-Heine University of Düsseldorf) Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center, Heidelberg) Material Description Published here are the presentation slides that were used for input from the trainers during the different sessions of the programme. Additionally, a Fiji Macro is published that depends on the OMERO Extensions Plugin by Pouchin et al, 2022, F100Research, https://doi.org/10.12688/f1000research.110385.2  Programme Overview Day 1 - April 29th, 2024 09.00 a.m. to 10.00 a.m.: Session 1 - Welcome and Introduction 10.00 a.m. to 10.30 a.m.:  Session 2 - Introduction to the FAIR principles & data annotation 10:30 a.m. to 10:45 a.m.: Coffee break 10.45 a.m. to 12.00 a.m.: Session 3 - Data structure (datasets in OMERO) and organization with Tags  12.00 a.m. to 1.00 p.m.:  Lunch Break 1.00 p.m. to 2.00 p.m.:  Session 4 - REMBI, Key-Value pair annotations in bioimaging 2:00 p.m. to 2.30 p.m.:  Session 5 - Ontologies for Key-Value Pairs in OMERO 2:30 p.m. to 2:45 p.m. Coffee break 2.45 p.m. to 3.45 p.m.:  Wrap-up, discussion, outlook on day 2 Day 2 - April 30th, 2024 09.00 a.m. to 09.30 a.m.:  Arrival and Start into day 2 09.30 a.m. to 11.30 a.m.:  Session 6 - Hands-on : REMBI-based Key-Value Pair annotation in OMERO 11.30 a.m. to 12.30 a.m.:  Lunch Break 12.30 a.m. to 1.15 p.m.: Session 7 - OMERO and OMERO.plugins 1.15 p.m. to 2.00 p.m.: Session 8 - Loading OMERO-hosted data into Fiji 2.00 p.m. to 2.15 p.m.: Coffee break  2.15 p.m. to 3.00 p.m.: Discussion, Outlook' license: CC-BY-4.0 name: '[Workshop] FAIR data handling for microscopy: Structured metadata annotation in OMERO' num_downloads: 234 proficiency_level: advanced beginner publication_date: '2024-05-06' url: - https://zenodo.org/records/11109616 - https://doi.org/10.5281/zenodo.11109616 uuid: b8b90ea6-b67d-4ca0-8315-ea9c8a53bfa4 language: en authors_with_orcid: - Vanessa Aphaia Fiona Fuchs https://orcid.org/0000-0002-4101-6987 - Christian Schmidt https://orcid.org/0000-0001-9568-895X - Tom Boissonnet https://orcid.org/0000-0002-3328-9467 file_formats: .ijm * .pdf * .pptx tags: - include in DALIA - authors: - Riccardo Massei description: 'YMIA python event 2024 Presentation :  "Getting started with Python: intro and set-up a conda environment with Dr. Riccardo Massei"' license: CC-BY-4.0 name: 'Getting started with Python: intro and set-up a conda environment' num_downloads: 6 proficiency_level: novice publication_date: '2024-10-09' url: - https://zenodo.org/records/13908480 - https://doi.org/10.5281/zenodo.13908480 uuid: 9b0e425e-30cb-4853-bfde-e52a7ed803ea authors_with_orcid: - Riccardo Massei https://orcid.org/0000-0003-2104-9519 file_formats: .odp tags: - include in DALIA - authors: - Robert Haase description: In this presentation I introduce bia-bob, an AI-based code generator that integrates into Jupyter Lab and allows for easy generation of Bio-Image Analysis Python code. It highlights how to get started with using large language models and prompt engineering to get high-quality bio-image analysis code. license: CC-BY-4.0 name: Bio-image Analysis Code Generation using bia-bob num_downloads: 101 publication_date: '2024-10-09' proficiency_level: advanced beginner tags: - artificial intelligence - bioimage analysis - include in DALIA url: - https://zenodo.org/records/13908108 - https://doi.org/10.5281/zenodo.13908108 uuid: 83205cc7-84f6-4823-a4b6-dd9073fe63ad language: en authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .pdf * .pptx - authors: - University of Ghent description: The website provides resources and guidelines for managing research data efficiently and responsibly. Its focus is to ensure that data are properly organized, stored, documented, and shared throughout a research project, and even beyond, in a way that aligns with Open Science principles. license: UNKNOWN name: Ghent University Research Data Management (RDM) - policy and support tags: - Research Data Management - exclude from DALIA type: - Website url: https://www.ugent.be/en/research/openscience/datamanagement uuid: c12aded1-ec0f-4a11-80e5-b73a89a991e4 language: en - authors: - GO FAIR description: This page is supposed to serve as a Starter Kit for research data management (RDM). It lists resources designed to help researchers get started to organize their data. license: CC-BY-4.0 name: RDM Starter Kit tags: - Research Data Management - include in DALIA type: - Website url: https://www.go-fair.org/resources/rdm-starter-kit/ uuid: ad24c08a-2367-462e-a187-ce4da251ffb8 - authors: - Robert Haase description: Overview about decision making and how to influence decisions in the bio-image analysis software context. license: CC-BY-4.0 name: Hitchhiking through a diverse Bio-image Analysis Software Universe tags: - Bioimage Analysis - exclude from DALIA type: - Slides - Presentation num_downloads: 86 publication_date: '2022-07-22' url: - https://f1000research.com/slides/11-746 - https://doi.org/10.7490/f1000research.1119026.1 uuid: 15d28ba4-2f3f-4f96-a71d-68ba3715c876 - authors: - Costantino Thanos description: This article discusses various aspects of data reusability in the context of scientific research, including technological, legal, and policy frameworks. license: CC-BY-4.0 name: Research Data Reusability - Conceptual Foundations, Barriers and Enabling Technologies tags: - Research Data Management - Open Science - Data Protection - exclude from DALIA type: - Publication publication_date: '2017-01-09' url: https://www.mdpi.com/2304-6775/5/1/2 uuid: de967b0c-3a26-4289-b7f8-cf0e13502ebd - authors: - Mark D. Wilkinson - Michel Dumontier - IJsbrand Jan Aalbersberg - Gabrielle Appleton - Myles Axton - et. al description: This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community. license: CC-BY-4.0 name: The FAIR Guiding Principles for scientific data management and stewardship tags: - FAIR-Principles - Research Data Management - exclude from DALIA type: - Publication publication_date: '2016-03-15' url: - https://www.nature.com/articles/sdata201618 - https://doi.org/10.1038/sdata.2016.18 uuid: cf136150-ae9e-4483-bcf5-0916bcbcc398 - authors: - Mark A Musen - Martin J O'Connor - Erik Schultes - Marcos Martínez-Romero - Josef Hardi - John Graybeal description: The authors have developed a model for scientific metadata, and they have made that model usable by both CEDAR and FAIRware. The approach shows that a formal metadata model can standardize reporting guidelines and that it can enable separate software systems to assist (1) in the authoring of standards-adherent metadata and (2) in the evaluation of existing metadata. license: CC-BY-4.0 name: Modeling community standards for metadata as templates makes data FAIR tags: - Data Stewardship - FAIR-Principles - Metadata - exclude from DALIA type: - Publication publication_date: '2022-11-12' url: - https://pubmed.ncbi.nlm.nih.gov/36371407/ - https://www.nature.com/articles/s41597-022-01815-3 uuid: aa6900c3-ebfd-4bd1-ac72-e13e4bb85257 language: en - authors: - Mathias Hammer - Maximiliaan Huisman - Alessandro Rigano - Ulrike Boehm - James J. Chambers - et al. description: Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments. license: UNKNOWN name: Towards community-driven metadata standards for light microscopy - tiered specifications extending the OME model tags: - Reproducibility - BioImage Analysis - Metadata - exclude from DALIA type: - Publication publication_date: '2022-07-10' url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/ uuid: 0a44aa70-7a95-460a-867d-7f5da5a680d9 language: en - authors: - Ugis Sarkans - Wah Chiu - Lucy Collinson - Michele C. Darrow - Jan Ellenberg - David Grunwald - et al. description: Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. license: UNKNOWN name: REMBI - Recommended Metadata for Biological Images—enabling reuse of microscopy data in biology tags: - Metadata - Research Data Management - exclude from DALIA type: - Publication publication_date: '2021-05-21' url: - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015/ - https://www.nature.com/articles/s41592-021-01166-8 - https://doi.org/10.1038/s41592-021-01166-8 uuid: 4bc500d3-1797-43a9-bb44-7c5e694b9880 language: en - authors: - Philippa C. Griffin - Jyoti Khadake - Kate S. LeMay - Suzanna E. Lewis - Sandra Orchard - et al. description: The authors provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on ‘omics’ datasets and computer-based data processing and analysis. license: UNKNOWN name: Best practice data life cycle approaches for the life sciences tags: - Bioinformatics - Reproducibility - Research Data Management - Sharing - Open Science - include in DALIA type: - Publication num_downloads: 1615 publication_date: '2018-06-04' url: https://doi.org/10.12688/f1000research.12344.2 uuid: 9a84ca9e-2563-45e5-ae0f-76563ac1916b language: en - authors: - Christian Schmidt - Elisa Ferrando-May description: Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle. license: CCY-BY-SA-4.0 name: NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data tags: - Nfdi4Bioimage - Research Data Management - exclude from DALIA type: - Conference Abstract - Slides publication_date: '2021-04-29' url: https://doi.org/10.11588/heidok.00029489 uuid: bb2fa570-0cd6-45ce-add3-666a3a1b97c0 - authors: - ELIXIR (2021) Research Data Management Kit description: In this section, information is organised according to the stages of the research data life cycle. license: CC-BY-4.0 name: Data life cycle tags: - Data Life Cycle - Research Data Management - exclude from DALIA type: - Collection - Website - Online Tutorial url: https://rdmkit.elixir-europe.org/data_life_cycle uuid: 3ab496b7-20e9-43cf-920c-9b48a7d54d8a - license: UNKNOWN name: Tess Search for Data Life Cycle tags: - Data Life Cycle - Research Data Management - exclude from DALIA type: - Collection url: https://tess.elixir-europe.org/search?q=data+life+cycle#materials uuid: 61e69915-e99e-47ef-af3a-ca92dea9f0ec - authors: - Uli Hahn - Kerstin Helbig - Gerald Jagusch - Jessica Rex description: Die vorliegende Empfehlung sowie die zugehörigen Erfahrungsberichte geben einen Überblick über die verschiedenen Möglichkeiten der Gestaltung einer Forschungsdatenmanagement Policy sowie Wege zu deren Erstellung. license: CC-BY-4.0 name: Erstellung und Realisierung einer institutionellen Forschungsdaten-Policy tags: - Research Data Management - include in DALIA type: - Publication publication_date: '2018-10-22' url: - https://bausteine-fdm.de/article/view/7945 - https://doi.org/10.17192/bfdm.2018.1.7945 uuid: 25f4dfd4-7a38-4aa3-b051-66a2f96d1edc language: de - authors: - Bea Hiemenz - Monika Kuberek description: As a methodological approach, research data policies of German universities are collected and evaluated, and compared to international recommendations on research data policies. license: CC-BY-4.0 name: Leitlinie? Grundsätze? Policy? Richtlinie? – Forschungsdaten-Policies an deutschen Universitäten tags: - Research Data Management - FAIR-Principles - exclude from DALIA type: - Publication publication_date: '2018-07-13' url: https://www.o-bib.de/bib/article/view/2018H2S1-13 uuid: 18c43864-c12d-4e8a-934a-637c98837648 - description: This document provides the essential elements of a Research Data Management (RDM) Policy and is part of the LEARN Toolkit containing the Model Policy for Research Data Management (RDM) at Research Institutions/Institutes. license: CC-BY-4.0 name: Guidance for Developing a Research Data Management (RDM) Policy tags: - Research Data Management - include in DALIA type: - Book num_downloads: 545 publication_date: '2017' url: - https://discovery.ucl.ac.uk/id/eprint/1546596/1/26_Learn_Guidance_137-140.pdf - https://doi.org/10.14324/000.learn.27 uuid: 061f3435-7a0f-408f-acb1-ceb05ddd87c5 language: en - description: Leading open-source platform for collaborative and living data management plans. license: UNKOWN name: Data Stewardship Wizard tags: - Data Stewardship - Open Source - Research Data Management - FAIR-Principles - exclude from DALIA type: - Website - Online Tutorial url: https://ds-wizard.org/ uuid: a579cd90-08d2-4b6a-905b-e1da1fd9b91f - description: Der Research Data Management Organiser (RDMO) unterstützt Forschungsprojekte bei der Planung, Umsetzung und Verwaltung aller Aufgaben des Forschungsdatenmanagements. license: UNKNOWN name: RDMO - Research Data Management Organiser tags: - Research Data Management - Open Source Software - exclude from DALIA type: - Website - Online Tutorial url: https://rdmorganiser.github.io/ uuid: cf442dd8-e4a1-4380-8808-343a0c3eaae5 - authors: - Robert Haase description: In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP. license: CC-BY-4.0 name: Creating a Research Data Management Plan using chatGPT proficiency_level: advanced beginner tags: - Research Data Management - Artificial Intelligence - include in DALIA type: - Blog Post publication_date: '2023-11-06' url: https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/ uuid: 1778a5a0-ad2a-4bbb-97c2-2a735da6ccfd - authors: - Till Kreutzer - Henning Lahmann description: Die Digitalisierung ermöglicht eine offene Wissenschaft (Open Science). Diese hat viele Aspekte, insbesondere den freien Zugang zu wissenschaftlichen Veröffentlichungen und Materialien (Open Access), transparente Begutachtungsverfahren (Open Peer Review) oder quelloffene Technologien (Open Source). Das Programm Hamburg Open Science (Laufzeit 2018–2020) unterstützt unter anderem den Kulturwandel in der Wissenschaft. In diesem Kontext entstand der nun vorliegende Leitfaden, der das rechtliche Umfeld greifbar machen soll. Der Leitfaden erarbeitet die betroffenen Rechtsgebiete zunächst systematisch. Im zweiten Teil werden rechtliche Fragen zu Open Science beantwortet, die direkt aus den Universitäten und Bibliotheken kommen. license: CC-BY-4.0 name: Rechtsfragen bei Open Science - Ein Leitfaden proficiency_level: advanced beginner tags: - Open Science - Open Access - Copyright - include in DALIA type: - Book publication_date: '2021-05-25' url: https://hup.sub.uni-hamburg.de/oa-pub/catalog/book/205 uuid: 159070fd-6827-46e4-8596-506947e3e66d language: de - description: Fiji is a popular free open-source image processing package based on ImageJ. license: BSD-2-CLAUSE name: Fiji proficiency_level: advanced beginner tags: - Imagej - OMERO - exclude from DALIA type: - Online Tutorial url: https://omero-guides.readthedocs.io/en/latest/fiji/docs/index.html uuid: 854470e9-75de-46a5-ba31-10ac44527463 - authors: - Christian Schmidt description: A Microscopy Research Data Management Resource. license: UNKNOWN name: I3D bio – Information Infrastructure for BioImage Data - Bioimage Metadata tags: - Metadata - I3Dbio - Research Data Management - exclude from DALIA type: - Collection url: - https://gerbi-gmb.de/i3dbio/i3dbio-rdm/i3dbio-bioimage-metadata/ uuid: 5ee1c3a9-8a4b-4f3f-80b4-3b1d9ad0812d - description: The key-value pairs are annotations in OMERO useful to describe thoroughly the data and can be added & edited via the OMERO.web interface. license: UNKNOWN name: Key-Value pairs scripts tags: - OMERO - exclude from DALIA type: - Documentation - Collection url: https://guide-kvpairs-scripts.readthedocs.io/en/latest/ uuid: 8ff1d027-c7e6-4fd1-97b0-3a291e341445 - authors: - Ilya G. Goldberg - Chris Allan - Jean-Marie Burel - Doug Creager - Andrea Falconi - et. al description: The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. license: CC-BY-4.0 name: The Open Microscopy Environment (OME) Data Model and XML file - open tools for informatics and quantitative analysis in biological imaging tags: - Bioimage Analysis - exclude from DALIA type: - Publication publication_date: '2005-05-03' url: - https://genomebiology.biomedcentral.com/articles/10.1186/gb-2005-6-5-r47 - https://doi.org/10.1186/gb-2005-6-5-r47 uuid: 72057227-5e93-493a-813f-2c289dfeab27 language: en - authors: - Marie-Hélène Bourget - Lee Kamentsky - Satrajit S. Ghosh - Giacomo Mazzamuto - Alberto Lazari - et al. description: The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. license: CC-BY-4.0 name: Microscopy-BIDS - An Extension to the Brain Imaging Data Structure for Microscopy Data tags: - Research Data Management - exclude from DALIA type: - Publication num_downloads: 768 publication_date: '2022-04-19' url: https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.871228/full uuid: 1ee51dd0-3493-4c46-975a-3a56b03c71a8 - description: Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data. license: CC0-1.0 name: REMBI Overview tags: - FAIR-Principles - Metadata - Research Data Management - include in DALIA type: - Collection url: https://www.ebi.ac.uk/bioimage-archive/rembi-help-overview/ uuid: 5e068085-7383-4889-9888-15f66a9843b9 - authors: - Rohola Hosseini - Matthijs Vlasveld - Joost Willemse - Bob van de Water - Sylvia E. Le Dévédec - Katherine J. Wolstencroft description: The authors show the utility of Minimum Information for High Content Screening Microscopy Experiments (MIHCSME) for High Content Screening (HCS) data using multiple examples from the Leiden FAIR Cell Observatory, a Euro-Bioimaging flagship node for high content screening and the pilot node for implementing FAIR bioimaging data throughout the Netherlands Bioimaging network. license: CC-BY-4.0 name: FAIR High Content Screening in Bioimaging tags: - FAIR-Principles - Metadata - Research Data Management - exclude from DALIA type: - Publication publication_date: '2023-07-17' url: https://www.nature.com/articles/s41597-023-02367-w uuid: 580d2609-c189-411a-9f1d-2d9b1bf3f870 language: en - authors: - Lienhard Wegewitz - F. Strauß description: Documentation for eLabFTW. With eLabFTW you get a secure, modern and compliant system to track your experiments efficiently but also manage your lab with a powerful and versatile database. license: AGPL-3.0 name: Dokumentation und Anleitung zum elektronischen Laborbuch (eLabFTW) proficiency_level: advanced beginner tags: - Research Data Management - exclude from DALIA type: - Documentation - Document - Tutorial publication_date: '2020-03-23' url: - https://www.fdm.tu-clausthal.de/fileadmin/FDM/documents/Manual_eLab_v0.3_20200323.pdf - https://www.elabftw.net/ uuid: 7966c25a-14e0-4f3a-bbbb-214ee2c784bc - authors: - Daniel Dietrich - Jonathan Gray - Tim McNamara - Antti Poikola - Rufus Pollock - et al. description: This handbook is about open data but what exactly is it? In particular what makes open data open, and what sorts of data are we talking about? license: CC-BY-4.0 name: What is Open Data? proficiency_level: advanced beginner tags: - Open Science - include in DALIA type: - Collection url: http://opendatahandbook.org/guide/en/what-is-open-data/ uuid: 8aff9ceb-4896-46a5-96fa-bd0be01ef5f5 - description: Sharing your data can benefit your career in some interesting ways. In this post, read why you should be making more of your research data openly available. license: UNKNOWN name: Five great reasons to share your research data proficiency_level: advanced beginner tags: - Research Data Management - Sharing - include in DALIA type: - Blog Post publication_date: '2022-03-18' url: https://web.library.uq.edu.au/blog/2022/03/five-great-reasons-share-your-research-data uuid: 4cea6882-9ae1-479c-b2e4-8ca9625c1954 - description: The website offers detailed advice on publishing research data, focusing on key issues like data management, FAIR data principles, legal considerations, and repository selection. license: UNKNOWN name: Research data - what are the key issues to consider when publishing this kind of material? tags: - Research Data Management - FAIR-Principles - Licensing - exclude from DALIA type: - Tutorial url: https://www.publisso.de/en/advice/publishing-advice-faqs/research-data uuid: e6a8b8e5-82f0-4fee-ba14-504438b674ab - authors: - Christian Schmidt description: Funding agencies may demand that original source data of a publication be published, too. So the question is - where should one publish the data? And how does it get there? license: UNKNOWN name: Finding and Choosing a Data Repository proficiency_level: advanced beginner tags: - I3Dbio - Research Data Management - include in DALIA type: - Tutorial publication_date: '2023-07-01' url: https://gerbi-gmb.de/2023/06/01/finding-and-choosing-a-repository/ uuid: 07126de5-9441-4fa1-8ccd-18137c77113a - description: Re3data is a global registry of research data repositories that covers research data repositories from different academic disciplines. It includes repositories that enable permanent storage of and access to data sets to researchers, funding bodies, publishers, and scholarly institutions. license: CC-BY-4.0 name: re3data.org - registry of Research Data Repositories tags: - Research Data Management - exclude from DALIA type: - Website url: https://www.re3data.org/ uuid: 342bf8a3-120f-42b0-9b73-a4ca7ee67dfe language: en - description: In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. license: CC-BY-4.0 name: FAIR Priciples proficiency_level: advanced beginner tags: - FAIR-Principles - Data Stewardship - Research Data Management - include in DALIA type: - Collection url: https://www.go-fair.org/fair-principles/ uuid: e690efc1-d9e7-4adf-bf0e-f190c12ef3a4 language: en - description: To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here. license: CC0-1.0 name: Submitting data to the BioImage Archive proficiency_level: advanced beginner tags: - Research Data Management - exclude from DALIA type: - Tutorial - Video url: https://www.ebi.ac.uk/bioimage-archive/submit/ uuid: 4f7b57eb-cbd2-428c-83fe-934b326749a1 - authors: - Matthew Hartley - Gerard J. Kleywegt - Ardan Patwardhan - Ugis Sarkans - Jason R. Swedlow - Alvis Brazma description: The BioImage Archive is a new archival data resource at the European Bioinformatics Institute (EMBL-EBI). license: UNKNOWN name: The BioImage Archive – Building a Home for Life-Sciences Microscopy Data tags: - Research Data Management - exclude from DALIA type: - Publication publication_date: '2022-06-22' url: - https://www.sciencedirect.com/science/article/pii/S0022283622000791?via%3Dihub - https://doi.org/10.1016/j.jmb.2022.167505 uuid: 9cbc5c1b-c0d8-4862-bbeb-d46dd032ee44 - description: Schritt für Schritt verbessern wir die Nutzungsmöglichkeiten von Daten für Wissenschaft und Gesellschaft. Durch unser Zusammenwirken im NFDI-Verein entsteht eine Dachorganisation für das Forschungsdatenmanagement in allen Wissenschaftszweigen. license: UNKNOWN name: NFDI - Daten als gemeinsames Gut für exzellente Forschung, organisiert durch die Wissenschaft in Deutschland. tags: - Nfdi4Bioimage - Research Data Management - exclude from DALIA type: - Website url: https://www.nfdi.de/ uuid: 0621e2e9-fb11-4edc-9c9b-d65a7b0dada4 language: de - authors: - Leyla Garcia - Bérénice Batut - Melissa L. Burke - Mateusz Kuzak - Fotis Psomopoulos - et al. description: The authors offer trainers some simple rules, to help make their training materials FAIR, enabling others to find, (re)use, and adapt them. license: CC-BY-4.0 name: Ten simple rules for making training materials FAIR proficiency_level: novice tags: - Metadata - Bioinformatics - FAIR-Principles - include in DALIA type: - Publication num_downloads: 4311 publication_date: '2020-05-21' url: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007854 uuid: 3c513830-fb3a-479b-863f-9c61f7e8c8fc - authors: - Martin Boeckhout - Gerhard A. Zielhuis - Annelien L. Bredenoord description: The FAIR guiding principles for research data stewardship (findability, accessibility, interoperability, and reusability) look set to become a cornerstone of research in the life sciences. A critical appraisal of these principles in light of ongoing discussions and developments about data sharing is in order. license: CC-BY-4.0 name: The FAIR guiding principles for data stewardship - fair enough? tags: - FAIR-Principles - Data Stewardship - Sharing - exclude from DALIA type: - Publication publication_date: '2018-05-17' url: https://www.nature.com/articles/s41431-018-0160-0 uuid: 1d27bfea-1ec8-49bc-a5d4-3f0c674cd0b7 language: en - license: UNKNOWN name: Data management at France BioImaging tags: - Research Data Management - Bioimage Analysis - Open Science - include in DALIA type: - Slides - Presentation publication_date: '2024-29-05' url: https://downloads.openmicroscopy.org/presentations/2024/Dundee/Day1/Research%20Data%20Management%20at%20France%20BioImaging.pdf uuid: 2546aa43-949d-4304-903f-e9c3a9a17947 - description: Explore fundamental topics on research data management (RDM), how DataPLANT implements these aspects to support plant researchers with RDM tools and services, read guides and manuals or search for some teaching materials. license: CC-BY-4.0 name: DataPLANT knowledge base tags: - Research Data Management - Dataplant - include in DALIA type: - Collection publication_date: '2022-12-14' url: https://nfdi4plants.org/nfdi4plants.knowledgebase/index.html uuid: c90cb9a3-f9de-45c7-889a-f8fc2e95b19d language: en - description: An easy to use and open source converter for bioimaging data. NGFF-Converter is a GUI application for conversion of bioimage formats into OME-NGFF (Next-Generation File Format) or OME-TIFF. license: GPL-2.0 name: NGFF Converter tags: - Open Source Software - exclude from DALIA type: - Application url: https://www.glencoesoftware.com/products/ngff-converter/ uuid: 55558f7d-5043-4b05-aff7-6fa14f27fc46 - authors: - Melissa Linkert - Chris Allan - Josh Moore - Sébastien Besson - David Gault - et al. description: Java application to convert image file formats, including .mrxs, to an intermediate Zarr structure compatible with the OME-NGFF specification. license: GPL-2.0 name: bioformats2raw Converter tags: - Open Source Software - exclude from DALIA type: - Application - Github repository url: https://github.com/glencoesoftware/bioformats2raw uuid: 0a24567c-7110-4d9a-be4f-2bdce3f3f629 - authors: - Melissa Linkert - Chris Allan - Sébastien Besson - Josh Moore description: Java application to convert a directory of tiles to an OME-TIFF pyramid. This is the second half of iSyntax/.mrxs => OME-TIFF conversion. license: GPL-2.0 name: raw2ometiff Converter tags: - Open Source Software - exclude from DALIA type: - Application - Github repository url: https://github.com/glencoesoftware/raw2ometiff uuid: f09968ac-248a-4756-b3ae-dc4680dd0363 - description: This Focus issue features a series of papers offering guidelines and tools for improving the tracking and reporting of microscopy metadata with an emphasis on reproducibility and data re-use. license: UNKNOWN name: Reporting and reproducibility in microscopy tags: - Reproducibility - Metadata - exclude from DALIA type: - Collection publication_date: '2021-12-03' url: https://www.nature.com/collections/djiciihhjh uuid: aef54970-e95c-44cf-80a5-d2dcdd5210f7 - authors: - Christopher Schmied description: In this paper we introduce two sets of checklists. One is concerned with the publication of images. The other one gives instructions for the publication of image analysis. license: CC0-1.0 name: Checklists for publishing images and image analysis tags: - BioImage Analysis - exclude from DALIA type: - Forum Post publication_date: '2023-09-14' url: https://forum.image.sc/t/checklists-for-publishing-images-and-image-analysis/86304 uuid: 6a266e96-c637-4989-bf9d-b32a22cf823f - authors: - Stian Soiland-Reyes - Finn Bacall - Bert Droesbeke - Alan R Williams - Johan Gustafsson - et al. description: A registry for describing, sharing and publishing scientific computational workflows. license: BSD-3-CLAUSE name: WorkflowHub tags: - Bioinformatics - Workflow - Workflow Engine - Python - R - exclude from DALIA type: - Collection url: https://workflowhub.eu/ uuid: 5a544a67-fc07-4628-854e-0d60e530d64b - license: CC-BY-4.0 name: Creating Workflows and Advanced Workflow Options proficiency_level: competent tags: - Workflow - include in DALIA type: - Tutorial - Online Tutorial url: https://galaxyproject.org/learn/advanced-workflow/ uuid: 1962ecf9-e012-4c47-a5dd-363f311bd7ee - description: A workflow is a chain of analysis steps. In Galaxy, we can create a workflow from an existing analysis history, or we can create one visually by adding tools to a canvas. This tutorial covers building a workflow to analyse a bacterial genome, from input FASTQ sequencing reads to assembly, annotation, and visualization. license: CC0-1.0 name: Galaxy workflows proficiency_level: advanced beginner tags: - Workflow - include in DALIA type: - Online Tutorial - Tutorial url: https://galaxy-au-training.github.io/tutorials/modules/workflows/ uuid: 4bc3f6ce-4a4a-4ec4-9381-d528cb8fd219 language: en - description: Collection of tutorials developed and maintained by the worldwide Galaxy community. license: CC-BY-4.0 name: Galaxy Training proficiency_level: advanced beginner tags: - Bioimage Analysis - Data Analysis - exclude from DALIA type: - Collection - Tutorial num_downloads: null publication_date: null url: https://training.galaxyproject.org/ uuid: abba4da1-44b1-4f68-8cb7-b71d72f3923c - authors: null description: The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME. license: GPL-3.0 name: KNIME Image Processing tags: - Imagej - OMERO - Workflow - exclude from DALIA type: - Tutorial - Online Tutorial - Documentation url: https://www.knime.com/community/image-processing uuid: 4599af42-a420-4677-9be7-f6d41cd13825 language: en - description: The NFDI Basic Service DMP4NFDI supports consortia in developing and providing data management plans (DMP) services for their community. license: CC-BY-4.0 name: Abstract - NFDI Basic Service for Data Management Plans tags: - Research Data Management - exclude from DALIA type: - Document url: https://base4nfdi.de/images/AbstractDMP4NFDI.pdf uuid: 0e615fec-d2b6-4e06-bb85-e7f1583fefcc - description: OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data license: CC-BY-4.0 name: Open Micoscropy Environment (OME) Youtube Channel tags: - Open Source Software - exclude from DALIA type: - Video - Collection num_downloads: null publication_date: null url: https://www.youtube.com/@OpenMicroscopyEnvironment uuid: a2399d73-3b86-4fc6-9bd3-fa7d06d97ac2 - authors: - SciPy - Erick Martins Ratamero license: YOUTUBE STANDARD LICENSE name: Erick Martins Ratamero - Expanding the OME ecosystem for imaging data management | SciPy 2024 tags: - OMERO - Bioimage Analysis - exclude from DALIA type: - Video - Presentation publication_date: '2024-08-19' url: https://www.youtube.com/watch?v=GmhyDNm1RsM uuid: 7c7b0d9a-3a21-458c-b012-819e5366d318 - authors: - Pierre Pouchin - Rdornier - kekunn - Jean-Marie Burel description: A wrapper library which can be called from scripts in Fiji, but can mostly be used in Maven projects to wrap calls to the underlying OMERO Java Gateway. license: GPL-2.0 name: Plugin "simple-omero-client" tags: - OMERO - Github - Fiji - exclude from DALIA type: - Github repository url: https://github.com/GReD-Clermont/simple-omero-client uuid: 2b6a2322-ede2-45c1-a0c8-202852b86d15 - description: An ImageJ plugin to run a script or macro on a batch of images from/to OMERO. license: GPL-2.0 name: Plugin "omero-batch-plugin" tags: - OMERO - Imagej - Imagej Macro - Github - exclude from DALIA type: - Github repository url: https://github.com/GReD-Clermont/omero_batch-plugin uuid: 8c1a97a4-9011-4071-a117-a3316533cfd8 - authors: - Torec T. Luik - Rodrigo Rosas-Bertolini - Eric A.J. Reits - Ron A. Hoebe - Przemek M. Krawczyk description: The authors introduce BIOMERO (bioimage analysis in OMERO), a bridge connecting OMERO, a renowned bioimaging data management platform, FAIR workflows, and high-performance computing (HPC) environments. license: CC-BY-4.0 name: BIOMERO - A scalable and extensible image analysis framework tags: - OMERO - Workflow - Bioimage Analysis - exclude from DALIA type: - Publication num_downloads: null publication_date: null url: https://doi.org/10.1016/j.patter.2024.101024 uuid: 95e45881-b0b7-4cd0-8e4f-d7e00a9fc908 language: en - authors: - Torec Luik - Johannes Soltwedel description: The BIOMERO framework, for BioImage analysis in OMERO, allows you to run (FAIR) bioimage analysis workflows directly from OMERO on a high-performance compute (HPC) cluster, remotely through SSH. license: APACHE-2.0 name: Biomero tags: - OMERO - Github - exclude from DALIA type: - Github repository publication_date: '2024-07-24' url: https://github.com/NL-BioImaging/biomero uuid: a2a8690a-53de-4997-8b08-6acdffabe200 - authors: - Erick Martins Ratamero - Jean-Marie Burel - Will Moore - Guillaume Gay - Christoph Moehl - et al. description: An OMERO CLI plugin for creating and using transfer packets between OMERO servers. license: GPL-2.0 name: Plugin "omero-cli-transfer" tags: - OMERO - exclude from DALIA type: - Github repository publication_date: '2024-09-14' url: https://github.com/ome/omero-cli-transfer uuid: 8ab42148-4985-4710-bcc3-995795e775d9 - authors: - Will Moore - Josh Moore - Yaroslav Halchenko - Sébastien Besson description: Web page for validating OME-NGFF files. license: BSD-2-CLAUSE name: ome-ngff-validator type: - Github repository - Application publication_date: '2022-09-29' url: - https://ome.github.io/ome-ngff-validator/ - https://github.com/ome/ome-ngff-validator uuid: 6781df06-9d08-4075-b79b-18f34d38405f tags: - exclude from DALIA - description: Bio-Formats is a standalone Java library for reading and writing life sciences image file formats. There are several scripts for using Bio-Formats on the command line, which are listed here. license: CC-BY-4.0 name: BioFormats Command line (CLI) tools type: - Documentation publication_date: '2024-10-24' url: https://bio-formats.readthedocs.io/en/v8.0.0/users/comlinetools/index.html uuid: 0211f6f7-6e54-4311-958a-f7ea99a459dd tags: - exclude from DALIA - authors: - Robert Haase description: The integration of Large Language Models (LLMs) in scientific research presents both opportunities and challenges for life scientists. Key challenges include ensuring transparency in AI-generated content and facilitating efficient knowledge exchange among researchers. These issues arise from the in-transparent nature of AI-driven code generation and the informal sharing of AI insights, which may hinder reproducibility and collaboration. This paper introduces git-bob, an innovative AI-assistant designed to address these challenges by fostering an interactive and transparent collaboration platform within GitHub. By enabling seamless dialogue between humans and AI, git-bob ensures that AI contributions are transparent and reproducible. Moreover, it supports collaborative knowledge exchange, enhancing the interdisciplinary dialogue necessary for cutting-edge life sciences research. The open-source nature of git-bob further promotes accessibility and customization, positioning it as a vital tool in employing LLMs responsibly and effectively within scientific communities. license: CC-BY-4.0 name: Towards Transparency and Knowledge Exchange in AI-assisted Data Analysis Code Generation num_downloads: 13 publication_date: '2024-10-14' url: - https://zenodo.org/records/13928832 - https://doi.org/10.5281/zenodo.13928832 uuid: 67d30313-fd89-4a7a-91fb-035bdeb9a9ad language: en authors_with_orcid: - Robert Haase file_formats: .pdf tags: - exclude from DALIA - authors: - Elnaz Fazeli - Haase Robert - Doube Michael - Miura Kota - Legland David description: Bioimaging has transformed our understanding of biological processes, yet extracting meaningful information from complex datasets remains a challenge, particularly for early career scientists. This paper proposes a simplified, systematic approach to bioimage analysis, focusing on categorizing commonly observed structures and shapes, and providing relevant analysis methods. Our approach includes illustrative examples and a visual flowchart, enabling researchers to define analysis objectives clearly. By understanding the diversity of bioimage structures and aligning them with appropriate analysis approaches, the framework empowers researchers to navigate bioimage datasets more efficiently. It also aims to foster a common language between researchers and analysts, thereby enhancing mutual understanding and facilitating effective communication. license: CC-BY-4.0 name: 'From Cells to Pixels: Bridging Biologists and Image Analysts Through a Common Language' num_downloads: 324 publication_date: '2024-08-16' url: - https://zenodo.org/records/13331351 - https://doi.org/10.5281/zenodo.13331351 uuid: f993d608-1bc9-4a59-a588-3318472e79ea language: en authors_with_orcid: - Elnaz Fazeli https://orcid.org/0000-0002-0770-0777 - Haase Robert - Doube Michael - Miura Kota - Legland David file_formats: .pdf tags: - include in DALIA - authors: - Rémy Jean Daniel Dornier license: CC-BY-NC-SA-4.0 name: OMERO - QuPath description: OMERO-RAW extension for QuPath allows to directly access to the raw pixels of images. All types of images (RGB, fluorescence, ...) are supported with this extension. tags: - Bioimage Analysis - OMERO - exclude from DALIA type: - Online Tutorial url: https://wiki-biop.epfl.ch/en/data-management/omero/qupath uuid: 449fdce8-2c37-4543-aa9e-29cdd82e6d4f - authors: - Shanghang Zhang - Gaole Dai - Tiejun Huang - Jianxu Chen license: CC-BY-NC-SA description: Multimodal large language models have been recognized as a historical milestone in the field of artificial intelligence and have demonstrated revolutionary potentials not only in commercial applications, but also for many scientific fields. Here we give a brief overview of multimodal large language models through the lens of bioimage analysis and discuss how we could build these models as a community to facilitate biology research name: Multimodal large language models for bioimage analysis tags: - Bioimage Analysis - FAIR-Principles - Workflow - exclude from DALIA type: - Publication url: - https://www.nature.com/articles/s41592-024-02334-2 - https://arxiv.org/abs/2407.19778 uuid: 67c073f3-8410-4e3f-b354-a343819750d0 language: en - authors: - Robert Haase description: 'Large Language Models are changing the way we interact with computers, especially how we write code. In this tutorial, we will generate bio-image analysis code using two LLM-based code generators, bia-bob and git-bob. https://github.com/haesleinhuepf/bia-bob https://github.com/haesleinhuepf/git-bob  ' license: CC-BY-4.0 name: Bio-image Analysis Code Generation num_downloads: 4 proficiency_level: advanced beginner publication_date: '2024-10-28' url: - https://zenodo.org/records/14001044 - https://doi.org/10.5281/zenodo.14001044 uuid: af33158e-7b1d-4002-a443-a4a490ee33ed language: en authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .pdf * .pptx tags: - include in DALIA - authors: - Alan O'Callaghan - Léo Leplat description: These are the notebooks and associated files for the i2k 2024 QuPath for Python programmers workshop. proficiency_level: advanced beginner tags: - Python - Notebooks - Open Source Software - Bioimage Analysis - include in DALIA type: - Github repository - Notebook - Collection license: UNKNOWN name: QuPath for Python programmers publication_date: '2024-10-23' url: https://github.com/qupath/i2k-qupath-for-python-programmers uuid: ffdb3697-4a31-423e-acf5-dc332827959a - authors: - Stephan Saalfeld - Tobias Pietzsch proficiency_level: competent tags: - Bioimage Analysis - include in DALIA type: - Workshop - Notebook - Github repository license: APACHE-2.0 name: I2K2024 workshop material - Lazy Parallel Processing and Visualization of Large Data with ImgLib2, BigDataViewer, the N5-API, and Spark num_downloads: null publication_date: null url: - https://saalfeldlab.github.io/i2k2024-lazy-workshop/ - https://github.com/saalfeldlab/i2k2024-lazy-workshop uuid: 206c398c-f678-4e2f-ac06-173e5d765cc8 - authors: - Jordão Bragantini - Teun Huijben proficiency_level: competent tags: - Bioimage Analysis - include in DALIA type: - Workshop - Github repository - Tutorial license: BSD3-CLAUSE name: Ultrack I2K 2024 Workshop Materials url: - https://github.com/royerlab/ultrack-i2k2024 - https://royerlab.github.io/ultrack-i2k2024/ uuid: c7d0ca9d-08b5-4f39-958f-a0a303c0a8c0 - authors: - Agustín Andrés Corbat - OmFrederic - Jonas Windhager - Kristína Lidayová description: Material for the I2K 2024 "Multiplexed tissue imaging - tools and approaches" workshop proficiency_level: competent tags: - Bioimage Analysis - include in DALIA type: - Github repository - Slides - Workshop license: CC-BY-4.0 name: Multiplexed tissue imaging - tools and approaches url: - https://github.com/BIIFSweden/I2K2024-MTIWorkshop - https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1 uuid: 4265abe7-0e3b-44d4-bb1f-584af61bb009 - authors: - Robert Haase description: This repository contains training materials for the Tutorial "Bio-Image Analysis Code Generation" at the From Images To Knowledge (I2K) Conference (virtual) October 28th-30th 2024. proficiency_level: advanced beginner tags: - Bioimage Analysis - Notebooks - Biabob - include in DALIA type: - Github repository - Tutorial - Notebook license: BSD-3-CLAUSE name: I2K2024(virtual) - Bio-Image Analysis Code Generation url: https://github.com/haesleinhuepf/i2k2024-ai-code-generation uuid: 280f8cd2-9962-4077-8430-5547da1fc990 - authors: - Joanna Pylvänäinen description: I2K 2024 workshop materials for "Object Tracking and Track Analysis using TrackMate and CellTracksColab" proficiency_level: advanced beginner tags: - Bioimage Analysis - include in DALIA type: - Github repository - Tutorial - Workshop - Slides license: GPL-3.0 name: Object Tracking and Track Analysis using TrackMate and CellTracksColab num_downloads: null publication_date: null url: https://github.com/CellMigrationLab/I2K_2024 uuid: c39b2920-f765-4bf8-afcc-eeb06d5a61a8 - authors: - Stephane Rigaud - Robert Haase description: Course and material for the clEsperanto workshop presented at I2K 2024 @ Human Technopol (Milan, Italy). The workshop is an hands-on demo of the clesperanto project, focussing on how to use the library for users who want use GPU-acceleration for their Image Processing pipeline. proficiency_level: competent tags: - Bioimage Analysis - include in DALIA type: - Github repository - Workshop - Tutorial - Notebook license: BSD-3-CLAUSE name: 'I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library' url: https://github.com/StRigaud/clesperanto_workshop_I2K24?tab=readme-ov-file uuid: b3fe1d29-990c-40d4-b69f-aad85d29012f language: en - authors: - Tim Monko description: Napari-ndev is a collection of widgets intended to serve any person seeking to process microscopy images from start to finish. The goal of this example pipeline is to get the user familiar with working with napari-ndev for batch processing and reproducibility (view Image Utilities and Workflow Widget). proficiency_level: advanced beginner tags: - Napari - Bioimage Analysis - exclude from DALIA type: - Documentation - Github repository - Tutorial license: BSD-3-CLAUSE name: Example Pipeline Tutorial publication_date: '2024-10-28' url: - https://timmonko.github.io/napari-ndev/tutorial/01_example_pipeline/ - https://github.com/timmonko/napari-ndev uuid: be6a6aaa-fe28-46bb-b508-d7d5eccdeb61 language: en - authors: - Josh Moore description: Presented at https://globalbioimaging.org/exchange-of-experience/exchange-of-experience-ix in Okazaki, Japan. license: CC-BY-4.0 name: "[GBI EoE IX] NFDI4BIOIMAGE\nNational Research Data Infrastructure \nfor Microscopy\ \ and BioImage Analysis" num_downloads: 23 publication_date: '2024-10-29' url: - https://zenodo.org/records/14001388 - https://doi.org/10.5281/zenodo.14001388 uuid: 7284eeb2-8c94-409d-b1a6-4fca8d3d58c7 authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X file_formats: .pdf tags: - exclude from DALIA - authors: - Josh Moore description: Presented at https://www.bioimagingnorthamerica.org/events/bina-2024-community-congress/ license: CC-BY-4.0 name: '[BINA CC] Scalable strategies for a next-generation of FAIR bioimaging' num_downloads: 37 publication_date: '2024-09-24' url: - https://zenodo.org/records/13831274 - https://doi.org/10.5281/zenodo.13831274 uuid: 5c0f0a6f-ec67-4e02-a334-aaac067af06f authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X file_formats: .pdf tags: - exclude from DALIA - authors: - Josh Moore description: 'or, "OME-Zarr: ''even a talk on formats [can be] interesting''" Presented at https://events.humantechnopole.it/event/1/' license: CC-BY-4.0 name: '[I2K] Scalable strategies for a next-generation of FAIR bioimaging' num_downloads: 279 publication_date: '2024-10-25' url: - https://zenodo.org/records/13991322 - https://doi.org/10.5281/zenodo.13991322 uuid: 54710395-1ce7-4672-9c8d-967f1b2ab3e4 authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X file_formats: .pdf tags: - include in DALIA - authors: - Thomas Zobel - Jens Wendt description: 'This Excel Workbook contains some simple Macros to help with the generation of a .csv in the necessary format for Key-Value pair annotations of images in OMERO. The format is tailored for the OMERO.web script "KeyVal_from_csv.py"  (from the version <=5.8.3 of the core omero-scripts). Attached is also a video of Thomas Zobel, the head of the imaging core facility Uni Münster, showcasing the use of the Excel workbook.The video uses a slightly older version of the workbook and OMERO, but the core functionality remains unchanged. Please keep in mind, that the OMERO.web script(s) to handle Key-Value Pairs from/to .csv files will undergo a major change very soon.This might break the compatibility with the format used now for the generated .csv from the workbook.' license: CC-BY-4.0 name: Excel template for adding Key-Value Pairs to images num_downloads: 30 publication_date: '2024-10-30' url: - https://zenodo.org/records/14014252 - https://doi.org/10.5281/zenodo.14014252 uuid: 171c8e4e-be57-4b85-ba1e-5b53713b34e9 language: en authors_with_orcid: - Thomas Zobel https://orcid.org/0000-0002-2101-8416 - Jens Wendt https://orcid.org/0009-0002-1826-7099 file_formats: .mp4 * .xlsm tags: - exclude from DALIA - authors: - Chris Allan - Emil Rozbicki description: Example Workflows / usage of the Glencoe Software. tags: - OMERO - exclude from DALIA type: - Video - Tutorial - Collection license: UNKNOWN name: Glencoe Software Webinars url: https://www.glencoesoftware.com/media/webinars/ uuid: 45c20672-7be4-47cf-9402-4df681b8f229 - authors: - Christian Schmidt - Janina Hanne - Josh Moore - Christian Meesters - Elisa Ferrando-May - et al. description: As an initiative within Germany's National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. tags: - Research Data Management - exclude from DALIA type: - Publication license: CC-BY-4.0 name: Research data management for bioimaging - the 2021 NFDI4BIOIMAGE community survey num_downloads: 321 publication_date: '2022-09-20' url: https://f1000research.com/articles/11-638/v2 uuid: 90748362-6f2e-4a52-9ce8-9cffca33693a language: en - authors: Marvin Albert description: Repository accompanying the multiview-stitcher tutorial for Virtual I2K 2024 license: BSD-3-CLAUSE name: Virtual-I2K-2024-multiview-stitcher publication_date: '2024-10-30T07:38:11+00:00' proficiency_level: advanced beginner tags: - Big Data - Bioimageanalysis - include in DALIA type: - Github repository - Tutorial url: - https://github.com/m-albert/Virtual-I2K-2024-multiview-stitcher uuid: e1b05d87-eea9-479c-9dd1-108d398bf106 - authors: - Steuerungsgremium Allianz-Schwerpunkt - Alexander von Humboldt Foundation - Deutsche Forschungsgemeinschaft - Fraunhofer Society - German Rectors' Conference - Leibniz Association - German National Academy of Sciences Leopoldina - German Academic Exchange Service - Helmholtz Association of German Research Centres - Max Planck Society description: Arbeitspapier des Steuerungsgremiums des Allianz-Schwerpunkts "Digitalität in der Wissenschaft" license: CC-BY-4.0 name: Forschungsdatenmanagement zukunftsfest gestalten – Impulse für die Strukturevaluation der Nationalen Forschungsdateninfrastruktur (NFDI) num_downloads: 237 publication_date: '2024-11-04' url: - https://zenodo.org/records/14032908 - https://doi.org/10.5281/zenodo.14032908 uuid: 92604000-df51-4b04-b020-a885099b8ea3 authors_with_orcid: - Steuerungsgremium Allianz-Schwerpunkt - Alexander von Humboldt Foundation - Deutsche Forschungsgemeinschaft - Fraunhofer Society - German Rectors' Conference - Leibniz Association - German National Academy of Sciences Leopoldina - German Academic Exchange Service - Helmholtz Association of German Research Centres - Max Planck Society file_formats: .pdf tags: - exclude from DALIA - authors: - Robert Haase description: 'This is a dataset of PNG images of [Bio-Image Data Science teaching slides](https://zenodo.org/records/12623730). The png_umap.yml file contains a list of all images and a dimensionality reduced embedding (Uniform Manifold Approximation Projection, UMAP) made using OpenAI''s text-embedding-ada-002 model. A notebook for visualizing this data is published here: https://github.com/haesleinhuepf/stackview/blob/main/docs/sliceplot.ipynb' license: CC-BY-4.0 name: Stackview sliceplot example data num_downloads: 3 publication_date: '2024-11-03' url: - https://zenodo.org/records/14030307 - https://doi.org/10.5281/zenodo.14030307 uuid: 4031c818-598a-44aa-80c4-ff5ce2707477 language: en authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .zip tags: - exclude from DALIA - authors: Isra Mekki description: '' license: MIT name: Prompt-Engineering-LLMs-Course publication_date: '2024-09-11T07:45:30+00:00' tags: - Llms - Prompt Engineering - Code Generation - include in DALIA type: - Github repository - Tutorial url: https://github.com/HelmholtzAI-Consultants-Munich/Prompt-Engineering-LLMs-Course uuid: 426d4c09-4401-4742-a33c-f34130ad2a24 - authors: - Riccardo Massei - Matthias Berndt - Beatriz Serrano-Solano - Wibke Busch - Stefan Scholz - Hannes Bohring - Jo Nyffeler - Luise Reger - Jan Bumberger - Lucille Lopez-Delisle description: 'Imaging is crucial across various scientific disciplines, particularly in life sciences, where it plays a key role in studies ranging from single molecules to whole organisms. However, the complexity and sheer volume of image data, especially from high-content screening (HCS) experiments involving cell lines or other organisms, present significant challenges. Managing and analysing this data efficiently requires well-defined image processing tools and analysis pipelines that align with the FAIR principles—ensuring they are findable, accessible, interoperable, and reusable across different domains. In the frame of NFDI4BioImaging (the National Research Data Infrastructure focusing on bioimaging in Germany), we want to find viable solutions for storing, processing, analysing, and sharing HCS data. In particular, we want to develop solutions to make findable and machine-readable metadata using (semi)automatic analysis pipelines. In scientific research, such pipelines are crucial for maintaining data integrity, supporting reproducibility, and enabling interdisciplinary collaboration. These tools can be used by different users to retrieve images based on specific attributes as well as support quality control by identifying appropriate metadata. Galaxy, an open-source, web-based platform for data-intensive research, offers a solution by enabling the construction of reproducible pipelines for image analysis. By integrating popular analysis software like CellProfiler and connecting with cloud services such as OMERO and IDR, Galaxy facilitates the seamless access and management of image data. This capability is particularly valuable in bioimaging, where automated pipelines can streamline the handling of complex metadata, ensuring data integrity and fostering interdisciplinary collaboration. This approach not only increases the efficiency of HCS bioimaging but also contributes to the broader scientific community''s efforts to embrace FAIR principles, ultimately advancing scientific discovery and innovation. In the present study, we proposed an automated analysis pipeline for storing, processing, analysing, and sharing HCS bioimaging data. The (semi)automatic workflow was developed by taking as a case study a dataset of zebrafish larvae and cell lines images previously obtained from an automated imaging system generating data in an HCS fashion. In our workflows, images are automatically enriched with metadata (i.e. key-value pairs, tags, raw data, regions of interest) and uploaded to the UFZ-OME Remote Objects (OMERO) server using a novel OMERO tool suite developed with GALAXY. Workflows give the possibility to the user to intuitively fetch images from the local server and perform image analysis (i.e. annotation) or even more complex toxicological analyses (dose response modelling). Furthermore, we want to improve the FAIRness of the protocol by adding a direct upload link to the Image Data Resource (IDR) repository to automatically prepare the data for publication and sharing.' license: CC-BY-4.0 name: Building FAIR image analysis pipelines for high-content-screening (HCS) data using Galaxy num_downloads: 1 publication_date: '2024-11-06' url: - https://zenodo.org/records/14044640 - https://doi.org/10.5281/zenodo.14044640 - https://galaxyproject.org/news/2024-11-08-galaxy-imaging-fair-pipelines/ uuid: 59d2650b-b35a-48d9-b6cb-eb7205044178 language: en authors_with_orcid: - Riccardo Massei https://orcid.org/0000-0003-2104-9519 - Matthias Berndt - Beatriz Serrano-Solano https://orcid.org/0000-0002-5862-6132 - Wibke Busch https://orcid.org/0000-0002-5497-6266 - Stefan Scholz https://orcid.org/0000-0002-6990-4716 - Hannes Bohring - Jo Nyffeler - Luise Reger - Jan Bumberger https://orcid.org/0000-0003-3780-8663 - Lucille Lopez-Delisle https://orcid.org/0000-0002-1964-4960 file_formats: .odp tags: - exclude from DALIA - authors: - Bruna Piereck - Alexander Botzki description: Course repository for Strategic Use of Generative AI license: CC-BY-4.0 name: introduction-to-generative-ai publication_date: '2024-09-27T14:38:51+00:00' proficiency_level: novice tags: - Artificial Intelligence - include in DALIA type: - Github repository - Tutorial url: - https://github.com/vibbits/introduction-to-generative-ai - https://liascript.github.io/course/?https://raw.githubusercontent.com/vibbits/introduction-to-generative-ai/refs/heads/main/README.md uuid: 53ac5bc3-7ff1-45c0-aeb1-3366ddfd86b8 - authors: - Tuur Muyldermans - Kris Davie - Alexander - Nicolas Vannieuwkerke - Kobe Lavaerts - Marcel Ribeiro-Dantas - Bruna Piereck - Steff Taelman description: Nextflow workshop materials March 2023 license: CC-BY-4.0 name: nextflow-workshop publication_date: '2023-03-29T10:40:04+00:00' proficiency_level: advanced beginner tags: - Workflow - Nextflow - include in DALIA type: - Github repository - Tutorial url: - https://github.com/vibbits/nextflow-workshop - https://liascript.github.io/course/?https://raw.githubusercontent.com/vibbits/nextflow-workshop/main/README.md#1 uuid: d965beed-3b7b-4eaf-8771-769d30b2fd34 - authors: - Curtis Rueden - Albane de la Villegeorges - Simon F. Nørrelykke - Romain Guiet - Olivier Burri - et al. license: UNKNOWN name: Upcoming Image Analysis Events tags: - Bioimage Analysis - exclude from DALIA type: - Collection - Event - Forum Post - Workshop url: https://forum.image.sc/t/upcoming-image-analysis-events/60018/67 uuid: 58105db4-ecb2-4dd4-9e13-b751879b1397 - authors: - Riccardo Massei - Robert Haase - ENicolay description: This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series. license: MIT name: YMIA - Python-Based Event Series Training Material publication_date: null proficiency_level: novice tags: - Python - artifical intelligence - Bioimage Analysis - include in DALIA type: - Github repository - Slides url: https://github.com/rmassei/ymia_python_event_series_material uuid: 0beb7c54-d72d-4bc1-af9e-5db27007c845 - authors: - SaibotMagd description: This tool is intended to link different research data management platforms with each other. license: UNKNOWN name: RDM_system_connector tags: - Research Data Management - exclude from DALIA type: - Github repository url: https://github.com/SaibotMagd/RDM_system_connector uuid: c7980923-d1e1-4826-86d0-e76e1d4c2311 - authors: - Anna Swan description: Sharing knowledge and data in the life sciences allows us to learn from each other and built on what others have discovered. This collection of online courses brings together a variety of training, covering topics such as biocuration, open data, restricted access data and finding publicly available data, to help you discover and make the most of publicly available data in the life sciences. license: CC-BY-4.0 name: Finding and using publicly available data publication_date: '2024-01-01' proficiency_level: novice tags: - Open Science - Teaching - Sharing - include in DALIA type: - Collection - Tutorial - Video url: https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/ uuid: 6a6d761a-ae7b-4a0b-b7e2-ca6cb1338de5 language: en - authors: - Tom Boissonnet - Bettina Hagen - Susanne Kunis - Christian Schmidt - Stefanie Weidtkamp-Peters description: 'Fit for OMERO: How imaging facilities and IT departments work together to enable RDM for bioimaging Description: Research data management (RDM) in bioimaging is challenging because of large file sizes, heterogeneous file formats and the variability of imaging methods. The image data management system OMERO (OME Remote Objects) allows for centralized and secure storage, organization, annotation, and interrogation of microscopy data by researchers. It is an internationally well-supported open-source software tool that has become one of the best-known image data management tools among bioimaging scientists. Nevertheless, the de novo setup of OMERO at an institute is a multi-stakeholder process that demands time, funds, organization and iterative implementation. In this workshop, participants learn how to begin setting up OMERO-based image data management at their institution. The topics include: Stakeholder identification at the university / research institute Process management, time line expectations, and resources planning Learning about each other‘s perspectives on chances and challenges for RDM Funding opportunities and strategies for IT and imaging core facilities Hands-on: Setting up an OMERO server in a virtual machine environment Target audience: This workshop was directed at universities and research institutions who consider or plan to implement OMERO, or are in an early phase of implementation. This workshop was intended for teams from IT departments and imaging facilities to participate together with one person from the IT department, and one person from the imaging core facility at the same institution. The trainers: Prof. Dr. Stefanie Weidtkamp-Peters (Imaging Core Facility Head, Center for Advanced Imaging, Heinrich Heine University of Düsseldorf) Dr. Susanne Kunis (Software architect, OMERO administrator, metadata specialist, University of Osnabrück) Dr. Tom Boissonnet (OMERO admin and image metadata specialist, Center for Advanced Imaging, Heinrich Heine University of Düsseldorf) Dr. Bettina Hagen (IT Administration and service specialist, Max Planck Institute for the Biology of Ageing, Cologne)  Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center (DKFZ), Heidelberg) Time and place The format was a two-day, in-person workshop (October 16-17, 2024). Location: Heidelberg, Germany Workshop learning goals Learn the steps to establish a local RDM environment fit for bioimaging data Create a network of IT experts and bioimaging specialists for bioimage RDM across institutions Establish a stakeholder process management for installing OMERO-based RDM Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution ' license: CC-BY-4.0 name: '[Workshop Material] Fit for OMERO - How imaging facilities and IT departments work together to enable RDM for bioimaging, October 16-17, 2024, Heidelberg' num_downloads: 101 proficiency_level: advanced beginner publication_date: '2024-10-30' url: - https://zenodo.org/records/14013026 - https://doi.org/10.5281/zenodo.14013026 uuid: 38fbd343-3e03-49e2-baf2-fbe534033e68 language: en authors_with_orcid: - Tom Boissonnet - Bettina Hagen - Susanne Kunis https://orcid.org/0000-0001-6523-7496 - Christian Schmidt https://orcid.org/0000-0001-9568-895X - Stefanie Weidtkamp-Peters https://orcid.org/0000-0001-7734-3771 file_formats: .pdf tags: - include in DALIA - authors: - Carl Herrmann - annavonbachmann - David Hoksza - Martin Schätz - Dario Malchiodi - jnguyenvan - Britta Velten - Elodie Laine - JanaBraunger - barwil name: Lecture-materials of the DeepLife course publication_date: '2023-12-06' license: UNKNOWN proficiency_level: competent tags: - Bioinformatics - exclude from DALIA type: - Github repository - Slides - Notebook url: https://github.com/deeplife4eu/Lecture-materials/ uuid: 169aecea-cddf-4bda-aeb4-953b0d658513 - authors: - Christian Tischer - Antonio Politi - Toby Hodges - maulakhan - grinic - bugraoezdemir - Tim-Oliver Buchholz - Elnaz Fazeli - Aliaksandr Halavatyi - Dominik Kutra - Stefania Marcotti - AnniekStok - Felix - jhennies - Severina Klaus - Martin Schorb - Nima Vakili - Sebastian Gonzalez Tirado - Stefan Helfrich - Yi Sun - Ziqiang Huang - Jan Eglinger - Constantin Pape - Joel Lüthi - Matt McCormick - Oane Gros description: Resources for teaching/preparing to teach bioimage analysis license: CC-BY-4.0 name: training-resources publication_date: '2020-04-23T07:51:38+00:00' proficiency_level: advanced beginner tags: - Bioimageanalysis - Neurobias - include in DALIA type: - Github repository url: https://github.com/NEUBIAS/training-resources uuid: b2557f1a-0ecc-4ca0-999f-ed0e8de6671c - authors: - Arif Khan - Christian Tischer - Sebastian Gonzalez - Dominik Kutra - Felix Schneider - et al. license: MIT name: cba-support-template publication_date: '2021-12-01' tags: - Workflow - Research Data Management - exclude from DALIA type: - Tutorial url: https://git.embl.de/grp-cba/cba-support-template uuid: 43f9bae1-731e-4d6c-9b73-bcca2f5f74a2 - authors: - Bret Fisher description: In this course you will learn how to use Docker, Compose and Kubernetes on your machine for better software building and testing. license: UNKNOWN name: Docker Mastery - with Kubernetes + Swarm from a Docker Captain proficiency_level: competent tags: - Docker - include in DALIA type: - Video - Tutorial - Online Course url: https://www.udemy.com/course/docker-mastery/?srsltid=AfmBOornR5gRqOg-4v8Nsap1z24CaPPUPxg8JzyqEGZ6MvW_dh-sf4Af&couponCode=ST2MT110724BNEW uuid: 608bbdad-a08b-4636-beee-4a61bf2261f2 - description: Computational skills training at the UCL Sainsbury Wellcome Centre and Gatsby Computational Neuroscience Unit, delivered by members of the Neuroinformatics Unit. license: CC-BY-4.0 name: SWC/GCNU Software Skills proficiency_level: competent type: - Collection - Online Course - Video - Tutorial url: https://software-skills.neuroinformatics.dev/index.html uuid: 29179f80-6007-42ca-841e-12c183e99765 tags: - include in DALIA - authors: - Daniele Procida description: Diátaxis is a systematic framework for technical documentation that organizes content into four types—tutorials, how-to guides, technical reference, and explanations—to address distinct user needs, enhancing both user understanding and the documentation process. license: CC-BY-SA-4.0 name: Diátaxis - A systematic approach to technical documentation authoring. tags: - Documentation - include in DALIA type: - Website - Tutorial - Workflow url: - https://www.diataxis.fr/ uuid: 090e6379-ab50-4856-a602-26a4ff1c5f9f language: en - authors: - Mark Meysenburg - Toby Hodges - Dominik Kutra - Erin Becker - David Palmquist - et al. description: This lesson shows how to use Python and scikit-image to do basic image processing. license: CC-BY-4.0 name: Image Processing with Python proficiency_level: advanced beginner tags: - Bioimage Analysis - Python - include in DALIA type: - Tutorial - Workflow url: https://datacarpentry.org/image-processing/key-points.html uuid: 0c3a2e37-788f-4cbc-85e7-e18f471f9210 - authors: - Nicolas Chiaruttini description: 'The files contained in this repository are confocal images taken with the Evident FV 4000 of a sample containing DAPI and mCherry stains, excited with a 405 nm laser and a 561 nm laser individual tiles are named `tiling-sample-brain-section_A01_G001_{i}.oir` The stiched image is named `Stitch_A01_G001` and contains an extra file `Stitch_A01_G001_00001` Some metadata like the tiles positions are stored in the extra files (omp2info)  ' license: CC-BY-4.0 name: Evident OIR sample files tiles + stitched image - FV 4000 num_downloads: 154 publication_date: '2024-09-04' url: - https://zenodo.org/records/13680725 - https://doi.org/10.5281/zenodo.13680725 uuid: 3583229a-ff8c-4157-84f4-d138938aa7d0 language: en authors_with_orcid: - Nicolas Chiaruttini file_formats: .hdf5 * .oir * .omp2info tags: - exclude from DALIA - authors: - Romain Guiet - Nicolas Chiaruttini description: "Mouse duodenum fixed in 4% PFA overnight at 4°C, processed for\ \ paraffin infiltration using a standard histology procedure and cut at 4 microns\ \ were dewaxed, rehydrated, permeabilized with 0.5% Triton X-100 in PBS 1x and\ \ stained with Azide - Alexa Fluor 555 (Thermo Fisher) to detect EdU and DAPI\ \ for nuclei. The images were taken using a Leica DM5500 microscope with a 40X\ \ N.A.1 objective (black&white camera: DFC350FXR2, pixel dimension: 0.161\ \ microns). Next, the slide was unmounted and stained using the fully automated\ \ Ventana Discovery xT autostainer (Roche Diagnostics, Rotkreuz, Switzerland).\ \ All steps were performed on automate with Ventana solutions. Sections were pretreated\ \ with heat using the CC1 solution under mild conditions. The primary rat anti\ \ BrDU (clone: BU1/75 (ICR1), Serotec, diluted 1:300) was incubated 1 hour at\ \ 37°C. After incubation with a donkey anti rat biotin diluted 1:200 (Jackson\ \ ImmunoResearch Laboratories), chromogenic revelation was performed with DabMap\ \ kit. The section was counterstained with Harris hematoxylin (J.T. Baker) before\ \ a second round of imaging on DM5500 PL Fluotar 40X N.A.1.0 oil (color camera:\ \ DFC 320 R2, pixel dimension: 0.1725 microns). Before acquisition, a white-balance\ \ as well as a shading correction is performed according to Leica LAS software\ \ wizard. The fluorescence and DAB images were converted in ome.tiff multiresolution\ \ file with the kheops Fiji Plugin.\n\nSampled prepared in the EPFL histology\ \ core facility by Nathalie Müller and Gian-Filippo Mancini.\n\nAssociated\ \ documents:\n\n\n\thttps://c4science.ch/w/bioimaging_and_optics_platform_biop/teaching/dab-intensity/\n\ \thttps://imagej.net/plugins/bdv/warpy/warpy\n\n\nThis document contains a full\ \ QuPath project with an example of registered image.\n\n " license: CC-BY-4.0 name: Test Dataset for Whole Slide Image Registration num_downloads: 1001 publication_date: '2021-04-12' url: - https://zenodo.org/records/5675686 - https://doi.org/10.5281/zenodo.5675686 uuid: 1cea6a77-395b-4881-8bf6-abd80bf928d7 language: en authors_with_orcid: - Romain Guiet https://orcid.org/0000-0001-6715-4897 - Nicolas Chiaruttini https://orcid.org/0000-0003-4722-6245 file_formats: .jpg * .zip tags: - exclude from DALIA - authors: - Nicolas Chiaruttini description: 'This is a microscopy image dataset generated by the Perkin Elmer Operetta HCS microscope by of the user of the PTBIOP EPFL facility. As of the 17th of July 2023, opening this file in ImageJ/Fiji using the BioFormats 6.14 library, this dataset generates a Null Pointer Exception. A post on forum.image.sc is linked to this issue: https://forum.image.sc/t/null-pointer-exception-in-perkin-elmer-operetta-dataset-with-bio-formats-6-14/83784    ' license: CC-BY-4.0 name: Example Operetta Dataset num_downloads: 3 publication_date: '2023-07-17' url: - https://zenodo.org/records/8153907 - https://doi.org/10.5281/zenodo.8153907 uuid: 0a89b7f2-53fc-41c9-8bac-8c9e5314533a language: en authors_with_orcid: - Nicolas Chiaruttini https://orcid.org/0000-0003-4722-6245 file_formats: .zip tags: - exclude from DALIA - authors: - Nicolas Chiaruttini description: 'A set of public CZI files. These can be used for testing CZI readers. - Demo LISH 4x8 15pct 647.czi: A cleared mouse brain acquired with a Zeiss LightSheet Z1 with 32 tiles. Courtesy of the Carl Petersen lab LSENS (https://www.epfl.ch/labs/lsens). Sampled prepared by Yanqi Liu an imaged by Olivier Burri. - test_gray.czi: a synthetically generated CZI file without metadata, made by Sebastian Rhode - Image_1_2023_08_18__14_32_31_964.czi: an example multi-part CZI file, containing only camera noise - a xt scan, xz scan, xzt scan - a set of multi angle, multi illumination, mutli tile acquisition, taken on the LightSheet Z1 microscope of the PTBIOP by Lorenzo Talà' license: CC-BY-4.0 name: CZI file examples num_downloads: 1192 publication_date: '2023-08-18' url: - https://zenodo.org/records/8305531 - https://doi.org/10.5281/zenodo.8305531 uuid: 9786c566-4887-4d42-b64b-24729f2defd8 language: en authors_with_orcid: - Nicolas Chiaruttini https://orcid.org/0000-0003-4722-6245 file_formats: .czi tags: - exclude from DALIA - authors: - Sarah Machado - Vincent Mercier - Nicolas Chiaruttini description: "Image datasets from the publication : LimeSeg: A coarse-grained\ \ lipid membrane simulation for 3D image segmentation\n\n\n\tVesicles.tif: spinning-disc\ \ confocal images of giant unilamellar vesicles\n\tHelaCell-FIBSEM.tif: a\ \ 3D Electron Microscopy (EM) dataset of nearly isotropic sections of\ \ a Hela cell, acquired with a focused ion beam scanning electron microscope (FIB-SEM).\ \ Sections are aligned with TrackEm2 (doi: ), without additional preprocessing.\n\ \tDrosophilaEggChamber.tif: point scanning confocal images of a Drosophila egg\ \ chamber. Channel 1: cell nuclei  stained with DAPI. Channel 2: cell\ \ membranes visualized with fused membrane proteins Nrg::GFP and Bsg::GFP. \n\ \n\nImage metadata contains extra information including voxel sizes.\n\n " license: CC-BY-4.0 name: LimeSeg Test Datasets num_downloads: 174 publication_date: '2018-10-27' url: - https://zenodo.org/records/1472859 - https://doi.org/10.5281/zenodo.1472859 uuid: 088ba7c8-49db-4377-9c5e-5fc38dc96433 language: en authors_with_orcid: - Sarah Machado - Vincent Mercier - Nicolas Chiaruttini https://orcid.org/0000-0003-4722-6245 file_formats: .tif tags: - exclude from DALIA - authors: - Yannick KREMPP description: A review of the tools, methods and concepts useful for biologists and life scientists as well as bioimage analysts. license: UNKNOWN name: AI ML DL in Bioimage Analysis - Webinar publication_date: '2024-11-14' proficiency_level: advanced beginner tags: - Artificial Intelligence - Bioimage Analysis - include in DALIA type: - Video - Slides - Webinar url: https://www.youtube.com/watch?v=TJXNMIWtdac uuid: 2f829294-f8cc-4362-b5b5-30b9b53af98a - authors: - Guillaume Jacquemet description: Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions. license: UNKNOWN name: Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy publication_date: '2024-10-24' tags: - artificial intelligence - BioImage Analysis - include in DALIA type: - Video - Slides url: https://www.youtube.com/watch?v=KTdZBgSCYJQ uuid: 87566bb8-e53b-4f58-bb5c-e722e0dc1a64 language: en - description: The mission of Metrics Reloaded is to guide researchers in the selection of appropriate performance metrics for biomedical image analysis problems, as well as provide a comprehensive online resource for metric-related information and pitfalls license: UNKNOWN name: Metrics Reloaded - A framework for trustworthy image analysis validation proficiency_level: competent tags: - Bioimage Analysis - Quality Control - exclude from DALIA type: - Website - Collection url: https://metrics-reloaded.dkfz.de/ uuid: 667f39e8-91c3-4c8c-8a5e-3a3b32f41da8 language: en - submission_date: '2024-11-12T10:17:25.985725' authors: - Wendt Jens description: Short presentation given at at PoL BioImage Analysis Symposium Dresden 2023 license: CC-BY-4.0 name: Metadata Annotation Workflow for OMERO with Tabbles num_downloads: 131 proficiency_level: advanced beginner publication_date: '2023-09-04' url: - https://zenodo.org/records/8314968 - https://doi.org/10.5281/zenodo.8314968 uuid: 0016dd82-e662-436f-8288-62313aa6508c authors_with_orcid: - Wendt Jens https://orcid.org/0009-0002-1826-7099 file_formats: .pdf * .pptx tags: - exclude from DALIA - authors: - Isabel Kemmer - Feriel Romdhane - Euro-BioImaging ERIC description: 'Depositing data in quality data repositories is one crucial step towards FAIR (Findable, Accessible, Interoperable, and Reusable) data. Accordingly, Euro-BioImaging strongly encourages sharing scientific imaging data in established, thematic repositories.  To guide you in the selection of appropriate repositories, we have created an overview of available repositories for different types of image data, including their scope and requirements. This decision tree guides you through questions about your data and directs you to the correct repository, and/or provides instructions for further processing to meet the critera of the repositories.  Three seperate trees are provided for different classes of imaging data: open bioimage data, preclinical data, and human imaging data. ' license: CC-BY-4.0 name: Image Repository Decision Tree - Where do I deposit my imaging data num_downloads: 129 publication_date: '2024-10-22' url: - https://zenodo.org/records/13945179 - https://doi.org/10.5281/zenodo.13945179 uuid: db00a320-cecb-4332-91ab-40df4aff3842 language: en authors_with_orcid: - Isabel Kemmer https://orcid.org/0000-0002-8799-4671 - Feriel Romdhane https://orcid.org/0000-0002-5854-9341 - Euro-BioImaging ERIC file_formats: .pdf tags: - include in DALIA - authors: - Alessandro Rigano - Ulrike Boehm - Claire M. Brown - Joel Ryan - James J. Chambers - Robert A. Coleman - Orestis Faklaris - Thomas Guilbert - Michelle S. Itano - Judith Lacoste - Alex Laude - Marco Marcello - Paula Montero-Llopis - Glyn Nelson - Roland Nitschke - Jaime A. Pimentel - Stefanie Weidtkamp-Peters - Caterina Strambio-De-Castillia description: "Example Microscopy Metadata (Microscope.JSON and Settings.JSON) files\ \ produced using Micro-Meta App to document the Hardware Specifications of example\ \ Microscopes and the Image Acquisition Settings utilized to acquire example images\ \ as listed in the table below.\n\n\nFor each facility, the dataset contains two\ \ JSON files:\n\n\n\tMicroscope.JSON file (e.g., 01_marcello_uliverpool_cci_zeiss_axioobserz1_lsm710.json)\n\ \tSettings.JSON file (indicated with the name of the image and with the _AS suffix)\n\ \n\n\nMicro-Meta App was developed as part of a global community initiative including\ \ the 4D Nucleome (4DN) Imaging Working Group, BioImaging North America (BINA)\ \ Quality Control and Data Management Working Group, and QUAlity and REProducibility\ \ for Instrument and Images in Light Microscopy (QUAREP-LiMi), to extend the Open\ \ Microscopy Environment (OME) data model.\n\n\nThe works of this global community\ \ effort resulted in multiple publications featured on a recent Nature Methods\ \ FOCUS ISSUE dedicated to Reporting and reproducibility in microscopy.\n\n\n\n\ Learn More! For a thorough description of Micro-Meta App consult our recent Nature\ \ Methods and BioRxiv.org publications!\n\n\n \n\n\n\t\n\t\t\n\t\t\tNr.\n\ \t\t\tManufacturer\n\t\t\tModel\n\t\t\tTier\n\t\t\tΕxperiment Type\n\t\ \t\tFacility Name\n\t\t\tDepartment and Institution\n\t\t\tURL\n\t\t\tReferences\n\ \t\t\n\t\t\n\t\t\t1\n\t\t\tCarl Zeiss Microscopy\n\t\t\tAxio Observer Z1 (with\ \ LSM 710 scan head)\n\t\t\t1\n\t\t\t3D visualization of superhydrophobic polymer-nanoparticles\n\ \t\t\tCentre for Cell Imaging (CCI)\n\t\t\tUniversity of Liverpool\n\t\t\thttps://cci.liv.ac.uk/equipment_710.html\n\ \t\t\tUpton et al., 2020\n\t\t\n\t\t\n\t\t\t2\n\t\t\tCarl Zeiss Microscopy\n\t\ \t\tAxio Observer (Axiovert 200M)\n\t\t\t2\n\t\t\tΜeasurement of illumination\ \ stability on Chinese Hamster Ovary cells expressing Paxillin-EGFP\n\t\t\tAdvanced\ \ BioImaging Facility (ABIF).\n\t\t\tMcGill University\n\t\t\thttps://www.mcgill.ca/abif/equipment/axiovert-1\n\ \t\t\tKiepas et al., 2020\n\t\t\n\t\t\n\t\t\t3\n\t\t\tCarl Zeiss Microscopy\n\t\ \t\tAxio Observer Z1 (with Spinning Disk)\n\t\t\t2\n\t\t\tImmunofluorescence imaging\ \ of cryosection of Mouse kidney\n\t\t\tImagerie Cellulaire; Quality Control managed\ \ by Miacellavie (https://miacellavie.com/)\n\t\t\tCentre de recherche du Centre\ \ Hospitalier Université de Montréal (CR CHUM), University of Montreal\n\ \t\t\thttps://www.chumontreal.qc.ca/crchum/plateformes-et-services  (the\ \ web site is for all core facilities, not specifically for the core facility\ \ hosting this microscope)\n\t\t\tPilliod et al., 2020\n\t\t\n\t\t\n\t\t\t4\n\t\ \t\tCarl Zeiss Microscopy\n\t\t\tAxio Imager Z2 (with Apotome)\n\t\t\t2\n\t\t\t\ Immunofluorescence imaging of mitotic division in Hela cells using  \n\ \t\t\tBioimaging Unit\n\t\t\tNewcastle University\n\t\t\thttps://www.ncl.ac.uk/bioimaging/\n\ \t\t\tWatson et al., 2020\n\t\t\n\t\t\n\t\t\t5\n\t\t\tCarl Zeiss Microscopy\n\t\ \t\tAxio Observer Z1\n\t\t\t2\n\t\t\tFluorescence microscopy of human skin fibroblasts\ \ from Glycogen Storage Disease patients.\n\t\t\tLife Imaging Center (LIC)\n\t\ \t\tCentre for Integrative Signalling Analysis (CISA), University of Freiburg\n\ \t\t\thttps://miap.eu/equipments/sd-i-abl/\n\t\t\tHannibal et al., 2020\n\t\t\n\ \t\t\n\t\t\t6\n\t\t\tLeica Microsystems\n\t\t\tDMI6000B\n\t\t\t2\n\t\t\t3D immunofluorescence\ \ imaging  rhinovirus infected macrophages \n\t\t\tIMAG'IC Confocal\ \ Microscopy Facility\n\t\t\tInstitut Cochin, CNRS, INSERM, Université\ \ de Paris\n\t\t\thttps://www.institutcochin.fr/core_facilities/confocal-microscopy/cochin-imaging-photonic-microscopy/organigram_team/10054/view\n\ \t\t\tJubrail et al., 2020\n\t\t\n\t\t\n\t\t\t7\n\t\t\tLeica Microsystems\n\t\t\ \tDM5500B\n\t\t\t2\n\t\t\tImmunofluorescence analysis of the colocalization of\ \ PML bodies with DNA double-strand breaks\n\t\t\tBioimaging Unit\n\t\t\tEdwardson\ \ Building on the Campus for Ageing and Vitality, Newcastle University\n\t\t\t\ https://www.ncl.ac.uk/bioimaging/equipment/leica-dm5500/#overview\n\t\t\tda Silva\ \ et al., 2019; Nelson et al., 2012\n\t\t\t  \n\t\t\n\t\t\n\t\t\t8\n\ \t\t\tLeica Microsystems\n\t\t\tDMI8-CS (with TCS SP8 STED 3X)\n\t\t\t2\n\t\t\t\ Live-cell imaging of N. benthamiana leaves cells-derived protoplasts\n\t\t\tCenter\ \ for Advanced Imaging (CAi)\n\t\t\tSchool of Mathematics/Natural Sciences, Heinrich-Heine-Universität\ \ Düsseldorf\n\t\t\thttps://www.cai.hhu.de/en/equipment/super-resolution-microscopy/leica-tcs-sp8-sted-3x\n\ \t\t\tSinger et al., 2017; Hänsch et al., 2020\n\t\t\n\t\t\n\t\t\t9\n\t\t\ \tNikon Instruments\n\t\t\tEclipse Ti\n\t\t\t2\n\t\t\tImmunofluorescence analysis\ \ of the cytoskeleton structure in COS cells\n\t\t\tAdvanced Imaging Center (AIC)\n\ \t\t\tJanelia Research Campus, Howard Hughes Medical Institute\n\t\t\thttps://www.janelia.org/support-team/light-microscopy/equipment\n\ \t\t\tAbdelfattah et al., 2019; Qian et al., 2019; Grimm et al., 2020\n\t\t\n\t\ \t\n\t\t\t10\n\t\t\tNikon Instruments\n\t\t\tEclipse Ti-E (HCA)\n\t\t\t2\n\t\t\ \tΤime-lapse analysis of the bursting behavior of amine-functionalized vesicular\ \ assemblies\n\t\t\tLight Microscopy Facility (IALS-LIF)\n\t\t\tInstitute for\ \ Applied Life Sciences, University of Massachusetts at Amherst\n\t\t\thttps://www.umass.edu/ials/light-microscopy\n\ \t\t\tFernandez et al., 2020\n\t\t\n\t\t\n\t\t\t11\n\t\t\tNikon Instruments/Coleman\ \ laboratory (customized)\n\t\t\tTIRF HILO Epifluorescence light Microscope (THEM)/\ \ Eclipse Ti\n\t\t\t2\n\t\t\tSingle-particle tracking of Halo-tagged PCNA in Lox\ \ cells\n\t\t\tColeman laboratory\n\t\t\tAnatomy and Structural Biology Department,\ \ The Albert Einstein College of Medicine\n\t\t\thttps://einsteinmed.org/faculty/12252/robert-coleman/\n\ \t\t\tDrosopoulos et al., 2020\n\t\t\n\t\t\n\t\t\t12\n\t\t\tNikon Instruments\n\ \t\t\tEclipse Ti (with Andor Dragon Fly Spinning Disk)\n\t\t\t2\n\t\t\tInvestigation\ \ of the 3D structure of cerebral organoids\n\t\t\tMontpellier Resources Imagerie\n\ \t\t\tCentre de Recherche de Biologie cellulaire de Montpellier (MRI-CRBM), CNRS,\ \ Univerity of Montpellier\n\t\t\thttps://www.mri.cnrs.fr/en/optical-imaging/our-facilities/mri-crbm.html\n\ \t\t\tAyala-Nunez et al., 2019\n\t\t\n\t\t\n\t\t\t13\n\t\t\tNikon Instruments\n\ \t\t\tEclipse Ti2\n\t\t\t2\n\t\t\tΙmmunofluorescence imaging of cryosections\ \ of mouse hearth myocardium \n\t\t\tNeuroscience Center Microscopy Core\n\ \t\t\tNeuroscience Center, University of North Carolina\n\t\t\thttps://www.med.unc.edu/neuroscience/core-facilities/neuro-microscopy/\n\ \t\t\tAghajanian et al., 2021\n\t\t\n\t\t\n\t\t\t14\n\t\t\tNikon Instruments\n\ \t\t\tEclipse Ti2\n\t\t\t2\n\t\t\tLive-cell imaging of bacterial cells expressing\ \ GFP-PopZ\n\t\t\tMicroscopy Resources on the North Quad (MicRoN)\n\t\t\tHarvard\ \ Medical School \n\t\t\thttps://micron.hms.harvard.edu/\n\t\t\tLim and Bernhardt\ \ 2019; Lim et al., 2019\n\t\t\n\t\t\n\t\t\t15\n\t\t\tOlympus/Biomedical Imaging\ \ Group (customized)\n\t\t\tTIRF Epifluorescence Structured light Microscope (TESM)/IX71\n\ \t\t\t3\n\t\t\t3D distribution of HIV-1 in the nucleus of human cells\n\t\t\t\ Biomedical Imaging Group\n\t\t\tProgram in Molecular Medicine, University of Massachusetts\ \ Medical School\n\t\t\thttps://trello.com/b/BQ8zCcQC/tirf-epi-fluorescence-structured-light-microscope\n\ \t\t\tNavaroli et al., 2012\n\t\t\n\t\t\n\t\t\t16\n\t\t\tOlympus/Computer Vision\ \ Laboratory (customized)\n\t\t\t3D BrightField Scanner/IX71\n\t\t\t3\n\t\t\t\ Transmitted light brightfield visualization of swimming spermatocytes\n\t\t\t\ Laboratorio Nacional de Microscopia Avanzada (LNMA) and Computer Vision Laboratory\ \ of the Institute of Biotechnology\n\t\t\tUniversidad Nacional Autonoma de Mexico\ \ (UNAM)\n\t\t\thttps://lnma.unam.mx/wp/\n\t\t\tPimentel et al., 2012; Silva-Villalobos\ \ et al., 2014\n\t\t\n\t\n\n\nGetting started\n\nUse these videos to get started\ \ with using Micro-Meta App after installation into OMERO and downloading the\ \ example data files:\n\n\n\tVideo 1\n\tVideo 2\n\n\nMore information\n\n\nFor\ \ full information on how to use Micro-Meta App please utilize the following resources:\n\ \n\n\tMicro-Meta App website\n\tFull documentation\n\tInstallation instructions\n\ \tStep-by-Step Instructions\n\tTutorial Videos\n\n\n\nBackground\n\nIf you want\ \ to learn more about the importance of metadata and quality control to ensure\ \ full reproducibility, quality and scientific value in light microscopy, please\ \ take a look at our recent publications describing the development of community-driven\ \ light 4DN-BINA-OME Microscopy Metadata specifications Nature Methods and BioRxiv.org\ \ and our overview manuscript entitled A perspective on Microscopy Metadata: data\ \ provenance and quality control.\n\n \n\n " license: CC-BY-4.0 name: Example Microscopy Metadata JSON files produced using Micro-Meta App to document example microscopy experiments performed at individual core facilities num_downloads: 1053 publication_date: '2022-01-15' url: - https://zenodo.org/records/5847477 - https://doi.org/10.5281/zenodo.5847477 uuid: 38a0638b-65f6-427a-8998-7afaf8af8f2e language: en authors_with_orcid: - Alessandro Rigano - Ulrike Boehm https://orcid.org/0000-0001-7471-2244 - Claire M. Brown https://orcid.org/0000-0003-1622-663X - Joel Ryan - James J. Chambers https://orcid.org/0000-0003-3883-8215 - Robert A. Coleman https://orcid.org/0000-0002-7367-9603 - Orestis Faklaris https://orcid.org/0000-0001-5965-5405 - Thomas Guilbert https://orcid.org/0000-0001-5069-0730 - Michelle S. Itano https://orcid.org/0000-0001-6853-1228 - Judith Lacoste https://orcid.org/0000-0002-8783-8599 - Alex Laude https://orcid.org/0000-0002-3853-1187 - Marco Marcello https://orcid.org/0000-0002-2392-8640 - Paula Montero-Llopis https://orcid.org/0000-0002-5983-2296 - Glyn Nelson https://orcid.org/0000-0002-1895-4772 - Roland Nitschke https://orcid.org/0000-0002-9397-8475 - Jaime A. Pimentel https://orcid.org/0000-0001-8569-0466 - Stefanie Weidtkamp-Peters https://orcid.org/0000-0001-7734-3771 - Caterina Strambio-De-Castillia https://orcid.org/0000-0002-1069-1816 file_formats: .czi_ * .json tags: - exclude from DALIA - authors: - Mohsen Ahmadi - Robert Wagner - Philipp Mattern - Nick Plathe - Sander Bekeschus - Markus M. Becker - Torsten Stöter - Stefanie Weidtkamp-Peters description: A more detailed understanding of the effect of plasmas on biological systems can be fostered by combining data from different imaging modalities, such as optical imaging, fluorescence imaging, and mass spectrometry imaging. This, however, requires the implementation and use of sophisticated research data management (RDM) solutions to incorporate the influence of plasma parameters and treatment procedures as well as the effects of plasma on the treated targets. In order to address this, RDM activities on different levels and from different perspectives are started and brought together within the framework of the NFDI consortium NFDI4BIOIMAGE. license: CC-BY-4.0 name: Data stewardship and research data management tools for multimodal linking of imaging data in plasma medicine num_downloads: 68 publication_date: '2023-11-03' url: - https://zenodo.org/records/10069368 - https://doi.org/10.5281/zenodo.10069368 uuid: 8c40ea77-c3ef-4617-a25e-ef50eeb49041 language: en authors_with_orcid: - Mohsen Ahmadi - Robert Wagner - Philipp Mattern - Nick Plathe - Sander Bekeschus - Markus M. Becker https://orcid.org/0000-0001-9324-3236 - Torsten Stöter - Stefanie Weidtkamp-Peters https://orcid.org/0000-0001-7734-3771 file_formats: .pdf tags: - include in DALIA - authors: - Maximilian Heber - Moritz Jakob - Matthias Landwehr - Jan Leendertse - Maximilian Müller - Gabriel Schneider - Dirk von Suchodoletz - Robert Ulrich description: "Im Zuge der stetig wachsenden Brisanz des Forschungsdatenmanagements\ \ fallen immer größere Mengen an Forschungsdaten an. Diese an sich\ \ begrüßenswerte Entwicklung führt zu technischen und organisatorischen\ \ Herausforderungen nicht nur im Bereich der Speicherung von Forschungsdaten,\ \ sondern in allen Phasen des Forschungsdatenlebenszyklus. Der vorliegende Beitrag\ \ erläutert vor diesem Hintergrund mögliche Motivationen hinter digitaler\ \ Datensparsamkeit mit Blick auf organisatorische, technische und ethische Kriterien,\ \ Datenschutz und Nachhaltigkeit. Anschließend werden vor dem Hintergrund\ \ zentraler Herausforderungen Umsetzungsvorschläge für das Vorfeld sowie\ \ den Verlauf eines Forschungsvorhabens gemacht. Zudem werden grundlegende Empfehlungen\ \ zur digitalen Datensparsamkeit ausgesprochen.\nEine kürzere Ausgabe des\ \ Leitfadens ist im Mai 2024 in der Zeitschrift o | bib erschienen: https://doi.org/10.5282/o-bib/6036\ \ \nDiese Ausgabe enthält ein zusätzliches Kapitel (4.2) mit konkreten\ \ Praxisbeispielen.\nDieser Artikel wurde ins Englische übersetzt:\nHeber,\ \ M., Jakob, M., Landwehr, M., Leendertse, J., Müller, M., Schneider, G.,\ \ von Suchodoletz, D., & Ulrich, R. (2024). A Users' Guide to Economical Digital\ \ Data Usage. Zenodo. https://doi.org/10.5281/zenodo.13752220" license: CC-BY-4.0 name: Leitfaden zur digitalen Datensparsamkeit (mit Praxisbeispielen) num_downloads: 255 proficiency_level: advanced beginner publication_date: '2024-06-03' url: - https://zenodo.org/records/11445843 - https://doi.org/10.5281/zenodo.11445843 uuid: 0f9891ad-edaa-43cb-916f-c67e02c40227 language: de authors_with_orcid: - Maximilian Heber https://orcid.org/0000-0003-3399-7532 - Moritz Jakob https://orcid.org/0009-0007-0772-3462 - Matthias Landwehr https://orcid.org/0000-0001-9274-2578 - Jan Leendertse https://orcid.org/0000-0001-5676-493X - Maximilian Müller https://orcid.org/0000-0003-2237-1147 - Gabriel Schneider https://orcid.org/0000-0001-6573-3115 - Dirk von Suchodoletz https://orcid.org/0000-0002-4382-5104 - Robert Ulrich https://orcid.org/0000-0001-9063-2703 file_formats: .pdf tags: - include in DALIA - authors: - C. Li description: 'Online R learning for applied statistics ' license: CC0-1.0 name: Online_R_learning publication_date: '2023-07-09T06:27:14+00:00' proficiency_level: advanced beginner tags: - Statistics - include in DALIA type: - Github repository url: https://github.com/cxli233/Online_R_learning uuid: 6eb1b5fd-0383-4954-bfd8-9e42bb29444b - authors: - C. Li description: 'Friends don''t let friends make certain types of data visualization - What are they and why are they bad. ' license: MIT name: FriendsDontLetFriends publication_date: '2024-03-10T15:34:07+00:00' proficiency_level: advanced beginner tags: - Visualization - include in DALIA type: - Github repository url: https://github.com/cxli233/FriendsDontLetFriends uuid: 9faa0f8b-a4ac-4e8a-80b0-22225c848110 - submission_date: '2024-11-18T14:39:37.692470' authors: - Tom Boissonnet - Bettina Hagen - Susanne Kunis - Christian Schmidt - Stefanie Weidtkamp-Peters description: 'Fit for OMERO: How imaging facilities and IT departments work together to enable RDM for bioimaging Description: Research data management (RDM) in bioimaging is challenging because of large file sizes, heterogeneous file formats and the variability of imaging methods. The image data management system OMERO (OME Remote Objects) allows for centralized and secure storage, organization, annotation, and interrogation of microscopy data by researchers. It is an internationally well-supported open-source software tool that has become one of the best-known image data management tools among bioimaging scientists. Nevertheless, the de novo setup of OMERO at an institute is a multi-stakeholder process that demands time, funds, organization and iterative implementation. In this workshop, participants learn how to begin setting up OMERO-based image data management at their institution. The topics include: Stakeholder identification at the university / research institute Process management, time line expectations, and resources planning Learning about each other‘s perspectives on chances and challenges for RDM Funding opportunities and strategies for IT and imaging core facilities Hands-on: Setting up an OMERO server in a virtual machine environment Target audience: This workshop was directed at universities and research institutions who consider or plan to implement OMERO, or are in an early phase of implementation. This workshop was intended for teams from IT departments and imaging facilities to participate together with one person from the IT department, and one person from the imaging core facility at the same institution. The trainers: Prof. Dr. Stefanie Weidtkamp-Peters (Imaging Core Facility Head, Center for Advanced Imaging, Heinrich Heine University of Düsseldorf) Dr. Susanne Kunis (Software architect, OMERO administrator, metadata specialist, University of Osnabrück) Dr. Tom Boissonnet (OMERO admin and image metadata specialist, Center for Advanced Imaging, Heinrich Heine University of Düsseldorf) Dr. Bettina Hagen (IT Administration and service specialist, Max Planck Institute for the Biology of Ageing, Cologne)  Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center (DKFZ), Heidelberg) Time and place The format was a two-day, in-person workshop (October 16-17, 2024). Location: Heidelberg, Germany Workshop learning goals Learn the steps to establish a local RDM environment fit for bioimaging data Create a network of IT experts and bioimaging specialists for bioimage RDM across institutions Establish a stakeholder process management for installing OMERO-based RDM Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution ' license: CC-BY-4.0 name: '[Workshop Material] Fit for OMERO - How imaging facilities and IT departments work together to enable RDM for bioimaging, October 16-17, 2024, Heidelberg' num_downloads: 144 proficiency_level: advanced beginner publication_date: '2024-11-18' tags: - nfdi4bioimage - research data management - exclude from DALIA url: - https://zenodo.org/records/14178789 - https://doi.org/10.5281/zenodo.14178789 uuid: b9c7adca-3f9d-472a-ab82-8dc201b9416a language: en authors_with_orcid: - Tom Boissonnet https://orcid.org/0000-0002-3328-9467 - Bettina Hagen - Susanne Kunis https://orcid.org/0000-0001-6523-7496 - Christian Schmidt https://orcid.org/0000-0001-9568-895X - Stefanie Weidtkamp-Peters https://orcid.org/0000-0001-7734-3771 file_formats: .pdf * .xlsx - submission_date: '2024-11-18T14:39:38.611036' authors: - Cornelia Wetzker description: 'The poster introduces the consortium NFDI4BIOIMAGE with its central objectives, provides an overview of challenges in bioimage data handling, sharing and analysis and lists support options by the consortium through its data stewardship team. It is part of the work of the German consortium NFDI4BIOIMAGE funded by the Deutsche Forschungsgemeinschaft (DFG grant number NFDI 46/1, project number 501864659) and has been presented at the conference FDM@Campus held in Göttingen September 23-25, 2024.' license: CC-BY-4.0 name: 'New Kid on the (NFDI) Block: NFDI4BIOIMAGE - A National Initiative for FAIR Data Management in Bioimaging and Bioimage Analysis' num_downloads: 29 publication_date: '2024-10-29' url: - https://zenodo.org/records/14006558 - https://doi.org/10.5281/zenodo.14006558 uuid: 7c794f67-5515-4f59-8734-b0df67755598 language: en authors_with_orcid: - Cornelia Wetzker https://orcid.org/0000-0002-8367-5163 file_formats: .pdf tags: - exclude from DALIA - submission_date: '2024-11-18T14:39:39.787873' authors: - Christian Jüngst - Peter Zentis description: Raw microscopy image from the NFDI4Bioimage calendar October 2024 license: CC-BY-4.0 name: NFDI4Bioimage Calendar 2024 October; original image num_downloads: 8 publication_date: '2024-09-25' tags: - nfdi4bioimage - research data management - exclude from DALIA url: - https://zenodo.org/records/13837146 - https://doi.org/10.5281/zenodo.13837146 uuid: 9cd198c8-d5c3-405d-bfca-9f6737d328a9 authors_with_orcid: - Christian Jüngst https://orcid.org/0000-0002-6586-7129 - Peter Zentis https://orcid.org/0000-0002-6999-132X file_formats: .lif - submission_date: '2024-11-18T14:39:41.141377' authors: - Stefan Dvoretskii license: CC-BY-4.0 name: Insights from Acquiring Open Medical Imaging Datasets for Foundation Model Development num_downloads: 41 publication_date: '2024-04-10' url: - https://zenodo.org/records/11503289 - https://doi.org/10.5281/zenodo.11503289 uuid: 54e4efd3-21a7-4493-983b-b951b37157db authors_with_orcid: - Stefan Dvoretskii https://orcid.org/0000-0001-7769-0167 - Lucas Kulla https://orcid.org/0000-0002-2484-2742 - Philipp Schader https://orcid.org/0000-0002-6075-0757 - Constantin Ulrich https://orcid.org/0000-0003-3002-8170 - Tassilo Wald https://orcid.org/0009-0007-5222-2683 - Paul F Jäger https://orcid.org/0000-0002-6243-2568 - Fabian Isensee https://orcid.org/0000-0002-3519-5886 - Josh Moore https://orcid.org/0000-0003-4028-811X - Marco Nolden https://orcid.org/0000-0001-9629-0564 - Klaus Maier-Hein https://orcid.org/0000-0002-6626-2463 file_formats: .pdf tags: - exclude from DALIA - submission_date: '2024-11-18T14:39:41.489967' authors: - Cornelia Wetzker - Michael Schlierf description: The poster is part of the work of the German consortium NFDI4BIOIMAGE funded by the Deutsche Forschungsgemeinschaft (DFG grant number NFDI 46/1, project number 501864659). license: CC-BY-4.0 name: RESEARCH DATA MANAGEMENT on Campus and in NFDI4BIOIMAGE num_downloads: 32 publication_date: '2024-08-29' url: - https://zenodo.org/records/13684187 - https://doi.org/10.5281/zenodo.13684187 uuid: c8d884fc-9302-46df-864c-442871438877 authors_with_orcid: - Cornelia Wetzker https://orcid.org/0000-0002-8367-5163 - Michael Schlierf https://orcid.org/0000-0002-6209-2364 file_formats: .pdf tags: - exclude from DALIA - submission_date: '2024-11-18T14:39:41.946082' authors: - Robert Haase description: This talk will present the initiatives of the NFDI4BioImage consortium aimed at the long-term preservation of life science data. We will discuss our efforts to establish metadata standards, which are crucial for ensuring data reusability and integrity. The development of sustainable infrastructure is another key focus, enabling seamless data integration and analysis in the cloud. We will take a look at how we manage training materials and communicate with our community. Through these actions, NFDI4BioImage seeks to enable FAIR bioimage data management for German researchers, across disciplines and embedded in the international framework. license: CC-BY-4.0 name: Towards Preservation of Life Science Data with NFDI4BIOIMAGE num_downloads: 228 publication_date: '2024-09-03' tags: - nfdi4bioimage - research data management - exclude from DALIA url: - https://zenodo.org/records/13640979 - https://doi.org/10.5281/zenodo.13640979 uuid: 555f04f1-d27e-465c-a882-3914b4d610aa language: en authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .pdf * .pptx - submission_date: '2024-11-18T14:39:42.386226' authors: - Stefan Dvoretskii license: CC-BY-4.0 name: Insights from Acquiring Open Medical Imaging Datasets for Foundation Model Development num_downloads: 46 publication_date: '2024-04-10' url: - https://zenodo.org/records/13380289 - https://doi.org/10.5281/zenodo.13380289 uuid: ce4fcc03-dcac-4635-9a11-ab0ecfaae60c authors_with_orcid: - Stefan Dvoretskii https://orcid.org/0000-0001-7769-0167 - Josh Moore https://orcid.org/0000-0003-4028-811X - Philipp Schader https://orcid.org/0000-0002-6075-0757 - Fabian Isensee https://orcid.org/0000-0002-3519-5886 - Paul F Jäger https://orcid.org/0000-0002-6243-2568 - Lucas Kulla https://orcid.org/0000-0002-2484-2742 - Tassilo Wald https://orcid.org/0009-0007-5222-2683 - Constantin Ulrich https://orcid.org/0000-0003-3002-8170 - Marco Nolden https://orcid.org/0000-0001-9629-0564 - Klaus Maier-Hein https://orcid.org/0000-0002-6626-2463 file_formats: .pdf * .pptx tags: - exclude from DALIA - submission_date: '2024-11-18T14:39:43.407105' authors: - Sophie Habinger - Maximilian Heber - Sonja Kralj - Emilia Mikautsch description: 'The rise of Open Science (OS) and the academic community’s needs that come with it bring about a range of challenges for academic libraries. To face these challenges, the University of Konstanz has created a competence unit called Team Open Science in the Communication, Information, Media Center (KIM) - a joint unit of library and IT infrastructure. The Team creates synergies within itself and across the library. In December 2023, it involved 12 staff members specialising in open access (OA), research data management (RDM), open educational resources (OER) and virtual research environments (VRE). It collaborates closely with other KIM departments. This submission shall serve as a best practice example for the impact of OS on research libraries and, beyond that, the impact of research libraries on universities. To enhance and foster OS, the Team provides individual consultations, services and office hours for researchers. Here, it collaborates closely with other librarians like subject specialists and the Team University Publications. Along similar lines, the KIM offers institutional repositories for publications (KOPS) and research data (KonDATA). Beyond that, the Team provides solutions to host OA journals and analyses researchers’ VRE needs to decide on implementation options. In sum, the Team is the central OS contact point for the entire university, underlining the major role the library holds in making institutional impact. Furthermore, the Team had the leading role in creating the University of Konstanz’ OS Policy, one of the first ones passed by a German university. This policy stands out because it encompasses various OS domains. It demands, among other things, that text publications be made OA and that research data be managed according to relevant subject-specific standards. If permissible and reasonable, it demands that research data should be made publicly available at the earliest possible time. Along these lines, the policy has a large impact on how the library handles closed access books and subscription-based journals. As a consequence, OA is pursued wherever possible, leading to the highest OA quota of all German universities. In that sense, the Team is a crucial driving force of OS in the University of Konstanz, which ties in with the library’s major role of open research transformation. Beyond the University of Konstanz, the Team is involved in a range of national and international projects collaborating with other libraries. On a national level, they lead the project open.access-network which provides an information platform for researchers and librarians and connects the German-speaking OA community through events like bar camps. The project KOALA-AV supports libraries in establishing consortial solutions for financing Diamond OA publications. Moreover, the Team is involved in the federal state initiative for RDM in Baden-Württemberg (bwFDM). Here, the Team is in charge of forschungsdaten.info, the German-speaking countries’ leading RDM information platform, which will be offered in English within the next years. Internationally, the Team cooperates with librarians and other OS professionals from the European Reform University Alliance (ERUA) and the European University for Well-Being (EUniWell), establishing formats for best practice exchange, such as monthly OS Meet-Ups.' license: CC-BY-4.0 name: 'Institutionalization and Collaboration as a Way of Addressing the Challenges Open Science Presents to Libraries: The University of Konstanz as a National Pioneer' num_downloads: 119 publication_date: '2024-07-09' url: - https://zenodo.org/records/12699637 - https://doi.org/10.5281/zenodo.12699637 uuid: 7357f51b-768e-4797-860d-773cc5bbe70a language: en authors_with_orcid: - Sophie Habinger https://orcid.org/0000-0002-5513-954X - Maximilian Heber https://orcid.org/0000-0003-3399-7532 - Sonja Kralj https://orcid.org/0000-0002-8516-8034 - Emilia Mikautsch https://orcid.org/0009-0005-1758-1790 file_formats: .pdf tags: - exclude from DALIA - submission_date: '2024-11-18T14:39:44.756942' authors: - Riccardo Massei - Christian Schmidt - Michele Bortolomeazzi - Julia Thoennissen - Jan Bumberger - Timo Dickscheid - Jan-Philipp Mallm - Elisa Ferrando-May description: Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond license: CC-BY-4.0 name: The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhancing FAIR data management for microscopy and bioimage analysis num_downloads: 54 publication_date: '2024-06-06' tags: - nfdi4bioimage - research data management - exclude from DALIA url: - https://zenodo.org/records/11501662 - https://doi.org/10.5281/zenodo.11501662 uuid: 3cc23473-e70e-4ef8-b6bb-30aee3e5978b language: en authors_with_orcid: - Riccardo Massei https://orcid.org/0000-0003-2104-9519 - Christian Schmidt https://orcid.org/0000-0001-9568-895X - Michele Bortolomeazzi https://orcid.org/0000-0001-5805-5774 - Julia Thoennissen https://orcid.org/0000-0002-5467-871X - Jan Bumberger https://orcid.org/0000-0003-3780-8663 - Timo Dickscheid https://orcid.org/0000-0002-9051-3701 - Jan-Philipp Mallm https://orcid.org/0000-0002-7059-4030 - Elisa Ferrando-May https://orcid.org/0000-0002-5567-8690 file_formats: .pdf - submission_date: '2024-11-18T14:39:45.213893' authors: - Riccardo Massei - Michele Bortolomeazzi - Christian Schmidt description: 'Here we share the material used in a workshop held on May 13th, 2024, at the German Cancer Research Center in Heidelberg (on-premise) Description:Microscopy experiments generate information-rich, multi-dimensional data, allowing us to investigate biological processes at high spatial and temporal resolution. Image processing and analysis is a standard procedure to retrieve quantitative information from biological imaging. Due to the complex nature of bioimaging files that often come in proprietary formats, it can be challenging to organize, structure, and annotate bioimaging data throughout a project. Data often needs to be moved between collaboration partners, transformed into open formats, processed with a variety of software tools, and exported to smaller-sized images for presentation. The path from image acquisition to final publication figures with quantitative results must be documented and reproducible. In this workshop, participants learn how to use OMERO to organize their data and enrich the bioimage data with structured metadata annotations.We also focus on image analysis workflows in combination with OMERO based on the Fiji/ImageJ software and using Jupyter Notebooks. In the last part, we explore how OMERO can be used to create publication figures and prepare bioimage data for publication in a suitable repository such as the Bioimage Archive. Module 1 (9 am - 10.15 am): Basics of OMERO, data structuring and annotation Module 2 (10.45 am - 12.45 pm): OMERO and Fiji Module 3 (1.45 pm - 3.45 pm): OMERO and Jupyter Notebooks Module 4 (4.15 pm - 6. pm): Publication-ready figures and data with OMERO The target group for this workshopThis workshop is directed at researchers at all career levels who plan to or have started to use OMERO for their microscopy research data management. We encourage the workshop participants to bring example data from their research to discuss suitable metadata annotation for their everyday practice. Prerequisites:Users should bring their laptops and have access to the internet through one of the following options:- eduroam- institutional WiFi- VPN connection to their institutional networks to access OMERO Who are the trainers? Dr. Riccardo Massei (Helmholtz-Center for Environmental Research, UFZ, Leipzig) - Data Steward for Bioimaging Data in NFDI4BIOIMAGE Dr. Michele Bortolomeazzi (DKFZ, Single cell Open Lab, bioimage data specialist, bioinformatician, staff scientist in the NFDI4BIOIMAGE project) Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center, Heidelberg, Project Coordinator of the NFDI4BIOIMAGE project)' license: CC-BY-4.0 name: '[Workshop] Bioimage data management and analysis with OMERO' num_downloads: 211 proficiency_level: advanced beginner publication_date: '2024-05-13' tags: - nfdi4bioimage - research data management - include in DALIA url: - https://zenodo.org/records/11350689 - https://doi.org/10.5281/zenodo.11350689 uuid: ef7a056b-cdc9-4a1d-be3c-290ba748db12 language: en authors_with_orcid: - Riccardo Massei https://orcid.org/0000-0003-2104-9519 - Michele Bortolomeazzi https://orcid.org/0000-0001-5805-5774 - Christian Schmidt https://orcid.org/0000-0001-9568-895X file_formats: .pdf - submission_date: '2024-11-18T14:39:45.586962' authors: - Josh Moore - Susanne Kunis description: Poster presented at the European Light Microscopy Initiative meeting in Liverpool (https://www.elmi2024.org/) license: CC-BY-4.0 name: '[ELMI 2024] AI''s Dirty Little Secret: Without FAIR Data, It''s Just Fancy Math' num_downloads: 54 publication_date: '2024-05-21' tags: - nfdi4bioimage - research data management - include in DALIA url: - https://zenodo.org/records/11235513 - https://doi.org/10.5281/zenodo.11235513 uuid: 49551a0c-eb32-4208-b843-b71137e6f0f1 authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X - Susanne Kunis https://orcid.org/0000-0001-6523-7496 file_formats: .pdf - submission_date: '2024-11-18T14:39:45.979235' authors: - Escobar Diaz Guerrero - Rodrigo description: First updates of LEO (Linking ELN with OMERO) license: CC-BY-4.0 name: 'LEO: Linking ELN with OMERO' num_downloads: 3 publication_date: '2024-05-08' url: - https://zenodo.org/records/11146807 - https://doi.org/10.5281/zenodo.11146807 uuid: 0967ecd2-06f6-462c-b9de-d514aed01b4b authors_with_orcid: - Rodrigo Escobar Diaz Guerrero file_formats: null tags: - exclude from DALIA - submission_date: '2024-11-18T14:39:46.762148' authors: - Carsten Fortmann-Grote description: This presentation was given at the 2nd MPG-NFDI Workshop on April 18th. license: CC-BY-4.0 name: NFDI4BIOIMAGE num_downloads: 81 publication_date: '2024-04-22' url: - https://zenodo.org/records/11031747 - https://doi.org/10.5281/zenodo.11031747 uuid: 2b5488e9-92ce-4f31-a8d6-290615f9bff6 authors_with_orcid: - Carsten Fortmann-Grote https://orcid.org/0000-0002-2579-5546 file_formats: .pdf tags: - exclude from DALIA - submission_date: '2024-11-18T14:39:47.147338' authors: - Christian Schmidt description: Short Talk about the NFDI4BIOIMAGE consortium presented at the RDM in (Bio-)Medicine Information Event on April 10th, 2024, organized C³RDM & ZB MED. license: CC-BY-4.0 name: '[Short Talk] NFDI4BIOIMAGE - A consortium in the National Research Data Infrastructure' num_downloads: 47 publication_date: '2024-04-10' url: - https://zenodo.org/records/10939520 - https://doi.org/10.5281/zenodo.10939520 uuid: 9017b456-8a53-49a8-a914-a4da2ffb9d66 authors_with_orcid: - Christian Schmidt https://orcid.org/0000-0001-9568-895X file_formats: .pdf tags: - exclude from DALIA - submission_date: '2024-11-18T14:39:47.561518' authors: - Anca Margineanu - Chiara Stringari - Marcelo Zoccoler - Cornelia Wetzker description: The presentations introduce open-source software to read in, visualize and analyse fluorescence lifetime imaging microscopy (FLIM) raw data developed for life scientists. The slides were presented at German Bioimaging (GerBI) FLIM Workshop held February 26 to 29 2024 at the Biomedical Center of LMU München by Anca Margineanu, Chiara Stringari and Conni Wetzker.  license: CC-BY-4.0 name: A Glimpse of the Open-Source FLIM Analysis Software Tools FLIMfit, FLUTE and napari-flim-phasor-plotter num_downloads: 219 publication_date: '2024-03-27' url: - https://zenodo.org/records/10886750 - https://doi.org/10.5281/zenodo.10886750 uuid: 1abe8f38-fd31-4f2a-967d-1b8d0e94651b language: en authors_with_orcid: - Anca Margineanu https://orcid.org/0000-0002-6634-9729 - Chiara Stringari https://orcid.org/0000-0002-0550-7463 - Marcelo Zoccoler https://orcid.org/0000-0002-6165-4679 - Cornelia Wetzker https://orcid.org/0000-0002-8367-5163 file_formats: .pdf * .pptx tags: - exclude from DALIA - submission_date: '2024-11-18T14:39:47.946546' authors: - Carsten Fortmann-Grote license: CC-BY-4.0 name: Linked (Open) Data for Microbial Population Biology num_downloads: 121 publication_date: '2024-03-12' url: - https://zenodo.org/records/10808486 - https://doi.org/10.5281/zenodo.10808486 uuid: 416298b9-cce7-4b61-9cff-a8e80e6ff459 authors_with_orcid: - Carsten Fortmann-Grote https://orcid.org/0000-0002-2579-5546 file_formats: .pdf tags: - exclude from DALIA - submission_date: '2024-11-18T14:39:48.292240' authors: - Riccardo Massei description: 'Results of the project "Conversion of KNIME image analysis workflows to Galaxy" during the Hackathon "Image Analysis in Galaxy" (Freiburg 26 Feb - 01 Mar 2024)  ' license: CC-BY-4.0 name: Hackaton Results - Conversion of KNIME image analysis workflows to Galaxy num_downloads: 46 publication_date: '2024-03-07' url: - https://zenodo.org/records/10793700 - https://doi.org/10.5281/zenodo.10793700 uuid: b5b6c806-9f1f-45c2-b445-55118f522be1 authors_with_orcid: - Riccardo Massei https://orcid.org/0000-0003-2104-9519 file_formats: .pptx tags: - exclude from DALIA - submission_date: '2024-11-18T14:39:48.632022' authors: - Vanessa Aphaia Fiona Fuchs - Jens Wendt - Maximilian Müller - Mohsen Ahmadi - Riccardo Massei - Cornelia Wetzker description: The Data Steward Team of the NFDI4BIOIMAGE consortium presents themselves and the services (including the Helpdesk) that we offer. license: CC-BY-4.0 name: Who you gonna call? - Data Stewards to the rescue num_downloads: 91 publication_date: '2024-03-01' url: - https://zenodo.org/records/10730424 - https://doi.org/10.5281/zenodo.10730424 uuid: 6cd74550-8eed-41e2-8664-26ccb1cd2488 authors_with_orcid: - Vanessa Aphaia Fiona Fuchs https://orcid.org/0000-0002-4101-6987 - Jens Wendt - Maximilian Müller https://orcid.org/0000-0003-2237-1147 - Mohsen Ahmadi https://orcid.org/0000-0002-7018-0460 - Riccardo Massei https://orcid.org/0000-0003-2104-9519 - Cornelia Wetzker https://orcid.org/0000-0002-8367-5163 file_formats: .pdf tags: - exclude from DALIA - authors: - Anett Jannasch - Silke Tulok - Vanessa Aphaia Fiona Fuchs - Tom Boissonnet - Christian Schmidt - Michele Bortolomeazzi - Gunar Fabig - Chukwuebuka Okafornta description: 'This is a Key-Value pair template used for the annotation of datasets in OMERO. It is tailored for a research study (PERIKLES project) on the biocompatibility of newly designed biomaterials out of pericardial tissue for cardiovascular substitutes (https://doi.org/10.1063/5.0182672) conducted in the research department of Cardiac Surgery at the Faculty of Medicine Carl Gustav Carus at the Technische Universität Dresden . A corresponding public example dataset is used in the publication "Setting up an institutional OMERO environment for bioimage data: perspectives from both facility staff and users" and is available here (https://omero.med.tu-dresden.de/webclient/?show=dataset-1557). The template is based on the REMBI recommendations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015) and it was developed during the PoL-Bio-Image Analysis Symposium in Dresden Aug 28th- Sept 1th 2023.  With this template it is possible to create a csv-file, that can be used to annotate a dataset in OMERO using the annotation script (https://github.com/ome/omero-scripts/blob/develop/omero/annotation_scripts/). How to use: select and copy the data range containing Keys and Values open a new excel sheet and paste transpose in column B1 type in A1 ''dataset'' insert in A2 the exact name of the dataset, which should be annotated in OMERO save the new excel sheet in csv- (comma seperated values) file format Example can be seen in sheet 1 ''csv import''. Important note; the code has to be 8-Bit UCS transformation format (UTF-8) otherwise several characters (for example µ, %,°) might not be able to decode by the annotation script. We encountered this issue with old Microsoft Office versions (e.g. MS Office 2016).  Note: By filling the values in the excel sheet, avoid the usage of decimal delimiter.   See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)' license: CC-BY-4.0 name: Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) num_downloads: 10 publication_date: '2024-06-26' tags: - nfdi4bioimage - research data management - exclude from DALIA url: - https://zenodo.org/records/12546808 - https://doi.org/10.5281/zenodo.12546808 uuid: 2293c563-2bc4-4fa6-ab48-958a7676309d language: en authors_with_orcid: - Anett Jannasch https://orcid.org/0000-0002-8047-2774 - Silke Tulok https://orcid.org/0009-0005-1473-6427 - Vanessa Aphaia Fiona Fuchs https://orcid.org/0000-0002-4101-6987 - Tom Boissonnet https://orcid.org/0000-0002-3328-9467 - Christian Schmidt https://orcid.org/0000-0001-9568-895X - Michele Bortolomeazzi https://orcid.org/0000-0001-5805-5774 - Gunar Fabig https://orcid.org/0000-0003-3017-0978 - Chukwuebuka Okafornta https://orcid.org/0000-0001-8094-1254 file_formats: .xlsx - authors: - Stefanie Weidtkamp-Peters - Janina Hanne - Christian Schmidt description: 'Oral presentation, 32nd MoMAN "From Molecules to Man" Seminar, Ulm, online. Monday February 6th, 2023 Abstract: Research data management is essential in nowadays research, and one of the big opportunities to accelerate collaborative and innovative scientific projects. To achieve this goal, all our data needs to be FAIR (findable, accessible, interoperable, reproducible). For data acquired on microscopes, however, a common ground for FAIR data sharing is still to be established. Plenty of work on file formats, data bases, and training needs to be performed to highlight the value of data sharing and exploit its potential for bioimaging data. In this presentation, Stefanie Weidtkamp-Peters will introduce the challenges for bioimaging data management, and the necessary steps to achieve data FAIRification. German BioImaging - GMB e.V., together with other institutions, contributes to this endeavor. Janina Hanne will present how the network of imaging core facilities, research groups and industry partners is key to the German bioimaging community’s aligned collaboration toward FAIR bioimaging data. These activities have paved the way for two data management initiatives in Germany: I3D:bio (Information Infrastructure for BioImage Data) and NFDI4BIOIMAGE, a consortium of the National Research Data Infrastructure. Christian Schmidt will introduce the goals and measures of these initiatives to the benefit of imaging scientist’s work and everyday practice.  ' license: CC-BY-4.0 name: A journey to FAIR microscopy data num_downloads: 68 publication_date: '2023-05-03' tags: - nfdi4bioimage - research data management - include in DALIA url: - https://zenodo.org/records/7890311 - https://doi.org/10.5281/zenodo.7890311 uuid: 9ca97882-53a0-4326-8662-8c50192b652d language: en authors_with_orcid: - Stefanie Weidtkamp-Peters https://orcid.org/0000-0001-7734-3771 - Janina Hanne https://orcid.org/0000-0002-5332-3589 - Christian Schmidt https://orcid.org/0000-0001-9568-895X file_formats: .pdf * .pptx - authors: - Silke Tulok - Gunar Fabig - Andy Vogelsang - Thomas Kugel - Thomas Müller-Reichert description: The Core Facility Cellular Imaging (CFCI) at the Faculty of Medicine Carl Gustav Carus (TU Dresden) is currently running a pilot project for testing the use and handling of the OMERO software. This is done together with interested users of the imaging facility and a research group. Currently, we are pushing forward this pilot study on a small scale without any data steward. Our experiences argue so far for giving data management issues into the hands of dedicated personnel not fully involved in research projects. As funding agencies will ask for higher and higher standards for implementing FAIRdata principles in the future, this will be a releva license: CC-BY-4.0 name: 'Report on a pilot study: Implementation of OMERO for microscopy data management' num_downloads: 80 publication_date: '2023-11-10' url: - https://zenodo.org/records/10103316 - https://doi.org/10.5281/zenodo.10103316 uuid: b408bd23-2304-4f93-a3d2-b1a5eec07a97 language: en authors_with_orcid: - Silke Tulok - Gunar Fabig https://orcid.org/0000-0003-3017-0978 - Andy Vogelsang - Thomas Kugel - Thomas Müller-Reichert file_formats: .pdf tags: - include in DALIA - authors: - Gunar Fabig - Anett Jannasch - Chukwuebuka Okafornta - Tom Boissonnet - Christian Schmidt - Michele Bortolomeazzi - Vanessa Aphaia Fiona Fuchs - Maria Koeckert - Aayush Poddar - Martin Vogel - Hanna-Margareta Schwarzbach - Andy Vogelsang - Michael Gerlach - Anja Nobst - Thomas Müller-Reichert - Silke Tulok description: 'This are a two Key-Value pair templates used for the annotation of datasets in OMERO. They are tailored for light- and electron microcopy data for all research projects of the research group of Prof. T. Mueller-Reichert.  All members of the Core Facility Cellular Imaging agreed for using these templates to annotate data in OMERO. Furthermore, there are a corresponding public example datasets used in the publication "Setting up an institutional OMERO environment for bioimage data: perspectives from both facility staff and users" and are available here: https://omero.med.tu-dresden.de/webclient/?show=dataset-1552 --> for lattice-light sheet microscopy https://omero.med.tu-dresden.de/webclient/?show=dataset-1555--> for electron microscopy data That templates are based on the REMBI recommendations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015) and were developed during the PoL-Bio-Image Analysis Symposium in Dresden Aug 28th- Sept 1st in 2023 and further adapeted during the usage of OMERO.  With every template it is possible to create a csv-file, that can be used to annotate a dataset in OMERO using the annotation script (https://github.com/ome/omero-scripts/blob/develop/omero/annotation_scripts/). How to use: fill the template with metadata select and copy the data range containing the Keys and Values open a new excel sheet and paste transpose in cell A1 Important: cell A1 contains always the name ''dataset'' and cell A2 contains the exact name of the dataset, which should be annotated in OMERO save the new excel sheet in csv-file (comma separated values) format Examples can be seen in sheet 3 ''csv_TOMO'' and sheet 5 csv_TEM''. Important note: The code has to be 8-Bit UCS transformation format (UTF-8) otherwise several characters (for example µ, %,°) might be not able to decode by the annotation script. We encountered this issue with old Microsoft-Office versions (MS Office 2016).  Note: By filling the values in the excel sheet, avoid the usage of comma as decimal delimiter. See cross reference: 10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)  ' license: CC-BY-4.0 name: Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Müller-Reichert num_downloads: 12 publication_date: '2024-06-26' url: - https://zenodo.org/records/12547566 - https://doi.org/10.5281/zenodo.12547566 uuid: 13784f68-4119-42d8-958e-47a4c828573d language: en authors_with_orcid: - Gunar Fabig https://orcid.org/0000-0003-3017-0978 - Anett Jannasch https://orcid.org/0000-0002-8047-2774 - Chukwuebuka Okafornta https://orcid.org/0000-0001-8094-1254 - Tom Boissonnet https://orcid.org/0000-0002-3328-9467 - Christian Schmidt https://orcid.org/0000-0001-9568-895X - Michele Bortolomeazzi https://orcid.org/0000-0001-5805-5774 - Vanessa Aphaia Fiona Fuchs https://orcid.org/0000-0002-4101-6987 - Maria Koeckert https://orcid.org/0009-0005-8062-2524 - Aayush Poddar https://orcid.org/0000-0002-8030-8851 - Martin Vogel https://orcid.org/0009-0005-5551-7570 - Hanna-Margareta Schwarzbach https://orcid.org/0009-0006-0353-3242 - Andy Vogelsang https://orcid.org/0009-0002-8722-9918 - Michael Gerlach https://orcid.org/0000-0002-6831-751X - Anja Nobst https://orcid.org/0009-0009-7468-180X - Thomas Müller-Reichert https://orcid.org/0000-0003-0203-1436 - Silke Tulok https://orcid.org/0009-0005-1473-6427 file_formats: .xlsx tags: - exclude from DALIA - submission_date: '2024-11-18T16:34:09.954845' authors: - Michael Gerlach description: 'This upload features a simple model for the creation (Manufacturing/Prototyping) of an abdominal imaging window (AIW) for use in mice intravital microscopy. Manufacture in titanium for chronic implantation. Measures in mm.' license: CC-BY-4.0 name: Abdominal Imaging Window (AIW) for Intravital Imaging num_downloads: 6 publication_date: '2024-11-15' url: - https://zenodo.org/records/14168603 - https://doi.org/10.5281/zenodo.14168603 uuid: 17eff95f-4651-4243-b7c7-3fd82076647f language: en authors_with_orcid: - Michael Gerlach https://orcid.org/0000-0002-6831-751X file_formats: .3mf * .f3d * .pdf * .png tags: - exclude from DALIA - submission_date: '2024-11-18T16:34:10.375459' authors: - Dr. Hellen Ishikawa-Ankerhold - Max Nobis description: Session 2 of a round table workshop. Features description of image processing methods useful in intravital imaging to compensate for the motion of living tissue. license: CC-BY-4.0 name: Round Table Workshop 2 - Correction of Drift and Movement num_downloads: 7 publication_date: '2024-11-14' url: - https://zenodo.org/records/14161633 - https://doi.org/10.5281/zenodo.14161633 uuid: b8a70f91-9bc4-40a9-95ea-86492658136f authors_with_orcid: - Dr. Hellen Ishikawa-Ankerhold https://orcid.org/0000-0003-0307-7022 - Max Nobis https://orcid.org/0000-0002-1861-1390 file_formats: .pdf tags: - exclude from DALIA - submission_date: '2024-11-18T16:34:10.757317' authors: - Michael Gerlach - Hans-Ulrich Fried - Christiane Peuckert description: 'Notes from a round table workshop on the 4th Day of Intravital Microscopy in Leuven, Belgium. Contains hands-on tips, tricks and schemes to improve sample stability during various models of Intravital Miroscopy.' license: CC-BY-4.0 name: Round Table Workshop 1 - Sample Stabilization in intravital Imaging num_downloads: 5 publication_date: '2024-11-14' url: - https://zenodo.org/records/14161289 - https://doi.org/10.5281/zenodo.14161289 uuid: f8698ee3-f2a9-4d92-9ddd-c6ad6021d52c language: en authors_with_orcid: - Michael Gerlach https://orcid.org/0000-0002-6831-751X - Hans-Ulrich Fried https://orcid.org/0000-0001-7557-1199 - Christiane Peuckert https://orcid.org/0000-0002-5973-1241 file_formats: .pptx tags: - exclude from DALIA - submission_date: '2024-11-18T16:34:11.147252' authors: - Dr. Hellen Ishikawa-Ankerhold description: 'Conference Slides for the presentation of GerBI e.V. at the 4th Day of Intravital Microscopy in Leuven, Belgium. Features Structure, activities and Links to join GerBI e.V.' license: CC-BY-4.0 name: Conference Slides - 4th Day of Intravital Microscopy num_downloads: 16 publication_date: '2024-11-13' url: - https://zenodo.org/records/14113714 - https://doi.org/10.5281/zenodo.14113714 uuid: 71ed4f50-f875-42a4-845b-f6e8ef9e2298 authors_with_orcid: - Dr. Hellen Ishikawa-Ankerhold https://orcid.org/0000-0003-0307-7022 file_formats: .pptx tags: - exclude from DALIA - submission_date: '2024-11-18T16:34:11.542022' authors: - Michael Gerlach description: 'This video describes the surgical process of implanting an abdominal imaging window (AIW) on the liver of mice. This window can be used for acute or longitudinal imaging. All experiments have been reviewed and approved by the local authorities (Landesdirektion Sachsen). Implantation of chronic abdominal windows allows for microscopical investigation of highly dynamic processes in physiological and pathological circumstances and is generally tolerated well by experimental animals. It enables insights which otherwise could only be obtained using high numbers of experimental animals. The method can be regarded as reduction approach in terms of 3R implementation. This upload contains the full version and is distributed under CC BY-ND 4.0 license to inhibit decontextualized misuse. Please check license terms for usage, especially for remixing/transforming! If you want to remix the material, get in contact with the author.' license: CC-BY-ND-4.0 name: Implantation of abdominal imaging windows on the mouse liver num_downloads: 103 publication_date: '2024-09-04' url: - https://zenodo.org/records/13683167 - https://doi.org/10.5281/zenodo.13683167 uuid: a0848ddf-9ac2-439f-9baa-099977f8312a language: en authors_with_orcid: - Michael Gerlach https://orcid.org/0000-0002-6831-751X file_formats: .mp4 tags: - exclude from DALIA - submission_date: '2024-11-18T16:34:11.914727' authors: - Michael Gerlach description: 'This video describes the surgical process of implanting an abdominal imaging window (AIW) on the liver of mice. This window can be used for acute or longitudinal imaging. All experiments have been reviewed and approved by the local authorities (Landesdirektion Sachsen). Implantation of chronic abdominal windows allows for microscopical investigation of highly dynamic processes in physiological and pathological circumstances and is generally tolerated well by experimental animals. It enables insights which otherwise could only be obtained using high numbers of experimental animals. The method can be regarded as reduction approach in terms of 3R implementation. This upload contains the short version and is distributed under CC BY-ND 4.0 license to inhibit decontextualized misuse. Please check license terms for usage, especially for remixing/transforming! If you want to remix the material, get in contact with the author.' license: CC-BY-ND-4.0 name: Implantation of abdominal imaging windows on the mouse liver - short version num_downloads: 26 publication_date: '2024-09-09' url: - https://zenodo.org/records/13736218 - https://doi.org/10.5281/zenodo.13736218 uuid: 6ff2fddb-942a-4e3b-9233-c9d7e1f3a9f6 language: en authors_with_orcid: - Michael Gerlach https://orcid.org/0000-0002-6831-751X file_formats: .mp4 tags: - exclude from DALIA - submission_date: '2024-11-18T16:34:12.275231' authors: - Michael Gerlach description: 'This video describes the surgical process of implanting an abdominal imaging window (AIW) on the kidney of mice. This window can be used for acute or longitudinal imaging. All experiments have been reviewed and approved by the local authorities (Landesdirektion Sachsen). Implantation of chronic abdominal windows allows for microscopical investigation of highly dynamic processes in physiological and pathological circumstances and is generally tolerated well by experimental animals. It enables insights which otherwise could only be obtained using high numbers of experimental animals. The method can be regarded as reduction approach in terms of 3R implementation. This upload contains the shortened version and is distributed under CC BY-ND 4.0 license to inhibit decontextualized misuse. Please check license terms for usage, especially for remixing/transforming! If you want to remix the material, get in contact with the author.' license: CC-BY-ND-4.0 name: Implantation of abdominal imaging windows on the mouse kidney - short version num_downloads: 50 publication_date: '2024-09-09' url: - https://zenodo.org/records/13736240 - https://doi.org/10.5281/zenodo.13736240 uuid: d835b656-b91e-4451-b147-5a669816bf3b language: en authors_with_orcid: - Michael Gerlach https://orcid.org/0000-0002-6831-751X file_formats: .mp4 tags: - exclude from DALIA - submission_date: '2024-11-18T16:34:12.641988' authors: - Michael Gerlach description: 'This video describes the surgical process of implanting an abdominal imaging window (AIW) on the kidney of mice. This window can be used for acute or longitudinal imaging. All experiments have been reviewed and approved by the local authorities (Landesdirektion Sachsen). Implantation of chronic abdominal windows allows for microscopical investigation of highly dynamic processes in physiological and pathological circumstances and is generally tolerated well by experimental animals. It enables insights which otherwise could only be obtained using high numbers of experimental animals. The method can be regarded as reduction approach in terms of 3R implementation. This upload contains the full version and is distributed under CC BY-ND 4.0 license to inhibit decontextualized misuse. Please check license terms for usage, especially for remixing/transforming! If you want to remix the material, get in contact with the author.' license: CC-BY-ND-4.0 name: Implantation of abdominal imaging windows on the mouse kidney num_downloads: 36 publication_date: '2024-09-04' url: - https://zenodo.org/records/13682928 - https://doi.org/10.5281/zenodo.13682928 uuid: b398a8f6-fbc7-4288-90aa-93c07e7ee5b9 language: en authors_with_orcid: - Michael Gerlach https://orcid.org/0000-0002-6831-751X file_formats: .mp4 tags: - exclude from DALIA - submission_date: '2024-11-18T16:34:13.156773' authors: - Financial & Legal Framework of Core Facilities - Elmar Endl - Jana Hedrich - Juliane Hoth - Julia Nagy - Astrid Schauss - Nina Schulze - Silke Tulok description: 'Die GermanBioImaging (GerBI-GMB) - Deutsche Gesellschaft für Mikroskopie und Bildanalyse e.V. bietet über regelmäßig stattfindende Treffen (GerBI-Chats) die Möglichkeit zum aktiven Austausch der Mitglieder untereinander. Das GerBI-GMB Team "Legal und Finacial Framwork", welches sich mit administrativen Aufgaben rund um das Core Facility Management beschäftigt, nutzt diese Möglichkeit zum aktiven Austausch innerhalb des Netzwerkes und darüber hinaus.  Der Beschaffungsprozess von Forschungsgroßgeräten ist komplex und je nach Institution unterschiedlich geregelt. Aus unserer Sicht lässt sich dieser Prozess grob in drei Stufen aufteilen: Bedarfsanmeldung Antragsvorbereitung und -fertigstellung Antragsbewilligung und Nutzung  Nach dem Initialvortrag der GerBI-Chat Reihe, in dem das Thema Bedarfsanmeldung im Fokus stand, geht es im hier enthaltenen zweiten Teil „Antragsvorbereitung und -fertigstellung: Wie schreibe ich am besten einen Großgeräteantrag?“ um die Beantragung von Forschungsgroßgeräten nach Art. 91b GG.' license: CC-BY-4.0 name: 'GerBI-Chat: Teil 2 - Wie schreibe ich am besten einen Großegräteantrag' num_downloads: 39 publication_date: '2024-10-02' url: - https://zenodo.org/records/13807114 - https://doi.org/10.5281/zenodo.13807114 uuid: 550b53b4-4db5-4f5d-bd4a-ce27e093dd05 language: de authors_with_orcid: - Financial & Legal Framework of Core Facilities - Elmar Endl - Jana Hedrich https://orcid.org/0000-0002-2664-0212 - Juliane Hoth https://orcid.org/0009-0004-1220-258X - Julia Nagy https://orcid.org/0009-0004-1220-258X - Astrid Schauss https://orcid.org/0000-0002-6658-2192 - Nina Schulze https://orcid.org/0009-0008-5503-6218 - Silke Tulok https://orcid.org/0009-0005-1473-6427 file_formats: .pdf tags: - exclude from DALIA - submission_date: '2024-11-18T16:34:13.609723' authors: - Financial & Legal Framework of Core Facilities - Elmar Endl - Jana Hedrich - Juliane Hoth - Julia Nagy - Astrid Schauss - Nina Schulze - Silke Tulok description: 'Die GermanBioImaging (GerBI-GMB) - Deutsche Gesellschaft für Mikroskopie und Bildanalyse e.V. bietet über regelmäßig stattfindende Treffen (GerBI-Chats) die Möglichkeit zum aktiven Austausch der Mitglieder untereinander. Das GerBI-GMB Team "Legal und Finacial Framwork", welches sich mit administrativen Aufgaben rund um das Core Facility Management beschäftigt, nutzt diese Möglichkeit zum aktiven Austausch innerhalb des Netzwerkes und darüber hinaus.  Der Beschaffungsprozess von Forschungsgroßgeräten ist komplex und je nach Institution unterschiedlich geregelt. Aus unserer Sicht lässt sich dieser Prozess grob in drei Stufen aufteilen: Bedarfsanmeldung Antragsvorbereitung und -fertigstellung Antragsbewilligung und Nutzung  Dieser hier enthaltene Beitrag ist der Initialvortrag des GerBi-Chats zum Teil 1 - Von der Bedarfsanmeldung bis zum Beginn der Antragststellung. Die weiteren Stufen der Großgerätebeschaffung werden in nachfolgenden Beiträgen behandelt.' license: CC-BY-4.0 name: 'GerBI-Chat: Teil 1 - Vom Bedarf bis zum Großgeräteantrag-Schreiben' num_downloads: 367 publication_date: '2024-09-11' url: - https://zenodo.org/records/13810879 - https://doi.org/10.5281/zenodo.13810879 uuid: cad67901-18c0-480c-8f1a-5c34fbcf93e2 language: de authors_with_orcid: - Financial & Legal Framework of Core Facilities - Elmar Endl - Jana Hedrich https://orcid.org/0000-0002-2664-0212 - Juliane Hoth https://orcid.org/0009-0005-4673-8419 - Julia Nagy https://orcid.org/0009-0004-1220-258X - Astrid Schauss https://orcid.org/0000-0002-6658-2192 - Nina Schulze https://orcid.org/0009-0008-5503-6218 - Silke Tulok https://orcid.org/0009-0005-1473-6427 file_formats: .pdf tags: - exclude from DALIA - submission_date: '2024-11-18T16:34:14.668170' authors: - Silke Tulok - Anja Nobst - Anett Jannasch - Tom Boissonnet - Gunar Fabig description: 'This Key-Value pair template is used for the data documentation during imaging experiments and the later data annotation in OMERO. It is tailored for the usage and image acquisition at the slide scanning system Zeiss AxioScan 7 in the Core Facility Cellular Imaging (CFCI). It contains important metadata of the imaging experiment, which are not saved in the corresponding imaging files. All users of the Core Facility Cellular Imaging are trained to use that file to document their imaging parameters directly during the data acquisition with the possibility for a later upload to OMERO. Furthermore, there is a corresponding public example image used in the publication "Setting up an institutional OMERO environment for bioimage data: perspectives from both facility staff and users" and is available here: https://omero.med.tu-dresden.de/webclient/?show=image-33248 This template was developed by the CFCI staff during the setup and usage of the AxioScan 7 and is based on the REMBI recommendations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015). With this template it is possible to create a csv-file, that can be used to annotate an image or dataset in OMERO using the annotation script (https://github.com/ome/omero-scripts/blob/develop/omero/annotation_scripts/). How to use: fill the template sheet  with your metadata select and copy the data range containing the Keys and Values open a new excel sheet and paste transpose in cell A1  Important: cell A1 contains always the name ''dataset'' and cell A2 contains the exact name of the image/dataset, which should be annotated in OMERO save the new excel sheet in csv-file (comma separated values) format An example can be seen in sheet 3 ''csv_AxioScan''. Important note: The code has to be 8-Bit UCS transformation format (UTF-8) otherwise several characters (for example µ, %,°) might be not able to decode by the annotation script. We encountered this issue with old Microsoft-Office versions (MS Office 2016).  Note: By filling the values in the excel sheet, avoid the usage of comma as decimal delimiter. See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Mueller-Reichert 10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study)' license: CC-BY-4.0 name: Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI) num_downloads: 12 publication_date: '2024-06-28' tags: - nfdi4bioimage - research data management - exclude from DALIA url: - https://zenodo.org/records/12578084 - https://doi.org/10.5281/zenodo.12578084 uuid: 7b7221bc-dfc9-4b7c-9a14-30837ba70953 language: en authors_with_orcid: - Silke Tulok https://orcid.org/0009-0005-1473-6427 - Anja Nobst https://orcid.org/0009-0009-7468-180X - Anett Jannasch https://orcid.org/0000-0002-8047-2774 - Tom Boissonnet https://orcid.org/0000-0002-3328-9467 - Gunar Fabig https://orcid.org/0000-0003-3017-0978 file_formats: .xlsx - submission_date: '2024-11-18T16:34:15.016548' authors: - Michael Gerlach description: 'A 3D- printable microscope stage adapter for the reproducible accomodation of 6 or 12-well plates at a Zeiss AxioZoom microscope. 4 cylindrical anchors are fixed to the glass plate of the stage. The stage adapter is reversibly placed on these anchors and acommodates a standard Greiner 6- or 12-well plate.' license: CC-BY-4.0 name: Zeiss AxioZoom Stage Adapter - 12/6Well Plate num_downloads: 2 publication_date: '2024-06-20' url: - https://zenodo.org/records/7944877 - https://doi.org/10.5281/zenodo.7944877 uuid: a3ab418c-93dd-4968-a7ee-3ad97764ef51 language: en authors_with_orcid: - Michael Gerlach https://orcid.org/0000-0002-6831-751X file_formats: .3mf * .png tags: - exclude from DALIA - submission_date: '2024-11-18T16:34:15.354820' authors: - Michael Gerlach description: 'A 3D- printable microscope stage adapter for the reproducible accomodation of samples at a Zeiss AxioZoom stereomicroscope. 4 cylindrical anchors are fixed to the glass plate of the stage. The stage adapter is reversibly placed on these anchors.  ' license: CC-BY-4.0 name: Zeiss AxioZoom Stage Adapter num_downloads: 1 publication_date: '2024-06-20' url: - https://zenodo.org/records/7963020 - https://doi.org/10.5281/zenodo.7963020 uuid: 570d8f83-7796-46e6-85bb-2a00c8326f7e language: en file_formats: .3mf * .png tags: - exclude from DALIA authors_with_orcid: - Michael Gerlach https://orcid.org/0000-0002-6831-751X - submission_date: '2024-11-18T16:34:15.720049' authors: - Michael Gerlach description: 'A 3D- printable microscope stage adapter for the reproducible accomodation of EM Blocks at a Zeiss AxioZoom microscope. 4 cylindrical anchors are fixed to the glass plate of the stage. The stage adapter is reversibly placed on these anchors and acommodates 70 standard resin EM blocks.' license: CC-BY-4.0 name: Zeiss AxioZoom Stage Adapter - EM block holder num_downloads: 3 publication_date: '2024-06-20' url: - https://zenodo.org/records/7963006 - https://doi.org/10.5281/zenodo.7963006 uuid: 9efdac58-b44c-49f3-99c8-05d6c88dfdc0 language: en file_formats: .3mf * .png tags: - exclude from DALIA authors_with_orcid: - Michael Gerlach https://orcid.org/0000-0002-6831-751X - submission_date: '2024-11-18T16:34:16.060174' authors: - Michael Gerlach description: 'A 3D- printable microscope stage adapter for the reproducible accomodation of microscopic slides at a Zeiss AxioZoom microscope. 4 cylindrical anchors are fixed to the glass plate of the stage. The stage adapter is reversibly placed on these anchors and acommodates 4 standard glass slides.' license: CC-BY-4.0 name: Zeiss AxioZoom Stage Adapter - Microscope slides num_downloads: 1 publication_date: '2024-06-21' url: - https://zenodo.org/records/7945018 - https://doi.org/10.5281/zenodo.7945018 uuid: b95c1dea-c736-46f8-93f7-34321d62a3fe language: en file_formats: .3mf * .png tags: - exclude from DALIA authors_with_orcid: - Michael Gerlach https://orcid.org/0000-0002-6831-751X - submission_date: '2024-11-18T16:34:16.445941' authors: - Michael Gerlach description: 'This is a set of databases containing published use of substances which can be applied to rodents in order to contrast specific structures for optical intravital microscopy. The first dataset contains applied final dosages, calculated for 25g-mice, as well as the orignally published amounts, concentrations and application routes of agents directly applied into the target organism. The second dataset contains dosages and cell numbers for the external contrastation and subsequent application of cells into the target organism. Filtering possible for organ system and contrasted structure/cell type in both datasets, substance class and fluorescent detection windows can be filtered in the dataset for direct agent application. Source publications are listed by DOI.  ' license: CC-BY-4.0 name: Intravital microscopy contrasting agents for application - Database num_downloads: 23 publication_date: '2024-06-19' url: - https://zenodo.org/records/12166710 - https://doi.org/10.5281/zenodo.12166710 uuid: a53e66ac-41e5-4c29-b0c7-f235cae942c2 language: en file_formats: .xlsx tags: - exclude from DALIA authors_with_orcid: - Michael Gerlach https://orcid.org/0000-0002-6831-751X - submission_date: '2024-11-18T16:34:16.785479' authors: - Nadine Utz - Sabine Reither - Ruth Hans - Christian Feldhaus description: 'In bio-medical research we often need to combine a broad range of expertise to run complex experiments and analyse and interpret their results. Also, it is desirable that all stakeholders of a project understand all parts of the experiment and analysis to draw and support the right conclusions. For imaging experiments this usually requires a basic understanding of the underlying physics. This has not necessarily been part of the professional training of all stakeholders, e.g. biologists or data scientists. Therefore an affordable platform for easily demonstrating and explaining imaging principles would be desirable. Building up on a commercially available STEM Optics kit we developed extensions with widely available and affordable components to demonstrate advanced imaging techniques like e.g. confocal, lightsheet, OPT, spectral imaging. All models are quick and easy to build, yet demonstrate the important physical principles each imaging technique is based on. Further use cases for this kit are training courses, demonstrations for imaging newbies when designing an experiment and outreach activities but also basic level prototyping.' license: CC-BY-4.0 name: Development of a platform for advanced optics education, training and prototyping num_downloads: 12 publication_date: '2023-10-05' url: - https://zenodo.org/records/10925217 - https://doi.org/10.5281/zenodo.10925217 uuid: abbcc42a-ec38-4b18-9e30-2e15a81d481b language: en file_formats: .pdf tags: - include in DALIA authors_with_orcid: - Nadine Utz - Sabine Reither - Ruth Hans - Christian Feldhaus https://orcid.org/0009-0008-5000-7645 - submission_date: '2024-11-18T16:34:17.179933' authors: - Stefanie Weidtkamp-Peters - Josh Moore - Christian Schmidt - Roland Nitschke - Susanne Kunis - Thomas Zobel description: 'Overview of GerBI RDM projects: why and how?' license: CC-BY-4.0 name: '[Community Meeting 2024] Supporting and financing RDM projects within GerBI' num_downloads: 58 publication_date: '2024-03-28' url: - https://zenodo.org/records/10889694 - https://doi.org/10.5281/zenodo.10889694 uuid: baa185b2-3557-4214-b578-40cbfb872409 file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Stefanie Weidtkamp-Peters https://orcid.org/0000-0001-7734-3771 - Josh Moore https://orcid.org/0000-0003-4028-811X - Christian Schmidt https://orcid.org/0000-0001-9568-895X - Roland Nitschke https://orcid.org/0000-0002-9397-8475 - Susanne Kunis https://orcid.org/0000-0001-6523-7496 - Thomas Zobel https://orcid.org/0000-0002-2101-8416 - submission_date: '2024-11-18T16:34:17.925836' authors: - Chiara Stringari description: 'This presentation introduces the open source software to analyze FLIM data: FLUTE – (F)luorescence (L)ifetime (U)ltima(T)e (E)xplorer: a Python GUI for interactive phasor analysis of FLIM data   The software is available on GitHub: https://github.com/LaboratoryOpticsBiosciences/FLUTE and it is published on Biological imaging Journal: Gottlieb, D., Asadipour, B., Kostina, P., Ung, T., & Stringari, C. (2023). FLUTE: A Python GUI for interactive phasor analysis of FLIM data. Biological Imaging, 1-22. doi:10.1017/S2633903X23000211 The lecture was part of the short talks on community developed FLIM-software at the German BioImaging workshop on FLIM in Munich.' license: CC-BY-4.0 name: 'Slides about FLUTE: a Python GUI for interactive phasor analysis of FLIM data' num_downloads: 454 publication_date: '2024-03-19' url: - https://zenodo.org/records/10839310 - https://doi.org/10.5281/zenodo.10839310 uuid: 22960997-36d3-4489-b5ae-fddfc5b5369b language: en file_formats: .pdf * .pptx tags: - include in DALIA authors_with_orcid: - Chiara Stringari https://orcid.org/0000-0002-0550-7463 - submission_date: '2024-11-18T16:34:18.393086' authors: - Christian Schmidt - Michele Bortolomeazzi - Tom Boissonnet - Julia Dohle - Tobias Wernet - Janina Hanne - Roland Nitschke - Susanne Kunis - Karen Bernhardt - Stefanie Weidtkamp-Peters - Elisa Ferrando-May description: Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations. license: CC-BY-4.0 name: The Information Infrastructure for BioImage Data (I3D:bio) project to advance FAIR microscopy data management for the community num_downloads: 44 publication_date: '2024-03-04' tags: - nfdi4bioimage - research data management - exclude from DALIA url: - https://zenodo.org/records/10805204 - https://doi.org/10.5281/zenodo.10805204 uuid: d8991538-e181-482e-aeb4-ada02d7f7671 language: en file_formats: .pdf authors_with_orcid: - Christian Schmidt https://orcid.org/0000-0001-9568-895X - Michele Bortolomeazzi https://orcid.org/0000-0001-5805-5774 - Tom Boissonnet https://orcid.org/0000-0002-3328-9467 - Julia Dohle - Tobias Wernet https://orcid.org/0009-0004-2093-7885 - Janina Hanne https://orcid.org/0000-0002-5332-3589 - Roland Nitschke https://orcid.org/0000-0002-9397-8475 - Susanne Kunis https://orcid.org/0000-0001-6523-7496 - Karen Bernhardt - Stefanie Weidtkamp-Peters https://orcid.org/0000-0001-7734-3771 - Elisa Ferrando-May https://orcid.org/0000-0002-5567-8690 - submission_date: '2024-11-18T16:34:18.767636' authors: - Susanne Kunis - Thomas Zobel description: 'Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V.  ' license: CC-BY-4.0 name: '[Community Meeting 2024] Overview Team Image Data Analysis and Management' num_downloads: 52 publication_date: '2024-03-08' tags: - nfdi4bioimage - research data management - exclude from DALIA url: - https://zenodo.org/records/10796364 - https://doi.org/10.5281/zenodo.10796364 uuid: 38117ab5-3161-4224-9b43-41426da1d052 file_formats: .pdf authors_with_orcid: - Susanne Kunis https://orcid.org/0000-0001-6523-7496 - Thomas Zobel https://orcid.org/0000-0002-2101-8416 - submission_date: '2024-11-18T16:34:21.337970' authors: - bugraoezdemir description: 'Changes implemented since v0.0.3 Support provided for file paths with spaces. Support provided for globbing filenames from s3 for one-to-one conversion (parse_s3_filenames.py modified). Support provided for single file import from s3 (parse_s3_filenames.py modified). run_conversion.py replaces batchconvert_cli.sh and construct_cli.py, uniting them. Error handling updated for each process ' license: CC-BY-4.0 name: 'Euro-BioImaging/BatchConvert: v0.0.4' num_downloads: 48 publication_date: '2024-02-19' url: - https://zenodo.org/records/10679318 - https://doi.org/10.5281/zenodo.10679318 uuid: 69df66f7-6309-45c8-8957-739cd53a5f4d language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - bugraoezdemir - submission_date: '2024-11-18T16:34:22.477169' authors: - Romain David - Arina Rybina - Jean-Marie Burel - Jean-Karim Heriche - Pauline Audergon - Jan-Willem Boiten - Frederik Coppens - Sara Crockett - Exter Katrina - Sven Fahrener - Maddalena Fratelli - Carole Goble - Philipp Gormanns - Tobias Grantner - Bjorn Gruning - Kim Tamara Gurwitz - John Hancock - Henriette Harmse - Petr Holub - Nick Juty - Geoffrey Karnbach - Emma Karoune - Antje Keppler - Jessica Klemeier - Carla Lancelotti - Jean-Luc Legras - L. Allyson Lister - Dario Livio Longo - Rebecca Ludwig - Benedicte Madon - Marzia Massimi - Vera Matser - Rafaele Matteoni - Mayrhofer Michaela Th. - Christian Ohmann - Maria Panagiotopoulou - Helen Parkinson - Isabelle Perseil - Claudia Pfander - Roland Pieruschka - Michael Raess - Andreas Rauber - Audrey S. Richard - Paolo Romano - Antonio Rosato - Alex Sanchez-Pla - Susanna-Assunta Sansone - Ugis Sarkans - Beatriz Serrano-Solano - Jing Tang - Ziaurrehman Tanoli - Jonathan Tedds - Harald Wagener - Martin Weise - Hans V. Westerhoff - Rudolf Wittner - Jonathan Ewbank - Niklas Blomberg - Philip Gribbon description: '"Be SURE - Be SUstainable REcommendations"The main goals and challenges for the Life Science (LS) communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable LS resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European LS Research Infrastructures (RIs), it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable FAIR data management, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences.IN PRESS EMBO Journal: https://www.embopress.org/journal/14602075 AVAILABLE SOON at : https://doi.org/10.15252/embj.2023115008 ' license: CC-BY-4.0 name: 'Preprint: "Be Sustainable", Recommendations for FAIR Resources in Life Sciences research: EOSC-Life''s Lessons' num_downloads: 2486 publication_date: '2023-09-12' url: - https://zenodo.org/records/8338931 - https://doi.org/10.5281/zenodo.8338931 uuid: cead4ffb-b2f6-4ec1-96ed-71eb44db1182 language: en file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Romain David https://orcid.org/0000-0003-4073-7456 - Arina Rybina https://orcid.org/0000-0002-5609-5710 - Jean-Marie Burel https://orcid.org/0000-0002-1789-1861 - Jean-Karim Heriche https://orcid.org/0000-0001-6867-9425 - Pauline Audergon https://orcid.org/0000-0002-1624-7388 - Jan-Willem Boiten https://orcid.org/0000-0003-0327-638X - Frederik Coppens https://orcid.org/0000-0001-6565-5145 - Sara Crockett https://orcid.org/0009-0008-7983-0360 - Exter Katrina https://orcid.org/0000-0002-5911-1536 - Sven Fahrener https://orcid.org/0000-0001-6240-9678 - Maddalena Fratelli https://orcid.org/0000-0002-1769-3427 - Carole Goble https://orcid.org/0000-0003-1219-2137 - Philipp Gormanns https://orcid.org/0000-0001-9823-1621 - Tobias Grantner https://orcid.org/0000-0001-5104-5004 - Bjorn Gruning https://orcid.org/0000-0002-3079-6586 - Kim Tamara Gurwitz https://orcid.org/0000-0003-1992-5073 - John Hancock https://orcid.org/0000-0003-2991-2217 - Henriette Harmse https://orcid.org/0000-0001-7251-9504 - Petr Holub https://orcid.org/0000-0002-5358-616X - Nick Juty https://orcid.org/0000-0002-2036-8350 - Geoffrey Karnbach https://orcid.org/0009-0005-9892-7799 - Emma Karoune https://orcid.org/0000-0002-6576-6053 - Antje Keppler https://orcid.org/0000-0003-4358-2269 - Jessica Klemeier https://orcid.org/0009-0006-9083-3808 - Carla Lancelotti https://orcid.org/0000-0003-1099-7329 - Jean-Luc Legras https://orcid.org/0000-0002-4006-4389 - L. Allyson Lister https://orcid.org/0000-0002-7702-4495 - Dario Livio Longo https://orcid.org/0000-0002-6906-9925 - Rebecca Ludwig https://orcid.org/0000-0001-9877-3287 - Benedicte Madon https://orcid.org/0000-0001-8608-3895 - Marzia Massimi https://orcid.org/0000-0001-5052-1822 - Vera Matser https://orcid.org/0000-0002-2010-6844 - Rafaele Matteoni https://orcid.org/0000-0002-0314-5948 - Michaela Th. Mayrhofer https://orcid.org/0000-0001-6932-0473 - Christian Ohmann https://orcid.org/0000-0002-5919-1003 - Maria Panagiotopoulou https://orcid.org/0000-0002-4221-7254 - Helen Parkinson https://orcid.org/0000-0003-3035-4195 - Isabelle Perseil https://orcid.org/0000-0001-9058-9290 - Claudia Pfander https://orcid.org/0000-0002-9574-9553 - Roland Pieruschka https://orcid.org/0000-0002-9774-2670 - Michael Raess https://orcid.org/0000-0002-8759-1186 - Andreas Rauber https://orcid.org/0000-0002-9272-6225 - Audrey S. Richard https://orcid.org/0000-0002-0207-0139 - Paolo Romano https://orcid.org/0000-0003-4694-3883 - Antonio Rosato https://orcid.org/0000-0001-6172-0368 - Alex Sanchez-Pla https://orcid.org/0000-0002-8673-7737 - Susanna-Assunta Sansone https://orcid.org/0000-0001-5306-5690 - Ugis Sarkans https://orcid.org/0000-0001-9227-8488 - Beatriz Serrano-Solano https://orcid.org/0000-0002-5862-6132 - Jing Tang https://orcid.org/0000-0001-7480-7710 - Ziaurrehman Tanoli https://orcid.org/0000-0003-2435-9862 - Jonathan Tedds https://orcid.org/0000-0003-2829-4584 - Harald Wagener https://orcid.org/0000-0003-1073-4991 - Martin Weise https://orcid.org/0000-0003-4216-302X - Hans V. Westerhoff https://orcid.org/0000-0002-0443-6114 - Rudolf Wittner https://orcid.org/0000-0002-0003-2024 - Jonathan Ewbank https://orcid.org/0000-0002-1257-6862 - Niklas Blomberg https://orcid.org/0000-0003-4155-5910 - Philip Gribbon https://orcid.org/0000-0001-7655-2459 - submission_date: '2024-11-18T16:34:22.816011' authors: - Beatriz Serrano-Solano description: Graduation presentation for the 7th cohort of the Open Seeds mentoring & training program for Open Science ambassadors. The project presented is called "Euro-BioImaging  Scientific Ambassadors Program". license: CC-BY-4.0 name: Euro-BioImaging Scientific Ambassadors Program num_downloads: 25 publication_date: '2023-07-25' url: - https://zenodo.org/records/8182154 - https://doi.org/10.5281/zenodo.8182154 uuid: 114b3dfb-33cc-45ae-8424-2ed18fb2f07a language: en file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Beatriz Serrano-Solano https://orcid.org/0000-0002-5862-6132 - submission_date: '2024-11-18T16:34:23.197583' authors: - Euro-BioImaging ERIC description: Euro-BioImaging ERIC is the European landmark research infrastructure for biological and biomedical imaging as recognized by the European Strategy Forum on Research Infrastructures (ESFRI). Euro-BioImaging is the gateway to world-class imaging facilities across Europe. This document is the Euro-BioImaging Annual Report for the year 2022. license: CC-BY-4.0 name: Euro-BioImaging ERIC Annual Report 2022 num_downloads: 83 publication_date: '2023-07-14' url: - https://zenodo.org/records/8146412 - https://doi.org/10.5281/zenodo.8146412 uuid: 18a583fe-c927-4a0c-98b0-89e2308d73ef language: en file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Euro-BioImaging ERIC - submission_date: '2024-11-18T16:34:23.860886' authors: - Isabel Kemmer - Antje Keppler - Beatriz Serrano-Solano - Arina Rybina - Bugra Özdemir - Johanna Bischof - Ayoub El Ghadraoui - John E. Eriksson - Aastha Mathur description: Bioimaging has now entered the era of big data with faster than ever development of complex microscopy technologies leading to increasingly complex datasets. This enormous increase in data size and informational complexity within those datasets has brought with it several difficulties in terms of common and harmonized data handling, analysis and management practices, which are currently hampering the full potential of image data being realized. Here we outline a wide range of efforts and solutions currently being developed by the microscopy community to address these challenges on the path towards FAIR bioimage data. We also highlight how different actors in the microscopy ecosystem are working together, creating synergies that develop new approaches, and how research infrastructures, such as Euro-BioImaging, are fostering these interactions to shape the field.  license: CC-BY-4.0 name: Building a FAIR image data ecosystem for microscopy communities num_downloads: 199 publication_date: '2023-03-31' url: - https://zenodo.org/records/7788899 - https://doi.org/10.5281/zenodo.7788899 uuid: bb24602f-d351-4309-8f9e-8e3ba8c28386 language: en file_formats: .pdf tags: - include in DALIA authors_with_orcid: - Isabel Kemmer https://orcid.org/0000-0002-8799-4671 - Antje Keppler https://orcid.org/0000-0003-4358-2269 - Beatriz Serrano-Solano https://orcid.org/0000-0002-5862-6132 - Arina Rybina https://orcid.org/0000-0002-5609-5710 - Bugra Özdemir https://orcid.org/0000-0001-9823-0581 - Johanna Bischof https://orcid.org/0000-0002-5668-841X - Ayoub El Ghadraoui https://orcid.org/0000-0002-5494-036X - John E. Eriksson https://orcid.org/0000-0002-1570-7725 - Aastha Mathur https://orcid.org/0000-0001-9734-9767 - submission_date: '2024-11-18T16:34:26.146506' authors: - Luke Sorensen - Ayame Saito - Sabrina Poon - Myat Noe Han - Ryan Hamnett - Peter Neckel - Adam Humenick - Keith Mutunduwe - Christie Glennan - Narges Mahdavian - Simon JH Brookes - Rachel M McQuade - Jaime PP Foong - Estibaliz Gómez-de-Mariscal - Arrate Muñoz Barrutia - Julia A. Kaltschmidt - Sebastian K. King - Robert Haase - Simona Carbone - Nicholas A. Veldhuis - Daniel P. Poole - Pradeep Rajasekhar description: 'What''s Changed Updating User Dialogs by @mattyrowey in https://github.com/pr4deepr/GutAnalysisToolbox/pull/18 Added Dialog Boxes and Grammar Corrections by @mattyrowey in https://github.com/pr4deepr/GutAnalysisToolbox/pull/19 Updated Dialog Prompts for Clarity by @mattyrowey in https://github.com/pr4deepr/GutAnalysisToolbox/pull/20 Batch analysis option added. fixed a bunch of bugs related to ganglia segmentation and user workflow New Contributors @mattyrowey made their first contribution in https://github.com/pr4deepr/GutAnalysisToolbox/pull/18 Full Changelog: https://github.com/pr4deepr/GutAnalysisToolbox/compare/v0.6...v0.7' license: CC-BY-4.0 name: Gut Analysis Toolbox num_downloads: 77 publication_date: '2024-09-10' url: - https://zenodo.org/records/13739137 - https://doi.org/10.5281/zenodo.13739137 uuid: 984b73bb-4dcd-4b21-91d5-50c444310723 language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Luke Sorensen - Ayame Saito - Sabrina Poon - Myat Noe Han https://orcid.org/0000-0003-3028-7359 - Ryan Hamnett https://orcid.org/0000-0002-9118-1585 - Peter Neckel https://orcid.org/0000-0003-1976-0512 - Adam Humenick - Keith Mutunduwe - Christie Glennan - Narges Mahdavian - Simon JH Brookes https://orcid.org/0000-0001-5635-0876 - Rachel M McQuade https://orcid.org/0000-0002-3510-1288 - Jaime PP Foong https://orcid.org/0000-0003-2082-5520 - Estibaliz Gómez-de-Mariscal https://orcid.org/0000-0003-2082-3277 - Arrate Muñoz Barrutia - Julia A. Kaltschmidt - Sebastian K. King https://orcid.org/0000-0001-5396-0265 - Robert Haase https://orcid.org/0000-0001-5949-2327 - Simona Carbone https://orcid.org/0000-0002-4350-6357 - Nicholas A. Veldhuis https://orcid.org/0000-0002-8902-9365 - Daniel P. Poole https://orcid.org/0000-0002-6168-8422 - Pradeep Rajasekhar https://orcid.org/0000-0002-1983-7244 - submission_date: '2024-11-18T16:34:26.566744' authors: - Luke Sorensen - Ayame Saito - Sabrina Poon - Myat Noe Han - Adam Humenick - Peter Neckel - Keith Mutunduwe - Christie Glennan - Narges Mahdavian - Simon JH Brookes - Rachel M McQuade - Jaime PP Foong - Sebastian K. King - Estibaliz Gómez-de-Mariscal - Arrate Muñoz-Barrutia - Robert Haase - Simona Carbone - Nicholas A. Veldhuis - Daniel P. Poole - Pradeep Rajasekhar description: "This upload is associated with the software, Gut Analysis Toolbox (GAT).\n\ If you use it please cite:\nSorensen et al. Gut Analysis Toolbox: Automating\ \ quantitative analysis of enteric neurons. J Cell Sci 2024; jcs.261950.\ \ doi: https://doi.org/10.1242/jcs.261950\nThe upload contains StarDist\ \ models for segmenting enteric neurons in 2D, enteric neuronal subtypes in 2D\ \ and UNet model for enteric ganglia in 2D in gut wholemount tissue. GAT is implemented\ \ in Fiji, but the models can be used in any software that supports StarDist and\ \ the use of 2D UNet models. The files here also consist of Python notebooks\ \ (Google Colab), training and test data as well as reports on model performance.\n\ The model files are located in the respective folders as zip files. The folders\ \ have also been zipped:\n\nNeuron (Hu; StarDist model):\n\nMain folder:\ \ 2D_enteric_neuron_model_QA.zip\nModel File:2D_enteric_neuron_v4_1.zip \n\ \n\nNeuronal subtype (StarDist model): \n\nMain folder: 2D_enteric_neuron_subtype_model_QA.zip\n\ Model File: 2D_enteric_neuron_subtype_v4.zip\n\n\nEnteric ganglia (2D UNet model;\ \ Use in FIJI with deepImageJ)\n\nMain folder: 2D_enteric_ganglia_model_QA.zip\n\ Model File: 2D_Ganglia_RGB_v2.bioimage.io.model.zip (Compatible with deepimageJ\ \ v3)\n\n\n\nFor the all models, files included are:\n\nModel for segmenting cells\ \ or ganglia in 2D FIJI. StarDist or 2D UNet.\nTraining and Test datasets used\ \ for training.\nGoogle Colab notebooks used for training and quality assurance\ \ (ZeroCost DL4Mic notebooks).\nQuality assurance reports generated from above\ \ notebooks.\nStarDist model exported for use in QuPath.\n\nThe model files can\ \ be used within can be used within the software, StarDist. They are\ \ intended to be used within FIJI or QuPath, but can be used in any software that\ \ supports the implementation of StarDist in 2D.\nData:\nAll the images were collected\ \ from 4 different research labs and a public database (SPARC database) to account\ \ for variations in image acquisition, sample preparation and immunolabelling.\n\ For enteric neurons the pan-neuronal marker, Hu has been used and the \ \ 2D wholemounts images from mouse, rat and human tissue.\nFor enteric neuronal\ \ subtypes, 2D images for nNOS, MOR, DOR, ChAT, Calretinin, Calbindin, Neurofilament,\ \ CGRP and SST from mouse tissue have been used..\n25 images were used from\ \ the following entries in the SPARC database:\n\nHoward, M. (2021). 3D imaging\ \ of enteric neurons in mouse (Version 1) [Data set]. SPARC Consortium. \nGraham,\ \ K. D., Huerta-Lopez, S., Sengupta, R., Shenoy, A., Schneider, S., Wright, C.\ \ M., Feldman, M., Furth, E., Lemke, A., Wilkins, B. J., Naji, A., Doolin, E.,\ \ Howard, M., & Heuckeroth, R. (2020). Robust 3-Dimensional visualization\ \ of human colon enteric nervous system without tissue sectioning (Version 1)\ \ [Data set]. SPARC Consortium.\nWang, L., Yuan, P.-Q., Gould, T. and Tache, Y.\ \ (2021). Antibodies Tested in theColon – Mouse (Version 1) [Data set].\ \ SPARC Consortium. doi:10.26275/i7dl-58h\n\nThe images have been acquired using\ \ a combination different microscopes. The images for the mouse tissue were acquired\ \ using: \n\n\nLeica TCS-SP8 confocal system (20x HC PL APO NA 1.33, 40 x\ \ HC PL APO NA 1.3) \n\n\nLeica TCS-SP8 lightning confocal system (20x HC\ \ PL APO NA 0.88) \n\n\nZeiss Axio Imager M2 (20X HC PL APO NA 0.3) \n\ \n\nZeiss Axio Imager Z1 (10X HC PL APO NA 0.45) \n\n\nHuman tissue images\ \ were acquired using: \n\n\nIX71 Olympus microscope (10X HC PL APO NA 0.3) \n\ \n\nFor more information, visit the Documentation website.\nNOTE: The images\ \ for enteric neurons and neuronal subtypes have been rescaled to 0.568 µm/pixel\ \ for mouse and rat. For human neurons, it has been rescaled to 0.9 µm/pixel\ \ . This is to ensure the neuronal cell bodies have similar pixel area across\ \ images. The area of cells in pixels can vary based on resolution of image, magnification\ \ of objective used, animal species (larger animals -> larger neurons) and\ \ potentially how the tissue is stretched during wholemount preparation \n\ Average neuron area for neuronal model: 701.2 ± 195.9 pixel2 (Mean\ \ ± SD, 6267 cells)\nAverage neuron area for neuronal subtype model: 880.9\ \ ± 316 pixel2 (Mean ± SD, 924 cells)\nSoftware References:\nStardist\n\ Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018, September). Cell\ \ detection with star-convex polygons. In International Conference on Medical\ \ Image Computing and Computer-Assisted Intervention (pp. 265-273). Springer,\ \ Cham.\ndeepImageJ\nGómez-de-Mariscal, E., García-López-de-Haro,\ \ C., Ouyang, W., Donati, L., Lundberg, E., Unser, M., Muñoz-Barrutia,\ \ A. and Sage, D., 2021. DeepImageJ: A user-friendly environment to run deep learning\ \ models in ImageJ. Nature Methods, 18(10), pp.1192-1195.\nZeroCost\ \ DL4Mic\nvon Chamier, L., Laine, R.F., Jukkala, J., Spahn, C., Krentzel, D.,\ \ Nehme, E., Lerche, M., Hernández-Pérez, S., Mattila, P.K., Karinou,\ \ E. and Holden, S., 2021. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature\ \ communications, 12(1), pp.1-18." license: CC-BY-4.0 name: 'Gut Analysis Toolbox: Training data and 2D models for segmenting enteric neurons, neuronal subtypes and ganglia' num_downloads: 176 publication_date: '2022-02-15' url: - https://zenodo.org/records/10460434 - https://doi.org/10.5281/zenodo.10460434 uuid: fdeeacba-ef2b-471d-9270-328c1006e7de language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Luke Sorensen - Ayame Saito - Sabrina Poon - Myat Noe Han https://orcid.org/0000-0003-3028-7359 - Adam Humenick - Peter Neckel - Keith Mutunduwe - Christie Glennan - Narges Mahdavian - Simon JH Brookes https://orcid.org/0000-0001-5635-0876 - Rachel M McQuade https://orcid.org/0000-0002-3510-1288 - Jaime PP Foong https://orcid.org/0000-0003-2082-5520 - Sebastian K. King https://orcid.org/0000-0001-5396-0265 - Estibaliz Gómez-de-Mariscal https://orcid.org/0000-0003-2082-3277 - Arrate Muñoz-Barrutia https://orcid.org/0000-0002-1573-1661 - Robert Haase https://orcid.org/0000-0001-5949-2327 - Simona Carbone https://orcid.org/0000-0002-4350-6357 - Nicholas A. Veldhuis https://orcid.org/0000-0002-8902-9365 - Daniel P. Poole https://orcid.org/0000-0002-6168-8422 - Pradeep Rajasekhar https://orcid.org/0000-0002-1983-7244 - submission_date: '2024-11-18T16:34:27.012540' authors: - Martin Schätz description: Research data management and how not to get overwhelmed with data presentation is an overview of bioimage analysis with a focus on the basics for data management planning, FAIR principles, and how to practically organize folders and prepares naming convention. The presentation includes an overview of metadata, Creative Common licenses, and a sum up of electronic laboratory notebooks. The last two slides go through how all of that works in practice in open access core microscopy facility. license: CC-BY-4.0 name: Research Data Managemet and how not to get overwhelmed with data num_downloads: 229 proficiency_level: advanced beginner publication_date: '2023-09-23' url: - https://zenodo.org/records/8372703 - https://doi.org/10.5281/zenodo.8372703 uuid: 5c0e14b8-d620-4c20-b2cb-d6c4c8d8553f language: en file_formats: .pdf * .pptx tags: - include in DALIA authors_with_orcid: - Martin Schätz https://orcid.org/0000-0003-0931-4017 - submission_date: '2024-11-18T16:34:28.045966' authors: - Romain description: A groovy script to use in Fiji to generate artificial images and labels, with example images. license: CC-BY-4.0 name: Artificial Blobs and Labels image num_downloads: 25 publication_date: '2023-05-10' url: - https://zenodo.org/records/7919117 - https://doi.org/10.5281/zenodo.7919117 uuid: 764b5dc0-ef3e-45ed-a227-2b3f1b83c946 file_formats: .groovy * .tif tags: - exclude from DALIA authors_with_orcid: - Romain https://orcid.org/0000-0001-6715-4897 - submission_date: '2024-11-18T16:34:28.513488' authors: - Martin Schätz - Olga Rubešová - Jan Mareš - Alan Spark description: 'The software tool is developed on demand of Radiological Department of Faculty Hospital of Královské Vinohrady, with the aim to provide a tool to estimate the percentage of pneumonia (or COVID-19 presence) in lungs. Paper Estimation of Covid-19 lungs damage based on computer tomography images analysis presenting the tool is available on F1000reserach DOI: 10.12688/f1000research.109020.1. The underlying dataset is published in Zenodo (DOI:10.5281/zenodo.5805939). One of the challenges was to design a tool that would be available without complicated install procedures and would process data in a reasonable time even on office computers. For this reason, 8-bit and 16-bit version of the tool exists. The FIJI software (or ImageJ with Bio-Formats plugin installed) was selected as the best candidate. Examples of use and tutorials are available at GitHub.  Underlying data: DOI:10.5281/zenodo.5805939 The first five datasets are analyzed using this tool, with results and parameters to repeat the analysis in results_csv.csv or results.xlsx. Contributions: Martin SCHÄTZ:       Coding, tool testing, data curation, data set analysis Olga RUBEŠOVÁ:    Code review, tutorial preparation, tool testing, data set analysis Jan MAREŠ:             Tool testing, data set analysis Alan SPARK:             Tool testing The work was funded by the Ministry of Education, Youth and Sports by grant ‘Development of Advanced Computational Algorithms for evaluating post-surgery rehabilitation’ number LTAIN19007. The work was also supported from the grant of Specific university research – grant No FCHI 2022-001.  ' license: CC-BY-4.0 name: ImageJ tool for percentage estimation of pneumonia in lungs num_downloads: 179 publication_date: '2023-05-02' url: - https://zenodo.org/records/7885379 - https://doi.org/10.5281/zenodo.7885379 uuid: 92d1fac3-90cb-4c2d-8e8e-4b24b5118c1d language: en file_formats: .csv * .xlsx * .zip tags: - exclude from DALIA authors_with_orcid: - Martin Schätz https://orcid.org/0000-0003-0931-4017 - Olga Rubešová https://orcid.org/0000-0003-2101-499X - Jan Mareš https://orcid.org/0000-0003-4693-2519 - Alan Spark - submission_date: '2024-11-18T16:34:29.533446' authors: - Anna Pascual Reguant - Ronja Mothes - Helena Radbruch - Anja E. Hauser description: 'Image-based data set of a post-mortem lung sample from a non-COVID-related pneumonia donor (CONTROL CASE 1, FOV1) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.' license: CC-BY-4.0 name: Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV1 num_downloads: 228 publication_date: '2022-12-16' url: - https://zenodo.org/records/7447491 - https://doi.org/10.5281/zenodo.7447491 uuid: 4e2427f6-8db4-4574-acfa-5a20d6914f83 language: en file_formats: .tif tags: - exclude from DALIA authors_with_orcid: - Anna Pascual Reguant https://orcid.org/0000-0002-5042-3699 - Ronja Mothes - Helena Radbruch - Anja E. Hauser https://orcid.org/0000-0002-7725-9526 - submission_date: '2024-11-18T16:34:29.961816' authors: - Romain Guiet - Olivier Burri - Mehmet Girgin - Matthias Lutolf description: This imagej macro analyses the reporter intensity activity and expression domain in EPI aggregates and Gastruloids. license: CC-BY-4.0 name: Measuring reporter activity domain in EPI aggregates and Gastruloids.ijm num_downloads: 34 publication_date: '2022-12-07' url: - https://zenodo.org/records/7409423 - https://doi.org/10.5281/zenodo.7409423 uuid: 69c6afd7-e1e1-4501-9bd9-6570d3384090 file_formats: .ijm * .nd2 tags: - exclude from DALIA authors_with_orcid: - Romain Guiet https://orcid.org/0000-0001-6715-4897 - Olivier Burri https://orcid.org/0000-0002-7100-3749 - Mehmet Girgin https://orcid.org/0000-0001-6191-3071 - Matthias Lutolf - submission_date: '2024-11-18T16:34:30.357363' authors: - Martin Schätz description: The slides were presented during the Macro programming with ImageJ workshop (https://www.16mcm.cz/programme/#workshops) which was part of the 16th Multinational Congress on Microscopy. It is a collection and "reshuffle" of slides originally made by Robert Haase on topics from Image Analysis in general up to User-friendly GPU-accelerated bio-image analysis and CLIJ2. license: CC-BY-4.0 name: Interactive Image Data Flow Graphs num_downloads: 14 publication_date: '2022-10-17' url: - https://zenodo.org/records/7215114 - https://doi.org/10.5281/zenodo.7215114 uuid: 09e19608-e0d8-4c5b-82b9-0e631b006124 language: en file_formats: .pptx tags: - include in DALIA authors_with_orcid: - Martin Schätz https://orcid.org/0000-0003-0931-4017 - submission_date: '2024-11-18T16:34:30.816975' authors: - Robert Haase description: In the field of radiooncological research, individualised therapy is one of the hot topics at the moment. As a key aspect biologically-adapted therapy is discussed. Therapy adaption based on biological parameters may include tomographic imaging to determine biological properties of the tumour. One often invoked imaging modality is positron emission tomography (PET) using the tracer [18F]-fluoromisonidazole (FMISO) for hypoxia imaging. Hypoxia imaging is of interest, because hypoxic tumours are known to be radiorestistant. Even further, patients with hypoxic tumours have worse prognosis compared to patients with normoxic tumours. Thus, hypoxia imaging appears promising for radiotherapy treatment adaption. For example, volumetric analysis of FMISO PET could deliver additional hypoxia target volumes, which may be irradiated with higher radiation doses to improve the therapeutic effect. However, limited contrast between target volume and background in FMISO PET images interferes image analysis.Established methods for target volume delineation in PET do not allow determination of reliable contours in FMISO PET. To tackle this aspect, this thesis focusses on an earlier developed swarm intelligence based segmentation algorithm for FMISO PET and rather, its optimisation and validation in a clinically relevant setting. In this setting, clinical FMISO PET images were used which were acquired as part of a clinical trial performed at the Clinic and Policlinic for Radiation Therapy and Radiooncology of the University Hospital Carl Gustav Carus Dresden. The segmentation algorithm was applied to these imaging data sets and optimised using a cross-validation approach incorporating reference contours from experienced observers who outlined FMISO PET positive volumes manually. Afterwards, the performance of the algorithm and the properties of the resulting contours were studied in more detail. The algorithm was shown to deliver contours which were similar to manually-created contours to a degree like manually-created contours were similar to each other. Thus, the application of the algorithm in clinical research is recommended to eliminate inter-observer-variabilities. Finally, it was shown that repeated FMISO PET imaging before and shortly after the beginning of combined radiochemotherapy lead to manually-created contours with significantly higher variations than the variations of automatically-created contours using the proposed algorithm. Increased contour similarity in subsequently acquired imaging data highlights the observer-independence of the algorithm. While several observers outline different volumes, in identical data sets as well as in subsequent imaging data sets, the algorithm outlines more stable volumes in both cases. Thus, increased contour reproducibility is reached by automation of the delineation process by the proposed algorithm.  license: CC-BY-4.0 name: Optimisation and Validation of a Swarm Intelligence based Segmentation Algorithm for low Contrast Positron Emission Tomography num_downloads: 170 publication_date: '2014-04-01' url: - https://zenodo.org/records/7209862 - https://doi.org/10.5281/zenodo.7209862 uuid: 49ebd316-a007-48e4-a316-eb425f55ac95 language: en file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 - submission_date: '2024-11-18T16:34:31.351869' authors: - Sebastian Rhode description: "Set of CZI test images created by using a simulated microscope with\ \ a test grayscale camera (no LSM or AiryScan or RGB). The filename indicates\ \ the used dimension(s) for the acquisition experiment. The files can be\ \ used to test the basic functionality of libraries reading CZI files.\n\nExamples:\n\ \n\n\tS=2_T=3_CH=1.czi = 2 Scenes, 3 TimePoints and 1 Channel\n\t\n\t\tZ-Stack\ \ was not activated inside acquisition experiment\n\t\n\t\n\tS=2_T=3_Z=5_CH=2.czi\ \ = 2 Scenes, 3 TimePoints, 5-Z-Planes and 1 Channels\n\t\n\t\tZ-Stack was activated\ \ inside acquisition experiment\n\t\n\t\n\n\nThe test files (so far) contain not\ \ any data with more "advanced" dimensions like AiryScan rawdata,\ \ illumination angles etc. Also no CZI files with pixel type RGB are\ \ included yet.\n\n \n\n \n\n " license: CC-BY-4.0 name: CZI (Carl Zeiss Image) dataset with artificial test camera images with various dimension for testing libraries reading num_downloads: 676 publication_date: '2022-08-22' url: - https://zenodo.org/records/7015307 - https://doi.org/10.5281/zenodo.7015307 uuid: fd76ff77-8804-4917-b784-37d70caff08e language: en file_formats: .czi tags: - exclude from DALIA authors_with_orcid: - Sebastian Rhode - submission_date: '2024-11-18T16:34:31.831739' authors: - Martin Schätz description: 'SciAugment v0.2.0 has pip installable version, channel-wise augmentation was added, and an option for all augmentations or no augmentation. Examples of how to use the tool are in README and in Google Colab notebooks. Practical examples of how to use results with YOLOv5 on scientific data can be found in the SciCount project. SciAugment aims to provide an option to create an augmented image set with similar changes in data as the imaging sensor and technique would do.' license: OTHER-OPEN name: SciAugment num_downloads: 8 publication_date: '2022-07-29' url: - https://zenodo.org/records/6991106 - https://doi.org/10.5281/zenodo.6991106 uuid: f6135086-5afb-4cd9-858e-413e5b2dfe32 language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Martin Schätz https://orcid.org/0000-0003-0931-4017 - submission_date: '2024-11-18T16:34:32.177486' authors: - Martin Schätz - Lukáš Mrazík - Karolina Máhlerova description: 'The first version contains an example of augmentation of scientific data and object detection with YOLO_v5 on colony counting (2 classes), object counting in blood smears (can be used as semisupervised learning for faster annotation), and wildlife detection from night records with a camera trap. The project is available on GitHub.' license: OTHER-OPEN name: 'martinschatz-cz/SciCount: v1.0.0 with reusable example notebooks' num_downloads: 3 publication_date: '2022-08-02' url: - https://zenodo.org/records/6953610 - https://doi.org/10.5281/zenodo.6953610 uuid: 7b23a071-59da-42b3-97ce-c8dfe852e3ff language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Martin Schätz https://orcid.org/0000-0003-0931-4017 - Lukáš Mrazík https://orcid.org/0000-0002-8847-5697 - Karolina Máhlerova https://orcid.org/0000-0001-7970-485X - submission_date: '2024-11-18T16:34:32.987735' authors: - Daniel Waiger description: 'A simple workflow to detect Soma and neurite paths, from light microscopy datasets. Using open-source tools for beginners.' license: CC-BY-4.0 name: Morphological analysis of neural cells with WEKA and SNT Fiji plugins num_downloads: 38 publication_date: '2022-07-14' url: - https://zenodo.org/records/6834214 - https://doi.org/10.5281/zenodo.6834214 uuid: 7e871b48-358b-4399-bf47-abb89c618047 file_formats: .pdf tags: - include in DALIA authors_with_orcid: - Daniel Waiger https://orcid.org/0000-0002-5519-1022 - submission_date: '2024-11-18T16:34:33.487847' authors: - Julie Meystre description: 'Name: Large tiling confocal acquisition (rat brain) Microscope: Zeiss LSM700 Microscopy data type: 108 tiles, each with 62 z-slices and 2 channels : Channel 1: DAPI Channel 2: cck staining File format: .lsm (16-bit) Image size: 1024x1024x62 (Pixel size: 0.152 x 0.152 x 1 micron), 2 channels.   NOTE : Some tiles were annotated and used to train a StarDist3D model (https://doi.org/10.5281/zenodo.6645978   )' license: CC-BY-4.0 name: Large tiling confocal acquisition (rat brain) num_downloads: 9 publication_date: '2022-06-15' url: - https://zenodo.org/records/6646128 - https://doi.org/10.5281/zenodo.6646128 uuid: 5f35772d-c257-4715-b993-1001797c8591 language: en file_formats: .lsm tags: - exclude from DALIA authors_with_orcid: - Julie Meystre - submission_date: '2024-11-18T16:34:34.152918' authors: - Romain Guiet description: 'Name: 3D Nuclei annotations and StarDist3D model(s) (rat brain) Images:  From a large tiling acquisition ( https://doi.org/10.5281/zenodo.6646128 ) individual Tile (xyz : 1024x1024x62) were downsampled and cropped (128x128x62). Four crops, from different tiles (./annotations_BIOP/images/) were manually annotated with ITK-SNAP (./annotations_BIOP/masks/) These four images, and their corresponding masks, were cropped into four quadrants (./crops_BIOP_v1/) in order to get 16 different images (64x64x62). Conda environment: A conda environment was created using the yml file  stardist0.8_TF1.15.yml Training : Training was performed using the jupyter notebook 1-Training_notebook.ipynb. Three different trainings (with the same random seed, same anisotropy, patch size and grid) were performed and produced three different models (./models/) Validation images (from the random seed used) were exported to ease the visual inspection of the results(./val_rdm42/). Validation:  To save metrics in a csv file and compare predictions to the annotations the jupyter notebook 2-QC_notebook.ipynb can be used on the validation folder. Large images: To test the model on larger images one can use Whole_ds441.tif (or Crop_ds441.tif ) These images were obtained using the plugin BigSticher on the raw data ( https://doi.org/10.5281/zenodo.6646128 ), resaved as h5 and exported the downsample by 4 version.    ' license: CC-BY-4.0 name: 3D Nuclei annotations and StarDist 3D model(s) (rat brain) num_downloads: 547 publication_date: '2022-06-15' url: - https://zenodo.org/records/6645978 - https://doi.org/10.5281/zenodo.6645978 uuid: 37c606c2-9538-4cce-839d-b0565cb6e27c language: en file_formats: .ipynb * .png * .tif * .yml * .zip tags: - exclude from DALIA authors_with_orcid: - Romain Guiet https://orcid.org/0000-0001-6715-4897 - submission_date: '2024-11-18T16:34:34.654130' authors: - Laurent Thomas description: 'This presentations describes Multi-Template-Matching, a novel method extending on template-matching for object-detection in images. The project was part of the PhD project of Laurent Thomas between 2017 and 2020, under supervision of Jochen Gehrig. The project was hosted at ACQUIFER Imaging with collaboration of the medical university of Heidelberg, and part of the ImageInLife Horizon2020 ITN (PhD program). ' license: CC-BY-4.0 name: Multi-Template-Matching for object-detection (slides) num_downloads: 169 publication_date: '2022-05-16' url: - https://zenodo.org/records/6554166 - https://doi.org/10.5281/zenodo.6554166 uuid: 52976c10-0ea8-4b40-a575-07a78bdfe73b language: en file_formats: .pdf * .pptx tags: - include in DALIA authors_with_orcid: - Laurent Thomas https://orcid.org/0000-0001-7686-3249 - submission_date: '2024-11-18T16:34:35.063614' authors: - Thomas Laurent description: 'This is a short introduction to light-microscopy, illustrated with widefield microscopy. It introduces : - upright and inverted widefield microscopes - the transmitted and fluorescent light-path - contrasting methods (optical and at the sample level) - the molecular principle of fluorescence (Perrin-Jablonski) - objective, resolution and limitations of the method (diffraction, diffusion/scattering) In addition to the PPT (with few animations), a lighter PDF version is provided for preview in Zenodo.   Illustrations are mostly extracted from the ThermoFisher Molecular Probes School of Fluorescence educator packet and from the course material from Micron Facility in Oxford. As stated in the presentation, illustrations are copyrighted but can be reproduced provided the original attribution is conserved.' license: OTHER-AT name: Introduction to light-microscopy / Widefield microscopy num_downloads: 104 proficiency_level: novice publication_date: '2022-05-10' url: - https://zenodo.org/records/6535296 - https://doi.org/10.5281/zenodo.6535296 uuid: b75c91df-a2fd-4ae4-8157-a5baf2bc90ba language: en file_formats: .pdf * .pptx tags: - exclude from DALIA authors_with_orcid: - Thomas Laurent https://orcid.org/0000-0001-7686-3249 - submission_date: '2024-11-18T16:34:35.407938' authors: - Laia Simó-Riudalbas - Romain Guiet - Olivier Burri - Julien Duc - Didier Trono description: 'Sample: Mouse (NSG) liver slices with human colorectal cancer cells metastases, stained with Hematoxylin & Eosin.  Image Acquisition: Images were acquired on an Olympus VS120 Whole Slide Scanner, using a 20x objective (UPLSAPO, N.A. 0.75) and a color camera (Pike F505 Color) with an image pixel size of 0.345 microns. Image Processing and Analysis: Obtained images were analyzed using the software QuPath [1] (version 0.3.2) using groovy scripts, making use of a pixel classifier to segment and measure cancer cell clusters. Files : Detailed_worflow.pdf : contains a detailed description of how pixel classifier was created images_for_classifier_training.zip : contains all the vsi file obtained from the microscope and used for the training project_for_classifier_training.zip : contains the QuPath project, with Training Image, annotations, classifiers and scripts for analysis PythonCode.txt : code ran to transform output results from QuPath to final results   [1] Bankhead, P. et al. QuPath: Open source software for digital pathology image analysis. Scientific Reports (2017). https://doi.org/10.1038/s41598-017-17204-5' license: CC-BY-4.0 name: Liver Micrometastases area quantification using QuPath and pixel classifier num_downloads: 168 publication_date: '2022-05-06' url: - https://zenodo.org/records/6523649 - https://doi.org/10.5281/zenodo.6523649 uuid: b38e68fe-0097-4352-ad82-9c6612ac2abc language: en file_formats: .pdf * .png * .txt * .zip tags: - include in DALIA authors_with_orcid: - Laia Simó-Riudalbas https://orcid.org/0000-0001-9294-3636 - Romain Guiet https://orcid.org/0000-0001-6715-4897 - Olivier Burri https://orcid.org/0000-0002-7100-3749 - Julien Duc https://orcid.org/0000-0001-8195-804X - Didier Trono https://orcid.org/0000-0002-3383-0401 - submission_date: '2024-11-18T16:34:35.773605' authors: - Romain Guiet - Olivier Burri description: "Name: Cellpose models for Brightfield and Digital Phase Contrast\ \ images\n\nData type: Cellpose models trained via transfer learning from\ \ the ‘nuclei’ and ‘cyto2’ pretrained model with additional\ \ Training Dataset . Includes corresponding csv files with 'Quality\ \ Control' metrics(§) (model.zip).\n\nTraining Dataset: Light microscopy\ \ (Digital Phase Contrast or Brightfield) and automatic annotations (nuclei or\ \ cyto) (https://doi.org/10.5281/zenodo.6140064)\n\nTraining Procedure: The cellpose\ \ models were trained using cellpose version 1.0.0 with GPU support (NVIDIA GeForce\ \ K40) using default settings as per the Cellpose documentation . Training\ \ was done using a Renku environment (renku template).\n\n \n\nCommand Line\ \ Execution for the different trained models\n\nnuclei_from_bf: \n\ncellpose --train\ \ --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei  --img_filter\ \ _bf --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose\n\ncyto_from_bf:\n\ \ncellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model\ \ cyto2 --img_filter _bf --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose\n\ \n \n\nnuclei_from_dpc:\n\ncellpose --train --dir 'data/train/' --test_dir\ \ 'data/test/' --pretrained_model nuclei  --img_filter _dpc --mask_filter _nuclei\ \ --chan 0 --chan2 0 --use_gpu --verbose\n\ncyto_from_dpc:\n\ncellpose --train\ \ --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter\ \ _dpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose\n\n \n\n\ nuclei_from_sqrdpc:\n\ncellpose --train --dir 'data/train/' --test_dir 'data/test/'\ \ --pretrained_model nuclei --img_filter _sqrdpc --mask_filter _nuclei --chan\ \ 0 --chan2 0 --use_gpu --verbose\n\ncyto_from_sqrdpc:\n\ncellpose --train --dir\ \ 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter\ \ _sqrdpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose\n\n \n\ \nNOTE (§): We provide a notebook for Quality Control, which\ \ is an adaptation of the "Cellpose (2D and 3D)" notebook from\ \ ZeroCostDL4Mic .\n\nNOTE: This dataset used a training dataset from the\ \ Zenodo entry(https://doi.org/10.5281/zenodo.6140064) generated from the “HeLa\ \ “Kyoto” cells under the scope”  dataset Zenodo entry(https://doi.org/10.5281/zenodo.6139958)\ \ in order to automatically generate the label images.\n\nNOTE: Make sure that\ \ you delete the “_flow” images that are auto-computed when running\ \ the training. If you do not, then the flows from previous runs will be used\ \ for the new training, which might yield confusing results.\n\n " license: CC-BY-4.0 name: Cellpose models for Label Prediction from Brightfield and Digital Phase Contrast images num_downloads: 78 publication_date: '2022-02-25' url: - https://zenodo.org/records/6140111 - https://doi.org/10.5281/zenodo.6140111 uuid: 9dc3a96a-6e4e-4237-9fe5-320e98849145 language: en file_formats: .ipynb * .zip tags: - exclude from DALIA authors_with_orcid: - Romain Guiet https://orcid.org/0000-0001-6715-4897 - Olivier Burri https://orcid.org/0000-0002-7100-3749 - submission_date: '2024-11-18T16:34:36.126990' authors: - Romain Guiet description: 'Name: HeLa “Kyoto” cells under the scope Microscope: Perkin Elmer Operetta microscope with a 20x N.A. 0.8 objective and an Andor Zyla 5.5 camera. Microscopy data type: The time-lapse datasets were acquired every 15 minutes, for 60 hours. From the individual plan images (channels, time-points, field of view exported by the PerkinElmer software Harmony) multi-dimension images were generated using the Operetta_Importer-0.1.21  with a downscaling of 4.  Channel 1 : Low Contrast DPC (Digital Phase Contrast) Channel 2 : High Contrast DPC Channel 3 : Brightfield Channel 4 : EGFP-α-tubulin Channel 5 : mCherry-H2B File format: .tif (16-bit) Image size: 540x540 (Pixel size: 0.299 nm), 5c, 1z , 240t   Cell type: HeLa “Kyoto” cells, expressing EGFP-α-tubulin and mCherry-H2B ( Schmitz et al, 2010 ) Protocol: Cells were resuspended in Imaging media and were seeded in a microscopy grade 96 wells plate ( CellCarrier Ultra 96, Perkin Elmer). The day after seeding, and for 60 hours, images were acquired in 3 wells, in 25 different fields of view, every 15 minutes. Imaging media: DMEM red-phenol-free media (FluoroBrite™ DMEM, Gibco) complemented with Fetal Calf Serum and Glutamax.   NOTE: This dataset was used to automatically generate label images in the following Zenodo entry:  https://doi.org/10.5281/zenodo.6140064 NOTE: This dataset was used to train the cellpose models in the following Zenodo entry: https://doi.org/10.5281/zenodo.6140111' license: CC-BY-4.0 name: HeLa "Kyoto" cells under the scope num_downloads: 309 publication_date: '2022-02-25' url: - https://zenodo.org/records/6139958 - https://doi.org/10.5281/zenodo.6139958 uuid: a42ed0b6-5ce6-4f7e-abab-4faa088a8bfa language: en file_formats: .png * .tif * .zip tags: - exclude from DALIA authors_with_orcid: - Romain Guiet https://orcid.org/0000-0001-6715-4897 - submission_date: '2024-11-18T16:34:36.472002' authors: - Laura Capolupo description: 'Name: Digital Phase Contrast on Primary Dermal Human Fibroblasts cells  Data type: Paired microscopy images (Digital Phase Contrast, square rooted) and corresponding labels/masks used for cellpose training (the corresponding Brightfield images are also present), organized as recommended by cellpose documentation. Microscopy data type: Light microscopy (Digital Phase Contrast and Brighfield ) Manual annotations: Labels/masks obtained via manual segmentation. For each region, all cells were annotated manually. Uncertain objects (Dust, fused cells) were left unannotated, so that the cellpose model (10.5281/zenodo.6023317) may mimic the same user bias during prediction. This was particularly necessary due to the accumulation of floating debris in the center of the well. Microscope: Perkin Elmer Operetta microscope with a 10x 0.35 NA objective Cell type: Primary Dermal Human Fibroblasts cells File format: .tif (16-bit for DPC and 16-bit for the masks) Image size: 1024x1024 (Pixel size: 634 nm) NOTE : This dataset was used to train cellpose model ( 10.5281/zenodo.6023317 )  ' license: CC-BY-4.0 name: Digital Phase Contrast on Primary Dermal Human Fibroblasts cells num_downloads: 101 publication_date: '2022-02-09' url: - https://zenodo.org/records/5996883 - https://doi.org/10.5281/zenodo.5996883 uuid: e895487c-5cd4-4d8c-b48c-2150acf9b1fb language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Laura Capolupo https://orcid.org/0000-0003-1254-5019 - submission_date: '2024-11-18T16:34:36.805571' authors: - Laura Capolupo - Olivier Burri - Romain Guiet description: 'Name: Cellpose model for Digital Phase Contrast images Data type: Cellpose model, trained via transfer learning from ‘cyto’ model. Training Dataset: Light microscopy (Digital Phase Contrast) and Manual annotations (10.5281/zenodo.5996883) Training Procedure: Model was trained using a Cellpose version 0.6.5 with GPU support (NVIDIA GeForce RTX 2080) using default settings as per the Cellpose documentation  python -m cellpose --train --dir TRAINING/DATASET/PATH/train --test_dir TRAINING/DATASET/PATH/test --pretrained_model cyto --chan 0 --chan2 0 The model file (MODEL NAME) in this repository is the result of this training. Prediction Procedure: Using this model, a label image can be obtained from new unseen images in a given folder with python -m cellpose --dir NEW/DATASET/PATH --pretrained_model FULL_MODEL_PATH --chan 0 --chan2 0 --save_tif --no_npy' license: CC-BY-4.0 name: Cellpose model for Digital Phase Contrast images num_downloads: 66 publication_date: '2022-02-09' url: - https://zenodo.org/records/6023317 - https://doi.org/10.5281/zenodo.6023317 uuid: 976a1aed-f247-430c-ab10-22c31a12ea8a language: en file_formats: '.171894' tags: - exclude from DALIA authors_with_orcid: - Laura Capolupo https://orcid.org/0000-0003-1254-5019 - Olivier Burri https://orcid.org/0000-0002-7100-3749 - Romain Guiet https://orcid.org/0000-0001-6715-4897 - submission_date: '2024-11-18T16:34:37.223826' authors: - Laurent Thomas - Pierre Trehin description: 'Fiji plugins for the creation of binary and semantic masks from ROIs in the RoiManager. Works with stacks too. Installation in Fiji: activate the Rois from masks update site in Fiji. See GitHub readme for the documentation. Latest tested with Fiji 2.1.0/ImageJ 1.53j' license: MIT name: 'LauLauThom/MaskFromRois-Fiji: Masks from ROIs plugins for Fiji - initial release' num_downloads: 36 publication_date: '2021-07-22' url: - https://zenodo.org/records/5121890 - https://doi.org/10.5281/zenodo.5121890 uuid: 31877350-3742-4a56-aa0c-c453b2e54013 language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Laurent Thomas https://orcid.org/0000-0001-7686-3249 - Pierre Trehin https://orcid.org/0000-0002-1724-2191 - submission_date: '2024-11-18T16:34:37.614706' authors: - Romain Guiet description: 'This a test dataset, HeLa cells stained for action using Phalloidin-488 acquired on confocal Zeiss LSM710, which contains - Ph488.czi (contains all raw metadata) - Raw_large.tif ( is the tif version of Ph488.czi, provided for conveninence as tif doesn't need Bio-Formats to be open in Fiji ) - Raw.tif , is a crop of the large image - PSFHuygens_confocal_Theopsf.tif , is a theoretical PSF generated with HuygensPro - PSFgen_WF_WBpsf.tif  , is a theoretical PSF generated with PSF generator - PSFgen_WFsquare_WBpsf.tif, is the result of the square operation on PSFgen_WF_WBpsf.tif , to approximate a confocal PSF' license: CC-BY-4.0 name: Deconvolution Test Dataset num_downloads: 353 publication_date: '2021-07-14' url: - https://zenodo.org/records/5101351 - https://doi.org/10.5281/zenodo.5101351 uuid: bb0e4a90-4b16-43b4-88b0-c314d9da51bc language: en file_formats: .czi * .tif tags: - exclude from DALIA authors_with_orcid: - Romain Guiet https://orcid.org/0000-0001-6715-4897 - submission_date: '2024-11-18T16:34:39.643990' authors: - Cavanagh description: A test data set for troublshooting. no scientific meaning. license: CC0-1.0 name: Ink in a dish num_downloads: 3 publication_date: '2024-09-03' url: - https://zenodo.org/records/13642395 - https://doi.org/10.5281/zenodo.13642395 uuid: 2896f410-3edd-42c7-9941-d8486872612f file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Cavanagh - submission_date: '2024-11-18T16:34:40.260008' authors: - Nicolas Chiaruttini description: 'The files contained in this repository are confocal images taken with the Evident FV 4000 of a sample containing DAPI and mCherry stains, excited with the 405 nm laser and images for different emission windows (lambda scan). They are public sample files which goal is to help test edge cases of the bio-formats library (https://www.openmicroscopy.org/bio-formats/), in particular for the proper handling of lambda scans. DAPI_mCherry_22Lambda-420-630-w10nm-s10nm.oir : 22 planes, each plane is an emission window, starting from 420 nm up to 630 nm by steps of 10 nm DAPI_mCherry_4T_5Lambda-420-630-w10nm-s50nm.oir : 20 planes, 5 lambdas from 420 to 630 nm by steps of 50 nm, 4 timepoints DAPI_mCherry_4Z_5Lambda-420-630-w10nm-s50nm.oir : 20 planes, 5 lambdas from 420 to 630 nm by steps of 50 nm, 4 slices DAPI-mCherry_3T_4Z_5Lambda-420-630-w10nm-s50nm.oir : 60 planes, 5 lambdas from 420 to 630 nm by steps of 50 nm, 4 slices, 3 timepoints ' license: CC-BY-4.0 name: Evident OIR sample files with lambda scan - FV 4000 num_downloads: 59 publication_date: '2024-07-18' url: - https://zenodo.org/records/12773657 - https://doi.org/10.5281/zenodo.12773657 uuid: 4f87cbbc-9f2b-4791-8153-a6b16d8f6dd3 language: en file_formats: .oir tags: - exclude from DALIA authors_with_orcid: - Nicolas Chiaruttini https://orcid.org/0000-0003-4722-6245 - submission_date: '2024-11-18T16:34:40.728131' authors: - j description: 'Hi @ome team,Please find the .czi file attached. When loaded into QuPath using BioFormats, the fluorescence channels populate the brightness/contrast panel but do not show up in the viewer. Re-exporting as OME.Tiff from Zen and loading into QuPath does not help either - the channels do not populate the brightness/contrast panel in this case, and it shows as a RGB image.Please let me know if any further info is needed from me to troubleshoot! Best,J' name: Axioscan 7 fluorescent channels not displaying in QuPath num_downloads: 5 publication_date: '2024-06-25' url: - https://zenodo.org/records/12533989 - https://doi.org/10.5281/zenodo.12533989 uuid: 22815dfe-8183-4604-b8da-35767ac239cf language: en file_formats: .czi tags: - exclude from DALIA authors_with_orcid: - j - submission_date: '2024-11-18T16:34:41.114444' authors: - IMCF description: "Hi @ome team !\nWe usually use ICS/IDS file formats as an\ \ output to our stitching pipeline as the reading and writing is pretty fast.\ \ However, it seems that since Bio-Formats 7.x opening the files is not working\ \ anymore.\nI tried with a Fiji with Bio-Formats 6.10.1 and the files open, but\ \ more recent versions give an issue.\n \njava.lang.NullPointerException\n\ \tat loci.formats.in.ICSReader.initFile(ICSReader.java:1481)\n\tat loci.formats.FormatReader.setId(FormatReader.java:1480)\n\ \tat loci.plugins.in.ImportProcess.initializeFile(ImportProcess.java:498)\n\t\ at loci.plugins.in.ImportProcess.execute(ImportProcess.java:141)\n\tat loci.plugins.in.Importer.showDialogs(Importer.java:156)\n\ \tat loci.plugins.in.Importer.run(Importer.java:77)\n\tat loci.plugins.LociImporter.run(LociImporter.java:78)\n\ \tat ij.IJ.runUserPlugIn(IJ.java:244)\n\tat ij.IJ.runPlugIn(IJ.java:210)\n\tat\ \ ij.Executer.runCommand(Executer.java:152)\n\tat ij.Executer.run(Executer.java:70)\n\ \tat ij.IJ.run(IJ.java:326)\n\tat ij.IJ.run(IJ.java:337)\n\tat ij.macro.Functions.doRun(Functions.java:703)\n\ \tat ij.macro.Functions.doFunction(Functions.java:99)\n\tat ij.macro.Interpreter.doStatement(Interpreter.java:281)\n\ \tat ij.macro.Interpreter.doStatements(Interpreter.java:267)\n\tat ij.macro.Interpreter.run(Interpreter.java:163)\n\ \tat ij.macro.Interpreter.run(Interpreter.java:93)\n\tat ij.macro.MacroRunner.run(MacroRunner.java:146)\n\ \tat java.lang.Thread.run(Thread.java:750)\n\nYou can find one example file at this\ \ link 1.\nThanks for your help !Best,Laurent" license: CC-BY-4.0 name: ICS/IDS stitched file num_downloads: 16 publication_date: '2024-06-13' url: - https://zenodo.org/records/11637422 - https://doi.org/10.5281/zenodo.11637422 uuid: a7b72ba1-7789-47b7-9865-573f9c07d238 language: en file_formats: .ics * .ids tags: - exclude from DALIA authors_with_orcid: - IMCF - submission_date: '2024-11-18T16:34:41.460603' authors: - Mario Garcia description: Human brain tissue with DAB immunostaining. Image acquired by BF microscopy in  Zeiss Axioscan at 20x.  license: CC-BY-4.0 name: Human DAB staining Axioscan BF 20x num_downloads: 9 publication_date: '2024-05-21' url: - https://zenodo.org/records/11234863 - https://doi.org/10.5281/zenodo.11234863 uuid: 15a6da47-2d74-409c-9c0f-a6e70e5daec8 file_formats: .czi tags: - exclude from DALIA authors_with_orcid: - Mario Garcia - authors: - Sarah Weischer - Jens Wendt - Thomas Zobel description: "Provides an overview of contexts, frameworks, and models from the\ \ world of bioimage data as well as metadata. Visualizes the techniques for structuring\ \ this data as Linked Data. (Walkthrough Video: https://doi.org/10.5281/zenodo.7018928\ \ )\n\nContent:\n\n\n\tTypes of metadata\n\tData formats\n\tData Models Microscopy\ \ Data\n\tTools to edit/gather metadata\n\tISA Framework\n\tFDO Framework\n\t\ Ontology\n\tRDF\n\tJSON-LD\n\tSPARQL\n\tKnowledge Graph\n\tLinked Data\n\tSmart\ \ Data\n\t...\n" license: CC-BY-4.0 name: 'Structuring of Data and Metadata in Bioimaging: Concepts and technical Solutions in the Context of Linked Data' num_downloads: 704 publication_date: '2022-07-12' submission_date: '2024-11-19T15:37:57.161475' tags: - nfdi4bioimage - research data management - include in DALIA url: - https://zenodo.org/records/7018750 - https://doi.org/10.5281/zenodo.7018750 uuid: 0967d465-a99f-49d8-bb21-2559a34ec4c3 language: en file_formats: .pdf authors_with_orcid: - Susanne Kunis https://orcid.org/0000-0001-6523-7496 - Julia Dohle - authors: - Kunis description: 'This thesis deals with concepts and solutions in the field of data management in everyday scientific life for image data from microscopy. The focus of the formulated requirements has so far been on published data, which represent only a small subset of the data generated in the scientific process. More and more, everyday research data are moving into the focus of the principles for the management of research data that were formulated early on (FAIR-principles). The adequate management of this mostly multimodal data is a real challenge in terms of its heterogeneity and scope. There is a lack of standardised and established workflows and also the software solutions available so far do not adequately reflect the special requirements of this area. However, the success of any data management process depends heavily on the degree of integration into the daily work routine. Data management must, as far as possible, fit seamlessly into this process. Microscopy data in the scientific process is embedded in pre-processing, which consists of preparatory laboratory work and the analytical evaluation of the microscopy data. In terms of volume, the image data often form the largest part of data generated within this entire research process. In this paper, we focus on concepts and techniques related to the handling and description of this image data and address the necessary basics. The aim is to improve the embedding of the existing data management solution for image data (OMERO) into the everyday scientific work. For this purpose, two independent software extensions for OMERO were implemented within the framework of this thesis: OpenLink and MDEmic. OpenLink simplifies the access to the data stored in the integrated repository in order to feed them into established workflows for further evaluations and enables not only the internal but also the external exchange of data without weakening the advantages of the data repository. The focus of the second implemented software solution, MDEmic, is on the capturing of relevant metadata for microscopy. Through the extended metadata collection, a corresponding linking of the multimodal data by means of a unique description and the corresponding semantic background is aimed at. The configurability of MDEmic is designed to address the currently very dynamic development of underlying concepts and formats. The main goal of MDEmic is to minimise the workload and to automate processes. This provides the scientist with a tool to handle this complex and extensive task of metadata acquisition for microscopic data in a simple way. With the help of the software, semantic and syntactic standardisation can take place without the scientist having to deal with the technical concepts. The generated metadata descriptions are automatically integrated into the image repository and, at the same time, can be transferred by the scientists into formats that are needed when publishing the data.' name: Engineering a Software Environment for Research Data Management of Microscopy Image Data in a Core Facility num_downloads: 60 publication_date: '2022-05-30' submission_date: '2024-11-19T15:37:57.429914' tags: - nfdi4bioimage - research data managementv - include in DALIA url: - https://zenodo.org/records/6905931 - https://doi.org/10.5281/zenodo.6905931 uuid: 4f173204-6c66-4dec-9d22-a66d274d22a8 language: en file_formats: .pdf authors_with_orcid: - Kunis https://orcid.org/0000-0001-6523-7496 - authors: - Marco Stucchi description: The files contained in this repository are cropped versions of Imaris demo images compressed with LZ4. license: CC-BY-4.0 name: LZ4-compressed Imaris ims example datasets. num_downloads: 4 publication_date: '2024-11-21' submission_date: '2024-11-26T11:19:38.819031' url: - https://zenodo.org/records/14197622 - https://doi.org/10.5281/zenodo.14197622 uuid: 6a09d253-1256-4e48-bfc1-f690dd915db0 file_formats: .ims tags: - exclude from DALIA authors_with_orcid: - Marco Stucchi - authors: - Josh Moore description: 'Presented at the 2024 FoundingGIDE event in Okazaki, Japan: https://founding-gide.eurobioimaging.eu/event/foundinggide-community-event-2024/ Note: much of the presentation was a demonstration of the OME2024-NGFF-Challenge -- https://ome.github.io/ome2024-ngff-challenge/ especially of querying an extraction of the metadata (https://github.com/ome/ome2024-ngff-challenge-metadata)  ' license: CC-BY-4.0 name: OME2024 NGFF Challenge Results num_downloads: 23 publication_date: '2024-11-01' submission_date: '2024-12-03T11:19:33.910078' tags: - nfdi4bioimage - research data management - exclude from DALIA url: - https://zenodo.org/records/14234608 - https://doi.org/10.5281/zenodo.14234608 uuid: ab00a09a-f8a9-4fa6-b8f7-55e20d1e691a language: en file_formats: .pdf authors_with_orcid: - Josh Moore - authors: - Marco Stucchi description: 'The files contained in this repository are example Imaris ims images.   Initially related to https://github.com/ome/bioformats/pull/4249' license: CC-BY-4.0 name: Example Imaris ims datasets. num_downloads: 3 publication_date: '2024-11-28' submission_date: '2024-12-03T11:20:19.287724' url: - https://zenodo.org/records/14235726 - https://doi.org/10.5281/zenodo.14235726 uuid: 66577876-eedd-402a-921c-78c3d1affbcb file_formats: .ims tags: - exclude from DALIA authors_with_orcid: - Marco Stucchi - authors: - Will Moore - Josh Moore - sherwoodf - jean-marie burel - Norman Rzepka - dependabot[bot] - JensWendt - Joost de Folter - Torsten St\xF6ter - AybukeKY - Eric Perlman - Tom Boissonnet description: Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data license: BSD 3-Clause name: ome2024-ngff-challenge publication_date: '2024-08-30T12:00:53+00:00' submission_date: '2024-12-04T07:10:23.182293' tags: - sharing - nfdi4bioimage - research data management - exclude from DALIA type: GitHub Repository url: https://github.com/ome/ome2024-ngff-challenge uuid: 23de46e4-2c21-4094-9669-6a9659260b6a - authors: - Birgitta König-Ries - Robert Haase - Daniel Nüst - Konrad Förstner - Judith Sophie Engel description: In diesem Slidedeck geben wir einen Einblick in Angebote und Dienste der Nationalen Forschungsdaten Infrastruktur (NFDI), die Relevant für die Zoologie und angrenzende Disziplinen relevant sein könnten. license: cc-by-4.0 name: Angebote der NFDI für die Forschung im Bereich Zoologie num_downloads: 91 publication_date: '2024-12-04' submission_date: '2024-12-10T11:19:45.214265' tags: - nfdi4bioimage - research data management - exclude from DALIA url: - https://zenodo.org/records/14278058 - https://doi.org/10.5281/zenodo.14278058 uuid: 5a85cdc8-f012-462f-bf56-39673e2e7f37 language: de file_formats: .pdf * .pptx authors_with_orcid: - Birgitta König-Ries https://orcid.org/0000-0002-2382-9722 - Robert Haase https://orcid.org/0000-0001-5949-2327 - Daniel Nüst https://orcid.org/0000-0002-0024-5046 - Konrad Förstner https://orcid.org/0000-0002-1481-2996 - Judith Sophie Engel https://orcid.org/0000-0001-8665-6382 - authors: - Zach Marin - Maohan Su description: Bead stack taken on a 4Pi. DCIMG 0x1000000 file with a 4-pixel correction requirement. license: cc-by-4.0 name: Astigmatic 4Pi bead stack num_downloads: 1 publication_date: '2024-12-06' submission_date: '2024-12-10T11:20:43.463988' url: - https://zenodo.org/records/14287640 - https://doi.org/10.5281/zenodo.14287640 uuid: 479722fa-1719-4db7-90c0-2906818d97fb file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Zach Marin https://orcid.org/0000-0001-5341-9911 - Maohan Su https://orcid.org/0000-0001-5523-9607 - authors: - Zach Marin - Maohan Su description: 10 frames of fluorescent particles. They don't do much, but they are a DCIMG version 0x7 file example. license: cc-by-4.0 name: 10 frames of fluorescent particles num_downloads: 2 publication_date: '2024-12-05' submission_date: '2024-12-10T11:20:44.051879' url: - https://zenodo.org/records/14281237 - https://doi.org/10.5281/zenodo.14281237 uuid: 7dbab1f5-49b4-4dc9-ad43-57f28eea1ea5 file_formats: .dcimg tags: - exclude from DALIA authors_with_orcid: - Zach Marin https://orcid.org/0000-0001-5341-9911 - Maohan Su https://orcid.org/0000-0001-5523-9607 - authors: - Zach Marin description: Bead stack taken on lower path of a 4Pi without deformable mirror corrections. DCIMG examples, not for other purposes. license: cc-by-4.0 name: Aberrated Bead Stack num_downloads: 3 publication_date: '2024-12-03' submission_date: '2024-12-10T11:20:44.701729' url: - https://zenodo.org/records/14268554 - https://doi.org/10.5281/zenodo.14268554 uuid: 7d97cbcf-01d0-48b1-864f-8d70afc30f65 file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Zach Marin https://orcid.org/0000-0001-5341-9911 - authors: - JanClusmann - Tim Lenz description: '' license: GPL-3.0 name: patho_prompt_injection publication_date: '2024-11-08T08:32:03+00:00' submission_date: '2024-12-17T03:35:48.039490' tags: - histopathology - bioimage analysis - exclude from DALIA type: - Github repository - notebook url: https://github.com/KatherLab/patho_prompt_injection uuid: 8b3e869c-7cd7-4414-adc9-c702e060cb65 - authors: - Dave Barry - Stefania Marcotti - Martin Jones description: '' license: cc-by-sa-4.0 name: introduction-to-image-analysis publication_date: '2024-10-23T14:05:55+00:00' submission_date: '2024-12-18T12:21:50.116196' proficiency_level: advanced beginner tags: - BioImage Analysis - include in DALIA type: - Github repository - notebooks url: https://github.com/RMS-DAIM/introduction-to-image-analysis uuid: ee2864c6-59bf-4a1e-b286-cdfbccd265b3 - authors: Jack Atkinson description: Teaching materials for improving research software writing abilities. license: GPL-3.0 name: rse-skills-workshop publication_date: '2023-12-22T17:39:48+00:00' submission_date: '2024-12-18T12:33:59.429229' proficiency_level: advanced beginner tags: - Research Software Engineering - include in DALIA type: - Github repository - Slides url: https://github.com/jatkinson1000/rse-skills-workshop uuid: 44f4837d-6941-4b7c-8fe2-015a0b070c94 - authors: - Robert Haase description: 'This slide deck introduces Large Language Models to an audience of life-scientists. We first dive into terminology: Different kinds of Language Models and what they can be used for. The remaining slides are optional slides to allow us to dive deeper into topics such as tools for using LLMs in Science, Quality Assurance, Techniques such as Retrieval Augmented Generation and Prompt Engineering.' license: cc-by-4.0 name: 'Large Language Models: An Introduction for Life Scientists' num_downloads: 24 proficiency_level: advanced beginner publication_date: '2024-12-12' submission_date: '2024-12-24T11:17:37.842980' tags: - globias - artificial intelligence - include in DALIA url: - https://zenodo.org/records/14418209 - https://doi.org/10.5281/zenodo.14418209 uuid: 4340cca4-96ed-42a4-8448-39a9807fbd10 language: en file_formats: .pdf * .pptx authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 - authors: - Markus M. Becker - Ihda Chaerony Siffa - Roman Baum description: "Abstract: \nTerminology services (TS) [1,2] play a pivotal role in\ \ achieving structured metadata by providing controlled vocabularies and ontologies\ \ that standardize the description of data. This is a crucial aspect of research\ \ data management (RDM) in all scientific disciplines. In addition, TS facilitate\ \ the use of a common vocabulary within a scientific community also in a more\ \ general context, e.g. to annotate scientific papers, patents or other content\ \ for better discoverability, as envisaged by the Open Research Knowledge Graph\ \ (ORKG) [3] or the Patents4Science project [4]. \nTo make use of these opportunities,\ \ terminologies, ontologies and knowledge graphs must be developed and made available\ \ as TS where they do not yet exist. This step is currently being taken by the\ \ research community in low-temperature plasma (LTP) physics. LTP physics explores\ \ partially ionized gases and its technological applications. This vibrant field\ \ offers innovative solutions for societal challenges, ranging from developing\ \ efficient lighting and solar cells to revolutionizing healthcare through plasma\ \ medicine. Various activities and projects have been started in the past years\ \ to support the RDM in LTP research and development and to facilitate the application\ \ of data-driven research methods. These activities are supported in parts by\ \ the NFDI4BIOIMAGE consortium, active work in the NFDI section “(Meta)data,\ \ Terminologies, Provenance”, and the basic service Terminology Services\ \ 4 NFDI (TS4NFDI) funded by Base4NFDI. \nRecently, the ontology Plasma-O\ \ [5–7] for LTP physics has been developed at INP in collaboration with\ \ FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, providing\ \ a framework for structuring metadata and building a knowledge graph for scientific\ \ information within the field. The present contribution will show how a TS utilizing\ \ this resource can support different aspects of RDM and knowledge discovery using\ \ concrete examples. The application cases include (i) standardizing data annotation:\ \ By providing researchers with a controlled vocabulary of LTP-specific terms\ \ and their relationships, ensuring consistent and unambiguous data descriptions;\ \ (ii) enabling semantic search: Moving beyond keyword-based searches, TS allow\ \ for complex queries based on the relationships between concepts, significantly\ \ improving data discoverability; (iii) facilitating data integration: By mapping\ \ data from different sources to a common ontology, TS enable seamless integration\ \ and analysis of heterogeneous datasets, which is crucial for data-driven research\ \ and development. The TS Suite of TS4NFDI with the provided widgets [8] fits\ \ perfectly to the requirements of these three application cases and will support\ \ the harmonization of metadata in LTP physics. The implementation of a public\ \ TS is required to provide the domain-specific metadata in a standardized format\ \ and will be instrumental in unlocking the full potential of the TS widgets for\ \ RDM and knowledge discovery by LTP researchers. Furthermore, the results can\ \ provide insights to other domains on how to apply TS to their specific needs.\n\  The work was supported in parts by the Deutsche Forschungsgemeinschaft (DFG,\ \ German Research Foundation) under the National Research Data Infrastructure\ \ – [NFDI46/1] – 501864659 and project number 496963457 as well as\ \ by the Federal Ministry of Education and Research (BMBF), project number 16KOA013A.\n\ References:\n\n\n\n\n[1]\n\n\nS. Jupp, T. Burdett, C. Leroy, H. Parkinson, “A\ \ new Ontology Lookup Service at EMBL-EBI”, Workshop on Semantic Web Applications\ \ and Tools for Life Sciences (2015), https://ceur-ws.org/Vol-1546/paper_29.pdf\ \ (accessed: 2024-09-20).\n\n\n\n\n[2]\n\n\nP. L. Whetzel, N. F. Noy, N. H. Shah,\ \ P. R. Alexander, C. Nyulas, T. Tudorache, M. A. Musen, “BioPortal: enhanced\ \ functionality via new Web services from the National Center for Biomedical Ontology\ \ to access and use ontologies in software applications”, Nucleic Acids\ \ Res. 39 (2011) W541–W545, https://doi.org/10.1093/nar/gkr469. \n\n\n\n\ \n[3]\n\n\nOpen Research Knowledge Graph, https://orkg.org/ (accessed: 2024-09-20).\n\ \n\n\n\n[4]\n\n\nPatents4Science – Establishing an Information Infrastructure\ \ for the Use of Patent Knowledge in Science, https://www.patents4science.org/\ \ (accessed: 2024-09-20).\n\n\n\n\n[5]\n\n\nH. Sack, F. Hoppe, “Verbundprojekt:\ \ Qualitätssicherung und Vernetzung von Forschungsdaten in der Plasmatechnologie\ \ - QPTDat; Teilvorhaben: Wissensgraph und Ontologieentwicklung zur Vernetzung\ \ von Metadaten : Schlussbericht des Teilvorhabens”, 2023, https://doi.org/10.2314/KXP:1883436974.\n\ \n\n\n\n[6]\n\n\nI. Chaerony Siffa, R. Wagner, L. Vilardell Scholten, M. M. Becker,\ \ “Semantic Information Management in Low-Temperature Plasma Science and\ \ Technology with VIVO”, 2024, preprint, https://doi.org/10.48550/arXiv.2409.11065.\n\ \n\n\n\n[7]\n\n\nI. Chaerony Siffa, R. Wagner, L. Vilardell Scholten, M. M. Becker,\ \ “Plasma Ontology and Knowledge Graph Initial Release v0.5.0”, 2024,\ \ Zenodo, https://doi.org/10.5281/zenodo.13325226. \n\n\n\n\n[8]\n\n\nJ. Sasse,\ \ V. Kneip, R. Baum, P. Zimmermann, J. Darms, J. Schneider, V. Clemens, P. Oladazimi,\ \ L. Kühnel, “ts4nfdi/terminology-service-suite: v2.6.0”, 2024,\ \ Zenodo, https://doi.org/10.5281/zenodo.13692297. \n\n\n\n" license: cc-by-4.0 name: Terminology service for research data management and knowledge discovery in low-temperature plasma physics num_downloads: 73 publication_date: '2024-12-11' submission_date: '2024-12-24T11:17:38.438350' url: - https://zenodo.org/records/14381522 - https://doi.org/10.5281/zenodo.14381522 uuid: 75380769-6c62-4487-88a3-d6d5ff0459ea language: en file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Markus M. Becker https://orcid.org/0000-0001-9324-3236 - Ihda Chaerony Siffa https://orcid.org/0000-0002-4232-4543 - Roman Baum https://orcid.org/0000-0001-5246-9351 - authors: - NFDI4BIOIMAGE Consortium description: 'These illustrations were contracted by the Heinrich Heine University Düsseldorf in the frame of the consortium NFDI4BIOIMAGE from Henning Falk for the purpose of education and public outreach. The illustrations are free to use under a CC-BY 4.0 license.AttributionPlease include an attribution similar to: "Data annoation matters", NFDI4BIOIMAGE Consortium (2024): NFDI4BIOIMAGE data management illustrations by Henning Falk, Zenodo, https://doi.org/10.5281/zenodo.14186100, is used under a CC-BY 4.0 license. Modifications to this illustration include cropping.  ' license: cc-by-4.0 name: NFDI4BIOIMAGE data management illustrations by Henning Falk num_downloads: 136 publication_date: '2024-11-29' submission_date: '2024-12-24T11:17:39.796497' tags: - nfdi4bioimage - research data management - include in DALIA url: - https://zenodo.org/records/14186101 - https://doi.org/10.5281/zenodo.14186101 uuid: 1ff5dfba-379a-4238-a08c-540f9c72af7a language: en file_formats: .jpg * .pdf authors_with_orcid: - NFDI4BIOIMAGE Consortium - authors: - Judith Engel - Patrick Helling - Robert Herrenbrück - MarinaLemaire - Hela Mehrtens - Marcus Schmidt - Martha Stellmacher - Lukas Weimer - Cord Wiljes - Wolf Zinke description: 'Support is an essential component of an efficient infrastructure for research data management (RDM). Helpdesks guide researchers through this complex landscape and provide reliable support about all questions regarding research data management, including support for technical services, best practices, requirements of funding organizations and legal topics. In NFDI, most consortia have already established or are planning to establish helpdesks to support their specific communities. On a local level, many institutions have set up RDM helpdesks that provide support for the researchers of their own institution. Additional RDM support services are offered by RDM federal state initiatives, by research data centers, by specialist libraries, by the EOSC, and by providers of RDM-relevant tools. Helpdesks cover a wide range of institutions, disciplines, topics, methodologies and target audiences. However, the individual helpdesks are not yet interconnected and therefore cannot complement one another in an efficient way: Given the wide and constantly increasing complexity of RDM, no single helpdesk can provide the expertise for all potential support requests. Therefore, we see great potential in combining the efforts and resources of the existing RDM helpdesks into an efficient and comprehensive national RDM support network in order to provide optimal and tailored RDM support to all researchers and research-related institutions in Germany and in an international context.' license: cc-by-4.0 name: Working Group Charter. RDM Helpdesk Network num_downloads: 215 publication_date: '2024-11-04' submission_date: '2024-12-24T11:17:41.996394' url: - https://zenodo.org/records/14035822 - https://doi.org/10.5281/zenodo.14035822 uuid: 9765f3c2-11b5-46ee-b45f-afbe97785c72 language: en file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Judith Engel https://orcid.org/0000-0001-8665-6382 - Patrick Helling https://orcid.org/0000-0003-4043-165X - Robert Herrenbrück https://orcid.org/0000-0002-1355-5043 - Marina Lemaire https://orcid.org/0000-0003-4726-2481 - Hela Mehrtens https://orcid.org/0000-0002-4526-2472 - Marcus Schmidt https://orcid.org/0000-0002-5546-5521 - Martha Stellmacher https://orcid.org/0000-0001-5655-0130 - Lukas Weimer https://orcid.org/0000-0001-6919-3646 - Cord Wiljes https://orcid.org/0000-0003-2528-5391 - Wolf Zinke https://orcid.org/0000-0001-5525-5973 - authors: - Christian Schmidt - Tom Boissonnet - Michele Bortolomeazzi - Ksenia Krooß description: 'Research Data Management for Microscopy and BioImage Analysis Introduction to BioImaging Research Data Management, NFDI4BIOIMAGE and I3D:bioChristian Schmidt /DKFZ Heidelberg OMERO as a tool for bioimaging data managementTom Boissonnet /Heinrich-Heine Universität Düsseldorf Reproducible image analysis workflows with OMERO software APIsMichele Bortolomeazzi /DKFZ Heidelberg Publishing datasets in public archives for bioimage dataKsenia Krooß /Heinrich-Heine Universität Düsseldorf Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt' license: cc-by-4.0 name: '[Workshop] Research Data Management for Microscopy and BioImage Analysis' num_downloads: 159 publication_date: '2024-09-30' submission_date: '2024-12-24T11:17:45.069538' tags: - nfdi4bioimage - research data management - include in DALIA url: - https://zenodo.org/records/13861026 - https://doi.org/10.5281/zenodo.13861026 uuid: aa83ee95-067f-4a26-b6c4-14b6d08b56aa language: en file_formats: .pdf authors_with_orcid: - Christian Schmidt https://orcid.org/0000-0001-9568-895X - Tom Boissonnet https://orcid.org/0000-0002-3328-9467 - Michele Bortolomeazzi https://orcid.org/0000-0001-5805-5774 - Ksenia Krooß https://orcid.org/0000-0003-1717-3138 - authors: - Salama Lab Fred Hutchinson Cancer Center license: cc-by-4.0 name: LSM example J. Dubrulle num_downloads: 23 publication_date: '2024-12-17' submission_date: '2024-12-24T11:18:35.007394' url: - https://zenodo.org/records/14510432 - https://doi.org/10.5281/zenodo.14510432 uuid: 4f324190-5e86-4ec8-aa03-78baa070da83 file_formats: .lsm tags: - exclude from DALIA authors_with_orcid: - Salama Lab Fred Hutchinson Cancer Center - authors: - Michael Chungyoun - Courtney Thomas - Britnie Carpentier - GabeAu79 - puv-sreev - Jeffrey Gray description: Colab Notebooks covering deep learning tools for biomolecular structure prediction and design name: DL4Proteins-notebooks publication_date: '2024-09-04T12:24:24+00:00' submission_date: '2025-01-07T13:44:29.467482' tags: - bioinformatics - exclude from DALIA type: - Github repository - collection url: https://github.com/Graylab/DL4Proteins-notebooks uuid: 079c6c01-382b-4c0e-b3ee-db26367cbf41 - authors: Joanna Pylvänäinen description: Training materials for V4SDB Student Winter School, 28th-31st January 2025 at ELTE Eötvös Loránd University in Budapest, Hungary name: V4SDB_Winter_School_2025 publication_date: '2025-01-13T08:29:22+00:00' submission_date: '2025-01-13T16:19:01.176180' proficiency_level: advanced beginner tags: - Cell Tracking - BioImage analysis - exclude from DALIA type: - Github repository - collection url: https://github.com/CellMigrationLab/V4SDB_Winter_School_2025 uuid: c7b8282b-9013-46de-abac-e4b6c0f2eb82 - authors: Robert Haase description: This slide deck introduces the version control tool git, related terminology and the Github Desktop app for managing files in Git[hub] repositories. We furthermore dive into:* Working with repositories* Collaborative with others* Github-Zenodo integration* Github pages* Artificial Intelligence answering Github Issues license: cc-by-4.0 name: Collaborative Working and Version Control with git[hub] num_downloads: 194 proficiency_level: advanced beginner publication_date: '2024-01-10' submission_date: '2025-01-14T08:13:50.559032' tags: - nfdi4bioimage - globias - research data management - research software management - include in DALIA url: - https://zenodo.org/records/14626054 - https://doi.org/10.5281/zenodo.14626054 uuid: 59719fe9-0c44-42d4-abb5-2e6f05c9910d language: en file_formats: .pdf * .pptx authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 - authors: Rafael Irizarry description: UMAP is a powerful tool for exploratory data analysis, but without a clear understanding of how it works, it can easily lead to confusion and misinterpretation. license: UNKNOWN name: Biologists, stop putting UMAP plots in your papers publication_date: '2024-12-23' tags: - Biology - Data Analysis - UMAP - exclude from DALIA type: - Blog Post url: https://simplystatistics.org/posts/2024-12-23-biologists-stop-including-umap-plots-in-your-papers/ uuid: c0bf8564-c856-4290-8e53-f42fa8ca00ff - authors: Joachim Goedhart license: CC-BY-NC-SA-4.0 name: DataViz protocols - An introduction to data visualization protocols for wet lab scientists publication_date: '2024-12-10' tags: - Data Visualization - R - include in DALIA type: - Book url: - https://zenodo.org/records/7257808 - https://joachimgoedhart.github.io/DataViz-protocols/ - https://doi.org/10.5281/zenodo.7257808 uuid: 6b73ed65-2d42-42ff-9b49-18f19a561e87 file_formats: .zip authors_with_orcid: - Joachim Goedhart - authors: Joachim Goedhart description: The author discusses a number of color palettes that are suitable for coloring graphical elements in plots. license: UNKNOWN name: Data Visualization with Flying Colors publication_date: '2019-08-29' tags: - Data Visualization - include in DALIA type: - Blog Post url: https://thenode.biologists.com/data-visualization-with-flying-colors/research/ uuid: 43d8a045-ec45-43ab-97b0-669f35bc71d3 - authors: - Riccardo Massei - Björn Grüning license: CC-BY-4.0 name: Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO using Galaxy publication_date: '2024-12-02' tags: - OMERO - Galaxy - Metadata - nfdi4bioimage - include in DALIA type: - Tutorial - Framework - Workflow url: https://training.galaxyproject.org/training-material/topics/imaging/tutorials/omero-suite/tutorial.html uuid: 415f41dd-5ccf-4b57-bce1-c62c051520f5 - authors: Iván Hidalgo-Cenalmor license: UNKNOWN name: DL4MicEverywhere – Overcoming reproducibility challenges in deep learning microscopy imaging publication_date: '2024-07-29' proficiency_level: advanced beginner tags: - Bio Image Analysis - Artifical Intelligence - include in DALIA type: - Blog Post url: https://focalplane.biologists.com/2024/07/29/dl4miceverywhere-overcoming-reproducibility-challenges-in-deep-learning-microscopy-imaging/ uuid: 30a65a8c-8cf1-496d-9ab4-df306eab284d - authors: UNKNOWN license: UNKNOWN name: New report highlights the scientific impact of open source software publication_date: UNKNOWN tags: - Open Source - AlphaFold - exclude from DALIA type: - Report - Blog Post url: https://www.statnews.com/sponsor/2024/11/26/new-report-highlights-the-scientific-impact-of-open-source-software/ uuid: a13da37f-fd0c-4f3a-b12a-e430e3296055 - authors: - Leonid Kostrykin - Diana Chiang Jurado license: cc-by-4.0 name: Tracking of mitochondria and capturing mitoflashes publication_date: '2024-11-20' tags: - Bioinformatics - Bioimage Analysis - include in DALIA type: - Workflow - Tutorial url: https://training.galaxyproject.org/training-material/topics/imaging/tutorials/detection-of-mitoflashes/tutorial.html#tracking-of-mitochondria-and-capturing-mitoflashes uuid: 9f3931f6-10dc-4f26-a199-696f8a1a2d6e - authors: - Virginie Uhlmann - Matthew Hartley - Josh Moore - Erin Weisbart - Assaf Zaritsky license: cc-by-4.0 name: Making the most of bioimaging data through interdisciplinary interactions publication_date: '2024-10-23' tags: - Bioimage Analysis - Open Science - Microscopy - include in DALIA type: - Publication url: https://journals.biologists.com/jcs/article/137/20/jcs262139/362478/Making-the-most-of-bioimaging-data-through uuid: d43e9b1d-0991-4a66-a122-53fe2f04d59c - authors: - Jakob Nikolas Kather - Faisal Mahmood - Florian Jug description: How can artificial intelligence be used for digital pathology? license: UNKNOWN name: Artificial Intelligence for Digital Pathology publication_date: '2024-11-08' tags: - Artificial Intelligence - include in DALIA type: - Video url: https://www.youtube.com/watch?v=Om9tl4Dh2yw uuid: 125beb87-3331-4cae-9371-01e33f8e99b1 - authors: Shanmugasundaram description: Introduction to RDM primarily for researchers. Can be seen as primer to all other materials in this catalogue. license: CC-BY-4.0 name: Introduction to Research Data Management and Open Research publication_date: '2024-05-17' tags: - Research Data Management - Open Science - include in DALIA type: - Slides url: - https://zenodo.org/records/4778265 - https://doi.org/10.5281/zenodo.4778265 uuid: dbbee207-410b-4486-a7d6-963a6e7f2545 file_formats: .pdf authors_with_orcid: - Shanmugasundaram https://orcid.org/0000-0002-3200-2698 - authors: Richard McElreath description: Video Lectures for Statistical Rethinking Course license: UNKNOWN name: Statistical Rethinking publication_date: '2023-01-02' tags: - Statistics - exclude from DALIA type: - Video url: https://www.youtube.com/playlist?list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus uuid: b980ce1f-56ec-46fc-9f22-e29f607554ac - authors: Richard McElreath description: This course teaches data analysis, but it focuses on scientific models. The unfortunate truth about data is that nothing much can be done with it, until we say what caused it. We will prioritize conceptual, causal models and precise questions about those models. We will use Bayesian data analysis to connect scientific models to evidence. And we will learn powerful computational tools for coping with high-dimension, imperfect data of the kind that biologists and social scientists face. license: CC0-1.0 name: Statistical Rethinking publication_date: '2024-03-01' tags: - Statistics - exclude from DALIA type: - Github repository url: https://github.com/rmcelreath/stat_rethinking_2024 uuid: d79b0567-0781-4d37-93cb-cae9b955b298 language: en - authors: Andrew Althouse description: Reference Collection to understand how to deal with common statistical myths. license: UNKNOWN name: Reference Collection to push back against “Common Statistical Myths” publication_date: '2023-06-29' tags: - Statistics - exclude from DALIA type: - Wiki url: https://discourse.datamethods.org/t/reference-collection-to-push-back-against-common-statistical-myths/1787 uuid: c6252eed-b79e-4889-8ed7-589c2e35b535 - authors: - Christian Tischer - Antonio Politi - Tim-Oliver Buchholz - Elnaz Fazeli - Nicola Gritti - Aliaksandr Halavatyi - Sebastian Gonzalez Tirado - Julian Hennies - Toby Hodges - Arif Khan - Dominik Kutra - Stefania Marcotti - Bugra Oezdemir - Felix Schneider - Martin Schorb - Anniek Stokkermans - Yi Sun - Nima Vakili description: Resources for teaching/preparing to teach bioimage analysis license: cc-by-4.0 name: Modular training resources for bioimage analysis num_downloads: 1 publication_date: '2024-12-03' submission_date: '2025-01-21T10:24:25.466809' tags: - neubias - bioimage analysis - include in DALIA url: - https://zenodo.org/records/14264885 - https://doi.org/10.5281/zenodo.14264885 uuid: 1741687d-e150-4d8d-b0c8-dff02e605a53 file_formats: .zip authors_with_orcid: - Christian Tischer https://orcid.org/0000-0003-4105-1990 - Antonio Politi https://orcid.org/0000-0003-4788-0933 - Tim-Oliver Buchholz https://orcid.org/0000-0001-6953-8915 - Elnaz Fazeli https://orcid.org/0000-0002-0770-0777 - Nicola Gritti https://orcid.org/0000-0002-7637-4258 - Aliaksandr Halavatyi https://orcid.org/0000-0002-9002-457X - Sebastian Gonzalez Tirado - Julian Hennies https://orcid.org/0000-0002-0555-151X - Toby Hodges https://orcid.org/0000-0003-1766-456X - Arif Khan https://orcid.org/0000-0003-4105-1990 - Dominik Kutra https://orcid.org/0000-0003-4202-3908 - Stefania Marcotti https://orcid.org/0000-0002-2877-0133 - Bugra Oezdemir https://orcid.org/0000-0001-9823-0581 - Felix Schneider https://orcid.org/0000-0002-9958-8961 - Martin Schorb - Anniek Stokkermans https://orcid.org/0000-0002-9013-9983 - Yi Sun https://orcid.org/0000-0002-7636-0200 - Nima Vakili - authors: - Alex Wolf - pre-commit-ci[bot] - Philipp A. - Isaac Virshup - Ilan Gold - Giovanni Palla - Fidel Ramirez - G\xF6k\xE7en Eraslan - Sergei Rybakov - Abolfazl (Abe) - Adam Gayoso - Dinesh Palli - Gregor Sturm - Jan Lause - Karin Hrovatin - Krzysztof Polanski - RaphaelBuzzi - Yimin Zheng - Yishen Miao - evanbiederstedt description: Scanpy Tutorials. license: bsd 3-clause name: scanpy-tutorials publication_date: '2018-12-16T03:42:46+00:00' tags: - single-cell analysis - bioimage analysis - include in DALIA type: GitHub Repository url: https://github.com/scverse/scanpy-tutorials uuid: 3a8e2767-9450-44ff-801f-836cf4e52bcc - authors: - Josh Moore description: 'Talk given at Georg-August-Universität Göttingen Campus Institute Data Science23rd January 2025 https://www.uni-goettingen.de/en/653203.html' license: cc-by-4.0 name: '[CIDAS] Scalable strategies for a next-generation of FAIR bioimaging' num_downloads: 66 publication_date: '2025-01-23' submission_date: '2025-01-28T11:17:32.405764' tags: - nfdi4bioimage - include in DALIA url: - https://zenodo.org/records/14716546 - https://doi.org/10.5281/zenodo.14716546 uuid: 93dd5805-f9db-44c7-affb-af125e8620b8 file_formats: .pdf authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X - authors: - Josh Moore description: 'CMCB LIFE SCIENCES SEMINARSTechnische Universität Dresden16th January 2025 https://tu-dresden.de/cmcb/crtd/news-termine/termine/cmcb-life-sciences-seminar-josh-moore-german-bioimaging-e-v-society-for-microscopy-and-image-analysis-constance  ' license: cc-by-4.0 name: '[CMCB] Scalable strategies for a next-generation of FAIR bioimaging' num_downloads: 190 publication_date: '2025-01-16' submission_date: '2025-01-28T11:17:32.840529' tags: - NFDI4Bioimage - include in DALIA url: - https://zenodo.org/records/14650434 - https://doi.org/10.5281/zenodo.14650434 uuid: e126a935-fa9f-4e56-84e3-d6ac2c4b3f48 language: en file_formats: .pdf authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X - authors: - Ben Vermaercke description: Intravital workshop 15/11/2024 license: cc-by-4.0 name: Optimized cranial window implantation for subcellular and functional imaging in vivo num_downloads: 20 publication_date: '2025-01-13' submission_date: '2025-01-28T11:17:47.507047' url: - https://zenodo.org/records/14641777 - https://doi.org/10.5281/zenodo.14641777 uuid: 2003eaea-e57e-4d01-9162-5d5d237e2d4d file_formats: .pptx tags: - exclude from DALIA authors_with_orcid: - Ben Vermaercke https://orcid.org/0000-0002-9645-0625 - authors: - Hoku West-Foyle license: CC0-1.0 name: Andor Dragonfly confocal image of BPAE cells stained for actin, IMS file format num_downloads: 5 publication_date: '2025-01-16' submission_date: '2025-01-28T11:18:25.537641' url: - https://zenodo.org/records/14675120 - https://doi.org/10.5281/zenodo.14675120 uuid: ce33ead5-cfec-442d-a1e5-4038e15f22f9 file_formats: .ims tags: - exclude from DALIA authors_with_orcid: - Hoku West-Foyle https://orcid.org/0000-0002-1603-7639 - authors: - Pylvänäinen, Joanna description: 'Description:This training package provides a guide to image processing and analysis using ImageJ/Fiji, an open-source software widely used in biological and medical imaging. The manual includes step-by-step exercises demonstrating practical workflows for measuring size distribution and signal intensity using both conventional thresholding and advanced tools like StarDist. This resource is ideal for researchers, students, and professionals looking to enhance their image analysis skills using Fiji. Key topics include: Image calibration and intensity adjustments Channel splitting and merging Projection techniques and scale bar addition Segmentation and thresholding methods Quantitative analysis of nuclei and fluorescence signal intensity Publication Date: January 2025 Keywords: Fiji, ImageJ, Image Analysis, Microscopy, Segmentation, Particle Analysis, 3D Visualization, StarDist License: MIT' license: mit-license name: Image handling using Fiji - training materials num_downloads: 34 publication_date: '2025-01-30' submission_date: '2025-01-30T19:21:32.417455' url: - https://zenodo.org/records/14771563 - https://doi.org/10.5281/zenodo.14771563 uuid: 7b5cdc1a-2cd1-430b-9d97-59a9443858f3 language: en file_formats: .lsm * .nd2 * .pdf * .tif tags: - include in DALIA authors_with_orcid: - Joanna W. Pylvänäinen https://orcid.org/0000-0002-3540-5150 - authors: - Haase, Robert description: In these two slide-decks we explore applications of large language models. In the first slide deck we dive into prompt engineering, function calling and how to build agentic workflows. In the second slide-deck we explore multi-modal large language models focusing on vision language models and image generation models.  license: cc-by-4.0 name: Prompt Engineering, Agentic Workflows and Multi-modal Large Language Models num_downloads: 79 publication_date: '2025-01-19' submission_date: '2025-01-29T13:05:13.342534' url: - https://zenodo.org/records/14692037 - https://doi.org/10.5281/zenodo.14692037 uuid: f07b6eec-b2c6-4871-b327-955fbd1e2e56 language: en file_formats: .pdf * .pptx tags: - include in DALIA authors_with_orcid: - Robert Haase https://orcid.org/0000-0003-2311-8004 - authors: - Fortmann-Grote, Carsten - Meireles, Mariana description: This Poster was presented at the 2025 All Hands Meeting of the NFDI4BIOIMAGE Consortium. It presents the current state of data integration activities at the MPI for Evolutionary Biology. Various data and metadata resources such as the internal image data repository OMERO and the Electronic Lab Notebook System OpenBIS are converted into a RDF Knowledge Graph utilizing a R2RML mapping scheme based on the Ontop-VKG framework. The materialized Knowledge Graph is then served via the QLever SPARQL endpoint and user interface. A graphical query editor (SPARNatural) assists users with no SPARQL knowledge in constructing their queries by selecting triple elements from dropdown menus and other widgets. We also present a benchmark comparison of query response times on 10 selected SPARQL queries run against three different endpoint/triplestore implementations.  name: Integration of Bioimage and *Omics data resources num_downloads: 29 publication_date: '2025-02-03' submission_date: '2025-02-04T11:17:47.530124' url: - https://zenodo.org/records/14792534 - https://doi.org/10.5281/zenodo.14792534 uuid: c721e166-d2c1-44a2-89d4-b1985161c849 language: en file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Carsten Fortmann-Grote https://orcid.org/0000-0002-2579-5546 - Mariana Meireles https://orcid.org/0000-0001-9227-9798 - authors: - Kuttler, Fabien - Dornier, Rémy description: 'The provided dataset contains 2 wells, 4 fields of view, 4 channels, no T but different number of Z according to the channel Cy3 : 1 Z DAPI : 16 Z FITC : 1 Z Brightfield : 1 Z The mix 2D/3D is not correctly supported and the .xcde file cannot be read. A discussion thread is already open on that topic. Bio-Formats version : 8.0.1  ' license: cc-by-4.0 name: InCell datasets with mix of 2D and 3D failed to be read num_downloads: 24 publication_date: '2025-01-31' submission_date: '2025-02-04T11:18:35.656931' url: - https://zenodo.org/records/14777242 - https://doi.org/10.5281/zenodo.14777242 uuid: 11938c17-43d7-4577-b7ae-fa4415bb5c89 language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Fabien Kuttler https://orcid.org/0000-0003-2575-1989 - Rémy Dornier https://orcid.org/0009-0006-1381-4582 - authors: - Kuttler, Fabien - Dornier, Rémy description: 'Two dummy datasets are provided in this repository :  Dataset_Ok : 96 wells, 9 fields of view per well, 4 different channels (DAPI, Cy3, FITC, Brightfield), no Z, no T. The .xcde file of this dataset is correctly read by BioFormats, as the dataset is recognized as a plate, and can be imported on OMERO Dataset_fail: 20 wells, 4 fields of view per well, 5 channels, with one duplicate (DAPI, FITC, Cy3, Cy5 wix 4 , Cy5 wix 5), no Z, no T. The .xcde file of this dataset is not correctly read by BioFormats and no images are imported on OMERO. BioFormats version: 8.0.1 A discussion thread has been open on this topic.' license: cc-by-4.0 name: Dataset from InCell 2200 microscope misread as a plate num_downloads: 34 publication_date: '2025-01-30' submission_date: '2025-02-04T11:18:36.941157' url: - https://zenodo.org/records/14769820 - https://doi.org/10.5281/zenodo.14769820 uuid: 9ce11efc-3000-4917-bc98-c69adb748846 language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Fabien Kuttler https://orcid.org/0000-0003-2575-1989 - Rémy Dornier https://orcid.org/0009-0006-1381-4582 - authors: - Matthew Mueller - Aaron - Advanced Bioimaging Center description: '' license: BSD 3-Clause "New" or "Revised" License name: Parallel_Fiji_Visualizer publication_date: '2024-05-15T06:14:24+00:00' submission_date: '2025-02-14T08:24:33.843320' tags: - Fiji - exclude from DALIA type: GitHub Repository url: https://github.com/abcucberkeley/Parallel_Fiji_Visualizer uuid: b5e49249-275a-47ef-abd9-66604230050f - authors: - Joran Deschamps - Damian Dalle Nogare description: '' license: BSD 3-Clause "New" or "Revised" License name: Advanced scripting with Fiji publication_date: '2023-06-14T08:25:35+00:00' submission_date: '2025-02-09T09:03:49.051347' tags: - Fiji - include in DALIA type: GitHub Repository url: https://github.com/nobias-fht/advanced-scripting uuid: 8c53b374-4855-4ed6-a59e-3a48936318b6 - authors: - Carsten Fortmann-Grote - andrawaag - Jerven Bolleman description: ' ONTOP module for querying OMERO with SPARQL' name: omero-ontop-mappings publication_date: '2024-09-13T08:01:09+00:00' submission_date: '2025-02-05T12:30:55.501203' tags: - Omero - exclude from DALIA type: GitHub Repository url: https://github.com/German-BioImaging/omero-ontop-mappings uuid: fdaba932-db79-47d9-97ae-196563ac3a45 - authors: - Michele Bortolomeazzi description: OMERO.web plugin for the Vitessce multimodal data viewer. license: GNU Affero General Public License v3.0 name: omero-vitessce publication_date: '2024-11-25T10:51:01+00:00' submission_date: '2025-02-05T12:26:41.939652' tags: - Omero - exclude from DALIA type: GitHub Repository url: https://github.com/NFDI4BIOIMAGE/omero-vitessce uuid: 15331066-c748-4275-81c5-dd85bcdc0c33 - authors: - Rodrigo Escobar Diaz Guerrero description: 'Linking Electronic Lab Notebooks and other sources with OMERO objects ' license: MIT License name: LEO publication_date: '2025-01-08T10:20:30+00:00' submission_date: '2025-02-05T12:26:11.745472' tags: - Omero - Research Data Management - Electronic lab notebooks - exclude from DALIA type: GitHub Repository url: https://github.com/NFDI4BIOIMAGE/LEO uuid: e1be7d60-d16a-437a-8060-a0fe24df85a0 - authors: - Zollitsch, Linda - Piotrowski, Swantje description: 'Im Rahmen von FDM-SH Kontor – einem Projekt, das im Kontext der AG Kompetenzentwicklung von der Landesinitiative FDM-SH durchgeführt wurde - haben wir zum Ziel, eine kuratierte Materialbasis für Fortbildungen und Schulungen zu schaffen. Dies stellte uns vor die Herausforderung, festzulegen, wie die Materialien ausgewählt werden sollen. Dieser Kriterienkatalog ist ein Versuch, erste Qualitätskriterien (insbesondere hinsichtlich der Nachnutzbarkeit und den FAIR-Prinzipien) für Materialien auf Basis von Metadaten zu erstellen. Dabei wurde das Vorgehen des Open Science Learning Gates (https://zenodo.org/records/12772135), als Vorbild genommen. Neben dem Metadatenschema der RDA (https://zenodo.org/records/6769695#.YrrP9-xBybQ) haben wir auf das Metadatenschema der DINI/nestor UAG Schulungen/Fortbildungen (https://zenodo.org/records/3760398) sowie das DALIA Interchange Format (https://zenodo.org/records/11521029) zurückgegriffen.' license: cc-by-4.0 name: Kriterienkatalog für Materialien aus dem Themenbereich Forschungsdatenmanagement num_downloads: 9 publication_date: '2025-01-24' submission_date: '2025-02-11T09:46:40.525798' url: - https://zenodo.org/records/14729452 - https://doi.org/10.5281/zenodo.14729452 uuid: c802daef-6b76-4236-9fb2-0a2d554d047e language: de file_formats: .odt tags: - include in DALIA authors_with_orcid: - Linda Zollitsch https://orcid.org/0000-0001-9592-3382 - Swantje Piotrowski https://orcid.org/0000-0002-5492-3746 - authors: - SaibotMagd description: 'firefox extension: reads datamatrix code from camera and create a sample in an inventory to link it into an ELN.' license: Apache License 2.0 name: dmtxSampleCreator publication_date: '2023-06-06T11:52:14+00:00' submission_date: '2025-02-05T12:25:17.287990' tags: - NFDI4BioImage - exclude from DALIA type: GitHub Repository url: https://github.com/SaibotMagd/dmtxSampleCreator uuid: b6b87689-9dab-4629-b9d6-16a140ac2081 - authors: - Galaxy Team description: Galaxy is an open, web-based platform for accessible, reproducible, and transparent computational research. license: UNKNOWN name: GalaxyProject YouTube Channel publication_date: '2020-06-16' tags: - Galaxy - Bioinformatics - exclude from DALIA type: - YouTube Channel url: https://www.youtube.com/c/galaxyproject uuid: b89c0ab6-c787-4840-8bef-2a4ac1a0a2f4 - authors: - Galaxy Team description: Galaxy is an open source tool that offers a filtered set of tools that you can assemble into workflows to manage image data, and perform image analysis and processing. license: - Academic Free License version 3.0 - Creative Commons Attribution 3.0 (CC BY 3.0) License name: Galaxy Imaging tags: - Galaxy - Bioinformatics - exclude from DALIA type: - Tool url: https://imaging.usegalaxy.eu uuid: 7b56bcd5-ac3f-49b2-a4d3-5dc0fbf581ec - authors: de.NBI description: The de.NBI (German Network for Bioformatics Infrastructure) Online Training & Media Library provides a collection of training materials for bioinformatics and computational biology. license: UNKNOWN name: deNBI Online Training Media Library tags: - Bioinformatics - Galaxy - exclude from DALIA type: - Website url: https://www.denbi.de/online-training-media-library uuid: 178e78a5-ece7-4dff-bb7d-2e73b5454c90 - authors: de.NBI description: Tutorial for accessing de.NBI cloud license: UNKNOWN name: Get started accessing the de.NBI cloud. tags: - Bioinformatics - Cloud Computing - exclude from DALIA type: - Tutorial url: https://cloud.denbi.de/get-started/ uuid: 5ec87832-7ed4-413c-b2a7-589828b6f6b4 - authors: de.NBI description: Tutorial for registering for de.NBI cloud access license: UNKNOWN name: de.NBI cloud access registration guide publication_date: '2024-11-04' tags: - Bioinformatics - Cloud Computing - exclude from DALIA type: - Tutorial url: https://cloud.denbi.de/wiki/registration/ uuid: 2e0eda84-574f-4786-a6ef-4c987c1610c0 - authors: - Kevin Frey - Kevin Schneider - Lukas Weil - Dominik Brilhaus - Timo Mühlhaus - Manuel Feser - Hannah-Doerpholz description: The ARC is a framework for organizing and documenting research data, as well as a container that continuously supports collaboration, data exchange, and adherence to FAIR principles among various researchers. The ARC can be checked for completeness and quality at any time and converted into a citable data publication without interrupting the research or documentation process. It is built on widely accepted research data standards such as RO-Crate, ISA, and abstract CWL. license: UNKNOWN name: Annotated Research Context (ARC tags: - Research Data Management - FAIR - exclude from DALIA type: - Framework url: https://arc-rdm.org/ uuid: 5110f584-b983-4aed-8479-7d7fb3559e5a language: en - authors: de.NBI description: The de.NBI (German Network for Bioformatics Infrastructure) Youtube channel (http://www.denbi.de/) license: UNKNOWN name: de.NBI YouTube Channel publication_date: '2015-08-28' tags: - Bioinformatics - exclude from DALIA type: - YouTube Channel url: https://www.youtube.com/@denbi5170 uuid: 110d54b6-660b-4a18-9595-7813ff5e639b - authors: - Sebastian Lobentanzer - Patrick Aloy - Jan Baumbach - Balazs Bohar - Vincent Carey - Pornpimol Charoentong - Katharina Danhauser - Tunca Doğan - Johann Dreo - Ian Dunham - Elias Farr - Adrià Fernandez-Torras - Benjamin Gyori - Michael Hartung - Charles Tapley Hoyt - Christoph Klein - Tamas Korcsmaros - Andreas Maier - Matthias Mann - David Ochoa - Elena Pareja-Lorente - Ferdinand Popp - Martin Preusse - Niklas Probul - Benno Schwikowski - Bünyamin Sen - Maximilian Strauss - Denes Turei - Erva Ulusoy - Dagmar Waltemath - Judith Wodke - Julio Saez-Rodriguez description: BioCypher is a framework to support users in creating KGs license: UNKNOWN name: Democratizing knowledge representation with BioCypher publication_date: '2023-06-19' tags: - Knowledge Graph - Bioinformatics - exclude from DALIA type: - Publication url: https://www.nature.com/articles/s41587-023-01848-y uuid: f69419b9-4156-4821-8127-ab29c0641506 - authors: - Jullia Jakiela - Laura Cooper - Katarzyna Kamieniecka - Krzysztof Poterlowicz description: The objective is to organise bioimage metadata, find out what REMBI is and why it is useful, categorise what metadata belongs to each of the submodules of REMBI and gather the metadata for an example bioimage dataset. license: CC-BY-4.0 International name: EMBI - Recommended Metadata for Biological Images – metadata guidelines for bioimaging data publication_date: '2025-02-05' tags: - Bioinformatics - Bioimage Analysis - include in DALIA type: - Tutorial url: https://training.galaxyproject.org/training-material/topics/imaging/tutorials/bioimage-REMBI/tutorial.html uuid: d9d12624-895f-4c04-a520-34db8976020d language: en - authors: David Barry description: Slides presented at Empowering Healthcare with Automated Analysis at London Metropolitan University. license: CC-BY-4.0 International name: Promoting Reproducibility in Biomedical Research through Image Analysis publication_date: '2025-01-29' tags: - Reproducibility - Image Analysis - include in DALIA type: - Slides url: https://doi.org/10.5281/zenodo.14767944 uuid: e554218d-2c80-409c-b0e3-3e6d6cc10180 - authors: GloBIAS description: This is the YouTube channel of GloBIAS, the Global BioImage Analysts Society. GloBIAS is a non-profit association officially constituted in October 2024. license: UNKNOWN name: GloBIAS publication_date: '2024-07-17' tags: - Bioimage Analysis - exclude from DALIA type: - YouTube Channel url: https://www.youtube.com/@globias uuid: a67bb34d-347f-411f-bba8-9c9a0828be91 - authors: GloBIAS description: The primary goal of this seminar series is to provide a dynamic platform for bioimage analysts, enabling the community to stay up to date with the latest developments in the field and foster community interactions. The seminars are designed to cater to intermediate and advanced analysts, focusing on practical, high-level content that extends beyond basic instruction. license: UNKNOWN name: BIA Seminar Series tags: - Bioimage Analysis - exclude from DALIA type: - Collection url: https://www.globias.org/activities/bia-seminar-series uuid: 4b254100-951b-489c-95c9-cab04dd2252e language: en - authors: - Torsten Stöter description: '' license: GNU General Public License v3.0 name: datenbiene publication_date: '2025-02-02T18:50:20+00:00' submission_date: '2025-02-05T12:27:52.033681' tags: - Research Data Management - exclude from DALIA type: - GitHub Repository - Software url: https://github.com/tstoeter/datenbiene uuid: 60926b5c-4d06-496b-8a8d-8356ecfa3a31 - authors: - Christoph Moehl - Peter Zentis - Niraj Kandpal description: Library to export OMERO projects to ARC repositories license: GNU General Public License v3.0 name: omero-arc publication_date: '2023-12-18T16:11:04+00:00' submission_date: '2025-02-05T12:09:56.325939' tags: - Omero - Research Data Management - exclude from DALIA type: - GitHub Repository - Software url: https://github.com/cmohl2013/omero-arc uuid: 06ccdf82-d61b-490a-b20d-6ad95a017c2d - authors: - Moore, Josh description: 'Talk given at Georg-August-Universität Göttingen Campus Institute Data Science23rd January 2025 https://www.uni-goettingen.de/en/653203.html' license: cc-by-4.0 name: '[CIDAS] Scalable strategies for a next-generation of FAIR bioimaging' num_downloads: 99 publication_date: '2025-01-23' submission_date: '2025-02-11T11:18:09.744263' url: - https://zenodo.org/records/14845059 - https://doi.org/10.5281/zenodo.14845059 uuid: 47115e0d-41db-442d-a469-aad7542c4bf5 file_formats: .pdf * .pptx tags: - NFDI4Bioimage - include in DALIA authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X - authors: - Bortolomeazzi, Michele - Schmidt, Christian - Mallm, Jan-Philipp description: 'omero-vitessce: an OMERO.web plugin for multi-modal data viewing. OMERO is the most used research data management system (RDM) in the bioimaging domain, and has been adopted as a centralized RDM solution by several academic and research institutions. A main reason for this is the ability to directly view and annotate images from a web-based interface. However, this feature of OMERO is currently underpowered for the visualization of very large or multimodal datasets. These datasets, are becoming a more and more common foundation for biological and biomedical studies, due to the recent developments in imaging, and sequencing technologies which enabled their application to spatial-omics. In order to begin to provide this multimodal-data capability to OMERO, we developed omero-vitessce (https://github.com/NFDI4BIOIMAGE/omero-vitessce/tree/main), a new OMERO.web plugin for viewing data stored in OMERO with the Vitessce (http://vitessce.io/) multimodal data viewer. omero-vitessce can be installed as an OMERO.web plugin with PiPy (https://pypi.org/project/omero-vitessce/), and allows users to set up interactive visualizations of their images of cells and tissues through interactive plots which are directly linked to the image. This enables the visual exploration of bioimage-analysis results and of multimodal data such as those generated through spatial-omics experiments. The data visualization is highly customizable and can be configured not only through custom configuration files, but also with the graphical interface provided by the plugin, thus making omero-vitessce a highly user-friendly solution for multimodal data viewing. most biological datasets. We plan to extend the interoperability of omero-vitessce with the OME-NGFF and SpatialData file formats to leverage the efficiency of these cloud optimized formats. The three files in this Zenodo Record are all the same poster saved in different format all with high resolution images.' license: cc-by-4.0 name: 'Introducing OMERO-vitessce: an OMERO.web plugin for multi-modal data' num_downloads: 111 publication_date: '2025-02-07' submission_date: '2025-02-11T11:18:10.238121' url: - https://zenodo.org/records/14832855 - https://doi.org/10.5281/zenodo.14832855 uuid: 63f5f077-197d-4538-91c7-1267c76ece9f language: en file_formats: .afdesign * .pdf * .svg tags: - NFDI4Bioimage - exclude from DALIA authors_with_orcid: - Michele Bortolomeazzi https://orcid.org/0000-0001-5805-5774 - Christian Schmidt https://orcid.org/0000-0001-9568-895X - Jan-Philipp Mallm https://orcid.org/0000-0002-7059-4030 - authors: - Fuster-Barcelo, Caterina description: The dynamic field of bioimage analysis continually seeks innovative tools to democratize access to analysis tools and its documentation. The BioImage.IO Chatbot, leveraging state-of-the-art AI technologies including Large Language Models (LLMs) and Retrieval Augmented Generation (RAG), provides an interactive platform that significantly integrates the exploration and application of bioimage analysis tools and models. This seminar will introduce the BioImage.IO Chatbot's capabilities, focusing on how it facilitates access to advanced analysis tools and documentation, allows for the execution of complex models, and enables users to create customized extensions adjusted to specific research needs. In a live demo, attendees will see how to interact with the chatbot and all its assistants and capabilities. Join us to explore how the BioImage.IO Chatbot ca transform your research by making sophisticated analysis more intuitive and accessible. license: cc-by-4.0 name: BioImage.IO Chatbot, GloBIAS Seminar num_downloads: 48 publication_date: '2024-10-02' submission_date: '2025-02-22T16:18:56.093547' url: - https://zenodo.org/records/13880367 - https://doi.org/10.5281/zenodo.13880367 uuid: e707f3ae-5811-4184-badc-7cee6fd46fe6 language: en file_formats: .pdf tags: - include in DALIA authors_with_orcid: - Caterina Fuster-Barcelo https://orcid.org/0000-0002-4784-6957 - authors: - Laurent Thomas - Pierre Trehin description: Also re-uploaded the compiled FilenameGetter.py$class to the update site, to fix https://github.com/LauLauThom/MaskFromRois-Fiji/issues/7 license: mit-license name: 'LauLauThom/MaskFromRois-Fiji: v1.0.1 - better handle "cancel"' num_downloads: 44 publication_date: '2025-02-24' submission_date: '2025-02-25T11:18:42.895597' url: - https://zenodo.org/records/14917722 - https://doi.org/10.5281/zenodo.14917722 uuid: 0105872f-4c54-4ff8-ad1c-cd9b3c3dc473 file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Laurent Thomas - Pierre Trehin - authors: - Sorensen, Luke - Saito, Ayame - Poon, Sabrina - Noe Han, Myat - Hamnett, Ryan - Neckel, Peter - Humenick, Adam - Mutunduwe, Keith - Glennan, Christie - Mahdavian, Narges - JH Brookes, Simon - M McQuade, Rachel - PP Foong, Jaime - Gómez-de-Mariscal, Estibaliz - Muñoz Barrutia, Arrate - Kaltschmidt, Julia A. - King, Sebastian K. - Haase, Robert - Carbone, Simona - A. Veldhuis, Nicholas - P. Poole, Daniel - Rajasekhar, Pradeep description: 'Full Changelog: https://github.com/pr4deepr/GutAnalysisToolbox/compare/v0.7...v1.0 Skip versions to 1.0 Fixed major bugs: Use deepImageJ to run Stardist models, due to issue with tensorflow in Fiji Fixed ganglia model to be compatible with new versions of deepImageJ Updated all scripts to accommodate for new deepImageJ workflow Added macros to generate user dialog when running GAT for first time ' license: bsd-3-clause name: Gut Analysis Toolbox num_downloads: 102 publication_date: '2025-02-23' submission_date: '2025-02-25T11:18:43.275561' url: - https://zenodo.org/records/14913673 - https://doi.org/10.5281/zenodo.14913673 uuid: 9b66afef-3cb6-4695-9517-f7629b178ade language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Luke Sorensen - Ayame Saito - Sabrina Poon - Myat Noe Han https://orcid.org/0000-0003-3028-7359 - Ryan Hamnett https://orcid.org/0000-0002-9118-1585 - Peter Neckel https://orcid.org/0000-0003-1976-0512 - Adam Humenick - Keith Mutunduwe - Christie Glennan - Narges Mahdavian - Simon JH Brookes https://orcid.org/0000-0001-5635-0876 - Rachel M McQuade https://orcid.org/0000-0002-3510-1288 - Jaime PP Foong https://orcid.org/0000-0003-2082-5520 - Estibaliz Gómez-de-Mariscal https://orcid.org/0000-0003-2082-3277 - Arrate Muñoz Barrutia - Julia A. Kaltschmidt - Sebastian K. King https://orcid.org/0000-0001-5396-0265 - Robert Haase https://orcid.org/0000-0001-5949-2327 - Simona Carbone https://orcid.org/0000-0002-4350-6357 - Nicholas A. Veldhuis https://orcid.org/0000-0002-8902-9365 - Daniel P. Poole https://orcid.org/0000-0002-6168-8422 - Pradeep Rajasekhar https://orcid.org/0000-0002-1983-7244 - authors: - Romette, Louis description: Acquired with an Nikon SIM, in 2D-SIM mode at 488nm of excitation with 30% laser power and 200ms of exposition.  Fluorescence is a knocked-in mStayGold-β2Spectrin. Cells are rat hippocampal neurons à DIV 3. The file is a reconstructed multiposition acquisition (10 positions). Uploaded to show a probable issue with Bio-Formats in Fiji, where SIM reconstrcuted multipositions files open like static noise.  license: cc-by-4.0 name: Reconstructed images of a 2DSIM multiposition acquisition. num_downloads: 3 publication_date: '2025-02-19' submission_date: '2025-02-25T11:19:01.034304' url: - https://zenodo.org/records/14893791 - https://doi.org/10.5281/zenodo.14893791 uuid: c9b5b1c2-6f9e-4921-bacf-7a35c7f98eee language: en file_formats: .nd2 tags: - exclude from DALIA authors_with_orcid: - Louis Romette https://orcid.org/0000-0003-2841-091X - authors: - Haase, Robert description: When training people in topics such as programming, bio-image analysis or data science, it makes sense to define a training strategy with a wider perspective than just trainees needs. This slide deck gives insights into aspects to consider when defining a training strategy. It considers funder's interests, financial aspects, metrics / goals, steps towards sustainability and opportunities for outreach and for founding future collaborations. license: cc-by-4.0 name: Developing a Training Strategy num_downloads: 149 publication_date: '2024-11-08' submission_date: '2025-02-25T11:19:03.838955' url: - https://zenodo.org/records/14053758 - https://doi.org/10.5281/zenodo.14053758 uuid: 1fe47d3d-e561-473c-9257-eb0c8cfba46a language: en file_formats: .pdf * .pptx tags: - NFDI4Bioimage - Artificial Intelligence - include in DALIA authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 - authors: - Haase, Robert description: Artificial intelligence (AI) and large language models (LLMs) are changing the way we use computers in science. This slide deck introduces ways for using AI and LLMs for making training materials and for exchanging knowledge about how to use AI in joint discussions between humans and LLM-based AI-systems. license: cc-by-4.0 name: Training Computational Skills in the Age of AI num_downloads: 318 publication_date: '2024-11-06' submission_date: '2025-02-25T11:19:04.474855' url: - https://zenodo.org/records/14043615 - https://doi.org/10.5281/zenodo.14043615 uuid: 2ace6a7f-809e-4126-9cea-0a345cd9b680 language: en file_formats: .pdf * .pptx tags: - NFDI4Bioimage - Artificial Intelligence - include in DALIA authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 - authors: - Draga Doncila Pop description: This is a three-part workshop guiding you through using napari to view images, a brief bioimaging analysis application, and extending napari's functionality with your own custom workflows. license: BSD 3-Clause "New" or "Revised" License name: napari-scipy2025-workshop publication_date: '2025-02-28T23:41:10+00:00' submission_date: '2025-03-01T11:30:43.571747' tags: - Python - Napari - include in DALIA type: GitHub Repository url: https://github.com/DragaDoncila/napari-scipy2025-workshop uuid: 7b668d50-e50c-4e1f-82d6-44a2500064ca - authors: - Schimmler, Sonja - Altenhöner, Reinhard - Bernard, Lars - Fluck, Juliane - Klinger, Axel - Lorenz, Sören - Mathiak, Brigitte - Miller, Bernhard - Ritz, Raphael - Schörner-Sadenius, Thomas - Sczyrba, Alexander - Stein, Regine description: 'Raw microscopy image from the NFDI4Bioimage calendar March 2025. The image shows 125x magnified microscopic details of a biofilm formed by Pseudomonas fluorescence on the surface of a liquid culture medium. The culture was inoculated with a cellulose-overexpressing and surface-colonizing mScarlet-tagged wild type and a GFP-tagged mutant that is unable to colonize the surface. The biofilm can collapse over time due to its own mass, so that new strategies have to be developed and thus a life cycle emerges. Image Metadata (using REMBI template): Study   Study description Biofilm formation Study Component   Imaging method Stereo microscopy Biosample   Biological entity Bacteria Organism Pseudomonas fluorescence Specimen   Signal/contrast mechanism Relief, fluorescence Channel 1 - content Relief, grey Channel 1 - biological entity Details of the biofilm in transmitted light Channel 2 - content mScarlet, red Channel 2 - biological entity WT over-expressing cellulose and colonizing the surface Channel 3 - content GFP, green Channel 3 - biological entity ∆wss mutant unable to colonize the surface Image Acquisition   Microscope model Zeiss Axio Zoom V16 Image Data   Magnification 125x Objective PlanNeoFluar Z 1.0x Dimension extents x: 2752, y: 2208 Pixel size description 0.91 µm x 0.91 µm Image area 2500µm x 2500µm  ' license: cc-by-4.0 name: NFDI4Bioimage Calendar 2025 March; original image num_downloads: 0 publication_date: '2025-02-27' submission_date: '2025-03-04T11:18:45.097222' url: - https://zenodo.org/records/14937632 - https://doi.org/10.5281/zenodo.14937632 uuid: 4a1d68a2-1ea0-4726-a5ab-2f098e5e7982 language: en file_formats: .czi tags: - exclude from DALIA authors_with_orcid: - Michael Schwarz https://orcid.org/0009-0002-1414-3291 - authors: - Rodriguez, Areli description: The blue and red channels get swapped when imported with Bio-formats. Happens consistently with .lif imports in QuPath and ImageJ. name: Leica (.lif) file with errors in channel order when imported with Bio-formats num_downloads: 1 publication_date: '2025-02-26' submission_date: '2025-03-04T11:19:47.062237' url: - https://zenodo.org/records/14933318 - https://doi.org/10.5281/zenodo.14933318 uuid: 7f7011c3-254d-4e66-968b-50508a7ec361 file_formats: .lif tags: - exclude from DALIA authors_with_orcid: - Areli Rodriguez - authors: - Serrano-Solano, Beatriz - Kostrykin, Leonid - Fouilloux, Anne - Massei, Riccardo description: 'GloBIAS seminar series   Part 3 in the topic:  Infrastructure for deploying image analysis workflows Image analysis using Galaxy Beatrix Serrano-Solano, Euro-BioImaging ERIC Bio-Hub, EMBL Heidelberg, Germany & Anne Fouilloux , Simula Research Laboratory, Oslo, Norway & Leonid Kostrykin, Biomedical Computer Vision Group, Heidelberg University, BioQuant, IPMB, Heidelberg, Germany & Ricardo Massei, Helmholtz Center for Environmental Research, UFZ, Leipzig, Germany Abstract: This webinar will introduce the Galaxy Image Analysis Community and highlight our mission to advance the development of FAIR and reproducible image analysis workflows. As part of our commitment to making image data analysis more accessible and collaborative, we will showcase how Galaxy can serve the imaging community. The session will explore Galaxy’s capabilities for integrating popular image analysis tools, interactive environments, and notebooks, making it a versatile platform for researchers across various scientific domains. We will also present how Galaxy facilitates the creation and sharing of reusable workflows, promoting open science and fostering collaboration. To give participants hands-on insight, we’ll provide a live demonstration on designing and running image analysis workflows within Galaxy.   ' license: cc-by-4.0 name: Image Analysis using Galaxy num_downloads: 58 publication_date: '2025-02-28' submission_date: '2025-03-04T11:19:58.814665' url: - https://zenodo.org/records/14944040 - https://doi.org/10.5281/zenodo.14944040 uuid: f30b1a4d-e87a-42b7-a3a6-b1e743d3a815 language: en file_formats: .pdf tags: - include in DALIA authors_with_orcid: - Beatriz Serrano-Solano https://orcid.org/0000-0002-5862-6132 - Leonid Kostrykin https://orcid.org/0000-0003-1323-3762 - Anne Fouilloux https://orcid.org/0000-0002-1784-2920 - Riccardo Massei https://orcid.org/0000-0003-2104-9519 - authors: - Marcelo Zoccoler - Johannes Soltwedel description: Course contents for biapol course at Trends in Microscopy conference 2025 license: Creative Commons Attribution 4.0 International name: TrendsInMicroscopy2025 publication_date: '2025-03-10T13:42:57+00:00' submission_date: '2025-03-17T20:49:27.146359' tags: - Bio-image analysis - exclude from DALIA type: GitHub Repository url: - https://biapol.github.io/TrendsInMicroscopy_2025/ - https://github.com/BiAPoL/TrendsInMicroscopy_2025 uuid: cd75c3db-fa37-4481-9d16-b02029238f17 - authors: - Moore, Josh description: Keynote at the NFDI4BIOIMAGE All-Hands Meeting in Düsseldorf, Germany, October 16, 2023. license: cc-by-4.0 name: '[N4BI AHM] Welcome to BioImage Town' num_downloads: 165 publication_date: '2023-10-16' submission_date: '2025-03-18T11:18:47.893951' url: - https://zenodo.org/records/15031842 - https://doi.org/10.5281/zenodo.15031842 uuid: c4b8c1bb-1945-47c0-8bbb-fdc800220d68 file_formats: .pdf * .pptx tags: - NFDI4Bioimage - exclude from DALIA authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X - authors: - Schmidt, Christian - Bortolomeazzi, Michele - Krooß, Ksenia - Mallm, Jan-Philipp - Ferrando-May, Elisa - Weidtkamp-Peters, Stefanie description: 'These slides were used in a workshop at the 2025 E-Science Tage in Heidelberg. Workshop Abstract: Effective Research Data Management (RDM) requires collaboration between infrastructure providers, support units, and domain-specific experts across scientific disciplines. Microscopy, or bioimaging, is a widely used technology at universities and research institutions, generating large, multi-dimensional datasets. Scientists now routinely produce microscopy data using advanced imaging modalities, often through centrally-provided instruments maintained by core facilities. Bioimaging data management presents unique challenges: files are often large (e.g., 15+ GB for whole slide images), come in various proprietary formats, and are accessed frequently for viewing as well as for complex image processing and analysis workflows. Collaboration between experimenters, clinicians, group leaders, core facility staff, and image analysts adds to the complexity, increasing the risk of data fragmentation and metadata loss. The DFG-funded project I3D:bio and the consortium NFDI4BIOIMAGE, part of Germany’s National Research Data Infrastructure (NFDI), are addressing these challenges by developing solutions and best practices for managing large, complex microscopy datasets. This workshop introduces the challenges of bioimaging RDM to institutional support personnel, including, for example, library staff, IT departments, and data stewards. Participants will explore the bioimaging RDM system OMERO, and apply structured metadata annotation and object-oriented data organization to a simple training dataset. OMERO offers centralized, secure access to data, allowing collaboration and reducing the data fragementation risk. Moreover, participants will experience the benefits of OME-Zarr, a chunked open file format designed for FAIR data sharing and remote access. OME-Zarr enables streaming of large, N-dimensional array-typed data over the Internet without the need to download whole files. An expanding toolbox for leveraging OME-Zarr for bioimaging data renders this file type a promising candidate for a standard file format suitable for use in FAIR Digital Object (FDO) implementations for microscopy data. OME-Zarr has become a pillar for imaging data sharing in two bioimaging-specific data repositories, i.e., the Image Data Resource (IDR) and the BioImage Archive (BIA). The team of Data Stewards from both abovenmentioned projects help researchers and research support staff to manage und publish bioimaging data. By the end of the workshop, participants will have gained hands-on experience with bioimaging data and will be aware of support resources like the NFDI4BIOIMAGE Help Desk for addressing specific local use cases. Our goal is to promote collaboration across disciplines to effectively manage complex bioimaging data in compliance with the FAIR principles.   ' license: cc-by-4.0 name: '[Workshop] Managing FAIR microscopy data at scale for universities and research institutions: an introduction for non-imaging stakeholders' num_downloads: 91 publication_date: '2025-03-14' submission_date: '2025-03-18T11:18:48.334126' url: - https://zenodo.org/records/15026373 - https://doi.org/10.5281/zenodo.15026373 uuid: 506bcd94-1b1a-40a2-94be-0017e4ff7ad9 language: en file_formats: .pdf * .pptx tags: - NFDI4Bioimage - Research Data Management - include in DALIA authors_with_orcid: - Christian Schmidt https://orcid.org/0000-0001-9568-895X - Michele Bortolomeazzi https://orcid.org/0000-0001-5805-5774 - Ksenia Krooß https://orcid.org/0000-0003-1717-3138 - Jan-Philipp Mallm https://orcid.org/0000-0002-7059-4030 - Elisa Ferrando-May https://orcid.org/0000-0002-5567-8690 - Stefanie Weidtkamp-Peters https://orcid.org/0000-0001-7734-3771 - authors: - Krooß, Ksenia - Fuchs, Vanessa Aphaia Fiona - Boissonnet, Tom - Weidtkamp-Peters, Stefanie description: At the Center for Advanced Imaging (CAi) at the Heinrich Heine University Düsseldorf, Germany, we have established a workflow to guide users through all aspects of bioimaging. The process begins with an initial consultation with our imaging specialists regarding microscopy techniques for their specific project. Users then receive training in microscope operation, ensuring they can handle the equipment effectively. If needed, our specialists also provide support in image analysis. Next, we introduce users to OMERO, highlighting its features and the advantages of using a bioimage data management system. They are then trained to structure and annotate their data within OMERO according to the Recommended Metadata for Biological Images (REMBI), taking their specific research topics into account. As users prepare for data publication, we assist with data organization and repository uploads. Our goal is to educate researchers in managing bioimage data throughout its entire lifecycle, with a strong emphasis on the FAIR (findable, accessible, interoperable, reusable) principles. license: cc-by-4.0 name: Workflow for user introduction into microscopy, OMERO and data management at Center for Advanced imaging num_downloads: 37 publication_date: '2025-03-07' submission_date: '2025-03-18T11:18:48.780814' url: - https://zenodo.org/records/14988921 - https://doi.org/10.5281/zenodo.14988921 uuid: 686572ec-e463-4a05-b809-5fbd751f73e1 language: en file_formats: .pdf tags: - NFDI4Bioimage - Research Data Management - include in DALIA authors_with_orcid: - Ksenia Krooß https://orcid.org/0000-0003-1717-3138 - Vanessa Aphaia Fiona Fuchs https://orcid.org/0000-0002-4101-6987 - Tom Boissonnet https://orcid.org/0000-0002-3328-9467 - Stefanie Weidtkamp-Peters https://orcid.org/0000-0001-7734-3771 - authors: - Chiang Jurado, Diana - Massei, Riccardo - Videm, Pavankumar - Kumar, Anup - Fouilloux, Anne - Kostrykin, Leonid - Serrano-Solano, Beatriz - Grüning, Björn license: cc-by-4.0 name: 'Advancing FAIR Image Analysis in Galaxy: Tools, Workflows, and Training' num_downloads: 49 publication_date: '2025-03-06' submission_date: '2025-03-18T11:18:49.182322' url: - https://zenodo.org/records/14979253 - https://doi.org/10.5281/zenodo.14979253 uuid: 49a28053-4f51-4e00-8e24-e8b54de16e21 file_formats: .pdf tags: - include in DALIA authors_with_orcid: - Diana Chiang Jurado https://orcid.org/0000-0002-5857-1477 - Riccardo Massei https://orcid.org/0000-0003-2104-9519 - Pavankumar Videm https://orcid.org/0000-0002-5192-126X - Anup Kumar https://orcid.org/0000-0002-2068-4695 - Anne Fouilloux https://orcid.org/0000-0002-1784-2920 - Leonid Kostrykin https://orcid.org/0000-0003-1323-3762 - Beatriz Serrano-Solano https://orcid.org/0000-0002-5862-6132 - Björn Grüning https://orcid.org/0000-0002-3079-6586 - authors: - Massei, Riccardo - Bernt, Matthias - Serrano-Solano, Beatriz - Lopez-Delisle, Lucille - Bumberger, Jan - Grüning, Björn - Kostrykin, Leonid license: cc-by-4.0 name: Galaxy meets OMERO! Overview on the Galaxy OMERO-suite and Vizarr Viewer num_downloads: 38 publication_date: '2025-03-05' submission_date: '2025-03-18T11:18:50.068927' url: - https://zenodo.org/records/14975462 - https://doi.org/10.5281/zenodo.14975462 uuid: e14f28b4-f50f-45b4-a982-cafce46f32c1 file_formats: .pdf tags: - include in DALIA authors_with_orcid: - Riccardo Massei https://orcid.org/0000-0003-2104-9519 - Matthias Bernt https://orcid.org/0000-0003-2380-8830 - Beatriz Serrano-Solano https://orcid.org/0000-0002-5862-6132 - Lucille Lopez-Delisle https://orcid.org/0000-0002-1964-4960 - Jan Bumberger https://orcid.org/0000-0003-3780-8663 - Björn Grüning https://orcid.org/0000-0002-3079-6586 - Leonid Kostrykin https://orcid.org/0000-0003-1323-3762 - authors: - Massei, Riccardo - Berndt, Matthias - Lopez-Delisle, Lucille - Serrano-Solano, Beatriz - Busch, Wibke - Scholz, Stefan - Bohring, Hannes - Nyffeler, Jo - Reger, Luise - Bumberger, Jan description: 'Imaging is crucial across various scientific disciplines, particularly in life sciences, where it plays a key role in studies ranging from single molecules to whole organisms. However, the complexity and sheer volume of image data, especially from high-content screening (HCS) experiments involving cell lines or other organisms, present significant challenges. Managing and analysing this data efficiently requires well-defined image processing tools and analysis pipelines that align with the FAIR principles—ensuring they are findable, accessible, interoperable, and reusable across different domains. In the frame of NFDI4BioImaging (the National Research Data Infrastructure focusing on bioimaging in Germany), we want to find viable solutions for storing, processing, analysing, and sharing HCS data. In particular, we want to develop solutions to make findable and machine-readable metadata using (semi)automatic analysis pipelines. In scientific research, such pipelines are crucial for maintaining data integrity, supporting reproducibility, and enabling interdisciplinary collaboration. These tools can be used by different users to retrieve images based on specific attributes as well as support quality control by identifying appropriate metadata. Galaxy, an open-source, web-based platform for data-intensive research, offers a solution by enabling the construction of reproducible pipelines for image analysis. By integrating popular analysis software like CellProfiler and connecting with cloud services such as OMERO and IDR, Galaxy facilitates the seamless access and management of image data. This capability is particularly valuable in bioimaging, where automated pipelines can streamline the handling of complex metadata, ensuring data integrity and fostering interdisciplinary collaboration. This approach not only increases the efficiency of HCS bioimaging but also contributes to the broader scientific community''s efforts to embrace FAIR principles, ultimately advancing scientific discovery and innovation. In the present study, we proposed an automated analysis pipeline for storing, processing, analysing, and sharing HCS bioimaging data. The (semi)automatic workflow was developed by taking as a case study a dataset of zebrafish larvae and cell lines images previously obtained from an automated imaging system generating data in an HCS fashion. In our workflows, images are automatically enriched with metadata (i.e. key-value pairs, tags, raw data, regions of interest) and uploaded to the UFZ-OME Remote Objects (OMERO) server using a novel OMERO tool suite developed with GALAXY. Workflows give the possibility to the user to intuitively fetch images from the local server and perform image analysis (i.e. annotation) or even more complex toxicological analyses (dose response modelling). Furthermore, we want to improve the FAIRness of the protocol by adding a direct upload link to the Image Data Resource (IDR) repository to automatically prepare the data for publication and sharing.' license: cc-by-4.0 name: Building FAIR image analysis pipelines for high-content-screening (HCS) data using Galaxy num_downloads: 130 publication_date: '2025-02-25' submission_date: '2025-03-18T11:18:51.201333' url: - https://zenodo.org/records/14909526 - https://doi.org/10.5281/zenodo.14909526 uuid: 66f41360-720b-48a5-98c0-d57a874da31f language: en file_formats: .pdf * .pptx tags: - include in DALIA authors_with_orcid: - Riccardo Massei https://orcid.org/0000-0003-2104-9519 - Matthias Berndt - Lucille Lopez-Delisle https://orcid.org/0000-0002-1964-4960 - Beatriz Serrano-Solano https://orcid.org/0000-0002-5862-6132 - Wibke Busch https://orcid.org/0000-0002-5497-6266 - Stefan Scholz https://orcid.org/0000-0002-6990-4716 - Hannes Bohring - Jo Nyffeler - Luise Reger - Jan Bumberger https://orcid.org/0000-0003-3780-8663 - authors: - Dubrulle, Julien description: This file cannot be read by bfGetReader() v8.1.1 on a Windows operating workstation. license: cc-by-4.0 name: imaris file not read by bfGetReader() num_downloads: 9 publication_date: '2025-03-10' submission_date: '2025-03-18T11:19:40.501625' url: - https://zenodo.org/records/15001649 - https://doi.org/10.5281/zenodo.15001649 uuid: 51d616eb-d80e-4023-996d-57f90f7afa1b file_formats: .ims tags: - exclude from DALIA authors_with_orcid: - Julien Dubrulle https://orcid.org/0000-0002-4186-7749 - authors: - Bohl, Jürgen description: 'With this file the problem addressed in PR#4284 can be reproduced: when runningbfconvert -series 4 -channel 0 2025_01_27__0007_offline_Zen_3_9_5.czi output.png the result is garbled.' license: cc-by-4.0 name: Sample data for PR#4284 (https://github.com/ome/bioformats/pull/4284) num_downloads: 38 publication_date: '2025-03-04' submission_date: '2025-03-18T11:19:46.553084' url: - https://zenodo.org/records/14968770 - https://doi.org/10.5281/zenodo.14968770 uuid: 6190a6dd-b467-40a1-ae3a-db982cd68409 file_formats: .czi tags: - exclude from DALIA authors_with_orcid: - Jürgen Bohl - authors: - Moore, Josh - Kunis, Susanne description: Presentation given to the Search & Harvesting workgroup of the Metadata section of NFDI on March 25th, 2025 license: cc-by-4.0 name: Metadata in Bioimaging num_downloads: 9 publication_date: '2025-03-25' submission_date: '2025-04-01T11:19:54.656989' url: - https://zenodo.org/records/15083018 - https://doi.org/10.5281/zenodo.15083018 uuid: bfed4738-50b9-4a42-8cb1-eb67991db18e file_formats: .pdf tags: - NFDI4Bioimage - exclude from DALIA authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X - Susanne Kunis https://orcid.org/0000-0001-6523-7496 - authors: - Massei, Riccardo - Bernt, Matthias - Kostrykin, Leonid - Bumberger, Jan description: Imaging plays a crucial role across various scientific disciplines, particularly in life sciences. However, image data often proves complex, and the volume of images requiring analysis is steadily increasing, especially in high-content screening (HCS) experiments involving cell lines or other organisms. Specifically, analysis pipelines must align to the FAIR principles, ensuring they are reusable and interchangeable across different domains license: mit-license name: Building FAIR image analysis pipelines for high-content-screening (HCS) data using Galaxy num_downloads: 9 publication_date: '2024-05-14' submission_date: '2025-04-01T11:19:55.125039' url: - https://zenodo.org/records/15047849 - https://doi.org/10.5281/zenodo.15047849 uuid: d5e2ad4e-1190-4720-877c-6a682bcb5935 language: en file_formats: .pdf tags: - NFDI4Bioimage - include in DALIA authors_with_orcid: - Riccardo Massei https://orcid.org/0000-0003-2104-9519 - Matthias Bernt https://orcid.org/0000-0003-3763-0797 - Leonid Kostrykin https://orcid.org/0000-0003-1323-3762 - Jan Bumberger https://orcid.org/0000-0003-3780-8663 - authors: - Wei Ouyang - Wanlu Lei - Caterina Fuster-Barceló - Gabe Reder - arratemunoz - Weize - Curtis Rueden - Matt McCormick description: Your Personal Assistant in Computational Bioimaging. license: MIT License name: bioimageio-chatbot publication_date: '2023-10-10T16:05:49+00:00' submission_date: '2025-04-10T09:13:40.282163' tags: - artificial intelligence - bioimage analysis - exclude from DALIA type: GitHub Repository url: https://github.com/bioimage-io/bioimageio-chatbot uuid: 31a8ab88-d436-4d52-af74-88c94ac589ff - authors: - Rensu Petrus Theart name: ABIC - Intermediate Fiji Image Analysis Course 2024 description: 'A structured beginner to intermediate-level course in image analysis using Fiji, developed for ABIC 2024. It includes a video lecture playlist, course documentation, and participant image files. ' proficiency_level: intermediate tags: - Bioimage Analysis - Image Processing - Teaching Resource - ImageJ - exclude from DALIA type: - Workshop - Video - Document url: - https://www.youtube.com/playlist?list=PL0RrV4sTNwh2S9Lb7d1TzJWPGgdw_YVnb - https://docs.google.com/document/d/1h-3oJDR7gd_y3tfgN3clPwt2O4g34QRPp_FJ4v2Q7kA/edit?usp=sharing - https://www.dropbox.com/scl/fi/7njq2wp680vubt6rwhn5f/ParticipantImages.zip?rlkey=pk3kttbsclk69ixxp1yv9j8p3&dl=0 license: CC-BY-4.0 uuid: 7ddb94c7-7d0e-40f0-8aa3-f7283b90e6a2 - authors: - Bernard, Lars - Brück, Maike - Busse, Christian - Engel, Judith - Eufinger, Jan - Ewert, Frank - Fluck, Juliane - Förstner, Konrad - Fürst, Julia - Gauza, Holger - Getzlaff, Klaus - Glöckner, Frank Oliver - Hunold, Johannes - Koepler, Oliver - Krooß, Ksenia - Lindstädt, Birte - McHardy, Alice C. - Mehrtens, Hela - Rey-Mazon, Elena - Schmidt, Marcus - Schober, Isabel - Schröter, Annett - Stegle, Oliver - Steinbeck, Christoph - Steinhart, Feray - von Suchodoletz, Dirk - Weidtkamp-Peters, Stefanie - Wendt, Jens - Wetzker, Conni description: In a Memorandum of Understanding, the undersigned consortia agree to work together to enhance their support capabilities (helpdesks) to meet the needs of interdisciplinary research in Earth-, Chemical and Life Sciences. license: cc-by-4.0 name: Memorandum of Understanding of NFDI consortia from Earth-, Chemical and Life Sciences to support a network called the Geo-Chem-Life Science Helpdesk Cluster num_downloads: 113 publication_date: '2025-04-02' submission_date: '2025-04-15T11:19:23.120722' url: - https://zenodo.org/records/15065070 - https://doi.org/10.5281/zenodo.15065070 uuid: 6fd444ff-698e-42be-acfb-ee28a3aacd09 language: en file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Lars Bernard https://orcid.org/0000-0002-3085-7457 - Maike Brück https://orcid.org/0009-0009-8508-8585 - Christian Busse https://orcid.org/0000-0001-7553-905X - Judith Engel https://orcid.org/0000-0001-8665-6382 - Jan Eufinger https://orcid.org/0000-0002-3439-1674 - Frank Ewert https://orcid.org/0000-0002-4392-8154 - Juliane Fluck https://orcid.org/0000-0003-1379-7023 - Konrad Förstner https://orcid.org/0000-0002-1481-2996 - Julia Fürst https://orcid.org/0000-0003-2547-933X - Holger Gauza - Klaus Getzlaff https://orcid.org/0000-0002-0347-7838 - Frank Oliver Glöckner https://orcid.org/0000-0001-8528-9023 - Johannes Hunold https://orcid.org/0000-0002-4378-6061 - Oliver Koepler https://orcid.org/0000-0003-3385-4232 - Ksenia Krooß https://orcid.org/0000-0003-1717-3138 - Birte Lindstädt https://orcid.org/0000-0002-8251-1597 - Alice C. McHardy https://orcid.org/0000-0003-2370-3430 - Hela Mehrtens https://orcid.org/0000-0002-4526-2472 - Elena Rey-Mazon https://orcid.org/0000-0003-4813-5927 - Marcus Schmidt https://orcid.org/0000-0002-5546-5521 - Isabel Schober https://orcid.org/0000-0002-4894-1913 - Annett Schröter https://orcid.org/0000-0002-2542-0867 - Oliver Stegle https://orcid.org/0000-0002-8818-7193 - Christoph Steinbeck https://orcid.org/0000-0001-6966-0814 - Feray Steinhart - Dirk von Suchodoletz https://orcid.org/0000-0002-4382-5104 - Stefanie Weidtkamp-Peters https://orcid.org/0000-0001-7734-3771 - Jens Wendt https://orcid.org/0009-0002-1826-7099 - Conni Wetzker https://orcid.org/0000-0002-8367-5163 - authors: - Marin, Zach description: DCIMG 0x1000000 images of beads over time (30 seconds, 0.03 s exposure).  license: cc-by-4.0 name: Beads imaged over time num_downloads: 1 publication_date: '2025-04-04' submission_date: '2025-04-15T11:20:21.288543' url: - https://zenodo.org/records/15150937 - https://doi.org/10.5281/zenodo.15150937 uuid: af78e254-13f6-4ed9-94dc-b1687486b293 file_formats: .dcimg tags: - exclude from DALIA authors_with_orcid: - Zach Marin https://orcid.org/0000-0001-5341-9911 - authors: - Walther, Christa description: This document reports on the first in-person workshop supported by GloBIAS. Each session has its own chapter provided by the people chairing the sessions, summarising the outputs achieved.  license: cc-by-4.0 name: GloBIAS in-person workshop 2024 num_downloads: 42 publication_date: '2025-04-07' submission_date: '2025-04-15T11:20:33.775844' url: - https://zenodo.org/records/15168241 - https://doi.org/10.5281/zenodo.15168241 uuid: 55d6cbd1-fa68-4942-9915-7c57bc0ab636 file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Christa Walther https://orcid.org/0000-0002-8962-3102 - authors: - Tischer, Christian - Politi, Antonio - Buchholz, Tim-Oliver - Fazeli, Elnaz - Gritti, Nicola - Halavatyi, Aliaksandr - Gonzalez Tirado, Sebastian - Hennies, Julian - Hodges, Toby - Khan, Arif - Kutra, Dominik - Marcotti, Stefania - Oezdemir, Bugra - Schneider, Felix - Schorb, Martin - Stokkermans, Anniek - Sun, Yi - Vakili, Nima description: 'The newly developed image data formats course was taught for the first time: https://github.com/NEUBIAS/training-resources/blob/master/courses/2025_01_EMBL_image_data_formats.md' license: cc-by-4.0 name: Modular training resources for bioimage analysis num_downloads: 6 publication_date: '2025-01-21' submission_date: '2025-04-15T11:42:16.691966' url: - https://zenodo.org/records/14710820 - https://doi.org/10.5281/zenodo.14710820 uuid: 79148d7b-3a4b-4702-839d-864ee2fea091 file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Christian Tischer https://orcid.org/0000-0003-4105-1990 - Antonio Politi https://orcid.org/0000-0003-4788-0933 - Tim-Oliver Buchholz https://orcid.org/0000-0001-6953-8915 - Elnaz Fazeli https://orcid.org/0000-0002-0770-0777 - Nicola Gritti https://orcid.org/0000-0002-7637-4258 - Aliaksandr Halavatyi https://orcid.org/0000-0002-9002-457X - Sebastian Gonzalez Tirado - Julian Hennies https://orcid.org/0000-0002-0555-151X - Toby Hodges https://orcid.org/0000-0003-1766-456X - Arif Khan https://orcid.org/0000-0003-4105-1990 - Dominik Kutra https://orcid.org/0000-0003-4202-3908 - Stefania Marcotti https://orcid.org/0000-0002-2877-0133 - Bugra Oezdemir https://orcid.org/0000-0001-9823-0581 - Felix Schneider https://orcid.org/0000-0002-9958-8961 - Martin Schorb - Anniek Stokkermans https://orcid.org/0000-0002-9013-9983 - Yi Sun https://orcid.org/0000-0002-7636-0200 - Nima Vakili - authors: - Dvoretskii, Stefan - Bortolomeazzi, Michele - Nolden, Marco - Schmidt, Christian - Maier-Hein, Klaus - Moore, Josh license: cc-by-4.0 name: 'OMExcavator: a tool for exporting and connecting domain-specific metadata in a wider knowledge graph' num_downloads: 41 publication_date: '2025-02-21' submission_date: '2025-04-29T11:20:07.445623' url: - https://zenodo.org/records/15268798 - https://doi.org/10.5281/zenodo.15268798 uuid: 249e0bd4-c3ee-4036-810e-e48191cc95c3 file_formats: .pdf tags: - NFDI4Bioimage - exclude from DALIA authors_with_orcid: - Stefan Dvoretskii https://orcid.org/0000-0001-7769-0167 - Michele Bortolomeazzi https://orcid.org/0000-0001-5805-5774 - Marco Nolden https://orcid.org/0000-0001-9629-0564 - Christian Schmidt https://orcid.org/0000-0001-9568-895X - Klaus Maier-Hein https://orcid.org/0000-0002-6626-2463 - Josh Moore https://orcid.org/0000-0003-4028-811X - authors: - Fernanda Fossa description: Course about image analysis with materials in English and videos in Portuguese. license: MIT License name: 2023_CourseImageAnalysis_NanoCell publication_date: '2023-08-24T10:15:11+00:00' submission_date: '2025-04-30T06:55:25.155955' tags: - cell profiler - bioimage analysis - include in DALIA type: GitHub Repository url: https://github.com/fefossa/2023_CourseImageAnalysis_NanoCell uuid: 12513d54-3aeb-4b4d-8aef-3aea2f8b5173 - authors: Nfdi4Bioimage license: UNKNOWN name: NFDI4BIOIMAGE - a consortium of the National Research Data Infrastructure tags: - BioImage Analysis - Research Data Management - nfdi4bioimage - exclude from DALIA type: - Collection url: https://nfdi4bioimage.de/home/ uuid: 192a064e-1f3f-4b7d-b462-f83d9a2236c8 - authors: - Torsten Stöter - Tobias Gottschall - Andrea Schrager - Peter Zentis - Monica Valencia-Schneider - Niraj Kandpal - Werner Zuschratter - Astrid Schauss - Timo Dickscheid - Timo Mühlhaus - Dirk von Suchodoletz description: Interdisciplinary collaboration and integration of large and diverse datasets are becoming increasingly important. Answering complex research questions requires combining and analysing multimodal datasets. Research data management follows the FAIR principles making data findable, accessible, interoperable, and reusable. However, there are challenges in capturing the entire research cycle and contextualizing data according, not only for the DataPLANT and NFDI4BIOIMAGE communities. To address these challenges, DataPLANT developed a data structure called Annotated Research Context (ARC). The Brain Imaging Data Structure (BIDS) originated from the neuroimaging community extended for microscopic image data. Both concepts provide standardised and file system based data storage structures for organising and sharing research data accompanied with metadata. We exemplarily compare the ARC and BIDS designs and propose structural and metadata mapping. license: CC-BY-4.0 name: Combining the BIDS and ARC Directory Structures for Multimodal Research Data Organization publication_date: '2023-09-12' tags: - NFDI4Bioimage - Research Data Management - include in DALIA type: - Poster url: - https://zenodo.org/records/8349563 - https://doi.org/10.5281/zenodo.8349563 uuid: 9d30fab4-4f91-476d-82f5-dd7ee41ab6d9 language: en file_formats: .pdf authors_with_orcid: - Torsten Stöter - Tobias Gottschall https://orcid.org/0000-0003-3001-1491 - Andrea Schrader https://orcid.org/0000-0002-3879-7057 - Peter Zentis https://orcid.org/0000-0002-6999-132X - Monica Valencia-Schneider https://orcid.org/0000-0003-3430-2683 - Niraj Kandpal https://orcid.org/0009-0007-5101-4786 - Werner Zuschratter https://orcid.org/0000-0002-9845-6393 - Astrid Schauss https://orcid.org/0000-0002-6658-2192 - Timo Dickscheid https://orcid.org/0000-0002-9051-3701 - Timo Mühlhaus https://orcid.org/0000-0003-3925-6778 - Dirk von Suchodoletz https://orcid.org/0000-0002-4382-5104 - authors: - Andrea Schrader - Michele Bortolomeazzi - Niraj Kandpal - Torsten Stöter - Kevin Schneider - Peter Zentis - Josh Moore - Jeam-Marie Burel - Tom Boissonnet description: 'Repository for documentation during the 2nd de.NBI BioHackathon Germany - 11.-15.12.2023 - OMERO-ARC project (in short: BHG2023-OMERO-ARC)' license: CC-BY-4.0 name: BHG2023-OMERO-ARC tags: - NFDI4Bioimage - Bioinformatics - Omero - exclude from DALIA type: - Github repository url: https://github.com/NFDI4BIOIMAGE/BHG2023-OMERO-ARC uuid: 897e08cf-1b92-41ef-9150-9cc23cc108a4 - authors: - Tong Li - David Horsfall - Daniela Basurto-Lozada description: Single cell and spatial transcriptomics illuminate complementary features of tissues. However, the online dissemination and exploration of multi-modal datasets is challenging. We introduce the WebAtlas pipeline for user-friendly sharing and interactive navigation of integrated datasets. WebAtlas unifies commonly used atlassing technologies into the cloud-optimised Zarr format and builds on Vitessce to enable remote data navigation. We showcase WebAtlas on the developing human lower limb to cross-query cell types and genes across single cell, sequencing- and imaging-based spatial transcriptomic data. license: null name: WebAtlas pipeline for integrated single cell and spatial transcriptomic data publication_date: '2023-04-28' tags: - Spatial transcriptomics - Single cell - Bioimage Analysis - exclude from DALIA type: - Collection - Atlas url: - https://developmental.cellatlas.io/webatlas - https://www.biorxiv.org/content/10.1101/2023.05.19.541329v1 uuid: f3e0beb1-baf6-47cc-bc25-4e55dc5138b8 language: en - authors: - Josh Moore - Susanne Kunis description: For decades, the sharing of large N-dimensional datasets has posed issues across multiple domains. Interactively accessing terabyte-scale data has previously required significant server resources to properly prepare cropped or down-sampled representations on the fly. Now, a cloud-native chunked format easing this burden has been adopted in the bioimaging domain for standardization. The format — Zarr — is potentially of interest for other consortia and sections of NFDI. license: CC-BY-4.0 name: Zarr - A Cloud-Optimized Storage for Interactive Access of Large Arrays publication_date: '2023-09-07' tags: - NFDI4Bioimage - Bioimage Analysis - exclude from DALIA type: - Publication url: https://www.tib-op.org/ojs/index.php/CoRDI/article/view/285 uuid: 7445618a-98d8-4ae0-9732-815d81906df8 language: en - authors: - Michael Brooks description: A plethora of standards mean shareable and verifiable microscopy data often get lost in translation. Biologists are working on a solution. license: UNKNOWN name: How open-source software could finally get the world’s microscopes speaking the same language publication_date: '2023-10-02' tags: - Research Data Management - Microscopy - exclude from DALIA type: - Blog post url: https://www.nature.com/articles/d41586-023-03064-9 uuid: eed235bb-0f7e-490e-8e21-153c9228f3f4 - authors: - Paul Zierep - Sanjay Kumar Srikakulam - Sebastian Schaaf - Bjoern Gruening description: The lifecycle of scientific tools comprises the creation of code releases, packages and containers which can be deployed into cloud platforms, such as the Galaxy Project, where they are run and integrated into workflows. The tools and workflows are further used to create training material that benefits a broad community. The need to organize and streamline this tool development lifecycle has led to a sophisticated development and deployment architecture. license: CC-BY-4.0 name: Conda, Container and Bots - How to Build and Maintain Tool Dependencies in Workflows and Training Materials publication_date: '2023-09-07' tags: - NFDI4Bioimage - Research Data Management - include in DALIA type: - Publication url: https://www.tib-op.org/ojs/index.php/CoRDI/article/view/417 uuid: 9769532e-a009-43dc-8a64-56547df02e8e language: en - authors: - Robert Wagner - Dagmar Waltemath - Kristina Yordanova - Markus Becker description: 'This paper focuses on the ongoing process of establishing a FAIR (Findable, Accessible, Interoperable and Reusable) data workflow for multidisciplinary research and development in applied plasma science. The presented workflow aims to support researchers in handling their project data while also fulfilling the requirements of modern digital research data management. ' license: CC BY-NC-ND 4.0 name: 'Towards FAIR Data Workflows for Multidisciplinary Science: Ongoing Endeavors and Future Perspectives in Plasma Technology' tags: - Research Data Management - exclude from DALIA type: - Publication url: https://www.scitepress.org/Link.aspx?doi=10.5220/0012808000003756 uuid: ec2c06ff-fc71-4318-b279-278192f36bdd language: en - authors: - Anett Jannasch - Silke Tulok - Chukwuebuka William Okafornta - Thomas Kugel - Michele Bortolomeazzi - Tom Boissonnet - Christian Schmidt - Andy Vogelsang description: 'Modern bioimaging core facilities at research institutions are essential for managing and maintaining high-end instruments, providing training and support for researchers in experimental design, image acquisition and data analysis. ' license: CC-BY-4.0 name: 'Setting up an institutional OMERO environment for bioimage data: Perspectives from both facility staff and users' publication_date: '2024-09-14' tags: - NFDI4Bioimage - OMERO - Bioimage Analysis - exclude from DALIA type: - Publication url: https://onlinelibrary.wiley.com/doi/10.1111/jmi.13360 uuid: 67f1a74b-eeb8-4b16-9540-1ef2fc37edbb language: en - authors: - Beth A. Cimini - Peter Bankhead - Rocco D' Antuono - Elnaz Fazeli - Julia Fernandez-Rondriguez - Caterina Fuster-Barcelo - Robert Haase - Helena Klara Jambor - Martin L. Jones - Florian Jug - Anna H. Klemm - Anna Kreshuk - Stefania Marcotti - Gabriel G. Martins - Sara Mc Ardle - Kota Miura - Arrate Muñoz-Barrutia - Laura C. Murphy - Michael S. Nelson - Simon F. Nørrelykke - Perrine Paul-Gilloteaux - Thomas Pengo - Joanna W. Pylvänäinen - Lior Pytowski - Arianna Ravera - Annika Reinke - Yousr Rekik - Caterina Strambio-De-Castillia - Daniel Thédié - Virginie Uhlmann - Oliver Umney - Laura Wiggins - Kevin W. Eliceiri description: Bioimage analysis (BIA), a crucial discipline in biological research, overcomes the limitations of subjective analysis in microscopy through the creation and application of quantitative and reproducible methods. The establishment of dedicated BIA support within academic institutions is vital to improving research quality and efficiency and can significantly advance scientific discovery. However, a lack of training resources, limited career paths and insufficient recognition of the contributions made by bioimage analysts prevent the full realization of this potential. This Perspective – the result of the recent The Company of Biologists Workshop ‘Effectively Communicating Bioimage Analysis’, which aimed to summarize the global BIA landscape, categorize obstacles and offer possible solutions – proposes strategies to bring about a cultural shift towards recognizing the value of BIA by standardizing tools, improving training and encouraging formal credit for contributions license: CC-BY-4.0 name: The crucial role of bioimage analysts in scientific research and publication publication_date: '2024-10-30' tags: - Bioimage Analysis - include in DALIA type: - Publication url: https://journals.biologists.com/jcs/article/137/20/jcs262322/362545/The-crucial-role-of-bioimage-analysts-in uuid: 35ddcee1-6800-4b33-9a55-c56574f712c5 language: en - authors: - Peter Bajcsy - Sreenivas Bhattiprolu - Katy Boerner - Beth A Cimini - Lucy Collinson - Jan Ellenberg - Reto Fiolka - Maryellen Giger - Wojtek Goscinski - Matthew Hartley - Nathan Hotaling - Rick Horwitz - Florian Jug - Anna Kreshuk - Emma Lundberg - Aastha Mathur - Kedar Narayan - Shuichi Onami - Anne L. Plant - Fred Prior - Jason Swedlow, - Adam Taylor - Antje Keppler description: 'Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects). ' license: CC-BY-NC-SA-4.0 International name: Enabling Global Image Data Sharing in the Life Sciences publication_date: '2024-01-23' tags: - Research Data Management - exclude from DALIA type: - Publication url: https://arxiv.org/abs/2401.13023 uuid: 5da8faf2-df36-4b8d-9eeb-a2c6684ba242 language: en - authors: - Nikki Bialy - Frank Alber - Brenda Andrews - Michael Angelo - Brian Beliveau - Lacramioara Bintu - Alistair Boettiger - Ulrike Boehm - Claire M. Brown - Mahmoud Bukar Maina - James J. Chambers - Beth A. Cimini - Kevin Eliceiri - Rachel Errington - Orestis Faklaris - Nathalie Gaudreault - Ronald N. Germain - Wojtek Goscinski - David Grunwald - Michael Halter - Dorit Hanein - John W. Hickey - Judith Lacoste - Alex Laude - Emma Lundberg - Jian Ma - Leonel Malacrida - Josh Moore - Glyn Nelson - Elizabeth Kathleen Neumann - Roland Nitschke - Shuichi Onami - Jaime A. Pimentel - Anne L. Plant - Andrea J. Radtke - Bikash Sabata - Denis Schapiro - Johannes Schöneberg - Jeffrey M. Spraggins - Damir Sudar - Wouter-Michiel Adrien Maria Vierdag - Niels Volkmann - Carolina Wählby - Siyuan (Steven)Wang - Ziv Yaniv - Caterina Strambio-De-Castillia description: Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. license: CC-BY-NC-SA-4.0 International name: Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data publication_date: '2024-08-30' tags: - Research Data Management - exclude from DALIA type: - Publication url: https://arxiv.org/abs/2401.13022 uuid: b3b968d2-4e1c-410d-92a8-59565200c24f language: en - authors: - Ruman Gerst - Zoltán Cseresnyés - Marc Thilo Figge name: 'JIPipe Spring Course (JSC) 2025: Workshop Recordings, Slides, Homework, and Materials' description: The course gives a basic introduction into microscopy, optics, and image analysis. This is followed by interactive tutorials that explain the basics of creating fully automated image analysis workflows in JIPipe using a simple blobs analysis and intermediate-level quantification of LSFM kidney images. JIPipe-specific features including annotation-guided batch processing, organization with graph compartments, expressions and path processing, and project-wide metadata and parameters are also established. Finally, an advanced real-world pipeline is showcased with detailed guidance through the individual components that include integrations of Cellpose and TrackMate. url: https://doi.org/10.5281/zenodo.15373555 type: - Workshop - Video - Tutorial - Slides license: cc-by-4.0 publication_date: '2025-05-12' tags: - NFDI4Bioimage - jipipe - bioimage analysis - include in DALIA uuid: 62c9a2ad-81b3-4ac2-9524-197a9e25be55 language: en - authors: - Cameron Watson - Allison Creason description: This tutorial will demonstrate how to use the Galaxy multiplex imaging tools to process and analyze publicly available TMA test data provided by MCMICRO (Figure 1); however, the majority of the steps in this tutorial are the same for both TMAs and WSIs and notes are made throughout the tutorial where processing of these two imaging types diverge. license: CC-BY-4.0 name: End-to-End Tissue Microarray Image Analysis with Galaxy-ME publication_date: '2023-02-14' tags: - Galaxy - Multiplex Imaging - include in DALIA type: - Tutorial url: https://training.galaxyproject.org/training-material/topics/imaging/tutorials/multiplex-tissue-imaging-TMA/tutorial.html#end-to-end-tissue-microarray-image-analysis-with-galaxy-me uuid: ec037b4d-74c3-4bd2-a890-2de3a6fdf4d5 language: en - authors: Michael Nielsen description: Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning. license: CC-BY-NC-3.0 Unported name: Neural Networks and Deep Learning publication_date: '2019-12-01' tags: - Deep Learning - Neural Networks - Machine Learning - exclude from DALIA type: - Book url: http://neuralnetworksanddeeplearning.com uuid: bbcaa95f-8650-457a-acd0-f60e249f2c0e language: en - authors: - Geeth Sethi - Sumith Kulal - Kevin Zakka - William Shen - Rachel Gardner description: ' ConvNet architectures make the explicit assumption that the inputs are images, which allows us to encode certain properties into the architecture. These then make the forward function more efficient to implement and vastly reduce the amount of parameters in the network.' license: UNKNOWN name: CS231n Convolutional Neural Networks for Visual Recognition tags: - Deep Learning - Computer Vision - Neural Networks - Machine Learning - include in DALIA type: - Blog post url: https://cs231n.github.io/convolutional-networks/ uuid: 11b7517a-098a-4094-be01-3e692ff61239 language: en - authors: GitHub description: Ressource that helps to choose an open source license for your project. license: CC-BY-3.0 Unported name: Choose an open source license tags: - Open Source - Licensing - exclude from DALIA type: - Website url: https://choosealicense.com uuid: 4b1549e0-0cd7-4c38-bd1a-ce7737e4958b - authors: - Joanna W. Pylvänäinen - Hanna Grobe - Guillaume Jacquemet description: This article emphasizes the importance of structured, hands-on data exploration in quantitative cell biology, offering practical advice for analyzing bioimage datasets. It also highlights how generative AI and large language models can enhance and streamline data workflows for more reliable and transparent research. license: CC-BY-4.0 name: Practical considerations for data exploration in quantitative cell biology publication_date: '2025-04-07' tags: - Bioimage Analysis - Data Exploration - include in DALIA type: - Publication url: https://journals.biologists.com/jcs/article/138/7/jcs263801/367617/Practical-considerations-for-data-exploration-in uuid: a0e7b743-5f75-4784-9b16-aa441e9d0eb1 language: en - authors: - Robert Kosara - Allison Horst description: This post explores how animation can enhance data visualizations by improving viewer understanding and engagement, while also acknowledging the risks of misuse. It presents five practical techniques, supported by examples and implementation tips using D3 or Observable Plot. license: UNKNOWN name: Five ways to effectively use animation in data visualization publication_date: '2024-12-05' tags: - Data Visualization - include in DALIA type: - Blog post url: https://observablehq.com/blog/effective-animation uuid: 7c63a86c-e134-4d25-b138-f6c2c182ec02 language: en - authors: - Beatriz Serrano-Solano - Anne Fouilloux - Leonid Kostrykin - Riccardo Massei description: This video is a tutorial on how to use the Galaxy platform for bioimage analysis. license: UNKNOWN name: Image Analysis using Galaxy publication_date: '2025-03-07' tags: - Galaxy - Bioimage Analysis - include in DALIA type: - Tutorial - Video url: https://www.youtube.com/watch?v=wAHOPT6lRV0 uuid: fada7380-babf-427d-8b90-a85b657f125e - authors: - Rachel Lee - Owen Puls - Wei Ouyang - Beth Cimini description: This is a repository containing a textbook written for BioImaging scientists and other microscopists. license: BSD-3-Clause name: Bioimaging AI Textbook publication_date: '2025-02-20' tags: - Bioimage Analysis - Artificial Intelligence - include in DALIA type: - Book - GitHub Repository url: https://github.com/aicjanelia/BioImagingAI uuid: 8a27fa76-09c0-4809-b166-b13fc9f90fbe - authors: Radiology Tutorials description: This is a playlist of videos about how CT works license: UNKNOWN name: CT Physics publication_date: '2025-01-01' tags: - Imaging - exclude from DALIA type: - Video url: https://youtube.com/playlist?list=PLWfaNqiSdtzW_muHrCkwJm9FyovAue-jN&si=-zIXh87DMkAEuJVd uuid: 57d920e9-a290-41d6-8aad-290e6a6ed965 - authors: Radiology Tutorials description: This is a playlist of videos about how MRI works license: UNKNOWN name: MRI Physics publication_date: '2025-01-01' tags: - Imaging - exclude from DALIA type: - Video url: https://youtube.com/playlist?list=PLWfaNqiSdtzVkfJW2gO-unAYjcDji7-9i&si=U5gvYtUYvmLHxi0z uuid: 0d0faea4-6139-4528-b53e-f1b1cc1a7b5d - authors: Julien Sindt description: This course introduces life science researchers to high-performance computing (HPC), covering essential concepts and providing hands-on experience using the UK’s ARCHER2 supercomputing service. It aims to help participants understand how HPC can benefit their research and prepare them to use it effectively for tasks like biomolecular simulation. license: CC-BY-4.0 name: Introduction to High Performance Computing for Life Scientists publication_date: '2021-03-22' tags: - High Performance Computing - include in DALIA type: - Github Repository url: https://epcced.github.io/20210322-intro-hpc-life-scientists/ uuid: 8a1aa389-2e16-44c8-8cc3-bd294b38cd47 language: en - authors: Joseph Scott description: In this blog post, the author emphasizes problematic characters (e.g., spaces, slashes, colons) that should be avoided in filenames to ensure cross-platform compatibility across operating systems like Windows, macOS, and Linux, emphasizing the chaos caused by differing filesystem rules and naming conventions. license: UNKNOWN name: Things that shouldn't be in file names for $1000 Alex publication_date: '2007-02-12' tags: - Reseach Data Management - include in DALIA type: - Blog post url: https://blog.josephscott.org/2007/02/12/things-that-shouldnt-be-in-file-names-for-1000-alex/ uuid: c6ed98db-ceaa-4581-b08c-17a19168d06f language: en - authors: Kristin Briney description: 'This worksheet walks researchers through the process of creating a file naming convention for a group of files. This process includes: choosing metadata, encoding and ordering the metadata, adding version information, and properly formatting the file names. Two versions of the worksheet are available: a Caltech Library branded version and a generic editable version.' license: CC-BY-4.0 name: File Naming Convention Worksheet publication_date: '2020-06-02' tags: - Research Data Management - include in DALIA type: - Worksheet url: https://authors.library.caltech.edu/records/mmnpf-cez11 uuid: 8b5b5726-2aff-4f04-b29b-b8c7d2b3cd32 language: en - authors: Nick Radcliffe description: This playful exploration delves into the quirky world of naming conventions in computing and data, humorously comparing them to Boston Box matrices and D&D alignments while poking fun at the chaos of categorizing what makes sense in metadata standards. license: UNKNOWN name: Name Styles publication_date: null tags: - Research Data Management - include in DALIA type: - Blog post url: https://www.tdda.info/name-styles uuid: 51b819a7-cb38-46e8-a3c6-3f75fa8b3f92 language: en - authors: Deepia description: 'This video provides information about three very common methods for data dimensionality reduction: PCA, t-SNE and UMAP. These are especially useful when you want to visualise the latent space of an autoencoder.' license: UNKNOWN name: 'Latent Space Visualisation: PCA, t-SNE, UMAP | Deep Learning Animated' publication_date: '2024-08-05' tags: - Dimensionality Reduction - include in DALIA type: - Video url: https://www.youtube.com/watch?v=o_cAOa5fMhE uuid: fab03e5f-60e2-4cb2-b817-097241c2593a language: en - authors: - Alexis Lebon - Anatole Chessel - Raphael Braud-Mussi - Marine Breuilly - Denis Ressnikoff - Dorian Kauffmann - Elvire Guiot - Emmanuel Faure - Perrine Gilloteaux - Guillaume Gay - Guillaume Jean-François - Jerome Mutterer - Paulette Lieby - Julio Mateos-Langerak - Guillaume Maucort - Marc Mongy - Mylene Pezet - Sotirios Papadiamantis - Théo Barnouin - Mathieu Vigneau description: omero-quay is a microscopy data transport layer between data management tools. Currently, it supports the iRODS — OMERO architecture built at France BioImaging fbi-omero. license: Mozilla Public License 2.0 name: omero-quay publication_date: '2023-06-16' tags: - Data Management - omero - exclude from DALIA type: - GitLab Repository url: https://gitlab.in2p3.fr/fbi-data/omero-quay uuid: f98c6ceb-4580-4478-8e81-836a1cdc317f - authors: - Jean-Yves Tinevez - Joanna W. Pylvänäinen - Guillaume Jacquemet description: '3D image of cells in a spheroid, imaged on a confocal microscope, used in a tutorial to demonstrate how to hack TrackMate to segment cells in 3D using the 2D segmentation algorithms it ships. Image by Guillaume Jacquemet. For more details see https://imagej.net/plugins/trackmate/trackmate-stardist#generation-of-3d-labels-by-tracking-2d-labels-using-trackmate  ' license: cc-by-4.0 name: Segmenting cells in a spheroid in 3D using 2D StarDist within TrackMate num_downloads: 452 publication_date: '2021-08-19' submission_date: '2025-05-29T19:38:24.237753' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/5220610 - https://doi.org/10.5281/zenodo.5220610 uuid: 79c64350-a63a-4789-a904-8c5a9d388ba3 language: en file_formats: .png * .tif authors_with_orcid: - Jean-Yves Tinevez https://orcid.org/0000-0002-0998-4718 - Joanna W. Pylvänäinen https://orcid.org/0000-0002-3540-5150 - Guillaume Jacquemet https://orcid.org/0000-0002-9286-920X - authors: - Kubow, Kristopoher - Pengo, Thomas description: 'Original image files, label (ground truth) files, and PSF files used in the ABRF Light Microscopy Research Group (LMRG) image analysis study. Simulated 3D confocal fluorescence images of sub-diffraction punctate staining (fluorescence in situ hybridization (FISH) in C. elegans). See https://github.com/ABRFLMRG/image-analysis-study for more details.' license: cc-by-4.0 name: LMRG Image Analysis Study - FISH datasets num_downloads: 53 publication_date: '2022-05-18' submission_date: '2025-05-29T19:38:24.766943' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/6560910 - https://doi.org/10.5281/zenodo.6560910 uuid: 94e58226-e1d1-41b3-9e9d-c49b41d20c7b language: en file_formats: .ics * .tiff authors_with_orcid: - Kristopoher Kubow - Thomas Pengo - authors: - Fuhui Long - Hanchuan Peng - Xiao Liu - Stuart K Kim - Eugene Myers - Dagmar Kainmüller - Martin Weigert description: 'The dataset consists of 28 confocal microscopy volumes of C. elegans worms at the L1 stage and  corresponding stacks of densely annotated nuclei instance segmentation masks. * 28 raw images and corresponding masks of average dimension (xyz) 1050 x 140 x 140 * Pixelsize (xyz): 0.116 x 0.116 x 0.122μm * Microscope: Leica confocal microscopy, 63x oil objective The original raw data and preliminary annotations were  part of the following publication (please cite if you use the dataset):   Long, F., Peng, H., Liu, X., Kim, S. K., & Myers, E. (2009). A 3D digital atlas of C. elegans and its application to single-cell analyses. Nature methods, 6(9), 667-672. The nuclei annotation masks were further manually curated by Dagmar Kainmueller (MDC Berlin) for the following publication: Hirsch, P., & Kainmueller, D. (2020). An auxiliary task for learning nuclei segmentation in 3d microscopy images. In Medical Imaging with Deep Learning (pp. 304-321). PMLR. We provide the dataset already structured into the train/validation/test split as used by the above as well as the following publications:  Weigert, M., Schmidt, U., Haase, R., Sugawara, K., & Myers, G. (2020). Star-convex polyhedra for 3d object detection and segmentation in microscopy. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 3666-3673).    ' license: cc-by-4.0 name: 3D nuclei instance segmentation dataset of fluorescence microscopy volumes of C. elegans num_downloads: 142 publication_date: '2022-02-01' submission_date: '2025-05-29T19:38:25.339506' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/5942575 - https://doi.org/10.5281/zenodo.5942575 uuid: c239c0da-c68f-4c7b-b28a-726f521cb2f0 language: en file_formats: .zip authors_with_orcid: - Fuhui Long - Hanchuan Peng - Xiao Liu - Stuart K Kim - Eugene Myers - Dagmar Kainmüller - Martin Weigert - authors: - Kubow, Kristopher - Pengo, Thomas description: 'Original image files, label (ground truth) files, and PSF files used in the ABRF Light Microscopy Research Group (LMRG) image analysis study. Simulated 3D widefield fluorescence images of nuclei. See https://github.com/ABRFLMRG/image-analysis-study for more details.' license: cc-by-4.0 name: LMRG Image Analysis Study - nuclei datasets num_downloads: 90 publication_date: '2022-05-18' submission_date: '2025-05-29T19:38:25.822989' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/6560759 - https://doi.org/10.5281/zenodo.6560759 uuid: 44fdab55-a58d-413e-ab6d-43d58719b4a4 language: en file_formats: .ics * .tif authors_with_orcid: - Kristopher Kubow - Thomas Pengo - authors: - Romain Guiet description: 'Name: Automatic labelling of HeLa “Kyoto” cells using Deep Learning tools Data type: Microscopy images from the dataset “HeLa “Kyoto” cells under the scope”, Brightfield (BF), Digital Phase Contrast (DPC, either “raw” or “square-rooted”), Tubulin and H2B fluorescent channel, paired with their corresponding nuclei or cell/cyto label images. Labels images: Labels images were generated using the script “prepare_trainingDataset_cellpose.ijm”. Briefly, for 5 defined time-points (1,10,50,100,150), channels of interest were duplicated, resaved and : -        nuclei label images were obtained using StarDist on H2B channel -        cell label images were obtained using Cellpose on Tubulin and H2B channels A quick visual inspection of the resulting label images concluded that they were satisfying enough, despite certainly not being perfect. Notes : -       This labelling strategy: o   will not produce 100% accurate labels, but they might be more reproducible than labels generated by humans and are (definitely) much faster to obtain. o   is NOT a recommended way of generating labels images, but for educational purposes. -       The fluorescent channels are part of the dataset to ease the process of review of the labels and are NOT used for training. We generated the labels from the fluorescent channels to later predict labels from the BF or DPC channels only. As such, the fluorescent channels should not be “reused” with our labels during training. File format: .tif (16-bit) Image size: 540x540 (Pixel size: 0.299 nm)   NOTE: This dataset uses the “HeLa “Kyoto” cells under the scope”  dataset (https://doi.org/10.5281/zenodo.6139958) to automatically generate annotations NOTE: This dataset was used to train cellpose models in the following Zenodo entry https://doi.org/10.5281/zenodo.6140111' license: cc-by-4.0 name: Automatic labelling of HeLa "Kyoto" cells using Deep Learning tools num_downloads: 151 publication_date: '2022-02-25' submission_date: '2025-05-29T19:38:26.752489' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/6140064 - https://doi.org/10.5281/zenodo.6140064 uuid: d170719b-7ff1-4c32-9b0e-6c680ac08df0 language: en file_formats: .ijm * .png * .zip authors_with_orcid: - Romain Guiet https://orcid.org/0000-0001-6715-4897 - authors: - Malou Arvidsson - Salma Kazemi Rashed - Sonja Aits description: 'Here we present a benchmarking dataset of fluorescence microscopy images with Hoechst 33342-stained nuclei together with annotations of nuclei, nuclear fragments and micronuclei. Images were randomly selected from an RNA interference screen with a modified U2OS osteosarcoma cell line, acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification. Labelling was performed by a single annotator and reviewed by a biomedical expert. The dataset contains 50 images showing over 2000 labelled nuclear objects in total, which is sufficiently large to train well-performing neural networks for instance or semantic segmentation. It is pre-split into training, development and test set, each in a zip file. The dataset should be referred to as Aitslab_bioimaging1. A brief article describing the dataset is also available (Arvidsson M, Kazemi Rashed S, Aits S. 10.1016/j.dib.2022.108769 ) Dataset description: Fluorescence microscopy images: original .C01 files and files converted to 8-bit .png format (Grayscale) Annotations: 24-bit .png format (RGB) Script used to convert C01 to png images: C01_to_png.py file with python code and readme.md file with instructions to run it' license: cc-by-4.0 name: An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines num_downloads: 518 publication_date: '2022-06-17' submission_date: '2025-05-29T19:38:27.205731' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/6657260 - https://doi.org/10.5281/zenodo.6657260 uuid: 1f6d2526-c0c4-4723-901e-db8d030c8f51 language: en file_formats: .md * .py * .zip authors_with_orcid: - Malou Arvidsson - Salma Kazemi Rashed https://orcid.org/0000-0002-2345-8167 - Sonja Aits https://orcid.org/0000-0002-1321-0678 - authors: - Follain, Gautier - Ghimire, Sujan - Pylvänäinen, Joanna - Ivaska, Johanna - Jacquemet, Guillaume description: 'This repository contains a StarDist deep learning model and its training and validation datasets for segmenting endothelial nuclei while ignoring cancer cells. The cancer cells were perfused over an endothelial cell monolayer. The initial dataset consisted of 17 images, where cancer cell nuclei were manually removed after segmentation with the StarDist Versatile Nuclei model. This dataset was augmented to 68 paired images using computational techniques like rotation and flipping. The model was trained for 200 epochs, achieving an average F1 Score of 0.976, demonstrating high accuracy in segmenting endothelial nuclei while excluding cancer cells. Specifications Model: StarDist for segmenting endothelial nuclei while ignoring cancer cells Training Dataset: Number of Original Images: 17 paired predictions of nuclei and label images Augmented Dataset: Expanded to 68 paired images using rotation and flipping Source Image Generation: Generated using a pix2pix model trained to predict nuclei from brightfield images of cancer cells on top of an endothelium (DOI: 10.5281/zenodo.10617532) Target Image Generation: Masks obtained via manual segmentation File Format: TIFF (.tif) Brightfield Images: 8-bit Masks: 8-bit Image Size: 1024 x 1022 pixels (uncalibrated) Training Parameters: Epochs: 200 Patch Size: 1024 x 1024 pixels Batch Size: 2 Performance: Average F1 Score: 0.976 Average IoU: 0.927 Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki) Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654' license: cc-by-4.0 name: StarDist_HUVEC_nuclei_dataset num_downloads: 19 publication_date: '2024-02-05' submission_date: '2025-05-29T19:38:28.073452' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/10617532 - https://doi.org/10.5281/zenodo.10617532 uuid: 7ac4316b-95e0-4613-b940-c6d4c575f200 language: en file_formats: .zip authors_with_orcid: - Gautier Follain https://orcid.org/0000-0003-0495-9529 - Sujan Ghimire https://orcid.org/0000-0003-3660-4300 - Joanna Pylvänäinen https://orcid.org/0000-0002-3540-5150 - Johanna Ivaska https://orcid.org/0000-0002-6295-6556 - Guillaume Jacquemet https://orcid.org/0000-0002-9286-920X - authors: - Follain, Gautier - Ghimire, Sujan - Pylvänäinen, Joanna - Ivaska, Johanna - Jacquemet, Guillaume description: 'This repository includes a StarDist deep learning model designed for segmenting AsPC1 cells labeled with Lifeact from fluorescence microscopy images. The model distinguishes individual AsPC1 cells within clusters and separates them from the background. The model was trained on a small dataset and achieved an Intersection over Union (IoU) score of 0.884 and an F1 Score of 0.967, indicating high accuracy in cell segmentation. Specifications Model: StarDist for segmenting AsPC1 cells in fluorescence microscopy images Training Dataset: Number of Images: 10 paired fluorescence microscopy images and label masks Microscope: Spinning disk confocal microscope (3i CSU-W1) with a 20x objective, NA 0.8 Data Type: Fluorescence microscopy images of the AsPC1 Lifeact channel with manually segmented masks File Format: TIFF (.tif) Fluorescence Images: 16-bit Masks: 8-bit Image Size: 1024 x 1024 pixels (Pixel size: 0.6337 x 0.6337 µm²) Model Capabilities: Segment AsPC1 Cells: Detects individual AsPC1 cells from a cluster and separates them from the background Measure Intensity: Enables measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity under individual cells in respective channels Performance: Average IoU: 0.884 Average F1 Score: 0.967 Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki) Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654  ' license: cc-by-4.0 name: StarDist_AsPC1_Lifeact num_downloads: 39 publication_date: '2024-08-29' submission_date: '2025-05-29T19:38:28.990624' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/13442128 - https://doi.org/10.5281/zenodo.13442128 uuid: eeb3909f-353d-40ed-80f5-4fd157dab77f language: en file_formats: .zip authors_with_orcid: - Gautier Follain https://orcid.org/0000-0003-0495-9529 - Sujan Ghimire https://orcid.org/0000-0003-3660-4300 - Joanna Pylvänäinen https://orcid.org/0000-0002-3540-5150 - Johanna Ivaska https://orcid.org/0000-0002-6295-6556 - Guillaume Jacquemet https://orcid.org/0000-0002-9286-920X - authors: - Follain, Gautier - Ghimire, Sujan - Pylvänäinen, Joanna - Ivaska, Johanna - Jacquemet, Guillaume description: 'This repository includes a StarDist deep learning model and its training and validation datasets for detecting mononucleated cells perfused over an endothelial cell monolayer. The model was trained on 27 manually annotated images and achieved an average F1 Score of 0.941. The dataset and model are helpful for biomedical research, especially in studying interactions between mononucleated and endothelial cells. Specifications Model: StarDist for mononucleated cell detection on endothelial cells Training Dataset: Number of Images: 27 paired brightfield microscopy images and label masks Microscope: Nikon Eclipse Ti2-E, 20x objective Data Type: Brightfield microscopy images with manually segmented masks File Format: TIFF (.tif) Brightfield Images: 16-bit Masks: 8-bit Image Size: 1024 x 1022 pixels (Pixel size: 650 nm) Training Parameters: Epochs: 400 Patch Size: 992 x 992 pixels Batch Size: 2 Performance: Average F1 Score: 0.941 Average IoU: 0.831 Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki) Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654' license: cc-by-4.0 name: StarDist_BF_Monocytes_dataset num_downloads: 25 publication_date: '2024-01-26' submission_date: '2025-05-29T19:38:29.421953' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/10572200 - https://doi.org/10.5281/zenodo.10572200 uuid: 93280692-a363-4bad-bdb6-649c28ae220f language: en file_formats: .zip authors_with_orcid: - Gautier Follain https://orcid.org/0000-0003-0495-9529 - Sujan Ghimire https://orcid.org/0000-0003-3660-4300 - Joanna Pylvänäinen https://orcid.org/0000-0002-3540-5150 - Johanna Ivaska https://orcid.org/0000-0002-6295-6556 - Guillaume Jacquemet https://orcid.org/0000-0002-9286-920X - authors: - Follain, Gautier - Ghimire, Sujan - Pylvänäinen, Joanna - Ivaska, Johanna - Jacquemet, Guillaume description: 'This repository contains a StarDist deep learning model and its training and validation datasets designed for segmenting cancer cells perfused over an endothelial cell monolayer captured at 20x magnification. Using computational methods, the initial dataset of 20 manually annotated images was augmented to 160 paired images. The model was trained over 400 epochs and achieved an average F1 Score of 0.921, demonstrating high accuracy in cell segmentation tasks. Specifications Model: StarDist for cancer cell segmentation on endothelial cells (20x magnification) Training Dataset: Number of Original Images: 20 paired brightfield microscopy images and label masks Microscope: Nikon Eclipse Ti2-E, 20x objective Data Type: Brightfield microscopy images with manually segmented masks File Format: TIFF (.tif) Brightfield Images: 16-bit Masks: 8-bit Image Size: 1024 x 1022 pixels (Pixel size: 650 nm) Training Parameters: Epochs: 400 Patch Size: 992 x 992 pixels Batch Size: 2 Performance: Average F1 Score: 0.921 Average IoU: 0.793 Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)   Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654' license: cc-by-4.0 name: StarDist_BF_cancer_cell_dataset_20x num_downloads: 51 publication_date: '2024-01-26' submission_date: '2025-05-29T19:38:29.860975' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/10572122 - https://doi.org/10.5281/zenodo.10572122 uuid: b3b05f51-70f7-4245-879d-5de96d1caf74 language: en file_formats: .zip authors_with_orcid: - Gautier Follain https://orcid.org/0000-0003-0495-9529 - Sujan Ghimire https://orcid.org/0000-0003-3660-4300 - Joanna Pylvänäinen https://orcid.org/0000-0002-3540-5150 - Johanna Ivaska https://orcid.org/0000-0002-6295-6556 - Guillaume Jacquemet https://orcid.org/0000-0002-9286-920X - authors: - Follain, Gautier - Ghimire, Sujan - Pylvänäinen, Joanna - Ivaska, Johanna - Jacquemet, Guillaume description: 'This repository includes a StarDist deep learning model and its training and validation datasets for detecting neutrophils perfused over an endothelial cell monolayer. The model was trained on 36 manually annotated images, achieving an average F1 Score of 0.969. The dataset and model are intended for use in biomedical research, particularly for analyzing interactions between neutrophils and endothelial cells. Specifications Model: StarDist for neutrophil detection on endothelial cells Training Dataset: Number of Images: 36 paired brightfield microscopy images and label masks Microscope: Nikon Eclipse Ti2-E, 20x objective Data Type: Brightfield microscopy images with manually segmented masks File Format: TIFF (.tif) Brightfield Images: 16-bit Masks: 8-bit Image Size: 1024 x 1022 pixels (Pixel size: 650 nm) Training Parameters: Epochs: 400 Patch Size: 992 x 992 pixels Batch Size: 2 Performance: Average F1 Score: 0.969 Average IoU: 0.914 Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki) Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654' license: cc-by-4.0 name: StarDist_BF_Neutrophil_dataset num_downloads: 22 publication_date: '2024-01-26' submission_date: '2025-05-29T19:38:30.285796' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/10572231 - https://doi.org/10.5281/zenodo.10572231 uuid: c4234bce-3ff9-4926-8cf4-d4c23c1a95f2 language: en file_formats: .zip authors_with_orcid: - Gautier Follain https://orcid.org/0000-0003-0495-9529 - Sujan Ghimire https://orcid.org/0000-0003-3660-4300 - Joanna Pylvänäinen https://orcid.org/0000-0002-3540-5150 - Johanna Ivaska https://orcid.org/0000-0002-6295-6556 - Guillaume Jacquemet https://orcid.org/0000-0002-9286-920X - authors: - Dohmen, Melanie - Mittermaier, Mirja - Hocke, Andreas description: 'The zip file contains 3 folders (annotations, images and training_splits).The annotation folder contains 3 folders (cell_instances, nuclei_instances and semantic). Cell and nuclei instance annotations are long int tif images, containing numbered instance ids and 0 in the background. Semantic annotations are 8-bit int png files containing the class ids (0: background, 1: normal tissue, 2: erythrocytes, 3: alveolar epithelial type 2 cells, 4: alveolar macrophages, 5: other nuclei, 6: alveolar epithelial type 2 cell nuclei, 7: alveolar macrophage nuclei, 8: cell debris). The image folder contains 4 folders (CD68, DAPI, DIC, proSPC), where DIC contains float valued background-corrected differential interference contrast images, the others contain normalized float-valued fluorescence channels of a multi-plex staining with CD-68 (whole alveolar macrophages), DAPI (any cell nuclei), proSPC (cytoplasm of alveolar epithelial type 2 cell). All images are in tif format. The training split folder contains 3 text files, with the image prefix (compared to images and annotations without ending, i.e. e.g. without "_DIC.tif") of all cases in the respective subset. With a total of 68 cases, there are 51 cases in the train set, 7 cases in the validation set and 10 cases in the test set.The lung tissue origins from lung surgery of patients, but does not include resected tumors. Please see reference [1]. The images were acquired with a laser scanning microscope with 40x magnification and 1024 x 1024 pixels per image.' name: Human Lung Tissue Microscopy (DIC, Fluorescence, Cell and Nuclei Semantic Instance Annotations) num_downloads: 7 publication_date: '2024-02-22' submission_date: '2025-05-29T19:38:30.751680' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/10669918 - https://doi.org/10.5281/zenodo.10669918 uuid: 739bab74-cbe1-40c8-a1b7-beeb52a28015 language: en file_formats: .zip authors_with_orcid: - Melanie Dohmen https://orcid.org/0000-0002-4447-0579 - Mirja Mittermaier https://orcid.org/0000-0003-0678-6676 - Andreas Hocke https://orcid.org/0000-0002-6935-8612 - authors: - Follain, Gautier - Ghimire, Sujan - Pylvänäinen, Joanna - Ivaska, Johanna - Jacquemet, Guillaume description: 'This repository includes a StarDist deep learning model and its training and validation datasets for detecting fluorescently labeled cancer cells perfused over an endothelial cell monolayer. The model was trained on 66 images labeled with CellTrace and demonstrated high accuracy, achieving an average F1 Score of 0.877. The dataset and the trained model can be used for biomedical image analysis, particularly in cancer research. Specifications Model: StarDist for cancer cell detection Training Dataset: Number of Images: 66 paired fluorescent microscopy images and label masks Microscope: Nikon Eclipse Ti2-E, 10x objective Data Type: Fluorescent microscopy images with manually segmented masks File Format: TIFF (.tif) Brightfield Images: 16-bit Masks: 8-bit Image Size: 1024 x 1024 pixels (Pixel size: 1.3205 μm) Training Parameters: Epochs: 200 Patch Size: 1024 x 1024 pixels Batch Size: 2 Performance: Average F1 Score: 0.877 Average IoU: 0.646 Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki) Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654' license: cc-by-4.0 name: StarDist_Fluorescent_cells num_downloads: 19 publication_date: '2024-01-26' submission_date: '2025-05-29T19:38:31.190324' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/10572310 - https://doi.org/10.5281/zenodo.10572310 uuid: 1adebe3d-8069-471d-9751-23943608202a language: en file_formats: .zip authors_with_orcid: - Gautier Follain https://orcid.org/0000-0003-0495-9529 - Sujan Ghimire https://orcid.org/0000-0003-3660-4300 - Joanna Pylvänäinen https://orcid.org/0000-0002-3540-5150 - Johanna Ivaska https://orcid.org/0000-0002-6295-6556 - Guillaume Jacquemet https://orcid.org/0000-0002-9286-920X - authors: - Hao Xu - Wei Ouyang description: Download RDF Package license: cc-by-4.0 name: HPA Nucleus Segmentation (DPNUnet) num_downloads: 57081 publication_date: '2023-03-02' submission_date: '2025-05-29T19:38:31.700518' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/7690494 - https://doi.org/10.5281/zenodo.7690494 uuid: 2a07498b-4a36-4e04-8f00-dcd077f55f4f file_formats: .ijm * .md * .npy * .png * .pt * .tif * .yaml authors_with_orcid: - Hao Xu - Wei Ouyang - authors: - Follain, Gautier - Ghimire, Sujan - Pylvänäinen, Joanna - Ivaska, Johanna - Jacquemet, Guillaume description: 'This repository contains a StarDist deep learning model designed for segmenting MiaPaCa2 cells from the CD44 channel in fluorescence microscopy images. The model is capable of accurately segmenting individual MiaPaCa2 cells while excluding HUVECs. Trained on a small dataset, the model achieved an Intersection over Union (IoU) score of 0.884 and an F1 Score of 0.950, indicating high precision in cell segmentation. Specifications Model: StarDist for segmenting MiaPaCa2 cells from the CD44 fluorescence channel Training Dataset: Number of Images: 8 paired fluorescence microscopy images and label masks Microscope: Spinning disk confocal microscope (3i CSU-W1) with a 20x objective, NA 0.8 Data Type: Fluorescence microscopy images of the CD44 channel, obtained after immunofluorescence staining with primary and secondary antibodies and manually segmented masks File Format: TIFF (.tif) Fluorescence Images: 16-bit Masks: 8-bit Image Size: 920 x 920 pixels (Pixel size: 0.6337 x 0.6337 µm²) Model Capabilities: Segment MiaPaCa2 Cells: Accurately detects individual MiaPaCa2 cells while ignoring HUVECs Measure CD44 Intensity: Allows for the measurement of CD44 intensity around MiaPaCa2 cells, specifically from the CD44 channel Performance: Average IoU: 0.884 Average F1 Score: 0.950 Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki) Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654' license: cc-by-4.0 name: Stardist_MiaPaCa2_from_CD44 num_downloads: 37 publication_date: '2024-08-29' submission_date: '2025-05-29T19:38:32.180777' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/13442877 - https://doi.org/10.5281/zenodo.13442877 uuid: 59f7e086-a93a-43d9-8f1d-66a20b5a9038 language: en file_formats: .zip authors_with_orcid: - Gautier Follain https://orcid.org/0000-0003-0495-9529 - Sujan Ghimire https://orcid.org/0000-0003-3660-4300 - Joanna Pylvänäinen https://orcid.org/0000-0002-3540-5150 - Johanna Ivaska https://orcid.org/0000-0002-6295-6556 - Guillaume Jacquemet https://orcid.org/0000-0002-9286-920X - authors: - Naylor Peter Jack - Walter Thomas - Laé Marick - Reyal Fabien description: 'This dataset has been annonced in our accepted paper "Segmentation of Nuclei in Histopathology Images by deep regression of the distance map" in Transcation on Medical Imaging on the 13th of August. This dataset consists of 50 annotated images, divided into 11 patients.   v1.1 (27/02/19): Small corrections to a few pixel that were labelled nuclei but weren't.' license: cc-by-4.0 name: Segmentation of Nuclei in Histopathology Images by deep regression of the distance map num_downloads: 6704 publication_date: '2018-02-16' submission_date: '2025-05-29T19:38:32.603510' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/2579118 - https://doi.org/10.5281/zenodo.2579118 uuid: 321f9795-473d-46bd-ab62-5ea6ca94a165 language: en file_formats: .zip authors_with_orcid: - Naylor Peter Jack https://orcid.org/0000-0002-9984-1736 - Walter Thomas https://orcid.org/0000-0001-7419-7879 - Laé Marick - Reyal Fabien - authors: - Follain, Gautier - Ghimire, Sujan - Pylvänäinen, Joanna - Ivaska, Johanna - Jacquemet, Guillaume description: 'This repository includes a StarDist deep learning model and its training dataset designed for segmenting cancer cells perfused over an endothelial cell monolayer captured at 10x magnification. The model was trained on 77 manually annotated images, with the dataset being computationally augmented during training by a factor of 8. The model was trained for 500 epochs and achieved an average F1 Score of 0.968, indicating high accuracy in segmenting cancer cells on endothelial cells. Specifications Model: StarDist for cancer cell segmentation on endothelial cells (10x magnification) Training Dataset: Number of Images: 77 paired brightfield microscopy images and label masks Augmented Dataset: Computational augmentation by a factor of 8 during training Microscope: Nikon Eclipse Ti2-E, 10x objective Data Type: Brightfield microscopy images with manually segmented masks File Format: TIFF (.tif) Brightfield Images: 16-bit Masks: 8-bit or 16-bit Image Size: 1024 x 1022 pixels (pixel size: 1.3148 μm) Training Parameters: Epochs: 500 Patch Size: 992 x 992 pixels Batch Size: 2 Performance: Average F1 Score: 0.968 Average IoU: 0.882 Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki) Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654' license: cc-by-4.0 name: StarDist_BF_cancer_cell_dataset_10x num_downloads: 15 publication_date: '2024-08-12' submission_date: '2025-05-29T19:38:33.127511' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/13304399 - https://doi.org/10.5281/zenodo.13304399 uuid: ac96e671-8549-47d0-a04b-34649430febd language: en file_formats: .zip authors_with_orcid: - Gautier Follain https://orcid.org/0000-0003-0495-9529 - Sujan Ghimire https://orcid.org/0000-0003-3660-4300 - Joanna Pylvänäinen https://orcid.org/0000-0002-3540-5150 - Johanna Ivaska https://orcid.org/0000-0002-6295-6556 - Guillaume Jacquemet https://orcid.org/0000-0002-9286-920X - authors: - Golani, Ofra - Mohan, Vishnu - Geiger, Tamar description: ' Training dataset:Paired microscopy images (fluorescence) and corresponding masks Microscopy data type: Fluorescence microscopy and masks obtained via manual correction of automatic segmentation with pre-trained StarDist model (see https://github.com/qupath/models/tree/main/stardist)  Cells were imaged using a 20x objective with a 1x camera adapter was used in conjunction with a pco.edge 4.2 4MP camera on Pannoramic SCAN 150 scanner. Cell type: FFPE tissue sections were sliced from all cancer-containing paraffin blocks File format: .tif (8-bit for fluorescence and 16-bit for the masks)   StarDist Model:The StarDist model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). This custom StarDist model was trained for 100 epochs using 80 manually annotated paired images (image dimensions: (257, 257)) with a batch size of 2, an augmentation factor of 10 and a mae loss function. The StarDist “Versatile fluorescent nuclei” model was used as a training starting point. Key python packages used include TensorFlow (v 2.2.0), Keras (v 1.1.2), CSBdeep (v 0.7.2), NumPy (v 1.21.6), Cuda (v 11..1.105). The training was accelerated using a Tesla P100GPU.The model weights can be used in the ZeroCostDL4Mic StarDist 2D notebook or in the StarDist Fiji plugin. a QuPath-compatible model is also provided.     ' license: cc-by-4.0 name: Breast Cancer Nuclei images for DL Training + ZeroCostDL4Mic StarDist Model num_downloads: 64 publication_date: '2024-05-21' submission_date: '2025-05-29T19:38:33.657404' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/11235393 - https://doi.org/10.5281/zenodo.11235393 uuid: 2f8aebb9-ba35-4f23-8cb5-802f28433eeb language: en file_formats: .zip authors_with_orcid: - Ofra Golani https://orcid.org/0000-0002-9793-236X - Vishnu Mohan https://orcid.org/0000-0002-0008-5513 - Tamar Geiger https://orcid.org/0000-0002-9526-197X - authors: - Hussein Al-Akhrass - Johanna Ivaska - Guillaume Jacquemet description: 'StarDist Model: The StarDist model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). This custom StarDist model was trained for 300 epochs using 46 manually annotated paired images (image dimensions: (1024, 1024)) with a batch size of 2, an augmentation factor of 4 and a mae loss function. The StarDist “Versatile fluorescent nuclei” model was used as a training starting point. Key python packages used include TensorFlow (v 0.1.12), Keras (v 2.3.1), CSBdeep (v 0.6.1), NumPy (v 1.19.5), Cuda (v 11.0.221). The training was accelerated using a Tesla P100GPU. The model weights can be used in the ZeroCostDL4Mic StarDist 2D notebook or in the StarDist Fiji plugin. StarDist Training dataset: Paired microscopy images (fluorescence) and corresponding masks Microscopy data type: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist for details about the segmentation) Cells were imaged using a 20x Nikon CFI Plan Apo Lambda objective (NA 0.75) one frame every 10 minutes for 16h. Cell type: MDA-MB-231 cells and BT20 cells File format: .tif (16-bit for fluorescence and 8 and 16-bit for the masks)' license: cc-by-4.0 name: Stardist model and training dataset for automated tracking of MDA-MB-231 and BT20 cells num_downloads: 127 publication_date: '2021-05-26' submission_date: '2025-05-29T19:38:34.087908' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/4811213 - https://doi.org/10.5281/zenodo.4811213 uuid: c494e040-24da-4786-b621-bb1c8cca5a57 language: en file_formats: .zip authors_with_orcid: - Hussein Al-Akhrass https://orcid.org/0000-0002-9630-3000 - Johanna Ivaska https://orcid.org/0000-0002-6295-6556 - Guillaume Jacquemet https://orcid.org/0000-0002-9286-920X - authors: - Sorzabal Bellido, Ioritz - Barbieri, Luca - Beckett, Alison J. - Prior, Ian A. - Susarrey-Arce, Arturo - Tiggelaar, Roald M. - Forthergill, Jo - Raval, Rasmita - Diaz Fernandez, Yuri A. description: 'Dataset.zip This dataset includes the raw and annotated images used to train a Stardist 2D deep learning model for segmentation of surface attached S.aureus as described in Effect of local topography on cell division of Staphylococci sp.   Stardist2d_Model.zip Stardist 2D deep learning model for segmentation of surface attached S.aureus, obtained using the StarDist 2D ZeroCostDL4Mic notebook (v 1.12.3).' license: cc-by-4.0 name: Effect of local topography on cell division of Staphylococci sp. num_downloads: 23 publication_date: '2021-05-16' submission_date: '2025-05-29T19:38:34.505684' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/4765599 - https://doi.org/10.5281/zenodo.4765599 uuid: edd47bc0-bf35-493f-8c4e-ae5dcd0df92b language: en file_formats: .zip authors_with_orcid: - Ioritz Sorzabal Bellido https://orcid.org/0000-0001-8050-1443 - Luca Barbieri - Alison J. Beckett - Ian A. Prior - Arturo Susarrey-Arce - Roald M. Tiggelaar - Jo Forthergill - Rasmita Raval - Yuri A. Diaz Fernandez - authors: - Spahn, Christoph - Diepold, Andreas - Ermoli, Francesca description: 'Dataset and StarDist model for the segmentation of Yersinia enterocolitica cells This dataset and StarDist model are part of the publication "Active downregulation of the type III secretion system at higher local cell densities promotes Yersinia replication and dissemination". It contains the dataset that was used for training the provided StarDist model using ZeroCostDL4Mic. Data: Yersinia enterocolitica cells were spotted on an agarose pad (1.5% low melting agarose (Sigma-Aldrich) in minimal medium, 1% Casamino acids, 5 mM EGTA,  glass depression slides (Marienfeld)). For imaging, a Deltavision Elite Optical Sectioning Microscope equipped with a UPlanSApo 100×/1.40 oil objective (Olympus) and an EDGE sCMOS_5.5 camera (Photometrics) was used. Z-stacks with 9 slices (∆z = 0.15 µm) per fluorescence channel were acquired and  5 slices were selected for network training. Images were annotated in Fiji using the Freehand selection tool, and brightlight and mask images were quartered to obtain the final dataset of 300 paired images. 260 images were used for training, while 40 images were used to test model performance. Model: The StarDist 2D model was trained from scratch for 100 epochs on 300 paired image patches (image dimensions: (480 x 480 px²), patch size: (480 x 480 px²)) with a batch size of 4 and a mae loss function, using the StarDist 2D ZeroCostDL4Mic notebook (v 1) (von Chamier & Laine et al., 2020). Grid parameter was set to 2 and the number of rays to 120. The model was trained with an initial learning rate of 0.0003 using a 80/20 train/test split. The dataset was augmented 4-fold by flipping and rotation. Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.7.2), numpy (v 1.21.6), cuda (v 11.1.105Build cuda_11.1.TC455_06.29190527_0). The training was accelerated using a Tesla T4 GPU.' license: cc-by-4.0 name: StarDist model and data for the segmentation of Yersinia enterocolitica cells in widefield images num_downloads: 16 publication_date: '2024-05-02' submission_date: '2025-05-29T19:38:35.530116' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/11105050 - https://doi.org/10.5281/zenodo.11105050 uuid: 1e02a908-276e-4219-9bab-784ac18f7022 language: en file_formats: .png * .zip authors_with_orcid: - Christoph Spahn https://orcid.org/0000-0001-9886-2263 - Andreas Diepold https://orcid.org/0000-0002-4475-3923 - Francesca Ermoli - authors: - Gautier Follain - Guillaume Jacquemet description: 'Description: Contains a StarDist example training dataset, a test dataset, and the StarDist model generated using ZeroCostDL4Mic (see https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist) Training dataset: Paired microscopy images (brightfield) and corresponding masks Microscopy data type: brightfield microscopy and masks obtained via manual segmentation (see https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist for details about the segmentation) Microscope: Images were acquired with a brightfield microscope (Zeiss Laser-TIRF 3 Imaging System, Carl Zeiss) and a 10X objective. File format: .tif (8-bit for brightfield images and 8 and 16-bit for the masks) Image size: 1024x1024 (Pixel size: 650 nm)' license: cc-by-4.0 name: Combining StarDist and TrackMate example 3 - Flow chamber dataset num_downloads: 131 publication_date: '2020-09-17' submission_date: '2025-05-29T19:38:35.970086' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/4034939 - https://doi.org/10.5281/zenodo.4034939 uuid: dbd2d690-e191-4f3e-a919-27ad6246a500 language: en file_formats: .zip authors_with_orcid: - Gautier Follain - Guillaume Jacquemet - authors: - Guillaume Jacquemet description: 'Description: Contains a StarDist example training dataset, a test dataset, and the StarDist model generated using ZeroCostDL4Mic (see https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist) Training dataset: 72 Paired microscopy images (fluorescence) and corresponding masks Microscopy data type: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist for details about the segmentation) Microscope: Spinning disk confocal microscope with a 20x 0.8 NA objective Cell type: DCIS.COM Lifeact-RFP cells File format: .tif (16-bit for fluorescence and 8 and 16-bit for the masks) Image size: 1024x1024 (Pixel size: 634 nm)' license: cc-by-4.0 name: Combining StarDist and TrackMate example 1 - Breast cancer cell dataset num_downloads: 164 publication_date: '2020-09-17' submission_date: '2025-05-29T19:38:36.414752' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/4034976 - https://doi.org/10.5281/zenodo.4034976 uuid: 3311a187-1bfb-42df-a186-2b9412d05637 language: en file_formats: .zip authors_with_orcid: - Guillaume Jacquemet - authors: - Sarkis Rita - Naveiras Olaia - Burri Olivier - Weigert Martin - De Leval Laurence description: 'Data from H&E human bone marrow whole slide scanner images used in the paper: "MarrowQuant 2.0: a digital pathology workflow assisting bone marrow evaluation in clinical and experimental hematology" (https://doi.org/10.21203/rs.3.rs-1860140/v1)   292 image patches Ground truth were manually annotated using QuPath and split into 263 images for training and 29 for validation. Training in StarDist was done on a Windows 10 PC with an RTX 2080 GPU. The requirements file for installing a Python 3.7 environment to run the attached notebooks is provided (stardist-val.txt). The StarDist model configuration can be found in the Jupyter Notebook : Adipocyte Training.ipynb Model validation and metrics can be performed by running the notebook after finishing the Adipocyte Training notebook. Quality Control.ipynb  ' license: cc-by-4.0 name: StarDist Adipocyte Segmentation Training data, Training Notebook and Model num_downloads: 197 publication_date: '2022-08-17' submission_date: '2025-05-29T19:38:36.879772' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/7003909 - https://doi.org/10.5281/zenodo.7003909 uuid: a6707b5a-38be-4408-818b-8e1d81a366d8 language: en file_formats: .ipynb * .pb * .txt * .zip authors_with_orcid: - Sarkis Rita https://orcid.org/0000-0003-4874-9937 - Naveiras Olaia https://orcid.org/0000-0003-3434-0022 - Burri Olivier https://orcid.org/0000-0002-7100-3749 - Weigert Martin https://orcid.org/0000-0002-7780-9057 - De Leval Laurence https://orcid.org/0000-0003-3994-516X - authors: - Nathan H. Roy - Guillaume Jacquemet description: 'Description: Contains a StarDist example training dataset, a test dataset, and the StarDist model generated using ZeroCostDL4Mic (see https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist) Training dataset: 209 Paired microscopy images (brightfield) and corresponding masks Microscopy data type: brightfield microscopy and masks obtained via manual segmentation (see https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist for details about the segmentation) Microscope: Imaging was done using a 10x phase contrast objective at 37°C on a Zeiss Axiovert 200M microscope equipped with an automated X-Y stage and a Roper EMCCD camera. Time-lapse images were collected every 30 sec for 10 min using SlideBook 6 software (Intelligent Imaging Innovations). File format: .tif (16-bit for brightfield images and 8 and 16-bit for the masks) Image size: 1024x1024 (Pixel size: 645 nm)' license: cc-by-4.0 name: Combining StarDist and TrackMate example 2 - T cell dataset num_downloads: 188 publication_date: '2020-09-17' submission_date: '2025-05-29T19:38:37.341752' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/4034929 - https://doi.org/10.5281/zenodo.4034929 uuid: 397c9592-60ce-4bed-84f1-e8d5075a378a language: en file_formats: .zip authors_with_orcid: - Nathan H. Roy - Guillaume Jacquemet - authors: - Zhongtian Shao description: 40 annotated immunofluorescence microscopy images (600 microns x 600 microns) of foreskin tissue stained for CD3/CD4/CCR5/Nuclei. These images were used to train StarDist models used for the identification of HIV Target Cells in foreskin tissue section scans.  license: cc-by-4.0 name: ProdgerLab-StarDist-HIV Target Cell Training Set num_downloads: 47 publication_date: '2023-06-28' submission_date: '2025-05-29T19:38:37.758199' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/8091914 - https://doi.org/10.5281/zenodo.8091914 uuid: 9ca14b2d-311b-4eeb-af24-19e702e05227 language: en file_formats: .zip authors_with_orcid: - Zhongtian Shao - authors: - Spahn, Christoph - Heilemann, Mike - Holden, Séamus - Conduit, Mia - Pereira, Pedro Matos - Pinho, Mariana description: 'Mixed training and test images of S. aureus, E. coli and B. subtilis for cell segmentation using StarDist, as well as the trained StarDist model. Additional information can be found on this github wiki.   Data type: Paired bright field / fluorescence and segmented mask images Microscopy data type: 2D widefield images; DIC and fluorescence for S. aureus, bright field images for E. coli, and fluorescence images for B. subtilis Microscopes:  S. aureus:  GE HealthCare Deltavision OMX system (with temperature and humidity control, 37°C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence) E.coli: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective B. subtilis: Custom-built 100x inverted microscope bearing a 100x TIRF objective (Nikon CFI Apochromat TIRF 100XC Oil); images were captured on a Prime BSI sCMOS camera (Teledyne Photometrics)   Cell types: S. aureus strain JE2, E. coli MG1655 (CGSC #6300) and B. subtilis strain SH130; all grown under agarose pads File format: .tif (8-bit and 16-bit) Image size: 512 x 512 px² @ 80 nm pixel size (S. aureus); 1024 x 1024 px² @ 79 nm pixel size (E. coli); 1024 x 1024 px² @ 65 nm pixel size (B. subtilis) Image preprocessing:  S. aureus: Raw images were manually annotated by drawing ellipses in the NR fluorescence image and segmented images were created using the LOCI plugin (“ROI Map”). For training, images and masks were quartered into four 256 x 256 px² patches. E. coli: Raw images were recorded in 16-bit mode (image size 512x512 px² @ 158 nm/px). Images were upscaled with a factor of 2 (no interpolation) to enable generation of higher-quality segmentation masks. B. subtilis: Images were denoised using PureDenoise and resulting 32-bit images were converted into 8-bit images after normalizing to 1% and 99.98% percentiles. Images were manually annotated using the Labkit Fiji plugin   StarDist model: The StarDist 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 200 epochs (120 steps/epoch) on 155 paired image patches (image dimensions: (1024, 1024), patch size: (256,256)) with a batch size of 4, 10% validation data, 64 rays on grid 2, a learning rate of 0.0003 and a mae loss function, using the StarDist 2D ZeroCostDL4Mic notebook (v 1.12.2). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), csbdeep (v 0.6.1), numpy (v 1.19.5), cuda (v 11.0.221). The training was accelerated using a Tesla P100GPU. The dataset was augmented by a factor of 3.   The model weights can be used in the ZeroCostDL4Mic StarDist 2D notebook, the StarDist Fiji plugin or the TrackMate Fiji plugin (v7+).   Author(s): Christoph Spahn1,2, Mike Heilemann1,3, Mia Conduit4, Séamus Holden4,5, Pedro Matos Pereira6,7, Mariana Pinho6,8 Contact email: christoph.spahn@mpi-marburg.mpg.de, Seamus.Holden@newcastle.ac.uk, pmatos@itqb.unl.pt and mgpinho@itqb.unl.pt   Affiliation(s):  1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263  3) ORCID: 0000-0002-9821-3578 4) Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK 5) ORCID: 0000-0002-7169-907X 6) Bacterial Cell Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal 7) ORCID: 0000-0002-1426-9540 8) ORCID: 0000-0002-7132-8842' license: cc-by-4.0 name: DeepBacs – Mixed segmentation dataset and StarDist model num_downloads: 525 publication_date: '2021-10-05' submission_date: '2025-05-29T19:38:38.194976' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/5551009 - https://doi.org/10.5281/zenodo.5551009 uuid: d4e9d735-2d39-40d8-837e-8482f69211ca language: en file_formats: .zip authors_with_orcid: - Christoph Spahn https://orcid.org/0000-0001-9886-2263 - Mike Heilemann https://orcid.org/0000-0002-9821-3578 - Séamus Holden https://orcid.org/0000-0002-7169-907X - Mia Conduit - Pedro Matos Pereira https://orcid.org/0000-0002-1426-9540 - Mariana Pinho https://orcid.org/0000-0002-7132-8842 - authors: - Alwes, Frederike - Sugawara, Ko - Averof, Michalis description: 'The Parhyale 3D Segmentation dataset consists of 50 timepoints (TP01-TP50) of 3D images (512x512x34), where the manual annotations can be found at discrete 6 timepoints (at TP01, TP11, TP21, TP31, TP41 and TP50). For further details, see README file. This version fixes the duplicated label IDs found in the previous version of label files. This version ensures that each instance has a unique ID. Thanks to Jackson Borchardt for reporting that error.' license: cc-by-4.0 name: Parhyale 3D segmentation dataset num_downloads: 195 publication_date: '2023-08-11' submission_date: '2025-05-29T19:38:38.748023' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/8252039 - https://doi.org/10.5281/zenodo.8252039 uuid: dda7b774-993f-4138-927e-33d38debc0cf language: en file_formats: .pdf * .tif authors_with_orcid: - Frederike Alwes - Ko Sugawara - Michalis Averof - authors: - Johanna Jukkala - Guillaume Jacquemet description: 'Name: ZeroCostDL4Mic - Stardist example training and test dataset (see our Wiki for details)   Data type: Paired microscopy images (fluorescence) and corresponding masks Microscopy data type: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist for details about the segmentation) Microscope: Spinning disk confocal microscope with a 20x 0.8 NA objective Cell type: DCIS.COM LifeAct-RFP cells File format: .tif (16-bit for fluorescence and 8 and 16-bit for the masks) Image size: 1024x1024 (Pixel size: 634 nm)   Author(s): Johanna Jukkala1,2 and Guillaume Jacquemet1,2 Contact email: guillaume.jacquemet@abo.fi Affiliation :  1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland 2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland   Associated publications: Unpublished Funding bodies: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.' license: cc-by-4.0 name: ZeroCostDL4Mic - Stardist example training and test dataset num_downloads: 2539 publication_date: '2020-03-17' submission_date: '2025-05-29T19:38:40.141372' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/3715492 - https://doi.org/10.5281/zenodo.3715492 uuid: 6e3d1986-21b1-482d-9235-ba0431a20038 language: en file_formats: .zip authors_with_orcid: - Johanna Jukkala - Guillaume Jacquemet https://orcid.org/0000-0002-9286-920X - authors: - Johanna Jukkala - Guillaume Jacquemet description: 'Name: ZeroCostDL4Mic - Stardist 2D example training and test dataset (light) (see our Wiki for details) Data type: Paired microscopy images (fluorescence) and corresponding masks Microscopy data type: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist for details about the segmentation) Microscope: Spinning disk confocal microscope with a 20x 0.8 NA objective Cell type: DCIS.COM LifeAct-RFP cells File format: .tif (16-bit for fluorescence and 8 and 16-bit for the masks) Image size: 1024x1024 (Pixel size: 634 nm)   Author(s): Johanna Jukkala1,2 and Guillaume Jacquemet1,2 Contact email: guillaume.jacquemet@abo.fi Affiliation :  1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland 2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland Funding bodies: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.' license: cc-by-4.0 name: ZeroCostDL4Mic - Stardist 2D example training and test dataset (light) num_downloads: 1654 publication_date: '2023-05-19' submission_date: '2025-05-29T19:38:41.093509' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/7949940 - https://doi.org/10.5281/zenodo.7949940 uuid: 26774451-dd2f-465f-999a-5e9dffc64886 language: en file_formats: .zip authors_with_orcid: - Johanna Jukkala - Guillaume Jacquemet - authors: - Follain, Gautier - Ghimire, Sujan - Pylvänäinen, Joanna - Ivaska, Johanna - Jacquemet, Guillaume description: 'This repository contains a StarDist deep learning model designed for segmenting tumor cell nuclei from the DAPI channel in fluorescence microscopy images while excluding HUVEC nuclei. The model was trained to accurately detect individual tumor cell nuclei for subsequent measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity around or under the nuclei. The model achieved an Intersection over Union (IoU) score of 0.558 and an F1 Score of 0.793, reflecting its capability to distinguish tumor cell nuclei from HUVEC nuclei. Specifications Model: StarDist for segmenting tumor cell nuclei from the DAPI fluorescence channel Training Dataset: Number of Images: 48 paired fluorescence microscopy images and label masks Microscope: Spinning disk confocal microscope (3i CSU-W1) with a 20x objective, NA 0.8 Data Type: Fluorescence microscopy images of the DAPI channel with manually segmented masks File Format: TIFF (.tif) Fluorescence Images: 16-bit Masks: 8-bit Image Size: 920 x 920 pixels (Pixel size: 0.6337 x 0.6337 µm²) Model Capabilities: Segment Tumor Cell Nuclei: Detects individual tumor cell nuclei in the DAPI channel while distinguishing them from HUVEC nuclei Measure Intensity: Enables measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity around or under tumor cell nuclei in respective channels Performance: Average IoU: 0.558 Average F1 Score: 0.793 Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki) Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654  ' license: cc-by-4.0 name: StarDist_TumorCell_nuclei num_downloads: 39 publication_date: '2024-08-29' submission_date: '2025-05-29T19:38:42.528986' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/13443221 - https://doi.org/10.5281/zenodo.13443221 uuid: 1ed3c5f5-04ab-4ae4-966e-7c38e16b0e1d language: en file_formats: .zip authors_with_orcid: - Gautier Follain https://orcid.org/0000-0003-0495-9529 - Sujan Ghimire https://orcid.org/0000-0003-3660-4300 - Joanna Pylvänäinen https://orcid.org/0000-0002-3540-5150 - Johanna Ivaska https://orcid.org/0000-0002-6295-6556 - Guillaume Jacquemet https://orcid.org/0000-0002-9286-920X - authors: - Bär, Julian description: 'Training data for the two StarDist2D models and the DeLTA 2.0 2D tracking model used in the publication on bioarxiv. The trained stardist models are included in the respective zip files of the training data. mm: mother-machine; cc: connected chamber. Each of them contains two folders, img and seg_label. They contain matching pairs of phasecontrast images (img) and label images (seg_label).    tracking_set_subset.zip contains the training data for the DeLTA tracking model following the default folder structure. We used custom weight functions to create the training weight maps in the folder wei. The folder wei_bck contains weights generated with the original function. The unet_pads_tracking.hdf5 is the retrained tracking model used in the associated publication. See associated GitHub repository for example code on how to use the models for segmentation and tracking. The four numbered zip files contain the data used to create all figures displaying image analysis output. Abstract: Staphylococcus aureus both colonizes humans and causes severe virulent infections. Virulence is regulated by the agr quorum sensing system and its autoinducing peptide (AIP), with dynamics at the single-cell level across four agr-types – each defined by distinct AIP sequences and capable of cross-inhibition – remaining elusive. Employing microfluidics, time-lapse microscopy, and deep-learning image analysis, we uncovered significant differences in AIP sensitivity among agr-types. We observed bimodal agr activation, attributed to intergenerational phenotypic stability and influenced by AIP concentration. Upon AIP stimulation, agr‑III showed AIP insensitivity, while agr‑II exhibited increased sensitivity and prolonged generation time. Beyond expected cross-inhibition of agr‑I by heterologous AIP‑II and ‑III, the presumably cross-activating AIP‑IV also inhibited agr‑I. Community interactions across different agr-type pairings revealed four main patterns: stable or switched dominance, and delayed or stable dual activation, influenced by community characteristics. These insights underscore the potential of personalized treatment strategies considering virulence and genetic diversity.' license: cc-by-4.0 name: Single-cell approach dissecting agr quorum sensing dynamics in Staphylococcus aureus num_downloads: 130 publication_date: '2024-02-28' submission_date: '2025-05-29T19:38:43.007344' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/10720439 - https://doi.org/10.5281/zenodo.10720439 uuid: 6e0f58d5-e64d-47ab-a59c-04ed8e7c137d language: en file_formats: .hdf5 * .zip authors_with_orcid: - Julian Bär https://orcid.org/0000-0003-1929-0338 - authors: - Pereira, Pedro Matos - Pinho, Mariana description: 'Training and test images of live S. aureus cells for the task of cell segmentation. Additional information can be found in the github wiki. The example shows the bright field and Nile Red fluorescence image of live S. aureus cells, as well as the manually annotated segmentation mask.   Data type: Paired DIC/fluorescence and segmented mask images Microscopy data type: 2D widefield images (DIC and fluorescence) Microscope:  GE HealthCare Deltavision OMX system (with temperature and humidity control, 37°C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence) Cell type: S. aureus strain JE2 grown under agarose pads File format: .tif (16-bit) Image size: 512 x 512 px² (80 nm/px) Image preprocessing: Raw images were manually annotated by drawing ellipses in the NR fluorescence image and segmented images were created using the LOCI plugin (“ROI Map”). For training, images and masks were quartered into four 256 x 256 px² patches.   Author(s): Pedro Matos Pereira1,2, Mariana Pinho1,3 Contact email: pmatos@itqb.unl.pt and mgpinho@itqb.unl.pt   Affiliation:  1) Bacterial Cell Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal 2) ORCID: https://orcid.org/0000-0002-1426-9540 3) ORCID: https://orcid.org/0000-0002-7132-8842' license: cc-by-4.0 name: DeepBacs – Staphylococcus aureus widefield segmentation dataset num_downloads: 455 publication_date: '2021-10-05' submission_date: '2025-05-29T19:38:43.922418' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/5550933 - https://doi.org/10.5281/zenodo.5550933 uuid: 6464b22a-ab55-46ba-9495-9c021377f585 language: en file_formats: .png * .zip authors_with_orcid: - Pedro Matos Pereira https://orcid.org/0000-0002-1426-9540 - Mariana Pinho https://orcid.org/0000-0002-7132-8842 - authors: - Holden, Séamus - Conduit, Mia description: 'Training and test images of live B. subtilis cells expressing FtsZ-GFP for the task of segmentation. Additional information can be found on this github wiki. The example shows the fluorescence widefield image of live B. subtilis cells expressing FtsZ-GFP and the manually annotated segmentation mask.   Data type: Paired fluorescence and segmented mask images Microscopy data type: 2D widefield images (fluorescence)  Microscope: Custom-built 100x inverted microscope bearing a 100x TIRF objective (Nikon CFI Apochromat TIRF 100XC Oil); images were captured on a Prime BSI sCMOS camera (Teledyne Photometrics) Cell type: B. subtilis strain SH130 grown under agarose pads File format: .tiff (8-bit) or .png (8-bit) For segmented masks, binary masks are used for training of CARE/U-Net models, 8-bit .tif ROI maps for training of StarDist models and .png images for training of pix2pix models Image size: 1024 x 1024 px² (Pixel size: 65 nm) Image preprocessing: Images were denoised using PureDenoise and resulting 32-bit images were converted into 8-bit images after normalizing to 1% and 99.98% percentiles. Images were manually annotated using the Labkit Fiji plugin   Author(s): Mia Conduit1,2, Séamus Holden1,3 Contact email: Seamus.Holden@newcastle.ac.uk   Affiliation: 1) Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK 2) ORCID: 0000-0002-7169-907X    Associated publications: Whitley et al., 2021, Nature Communications, https://doi.org/10.15252/embj.201696235' license: cc-by-4.0 name: DeepBacs – Bacillus subtilis fluorescence segmentation dataset num_downloads: 294 publication_date: '2021-10-05' submission_date: '2025-05-29T19:38:44.386655' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/5550968 - https://doi.org/10.5281/zenodo.5550968 uuid: 71c9db70-523d-404e-ab28-d883e9caa1f7 language: en file_formats: .png * .zip authors_with_orcid: - Séamus Holden https://orcid.org/0000-0002-7169-907X - Mia Conduit - authors: - Spahn, Christoph - Heilemann, Mike description: 'Training and test images of live E. coli cells imaged under bright field for the task of segmentation. Additional information can be found on this github wiki. The example shows a bright field image of live E. coli cells and the manually annotated segmentation mask.   Data type: Paired bright field and segmented mask images  Microscopy data type: 2D bright field images recorded at 1 min interval Microscope: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective Cell type: E. coli MG1655 wild type strain (CGSC #6300). File format: .tif (8-bit) Image size: 1024 x 1024 px² (79 nm / pixel), 19/15 individual frames (training/test dataset) 1024 x 1024 px² (79 nm / pixel), 9 regions of interest with 80 frames @ 1 min time interval (live-cell time series) Image preprocessing: Raw images were recorded in 16-bit mode (image size 512 x 512 px² @ 158 nm/px). Images were upscaled with a factor of 2 (no interpolation) to enable generation of higher-quality segmentation masks. Two sets of mask images are provided: RoiMaps for instance segmentation using e.g. StarDist or binary images for CARE or U-Net. Author(s): Christoph Spahn1,2, Mike Heilemann1,3 Contact email: christoph.spahn@mpi-marburg.mpg.de   Affiliation(s):  1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263  3) ORCID: 0000-0002-9821-3578' license: cc-by-4.0 name: DeepBacs – Escherichia coli bright field segmentation dataset num_downloads: 739 publication_date: '2021-10-05' submission_date: '2025-05-29T19:38:44.860796' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/5550935 - https://doi.org/10.5281/zenodo.5550935 uuid: 9904b1da-b46f-4b1d-b60c-f2512f6e03e2 language: en file_formats: .png * .zip authors_with_orcid: - Christoph Spahn https://orcid.org/0000-0001-9886-2263 - Mike Heilemann https://orcid.org/0000-0002-9821-3578 - authors: - Cortada, Maurizio - Sauteur, Loïc - Lanz, Michael - Levano, Soledad - Bodmer, Daniel description: StarDist 2D deep learning model and training dataset. license: cc-by-4.0 name: A deep learning approach to quantify auditory hair cells num_downloads: 91 publication_date: '2021-03-09' submission_date: '2025-05-29T19:38:45.337769' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/4590066 - https://doi.org/10.5281/zenodo.4590066 uuid: 26e5244e-59cd-413c-bde5-889f58c8d5fa file_formats: .zip authors_with_orcid: - Maurizio Cortada - Loïc Sauteur https://orcid.org/0000-0002-9163-0424 - Michael Lanz - Soledad Levano - Daniel Bodmer - authors: - Qamar, Saqib - Baba, Abu Imran - Verger, Stèphane - Andersson, Magnus description: This repository hosts a comprehensive collection of datasets used to develop an innovative deep learning model designed to enhance the segmentation and characterization of macerated fibers and vessel forms in microscopy images. Included in the deposit are raw images, alongside meticulously prepared training and validation datasets. We present an automated segmentation approach that utilizes the one-stage YOLOv8 model, which has been specifically adapted to process high-resolution microscopy images up to 32640 x 25920 pixels. Our model excels in cell detection and segmentation, demonstrating exceptional proficiency. license: cc-by-4.0 name: Fiber and vessel dataset for segmentation and characterization num_downloads: 124 publication_date: '2024-05-03' submission_date: '2025-05-29T19:38:45.843825' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/10913446 - https://doi.org/10.5281/zenodo.10913446 uuid: 87dc692c-e771-4fd3-9097-f5e25584fe55 language: en file_formats: .zip authors_with_orcid: - Saqib Qamar https://orcid.org/0000-0002-5980-5976 - Abu Imran Baba - Stèphane Verger https://orcid.org/0000-0003-3643-3978 - Magnus Andersson https://orcid.org/0000-0002-9835-3263 - authors: - Wanner, Julian - Kuhn Cuellar, Luis - Wanke, Friederike description: 'The PHDFM dataset is composed of fluorescence microscopy images of root tissue samples from A. thaliana, using the ratiometric fluorescent indicator 8‐hydroxypyrene‐1,3,6‐trisulfonic acid trisodium salt (HPTS). This semantic segmentation training dataset consists of 2D microscopy images (the brightfield channel for excitation at 405 nm), each containing a segmentation mask as an additional image channel (manually annotated by plant biologists). The segmentation masks classify pixels into the following 5 labels with the corresponding IDs: background (0), root tissue (1), early elongation zone (2), late elongation zone (3), and meristematic zone (4).' license: cc-by-4.0 name: Root tissue segmentation dataset num_downloads: 554 publication_date: '2022-01-12' submission_date: '2025-05-29T19:38:46.275062' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/5841376 - https://doi.org/10.5281/zenodo.5841376 uuid: 17685617-b2a4-43f2-9be1-6bd3c6cedd04 language: en file_formats: .gz authors_with_orcid: - Julian Wanner https://orcid.org/0000-0002-1487-7909 - Luis Kuhn Cuellar https://orcid.org/0000-0002-6950-6929 - Friederike Wanke - authors: - Constantin Pape description: 'Training data for Convolutional Neural Networks used in the publication Whole-body integration of gene expression and single-cell morphology. We provide training data for segmenting structures in the SerialBlockface Electron Microscopy data-set containing a complete 6 day old Platynereis dumerilii larva, in particular for: - cell membranes: 9 training blocks @ resolution 20x20x25 nm. Based on initial training data provided by https://ariadne.ai/. - cilia: 3 training and 2 validation blocks @ resolution 20x20x25 nm. - cuticle: 5 training blocks @ resolution 40x40x50 nm. - nuclei: 12 training blocks @ resolution 80x80x100 nm. Based on initial training data provided by https://ariadne.ai/. For details on how to use this data for training, see https://github.com/platybrowser/platybrowser-backend/tree/master/segmentation.' license: cc-by-4.0 name: Platynereis EM training data num_downloads: 382 publication_date: '2020-02-19' submission_date: '2025-05-29T19:38:46.745073' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/3675220 - https://doi.org/10.5281/zenodo.3675220 uuid: a5fedfe4-8006-4b2e-bb46-f2ed9bc519f9 language: en file_formats: .zip authors_with_orcid: - Constantin Pape https://orcid.org/0000-0001-6562-7187 - authors: - Ilya, Belevich - Jokitalo, Eija description: This submission includes ground truth datasets that were used to segment the nuclear envelope (NE), mitochondria, endoplasmic reticulum (ER) and Golgi from a human bone osteosarcoma epithelial cell (U2-OS) imaged using focused-ion beam scanning electron microscopy (FIB-SEM).The full FIB-SEM dataset is deposited to EMPIAR (https://www.ebi.ac.uk/empiar, EMPIAR-11746).  license: cc-by-4.0 name: Deep learning segmentation projects of FIB-SEM dataset of U2-OS cell num_downloads: 204 publication_date: '2023-10-26' submission_date: '2025-05-29T19:38:48.150464' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/10043461 - https://doi.org/10.5281/zenodo.10043461 uuid: 54e29da3-0cbc-4f25-8c21-3efa43df9fa2 language: en file_formats: .pdf * .zip authors_with_orcid: - Belevich Ilya https://orcid.org/0000-0003-2190-4909 - Eija Jokitalo https://orcid.org/0000-0002-4159-6934 - authors: - Pape, Constantin description: 'This dataset contains room-temperature single-axis TEM tomograms from Schaffer collateral and mossy fiber synapses in organotypic hippocampal slices. The tomograms were published in the two studies [1, 2]. The data was re-used for training deep neural networks to segment different synaptic structures in electron micrographs in [3]. For the tomograms, organotypic slices were prepared from the hippocampi of neonatal mice according to the interface protocol55 and vitrified after 28 days in vitro in culture medium supplemented with 20% (w/v) bovine serum albumin using an HPM100 (Leica) high-pressure freezing device. The dataset also contains 23 tomograms resulting from chemically-fixed material, which were also published in (Maus et al., 2020). For these tomograms, wild-type animals at postnatal day 28 were transcardially perfused under deep anesthesia, first with 0.9% sodium chloride solution, and then one of two fixatives (Fixative 1: Ice-cold 4% paraformaldehyde, 2.5% glutaraldehyde in 0.1 M phosphate buffer16; Fixative 2: 37° C 2% paraformaldehyde, 2.5% glutaraldehyde, 2 mM CaCl2, in 0.1 M cacodylate buffer56). Brains were rinsed and sectioned coronally through the dorsal hippocampus in an ice-cold 0.1 M phosphate buffer using a VT 1200S vibratome (Leica) (step size 100 µm; amplitude 1.5 mm, speed 0.1 mm/sec). Hippocampal CA3 subregions were excised using a 1.5 mm diameter biopsy punch and high-pressure frozen on the same day in 20% (w/v) bovine serum albumin using an HPM100 (Leica) high-pressure freezing device. For both sample preparations, automated freeze-substitution was performed. Tomograms were collected using a 200 kV JEM-2100 (JEOL) transmission electron microscope equipped with an 11 MP Orius SC1000 CCD camera (Gatan). Tilt-series (tilt range +/- 60°; 1° angular increments) were acquired at 30 000x magnification using SerialEM58. Tomographic reconstructions were generated using weighted back-projection with etomo.The data is organized into two different subfolders for data with annotations for "vesicles" and "active_zones". Each of these subfolders is further subdivided into "train" and "test" folders, which containtomograms for the two different sample preparations in "chemical_fixation" and "single_axis_tem".Each tomogram and the corresponding annotation is stored as a hdf5 file, containing the following internal datasets:- raw: The tomogram data.- labels/vesicles: Annotations for the synaptic vesicles, annotated with IMOD, further postprocessed and then exported to instance masks. (for tomograms in "vesicles")- labels/AZ: Annotations for the active zone, annotated with IMOD and exported to binary masks. [1] Imig et al., The Morphological and Molecular Nature of Synaptic Vesicle Priming at Presynaptic Active Zones, Neuron, 2014, DOI:10.1016/j.neuron.2014.10.009[2] Maus et al., Ultrastructural Correlates of Presynaptic Functional Heterogeneity in Hippocampal Synapses, Cell Reports, 2020, DOI: 10.1016/j.celrep.2020.02.083[3] Muth, Moschref et al., 2024, Preprint to be published' license: cc-by-4.0 name: SynapseNet Training Data num_downloads: 15 publication_date: '2024-12-01' submission_date: '2025-05-29T19:38:48.620043' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/14330011 - https://doi.org/10.5281/zenodo.14330011 uuid: 49420074-9e04-47d0-b673-9ba1278d84be language: en file_formats: .zip authors_with_orcid: - Constantin Pape https://orcid.org/0000-0001-6562-7187 - authors: - Dietler, Nicola - Minder, Matthias - Gligorovski, Vojislav - Economou, Augoustina Maria - Joly, Denis Alain Henri Lucien - Sadeghi, Ahmad - Chan, Chun Hei Michael - Kozinski, Mateusz - Weigert, Martin - Bitbol, Anne-Florence - Rahi, Sahand Jamal description: Training set of microscopy images for Dietler et al. Nature Communications 2020 license: cc-by-4.0 name: Training set of microscopy images for Dietler et al. Nature Communications 2020 num_downloads: 18 publication_date: '2021-12-07' submission_date: '2025-05-29T19:38:49.073075' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/5765648 - https://doi.org/10.5281/zenodo.5765648 uuid: deddb142-d5c3-47c7-9aef-4c3fc176447b file_formats: .gz authors_with_orcid: - Nicola Dietler - Matthias Minder - Vojislav Gligorovski - Augoustina Maria Economou - Denis Alain Henri Lucien Joly - Ahmad Sadeghi - Chun Hei Michael Chan - Mateusz Kozinski - Martin Weigert - Anne-Florence Bitbol - Sahand Jamal Rahi - authors: - Lamm, Lorenz description: 'This dataset contains training data for segmenting membranes in cryo-electron tomograms. More details will follow.' license: cc-by-4.0 name: MemBrain-seg training data num_downloads: 5 publication_date: '2023-03-16' submission_date: '2025-05-29T19:38:49.958446' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/7739793 - https://doi.org/10.5281/zenodo.7739793 uuid: bb88717c-85f2-494a-8506-a45eefdd8b38 file_formats: .zip authors_with_orcid: - Lorenz Lamm - authors: - Heebner, Jessica - Purnell, Carson - Hylton, Ryan - Marsh, Mike - Grillo, Michael - Swulius, Matt description: Cryo-electron tomography (cryo-ET) allows researchers to image cells in their native, hydrated state at the highest resolution currently possible. However, the technique has several limitations that make analyzing the data it generates time-intensive and difficult. Hand-segmenting a single tomogram can take hours to days of human effort, but the microscope can easily generate 50 or more tomograms a day. Current deep learning segmentation programs for cryo-ET do exist but are limited to segmenting one structure at a time. Here multi-slice U-Net convolutional neural networks are trained and applied to automatically segment multiple structures simultaneously within cryo-tomograms. With proper preprocessing, these networks can be robustly inferred to many tomograms without the need for training individual networks for each tomogram. This workflow dramatically improves the speed with which cryo-electron tomograms can be analyzed by cutting segmentation time down to under 30 min in most cases. Further, segmentations can be used to improve the accuracy of filament tracing within a cellular context and to rapidly extract coordinates for subtomogram averaging. license: cc-zero name: Deep learning training data (JOVE) num_downloads: 15 publication_date: '2022-11-18' submission_date: '2025-05-29T19:38:50.490184' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/7335439 - https://doi.org/10.5061/dryad.rxwdbrvct uuid: 3ac03ab8-2d28-48af-9fb5-f1d8bb344f08 language: en file_formats: .md * .zip authors_with_orcid: - Jessica Heebner - Carson Purnell - Ryan Hylton - Mike Marsh - Michael Grillo - Matt Swulius https://orcid.org/0000-0002-8147-208X - authors: - Wohlwend, Martin - Burri, Olivier - Auwerx, Johan description: 'This Workflow contains all the material necessary to reproduce the results of the QuPath analysis performed in the paper  "Inhibition of CERS1 in aging skeletal muscle exacerbates age-related muscle impairments" Inside this workflow and dataset, you will find the following folders QuPath Training Project: A QuPath 0.3.2 project containing all the manual annotations (ground truths) used to train the cellpose model, as well as the script to start the training QuPath Demo Project: A QuPath 0.3.2 project containing an example image that can be segmented using cellpose, followed by the classification of the CD45 expressing fibers Training Images and Demo Images: The raw whole slide scanner 20x images needed by the above QuPath projects Model: The fodler contianing the trained cellpose model Cellpose Training Folder: The exported raw and ground truth images that the above cellpose model was trained on Scripts: The QuPath scripts, also located in their respective QuPath projects, that were created for this whole workflow QC: A Jupyter notebook, based on ZeroCostDL4Mic that computes quality metrics in order to assess the performance of the trained cellpose model. The folder also contains the resulting metrics. Installation and Use If you are going to use the QuPath projects, you need a local QuPath Installation https://qupath.github.io/ that is configured to run the QuPath Cellpose Extension https://github.com/BIOP/qupath-extension-cellpose as well as a working Cellpose installation https://github.com/MouseLand/cellpose Instructions for installation are available from the links above. After that, you should be able to open the QuPath project, navigate to the "Automate > Project scripts" menu and locate the script you wish to run.' license: cc-by-4.0 name: Cellpose training data and scripts from "Inhibition of CERS1 in aging skeletal muscle exacerbates age-related muscle impairments" num_downloads: 84 publication_date: '2024-02-27' submission_date: '2025-05-29T19:38:50.949394' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/7041137 - https://doi.org/10.5281/zenodo.7041137 uuid: a4a93984-060e-4308-8717-f404f680c885 language: en file_formats: .md * .zip authors_with_orcid: - Martin Wohlwend https://orcid.org/0000-0001-9851-0364 - Olivier Burri https://orcid.org/0000-0002-7100-3749 - Johan Auwerx https://orcid.org/0000-0002-5065-5393 - authors: - Jacquemier, Jean - Meystre, Julie - Burri, Olivier description: 'This Workflow contains all the material necessary to reproduce the cells detection, thanks to the QuPath performed in the paper  "Machine learning for histological annotation and quantification of cortical layers" Inside this workflow and dataset, you will find the following folders QuPath Training Project: A QuPath 0.5.0 project containing all the manual annotations (ground truths) used to train the cellpose model, as well as the script to start the training Training Images and Demo Images: The raw whole slide scanner images needed by the above QuPath project Model: The fodler containing the trained cellpose model cellpose-training Folder: The exported raw and ground truth images that the above cellpose model was trained on Scripts: The QuPath scripts, also located in their respective QuPath projects, that were created for this whole workflow QC: A Jupyter notebook, based on ZeroCostDL4Mic that computes quality metrics in order to assess the performance of the trained cellpose model. The folder also contains the resulting metrics. Installation and Use If you are going to use the QuPath projects, you need a local QuPath Installation https://qupath.github.io/ that is configured to run the QuPath Cellpose Extension https://github.com/BIOP/qupath-extension-cellpose as well as a working Cellpose installation https://github.com/MouseLand/cellpose Instructions for installation are available from the links above. After that, you should be able to open the QuPath project, navigate to the "Automate > Project scripts" menu and locate the script you wish to run. 1. train a cell segmentation algorithm in the context of the rat brain Layer Boundaries project  2. trigger cell segmentation from a QuPath project in a semi-automated pipeline' license: cc-by-4.0 name: Cellpose training data and scripts from "Machine learning for histological annotation and quantification of cortical layers" num_downloads: 135 publication_date: '2024-07-04' submission_date: '2025-05-29T19:38:51.500727' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/12656468 - https://doi.org/10.5281/zenodo.12656468 uuid: e5c3eee0-3cb9-4f3e-9e54-62973e42abc0 language: en file_formats: .pdf * .zip authors_with_orcid: - Jean Jacquemier https://orcid.org/0009-0002-0029-7951 - Julie Meystre https://orcid.org/0009-0000-9093-9385 - Olivier Burri https://orcid.org/0000-0002-7100-3749 - authors: - Martyna Mazur - Wojciech Krauze description: "This dataset includes 4 files with segmentation results for 4 different\ \ ODT reconstructions of SH-SY5Y neuroblastoma cell. The segmentation results\ \ contain:\n\n\n\t3D binary masks of biological cells obtained through Cellpose\ \ [1] and ODT-SAS;\n\t3D binary masks of organelles: nucleoli and lipid structures\ \ (LS) obtained through slice-by-slice manual segmentation and ODT-SAS.\n\ \n\nAll files are .*mat files.\n\nThe files REC_SH-SY5Y_1.mat, REC_SH-SY5Y_2.mat\ \ and REC_SH-SY5Y_3.mat consist of 7 variables:\n\nRECON – tomographic\ \ reconstruction of SH-SY5Y neuroblastoma cell;\nn_imm – refractive\ \ index of object immersion medium;\ndx – object space sample size\ \ in XY [\\(\\mu m\\)];\nrayXY – xy-coordinates of illumination vectors;\n\ \nmaskManual – table with manually determined 3D binary masks of organelles;\n\ maskCellpose – 3D binary mask of biological cell obtained through Cellpose;\n\ maskODTSAS – table with 3D binary masks of biological cell and their\ \ organelles obtained through ODT-SAS.\n\nFile REC_SH-SY5Y_4.mat includes\ \ masks for the ODT-SAS and Cellpose segmentation of three closely packed cells\ \ and consists of 5 variables: RECON, n_imm, dx, maskCellpose and maskODTSAS.\n\ \nAccess a particular 3D binary mask from 'maskManual' and 'maskODTSAS'\ \ tables, using the following names: 'Cell', 'Nucleoli', 'LS'.\n\ For example:\n\ncellMask = maskODTSAS.Cell{1};\n\n\n[1] Stringer, C., Wang, T.,\ \ Michaelos, M., & Pachitariu, M. (2021). Cellpose: a generalist algorithm\ \ for cellular segmentation. Nature methods, 18(1), 100-106.\n\n " license: cc-by-4.0 name: Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images - dataset num_downloads: 202 publication_date: '2023-06-16' submission_date: '2025-05-29T19:38:52.025441' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/8188948 - https://doi.org/10.5281/zenodo.8188948 uuid: 0596c4ec-d64b-4e85-9539-86992a955f6e language: en file_formats: .mat authors_with_orcid: - Martyna Mazur https://orcid.org/0000-0002-7386-4243 - Wojciech Krauze https://orcid.org/0000-0002-5248-3986 - authors: - Shi, Can - Fan, Jinghong - Deng, Zhonghan - Liu, Huanlin - Kang, Qiang - Li, Yumei - Guo, Jing - Wang, Jingwen - Gong, Jinjiang - Liao, Sha - Chen, Ao - Zhang, Ying - Li, Mei description: 'CellBinDB is a large-scale, multimodal annotated dataset for cell segmentation. It contains 1,044 annotated microscope images and 109,083 cell annotations, covering four staining types: DAPI, ssDNA, H&E, and mIF. CellBinDB contains samples from two species, human and mouse, covering more than 30 histologically different tissue types, including disease-related tissues. The images in CellBinDB come from two sources: 844 mouse images from internal experiments and 200 human images from the open access platform 10x Genomics. We annotated all images in CellBinDB and provide two types of image annotations: semantic and instance masks. A xlsx file is attached to record the detailed information of each image. In addition, we provide the images and annotations of nine other widely used publicly available cell segmentation datasets downloaded from their original sources, retaining their original formats for ease of use.  The file ''mixed_licenses.txt'' contains the original accessions of the public datasets used in our project and their associated licenses. Please refer to these links for more information about each dataset and its licensing terms, and use it according to the specifications.' license: cc-zero name: 'CellBinDB: A Large-Scale Multimodal Annotated Dataset' num_downloads: 600 publication_date: '2024-11-20' submission_date: '2025-05-29T19:38:52.495957' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/15370205 - https://doi.org/10.5281/zenodo.15370205 uuid: 60d7e30c-eee5-44dc-be52-dc6c482c8c72 language: en file_formats: .txt * .xlsx * .zip authors_with_orcid: - Can Shi https://orcid.org/0009-0007-0003-2378 - Jinghong Fan https://orcid.org/0009-0001-9556-4831 - Zhonghan Deng - Huanlin Liu - Qiang Kang - Yumei Li - Jing Guo - Jingwen Wang - Jinjiang Gong - Sha Liao - Ao Chen - Ying Zhang - Mei Li - authors: - Ma, Jun - Xie, Ronald - Ayyadhury, Shamini - Ge, Cheng - Gupta, Anubha - Gupta, Ritu - Gu, Song - Zhang, Yao - Lee, Gihun - Kim, Joonkee - Lou, Wei - Li, Haofeng - Upschulte, Eric - Dickscheid, Timo - de Almeida, José Guilherme - Wang, Yixin - Han, Lin - Yang, Xin - Labagnara, Marco - Gligorovski, Vojislav - Scheder, Maxime - Rahi, Sahand Jamal - Kempster, Carly - Pollitt, Alice - Espinosa, Leon - Mignot, Tam - Middeke, Jan Moritz - Eckardt, Jan-Niklas - Li, Wangkai - Li, Zhaoyang - Cai, Xiaochen - Bai, Bizhe - Greenwald, Noah F. - Van Valen, David - Weisbart, Erin - Cimini, Beth A - Cheung, Trevor - Brück, Oscar - Bader, Gary D. - Wang, Bo description: "The official data set for the NeurIPS 2022 competition: cell segmentation\ \ in multi-modality microscopy images.\nhttps://neurips22-cellseg.grand-challenge.org/\n\ Please cite the following paper if this dataset is used in your research. \n\  \n@article{NeurIPS-CellSeg,\n title = {The Multi-modality Cell Segmentation\ \ Challenge: Towards Universal Solutions},\n author = {Jun Ma and Ronald\ \ Xie and Shamini Ayyadhury and Cheng Ge and Anubha Gupta and Ritu Gupta and Song\ \ Gu and Yao Zhang and Gihun Lee and Joonkee Kim and Wei Lou and Haofeng Li and\ \ Eric Upschulte and Timo Dickscheid and José Guilherme de Almeida and\ \ Yixin Wang and Lin Han and Xin Yang and Marco Labagnara and Vojislav Gligorovski\ \ and Maxime Scheder and Sahand Jamal Rahi and Carly Kempster and Alice Pollitt\ \ and Leon Espinosa and Tâm Mignot and Jan Moritz Middeke and Jan-Niklas\ \ Eckardt and Wangkai Li and Zhaoyang Li and Xiaochen Cai and Bizhe Bai and Noah\ \ F. Greenwald and David Van Valen and Erin Weisbart and Beth A. Cimini and Trevor\ \ Cheung and Oscar Brück and Gary D. Bader and Bo Wang},\n journal =\ \ {Nature Methods},      volume={21}, pages={1103–1113},\ \ year = {2024},\n doi = {https://doi.org/10.1038/s41592-024-02233-6}\n\ \ }\n \nThis is an instance segmentation task where each cell has an individual\ \ label under the same category (cells). The training set contains both labeled\ \ images and unlabeled images. You can only use the labeled images to develop\ \ your model but we encourage participants to try to explore the unlabeled images\ \ through weakly supervised learning, semi-supervised learning, and self-supervised\ \ learning.\n \nThe images are provided with original formats, including\ \ tiff, tif, png, jpg, bmp... The original formats contain the most amount of\ \ information for competitors and you have free choice over different normalization\ \ methods. For the ground truth, we standardize them as tiff formats.\n \n\ We aim to maintain this challenge as a sustainable benchmark platform. If you\ \ find the top algorithms (https://neurips22-cellseg.grand-challenge.org/awards/)\ \ don't perform well on your images, welcome to send us the dataset (neurips.cellseg@gmail.com)!\ \ We will include them in the new testing set and credit your contributions on\ \ the challenge website!\n \nDataset License: CC-BY-NC-ND" license: cc-by-nc-nd-4.0 name: NeurIPS 2022 Cell Segmentation Competition Dataset num_downloads: 8259 publication_date: '2024-02-27' submission_date: '2025-05-29T19:38:52.964225' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/10719375 - https://doi.org/10.5281/zenodo.10719375 uuid: 4fbb34f6-9d7d-47f0-a63b-c0abbad5b541 language: en file_formats: .md * .zip authors_with_orcid: - Jun Ma - Ronald Xie - Shamini Ayyadhury - Cheng Ge - Anubha Gupta - Ritu Gupta - Song Gu - Yao Zhang - Gihun Lee - Joonkee Kim - Wei Lou - Haofeng Li - Eric Upschulte - Timo Dickscheid - José Guilherme de Almeida - Yixin Wang - Lin Han - Xin Yang - Marco Labagnara - Vojislav Gligorovski - Maxime Scheder - Sahand Jamal Rahi - Carly Kempster - Alice Pollitt - Leon Espinosa - Tam Mignot - Jan Moritz Middeke - Jan-Niklas Eckardt - Wangkai Li - Zhaoyang Li - Xiaochen Cai - Bizhe Bai - Noah F. Greenwald - David Van Valen - Erin Weisbart - Beth A Cimini - Trevor Cheung - Oscar Brück - Gary D. Bader - Bo Wang - authors: - Naji Hussein - Büttner Reinhard - Simon Adrian - Eich Marie-Lisa - Lohneis Philipp - Bozek Katarzyna description: 'Over the last years, there has been large progress in automated segmentation and classification methods in histological whole slide images (WSIs) stained with hematoxylin and eosin (H&E). Current state-of-the-art techniques are based on diverse datasets of H&E-stained WSIs of different types of predominantly solid cancer. However, there is a lack of publicly available annotated datasets of lymphoma, which is why we generated a labeled diffuse large B-cell lymphoma dataset and denoted it LyNSeC (lymphoma nuclear segmentation and classification). LyNSeC comprises three subsets: LyNSeC 1 consists of 379 IHC images of size 512 x 512 pixels at 40x magnification. In the images, we annotated the contours of each cell nuclei and the cell class: marker-positive or marker-negative. In total, LyNSeC 1 contains 87,316 annotated cell nuclei of four different cases, with 48,171 of them assigned the class negative and 39,145 positive. We included three markers in this dataset showing visually different staining patterns: cluster of differentiation 3 (CD3), Ki67 as a marker of proliferation, and erythroblast transformation-specific (EST)-related gene (ERG). LyNSeC 2 and 3 contain H&E-stained images of 70 different patients. LyNSeC 2 consists of 280 images and LyNSeC 3 of 40 images of size 512 x 512 pixels at 40x magnification. 65,479 and 8,452 nuclei were annotated in LyNSeC 2 and 3, respectively. In LyNSeC 3, the nuclei were also assigned a class label (tumor and non-tumor). 3,747 nuclei were identified as tumors and 4,705 as non-tumors. In the annotation procedure, the contours of the H&E images (LyNSeC 2 and LyNSeC 3) were annotated by two pathologists and by two students (trained by the pathologists). Annotation of the cell classes in LyNSeC 3 was done by the pathologists only. LyNSeC 1 was annotated by the two students who were additionally trained to annotate the contours and to distinguish marker-positive and marker-negative cells. The pathologists inspected and (if necessary) adjusted the LyNSeC 3 annotations. The files are uploaded in '.npy' format. The files of LyNSeC 1 (x_l1.npy) and LyNSeC 3 (x_l3.npy) contain five channels, respectively: the first three are the RGB channels of the images, channel 4 contains the instance maps, and channel 5 the class type maps (for LyNSeC 1 a pixel value of 1 corresponds to the class negative and 2 to the class positive, whereas in LyNSeC 3 1 corresponds to the class non-tumor and 2 to the class tumor). The files of LyNSeC 2 (x_l2.npy) have 4 channels (without the class type map). Additionally, we also make our HoVer-Net-based pre-trained nuclei segmentation and classification models available (he.tar for H&E images and ihc.tar for IHC images).' license: cc-by-4.0 name: 'LyNSeC: Lymphoma Nuclear Segmentation and Classification' num_downloads: 843 publication_date: '2023-06-21' submission_date: '2025-05-29T19:38:53.423116' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/8065174 - https://doi.org/10.5281/zenodo.8065174 uuid: 6320c301-bad6-4d68-a6f0-9ff0e22cd8b6 language: en file_formats: .tar * .zip authors_with_orcid: - Naji Hussein - Büttner Reinhard - Simon Adrian - Eich Marie-Lisa - Lohneis Philipp - Bozek Katarzyna - authors: - Schuiveling, Mark description: 'Description: This dataset is designed for development of deep learning models for segmentation of nuclei and tissue in melanoma H&E stained histopathology. Existing nuclei segmentation models that are trained on non-melanoma specific datasets have low performance due to the ability of melanocytes to mimic other cell types, whereas existing melanoma specific models utilize older, sub-optimal techniques. Moreover, these models do not provide tissue annotations necessary for determining the localization of tumor-infiltrating lymphocytes, which may hold value for predictive and prognostic tasks. To address this, we created a melanoma specific dataset with nuclei and tissue annotations.  Methodology: Sample Collection: Regions of interest (ROIs) were sampled from H&E stained slides of 103 primary melanoma specimens and 102 metastatic melanoma specimens, scanned using a Hamamatsu scanner at 40× magnification (0.23 μm per pixel). All slides were obtained from regular diagnostic procedures.From each specimen, a 40× magnified ROI of 1024×1024 pixels was selected for annotation. Additionally, a context ROI of 5120×5120 pixels was sampled to provide information about the broader context for the annotation process. Selection was performed by a trained medical expert (M.S.) and subsequently verified by a dermatopathologist (W.B.). Manual ROI selection ensured the inclusion of diverse tissue and nuclei types. Annotation Process: Nuclei segmentationNuclei segmentations were generated using Hover-Net pretrained on the PanNuke dataset. Manual annotation adjustments were performed by author M.S. using QuPath, with the following nuclei categories: tumor, stroma, vascular endothelium, histiocyte, melanophage, lymphocyte, plasma cell, neutrophil, apoptotic cell, and epithelium. All annotations were reviewed and corrected, where needed, by a dermatopathologist (W.B.). Tissue segmentationTissue segmentations were created manually using QuPath by M.S., with the following categories: tumor, stroma, epidermis, necrosis, blood vessel, and background. Annotations were reviewed and corrected, where needed, by a dermatopathologist (W.B.). Quality Control: To assess the reliability of the annotations, intra- and interobserver agreement (by pathologist G.B.) were determined on 12 randomly selected ROIs. Nuclei segmentationThe intraobserver overall precision was 84.89%, with a recall of 86.45%, and an F1 score of 85.66%. Interobserver overall precision was 80.34%, with a recall of 80.62%, and an F1 score of 80.20%. These results are based on the sum of all true positive, false positive, and false negative counts for the 12 ROIs. Tissue segmentationThe DICE score was determined on the same 12 randomly selected ROIs. The average intraobserver DICE score was 0.90, and the interobserver DICE score was also 0.90.   Version 3:Removed sample "training_set_metastatic_roi_103" due to inconsistencies in annotation file. Version 4:Sample training_set_metastatic_roi_088 missed one color annotation for a nuclei_apoptosis in the geojson file rendering it qupath uncompatible. This is fixed in the new version.  Version 5:Addition of correct sample of training_set_metastatic_roi_103" after deadline of panoptic segmentation of nuclei and tissue in advanced melanoma challenge test phase. ' license: cc-zero name: Melanoma Histopathology Dataset with Tissue and Nuclei Annotations num_downloads: 7595 publication_date: '2025-03-19' submission_date: '2025-05-29T19:38:53.883896' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/15050523 - https://doi.org/10.5281/zenodo.15050523 uuid: 00df53b9-3104-4128-b66d-5a89d5a346ce language: en file_formats: .zip authors_with_orcid: - Mark Schuiveling https://orcid.org/0000-0002-2631-7271 - authors: - Jeongun Ryu - Aaron Valero Puche - JaeWoong Shin - Seonwook Park - Biagio Brattoli - Mohammad Mostafavi - Jinhee Lee - Sérgio Pereira - Wonkyung Jung - Soo Ick Cho - Chan-Young Ock - Kyunghyun Paeng - Donggeun Yoo description: 'The OCELOT dataset is a histopathology dataset designed to facilitate the development of methods that utilize cell and tissue relationships. The dataset comprises both small and large field-of-view (FoV) patches extracted from digitally scanned whole slide images (WSIs), with overlapping regions. The small and large FoV patches are accompanied by annotations of cells and tissues, respectively. The WSIs are sourced from the publicly available TCGA database and were stained using the H&E method before being scanned with an Aperio scanner. For more details, please check https://lunit-io.github.io/research/ocelot_dataset/.   Before downloading the dataset, please make sure to carefully read and agree to the Terms and Conditions at (https://lunit-io.github.io/research/ocelot_tc/). Also, please provide 1. name, 2. e-mail address, 3. organization/company name.   ----------------------------------------------------------------------------------- Release note. In version 1.0.1, we exclude four test cases (586, 589, 609, 615) due to under-annotated issue. In version 1.0.0, we include images and annotations of validation and test splits. In version 0.1.2, we modified the coordinates of cell labels to range from 0 to 1023 (-1 from the previous coordinates). In version 0.1.1, we removed non-H&E stained patches from the dataset.' name: 'OCELOT: Overlapped Cell on Tissue Dataset for Histopathology' num_downloads: 1044 publication_date: '2023-03-23' submission_date: '2025-05-29T19:38:54.561573' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/8417503 - https://doi.org/10.5281/zenodo.8417503 uuid: 3ebc596f-51f7-4d32-b480-9b2f4f348c10 language: en file_formats: .zip authors_with_orcid: - Jeongun Ryu - Aaron Valero Puche - JaeWoong Shin - Seonwook Park - Biagio Brattoli - Mohammad Mostafavi - Jinhee Lee - Sérgio Pereira - Wonkyung Jung - Soo Ick Cho - Chan-Young Ock - Kyunghyun Paeng - Donggeun Yoo - authors: - Estibaliz Gómez-de-Mariscal - Hasini Jayatilaka - Denis Wirtz - Arrate Muñoz-Barrutia description: 'Human fibrosarcoma HT1080WT (ATCC) cells at low cell densities embedded in 3D collagen type I matrices [1]. The time-lapse videos were recorded every 2 minutes for 16.7 hours and covered a field of view of 1002 pixels × 1004 pixels with a pixel size of 0.802 μm/pixel The videos were pre-processed to correct frame-to-frame drift artifacts, resulting in a final size of 983 pixels × 985 pixels pixels. Hasini Jayatilaka, Anjil Giri, Michelle Karl, Ivie Aifuwa, Nicholaus J Trenton, Jude M Phillip, Shyam Khatau, and Denis Wirtz. EB1 and cytoplasmic dynein mediate protrusion dynamics for efficient 3-dimensional cell migration. FASEB J., 32(3):1207–1221, 2018. ISSN 0892-6638. doi: 10.1096/fj.201700444RR. Further information about how to use this data is given in https://github.com/esgomezm/microscopy-dl-suite-tf This dataset is provided together with the following preprint and if you use it, we would like to kindly ask you to cite it properly: Estibaliz Gómez-de-Mariscal, Hasini Jayatilaka, Özgün Çiçek, Thomas Brox, Denis Wirtz, Arrate Muñoz-Barrutia, *Search for temporal cell segmentation robustness in phase-contrast microscopy videos*, arXiv 2021 (arXiv:2112.08817)' license: cc-by-4.0 name: HT1080WT cells embedded in 3D collagen type I matrices - manual annotations for cell instance segmentation and tracking num_downloads: 364 publication_date: '2021-12-13' submission_date: '2025-05-29T19:38:55.457337' tags: - AI-ready - exclude from DALIA type: - Data url: - https://zenodo.org/records/5979761 - https://doi.org/10.5281/zenodo.5979761 uuid: 65c10444-143a-48d1-88a0-1ec439cdce85 language: en file_formats: .zip authors_with_orcid: - Estibaliz Gómez-de-Mariscal https://orcid.org/0000-0003-2082-3277 - Hasini Jayatilaka https://orcid.org/0000-0001-7418-9393 - Denis Wirtz https://orcid.org/0000-0001-6147-3045 - Arrate Muñoz-Barrutia https://orcid.org/0000-0002-1573-1661 - authors: - Dvoretskii, Stefan - Maier-Hein, Klaus - Nolden, Marco - Schmidt, Christian - Bortolomeazzi, Michele - Moore, Josh license: cc-by-4.0 name: 'OMExcavator: a tool for exporting and connecting Bioimaging-specific metadata in wider knowledge graphs' num_downloads: 38 publication_date: '2025-05-15' submission_date: '2025-05-27T11:20:43.144497' url: - https://zenodo.org/records/15423904 - https://doi.org/10.5281/zenodo.15423904 uuid: 16fa61ee-9c45-44b7-a9b9-7cd006b288b1 file_formats: .pdf tags: - include in DALIA authors_with_orcid: - Stefan Dvoretskii https://orcid.org/0000-0001-7769-0167 - Klaus Maier-Hein https://orcid.org/0000-0002-6626-2463 - Marco Nolden https://orcid.org/0000-0001-9629-0564 - Christian Schmidt https://orcid.org/0000-0001-9568-895X - Michele Bortolomeazzi https://orcid.org/0000-0001-5805-5774 - Josh Moore https://orcid.org/0000-0003-4028-811X - authors: - Bortolomeazzi, Michele - Boissonnet, Tom description: 'These slides were presented during an online introductory session to OMERO for the UB Frankfurt. The two-hour session consisted of a first part highlighting the benefits that image data management brings to the lab. The second part showcased image analysis workflows with a Fiji macro and a Python notebook.  ' license: cc-by-4.0 name: Introduction to OMERO - Frankfurt - online num_downloads: 65 publication_date: '2025-04-05' submission_date: '2025-05-27T11:20:45.962153' url: - https://zenodo.org/records/15152576 - https://doi.org/10.5281/zenodo.15152576 uuid: 3d7e262e-c4af-4301-bc61-fd5f8da193a6 language: en file_formats: .ijm * .ipynb * .pptx tags: - NFDI4Bioimage - Research Data Management - include in DALIA authors_with_orcid: - Michele Bortolomeazzi https://orcid.org/0000-0001-5805-5774 - Tom Boissonnet https://orcid.org/0000-0002-3328-9467 - authors: - Kemmer, Isabel - Romdhane, Feriel - Euro-BioImaging ERIC description: 'Depositing data in quality data repositories is one crucial step towards FAIR (Findable, Accessible, Interoperable, and Reusable) data. Accordingly, Euro-BioImaging strongly encourages sharing scientific imaging data in established, thematic repositories.  To guide you in the selection of appropriate repositories, we have created an overview of available repositories for different types of image data, including their scope and requirements. This decision tree guides you through questions about your data and directs you to the correct repository, and/or provides instructions for further processing to meet the critera of the repositories.  Three seperate trees are provided for different classes of imaging data: open bioimage data, preclinical data, and human imaging data. These versions with three trees can be used for web-view. Update: also the editable versions in powerpoint format (.pptx) are now provided. Please be aware that opening the versions with another program might lead to shifted formatting. Update: we now also provide ready-to-print versions designed to be printed on A3 format. One page shows the open bioimaging data tree and one page combines the preclinical and human imaging data trees. Also the editable versions of these are provided.' license: cc-by-4.0 name: Image Repository Decision Tree - Where do I deposit my imaging data num_downloads: 529 publication_date: '2025-05-15' submission_date: '2025-05-27T11:21:17.112182' url: - https://zenodo.org/records/15425770 - https://doi.org/10.5281/zenodo.15425770 uuid: 88da6030-f8df-4f49-9678-e79899595ea2 language: en file_formats: .pdf * .pptx tags: - include in DALIA authors_with_orcid: - Isabel Kemmer https://orcid.org/0000-0002-8799-4671 - Feriel Romdhane https://orcid.org/0000-0002-5854-9341 - Euro-BioImaging ERIC - authors: - Luke Sorensen - Ayame Saito - Sabrina Poon - Myat Noe Han - Adam Humenick - Peter Neckel - Keith Mutunduwe - Christie Glennan - Narges Mahdavian - Simon JH Brookes - Rachel M McQuade - Jaime PP Foong - Sebastian K. King - Estibaliz Gómez-de-Mariscal - Arrate Muñoz-Barrutia - Robert Haase - Simona Carbone - Nicholas A. Veldhuis - Daniel P. Poole - Pradeep Rajasekhar description: "This upload is associated with the software, Gut Analysis Toolbox (GAT).\n\ If you use it please cite:\nSorensen et al. Gut Analysis Toolbox: Automating\ \ quantitative analysis of enteric neurons. J Cell Sci 2024; jcs.261950.\ \ doi: https://doi.org/10.1242/jcs.261950\nThe upload contains StarDist models\ \ for segmenting enteric neurons in 2D, enteric neuronal subtypes in 2D and FPN+ResNet101\ \ model for enteric ganglia in 2D in gut wholemount tissue. GAT is implemented\ \ in Fiji, but the models can be used in any software that supports StarDist and\ \ the use of 2D UNet models. The files here also consist of Python notebooks\ \ (Google Colab), training and test data as well as reports on model performance.\n\ Note: The enteric ganglia model is has been updated to v3 which uses pytorch and\ \ is a different architecture (FPN+ResNet101).\nThe model files are located in\ \ the respective folders as zip files. The folders have also been zipped:\n\n\ Neuron (Hu; StarDist model):\n\nMain folder: 2D_enteric_neuron_model_QA.zip\n\ StarDist Model File:2D_enteric_neuron_v4_1.zip \nDeepImageJ compatible model:\ \ 2D_enteric_neuron.bioimage.io.model.zip (used currently in GAT)\n\n\nNeuronal\ \ subtype (StarDist model): \n\nMain folder: 2D_enteric_neuron_subtype_model_QA.zip\n\ Model File: 2D_enteric_neuron_subtype_v4.zip\nDeepImageJ compatible model: 2D_enteric_neuron_subtype.bioimage.io.model.zip\ \ (used currently in GAT)\n\n\nEnteric ganglia (2D FPN_ResNet101; Use in FIJI\ \ with deepImageJ)\n\nMain folder: 2D_enteric_ganglia_v3_training.zip\nModel\ \ File: 2D_Ganglia_RGB_v3.bioimage.io.model.zip (used currently in GAT)\n\n\n\n\ For the all models, files included are:\n\nModel for segmenting cells or ganglia\ \ in 2D FIJI. StarDist or 2D UNet.\nTraining and Test datasets used for training.\n\ Google Colab notebooks used for training and quality assurance (ZeroCost DL4Mic\ \ notebooks).\nPython notebook and code for training ganglia model with QA.\n\ Quality assurance reports generated from above notebooks.\nStarDist model exported\ \ for use in QuPath.\n\nThe model files can be used within can be used within\ \ the software, StarDist. They are intended to be used within FIJI or\ \ QuPath, but can be used in any software that supports the implementation of\ \ StarDist in 2D.\nData:\nAll the images were collected from 4 different research\ \ labs and a public database (SPARC database) to account for variations in image\ \ acquisition, sample preparation and immunolabelling.\nFor enteric neurons the\ \ pan-neuronal marker, Hu has been used and the  2D wholemounts images\ \ from mouse, rat and human tissue.\nFor enteric neuronal subtypes, 2D images\ \ for nNOS, MOR, DOR, ChAT, Calretinin, Calbindin, Neurofilament, CGRP and SST\ \ from mouse tissue have been used..\n25 images were used from the following\ \ entries in the SPARC database:\n\nHoward, M. (2021). 3D imaging of enteric\ \ neurons in mouse (Version 1) [Data set]. SPARC Consortium. \nGraham, K. D.,\ \ Huerta-Lopez, S., Sengupta, R., Shenoy, A., Schneider, S., Wright, C. M., Feldman,\ \ M., Furth, E., Lemke, A., Wilkins, B. J., Naji, A., Doolin, E., Howard, M.,\ \ & Heuckeroth, R. (2020). Robust 3-Dimensional visualization of human colon\ \ enteric nervous system without tissue sectioning (Version 1) [Data set]. SPARC\ \ Consortium.\nWang, L., Yuan, P.-Q., Gould, T. and Tache, Y. (2021). Antibodies\ \ Tested in theColon – Mouse (Version 1) [Data set]. SPARC Consortium. doi:10.26275/i7dl-58h\n\ \nAdditional images for new ganglia model:\n\nHamnett, R., Dershowitz, L. B.,\ \ Sampathkumar, V., Wang, Z., Gomez-Frittelli, J., De Andrade, V., Kasthuri, N.,\ \ Druckmann, S. and Kaltschmidt, J. A. (2022b). Regional cytoarchitecture of the\ \ adult and developing mouse enteric nervous system. Curr. Biol. 32, 4483-4492.e5.\n\ \nThe images have been acquired using a combination different microscopes. The\ \ images for the mouse tissue were acquired using: \n\n\nLeica TCS-SP8 confocal\ \ system (20x HC PL APO NA 1.33, 40 x HC PL APO NA 1.3) \n\n\nLeica TCS-SP8\ \ lightning confocal system (20x HC PL APO NA 0.88) \n\n\nZeiss Axio Imager\ \ M2 (20X HC PL APO NA 0.3) \n\n\nZeiss Axio Imager Z1 (10X HC PL APO NA\ \ 0.45) \n\n\nHuman tissue images were acquired using: \n\n\nIX71 Olympus\ \ microscope (10X HC PL APO NA 0.3) \n\n\nFor more information, visit the Documentation\ \ website.\nNOTE: The images for enteric neurons and neuronal subtypes have been\ \ rescaled to 0.568 µm/pixel for mouse and rat. For human neurons, it has\ \ been rescaled to 0.9 µm/pixel . This is to ensure the neuronal cell bodies\ \ have similar pixel area across images. The area of cells in pixels can vary\ \ based on resolution of image, magnification of objective used, animal species\ \ (larger animals -> larger neurons) and potentially how the tissue is stretched\ \ during wholemount preparation \nAverage neuron area for neuronal model: 701.2\ \ ± 195.9 pixel2 (Mean ± SD, 6267 cells)\nAverage neuron area for\ \ neuronal subtype model: 880.9 ± 316 pixel2 (Mean ± SD, 924\ \ cells)\nSoftware References:\nStardist\nSchmidt, U., Weigert, M., Broaddus,\ \ C., & Myers, G. (2018, September). Cell detection with star-convex polygons.\ \ In International Conference on Medical Image Computing and Computer-Assisted\ \ Intervention (pp. 265-273). Springer, Cham.\ndeepImageJ\nGómez-de-Mariscal,\ \ E., García-López-de-Haro, C., Ouyang, W., Donati, L., Lundberg,\ \ E., Unser, M., Muñoz-Barrutia, A. and Sage, D., 2021. DeepImageJ: A user-friendly\ \ environment to run deep learning models in ImageJ. Nature Methods, 18(10),\ \ pp.1192-1195.\nZeroCost DL4Mic\nvon Chamier, L., Laine, R.F., Jukkala, J., Spahn,\ \ C., Krentzel, D., Nehme, E., Lerche, M., Hernández-Pérez, S.,\ \ Mattila, P.K., Karinou, E. and Holden, S., 2021. Democratising deep learning\ \ for microscopy with ZeroCostDL4Mic. Nature communications, 12(1),\ \ pp.1-18." license: cc-by-4.0 name: 'Gut Analysis Toolbox: Training data and 2D models for segmenting enteric neurons, neuronal subtypes and ganglia' num_downloads: 663 publication_date: '2025-05-01' submission_date: '2025-05-27T11:21:25.128788' url: - https://zenodo.org/records/15314214 - https://doi.org/10.5281/zenodo.15314214 uuid: 0ff88548-3496-4cb7-abe1-549429c09593 language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Luke Sorensen - Ayame Saito - Sabrina Poon - Myat Noe Han https://orcid.org/0000-0003-3028-7359 - Adam Humenick - Peter Neckel - Keith Mutunduwe - Christie Glennan - Narges Mahdavian - Simon JH Brookes https://orcid.org/0000-0001-5635-0876 - Rachel M McQuade https://orcid.org/0000-0002-3510-1288 - Jaime PP Foong https://orcid.org/0000-0003-2082-5520 - Sebastian K. King https://orcid.org/0000-0001-5396-0265 - Estibaliz Gómez-de-Mariscal https://orcid.org/0000-0003-2082-3277 - Arrate Muñoz-Barrutia https://orcid.org/0000-0002-1573-1661 - Robert Haase https://orcid.org/0000-0001-5949-2327 - Simona Carbone https://orcid.org/0000-0002-4350-6357 - Nicholas A. Veldhuis https://orcid.org/0000-0002-8902-9365 - Daniel P. Poole https://orcid.org/0000-0002-6168-8422 - Pradeep Rajasekhar https://orcid.org/0000-0002-1983-7244 - authors: - Young, Pamela description: .lif files misbehaving in fiji but fine in LASX.  This data opens fine in LASX but FIJI only likes some of the files.  I think it was captured during a poweroutage so may have lived on a temp drive and been recovered when the power came back.  LASX uses the .lifext but I don't think FIJI does.  I have included it however since it is part of the dataset output from the microscope. license: cc-by-4.0 name: .lif files misbehaving in fiji but fine in LASX num_downloads: 117 publication_date: '2025-05-07' submission_date: '2025-05-27T11:21:43.215680' url: - https://zenodo.org/records/15353569 - https://doi.org/10.5281/zenodo.15353569 uuid: 424e6f7a-7dfe-4775-9e4c-85d2e3095220 language: en file_formats: .lif * .lifext tags: - exclude from DALIA authors_with_orcid: - Pamela Young https://orcid.org/0009-0009-2068-1521 - authors: - Haase, Robert description: These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material will develop here and in the corresponding github repository between April and July 2025. license: cc-by-4.0 name: Bio-image Data Science Lectures 2025 @ Uni Leipzig / ScaDS.AI num_downloads: 1228 publication_date: '2025-05-29' submission_date: '2025-05-29T20:42:31.742051' url: - https://zenodo.org/records/15546497 - https://doi.org/10.5281/zenodo.15546497 uuid: 0d46d6ca-2417-4722-863c-69275c111cd8 language: en file_formats: .pdf * .pptx tags: - include in DALIA authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 - authors: - Haase, Robert description: 'Working together on the internet presents us with new challenges: Who uploaded a file and when? Who contributed to the project when and why? How can content be merged when multiple team members make changes at the same time? The version control tool Git offers a comprehensive solution to these questions. The online platform GitHub.com provides a Git-driven platform that enables effective collaboration. Attendees of this hands-on tutorial will learn: Introduction to version control with Git[Hub] Working with Git: Pull requests Resolving merge conflicts Artificial intelligence that can respond to GitHub issues ' license: cc-by-4.0 name: Collaborative working and Version Control with Git[Hub] num_downloads: 68 publication_date: '2025-05-10' submission_date: '2025-05-29T20:42:32.156027' url: - https://zenodo.org/records/15379632 - https://doi.org/10.5281/zenodo.15379632 uuid: 145a5dc8-29e5-4e66-bced-7ffb15fcbe36 language: en file_formats: .pdf * .pptx tags: - NFDI4Bioimage - Research Data Management - exclude from DALIA authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 - authors: - Haase, Robert description: The advent of large language models (LLMs) such as ChatGPT changes the way we analyse images. We ask LLMs to generate code, apply it to images and spend less time on learning implementation details. This also has impact on how we learn image analysis. While coding skills are still required, we can use LLMs to explain code, make proposals how to analyse the images and yet still decide how the analysis is done. license: cc-by-4.0 name: Learning and Training Bio-image Analysis in the Age of AI num_downloads: 304 publication_date: '2025-04-07' submission_date: '2025-05-29T20:42:32.571441' url: - https://zenodo.org/records/15165424 - https://doi.org/10.5281/zenodo.15165424 uuid: 586accd4-855b-4cad-9fcb-40ce1c81c7a0 language: en file_formats: .pdf * .pptx tags: - include in DALIA authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 - authors: - Haase, Robert description: 'In this slide deck we learn about the basics of Explainable Artificial Intelligence with a soft focus on Computer Vision. We take a deeper dive in one method: Gradient Class Activation Maps. Releated exercise materials are available online: https://haesleinhuepf.github.io/xai/' license: cc-by-4.0 name: Explainable AI for Computer Vision num_downloads: 78 publication_date: '2025-03-09' submission_date: '2025-05-29T20:42:32.965385' url: - https://zenodo.org/records/14996127 - https://doi.org/10.5281/zenodo.14996127 uuid: b027a9ac-a79b-4388-b00f-11accdc67184 language: en file_formats: .pdf * .pptx tags: - NFDI4Bioimage - Bioimage Analysis - Artificial Intelligence - include in DALIA authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 - authors: - Vebjorn Ljosa - Katherine L. Sokolnicki - Anne E. Carpenter description: Since robust foreground/background separation and segmentation of cellular objects (i.e.,identification of which pixels below to which objects) strongly depends on image quality, focus artifacts are detrimental to data quality. This image set provides examples of in- and out-of-focus synthetic images, which can be used for validation of focus metrics. license: CC0-1.0 name: Synthetic cells publication_date: '2012-06-28' tags: - AI-ready - exclude from DALIA type: - Data url: https://bbbc.broadinstitute.org/BBBC005 uuid: ef5e9e09-7c32-4ea4-a7bf-1755cf093c47 language: en - authors: - Vebjorn Ljosa - Katherine L. Sokolnicki - Anne E. Carpenter description: These images are of human HT29 colon cancer cells, a cell line that has been widely used for the study of many normal and neoplastic processes. A set of about 43,000 such images was used by Moffat et al. (Cell, 2006) to screen for mitotic regulators. The analysis followed the common pattern of identifying and counting cells with a phenotype of interest (in this case, cells that were in mitosis), then normalizing the count by dividing by the total number of cells. Such experiments present two image analysis problems. First, identifying the cells that have the phenotype of interest requires that the nuclei and cells be segmented. Second, normalizing requires an accurate cell count. license: CC-BY-NC-SA-3.0 name: Human HT29 colon-cancer cells publication_date: '2012-06-28' tags: - AI-ready - exclude from DALIA type: - Data url: https://bbbc.broadinstitute.org/BBBC008 uuid: 371279a9-ba71-451c-a3eb-c8f9a111befb language: en - authors: - David Svoboda - Michal Kozubkek - Stanislav Stejskal description: 'One of the principal challenges in counting or segmenting nuclei is dealing with clustered nuclei. To help assess algorithms performance in this regard, this synthetic image set consists of four subsets with increasing degree of clustering. Each subset is also provided in two diferent levels of quality: high SNR and low SNR.' license: CC-BY-3.0 name: 3D HL60 Cell line (synthetic data) publication_date: '2009-06-01' tags: - AI-ready - exclude from DALIA type: - Data url: https://bbbc.broadinstitute.org/BBBC024 uuid: 4a46c9c7-9738-4d49-a1ed-089f09c383de language: en - authors: - Vebjorn Ljosa - Katherine L. Sokolnicki - Anne E. Carpenter description: Segmenting nuclei in 3D images can be challenging especially when nuclei are clustered not only in XY plane but also in XZ and YZ planes. Manually annotated ground truth provides a reference for image analysis software testing purposes. These images of mouse embryo blastocyst cells also have changing nuclei intensity in Z plane which makes finding the right threshold for successful segmentation a difficult task. This image set also contains GAPDH transcripts that can be quantified in each cell. license: CC0-1.0 name: Mouse embryo blastocyst cells publication_date: '2012-06-28' tags: - AI-ready - exclude from DALIA type: - Data url: https://bbbc.broadinstitute.org/BBBC032 uuid: 1ad5b4e1-add7-4127-be32-b71d689ab129 language: en - authors: - Vebjorn Ljosa - Katherine L. Sokolnicki - Anne E. Carpenter description: This image set is part of a high-throughput chemical screen on U2OS cells, with examples of 200 bioactive compounds. The effect of the treatments was originally imaged using the Cell Painting assay (fluorescence microscopy). This data set only includes the DNA channel of a single field of view per compound. These images present a variety of nuclear phenotypes, representative of high-throughput chemical perturbations. The main use of this data set is the study of segmentation algorithms that can separate individual nucleus instances in an accurate way, regardless of their shape and cell density. The collection has around 23,000 single nuclei manually annotated to establish a ground truth collection for segmentation evaluation. license: CC0-1.0 name: Nuclei of U2OS cells in a chemical screen publication_date: '2012-06-28' tags: - AI-ready - exclude from DALIA type: - Data url: https://bbbc.broadinstitute.org/BBBC039 uuid: dfacb5ce-3d44-4c98-af7c-da0d1b51a4e0 language: en - authors: - Vebjorn Ljosa - Katherine L. Sokolnicki - Anne E. Carpenter description: Since robust foreground/background separation and segmentation of cellular objects (i.e., identification of which pixels below to which objects) strongly depends on image quality, focus artifacts are detrimental to data quality. This image set provides examples of in- and out-of-focus HCS images which can be used for validation of focus metrics. license: CC0-1.0 name: Human U2OS cells (out of focus) publication_date: '2012-06-28' tags: - AI-ready - exclude from DALIA type: - Data url: https://bbbc.broadinstitute.org/BBBC006 uuid: 7e9d729e-d9d3-4ab6-b811-11bf8a61ddad language: en - authors: - Vebjorn Ljosa - Katherine L. Sokolnicki - Anne E. Carpenter description: One of the principal challenges in counting or segmenting nuclei is dealing with clustered nuclei. To help assess algorithms performance in this regard, this synthetic image set consists of five subsets with increasing degree of clustering. license: CC-BY-NC-SA-3.0 name: Synthetic cells publication_date: '2012-06-28' tags: - AI-ready - exclude from DALIA type: - Data url: https://bbbc.broadinstitute.org/BBBC004 uuid: 047cafa8-6b41-4670-af7c-8d88e768124f language: en - authors: - Vebjorn Ljosa - Katherine L. Sokolnicki - Anne E. Carpenter description: These are synthetic images from the Cell Tracking Challenge. The images depict simulated nuclei of HL60 cells stained with Hoescht (training datasets). These synthetic images of HL60 cells provide an opportunity to test image analysis software by comparing segmentation results to the available ground truth for each time point. The number of clustered nuclei increases with time adding more complexity to the problem. This time-laps dataset can be used for simple segmentation or for nuclei tracking. license: CC0-1.0 name: Simulated HL60 cells (from the Cell Tracking Challenge) publication_date: '2012-06-28' tags: - AI-ready - exclude from DALIA type: - Data url: https://bbbc.broadinstitute.org/BBBC035 uuid: ca257f74-43ea-4b44-8030-581248e70cc4 language: en - authors: - David J. Logan - Jing Shan - Sangeeta N. Bhatia - Anne E. Carpenter description: This 384-well plate has images of co-cultured hepatocytes and fibroblasts. Every other well is populated (A01, A03, ..., C01, C03, ...) such that 96 wells comprise the data. Each well has 9 sites and thus 9 images associated, totaling 864 images. license: CC-BY-3.0 name: Human Hepatocyte and Murine Fibroblast cells Co-culture experiment publication_date: '2016-03-01' tags: - AI-ready - exclude from DALIA type: - Data url: https://bbbc.broadinstitute.org/BBBC026 uuid: 9ad4fa3c-4812-4e18-8f4b-7fee453b19b2 language: en - authors: - Giulia Paci - Ines Fernandez Mosquera - Pablo Vicente Munuera - Yanlan Mao description: Segmentation masks of individual cells in Drosophila wing discs license: CC0-1.0 name: 3D cell shape of Drosophila Wing Disc publication_date: '2023-08-14' tags: - AI-ready - exclude from DALIA type: - Data url: https://www.ebi.ac.uk/bioimage-archive/galleries/S-BIAD843-ai.html uuid: 82d9b4ee-ca5a-4f8d-b20b-ffb09cf2a899 - authors: - Vebjorn Ljosa - Katherine L. Sokolnicki - Anne E. Carpenter description: Cell dynamics during the early mouse embryogenesis change spatiotemporally. For understanding the mechanism of this developmental process, imaging cell dynamics by live-cell imaging of fluorescently labeled nuclei and performing nuclei segmentation of these images by image processing are essential. This dataset contains the fluorescence images and Ground Truth used when performing nuclei segmentation using deep learning. Fluorescence images are time-series images from fertilization to blastocyst formation. Ground Truth is supervised data of the cell nuclear region. license: CC0-1.0 name: Nuclei of mouse embryonic cells publication_date: '2012-06-28' tags: - AI-ready - exclude from DALIA type: - Data url: https://bbbc.broadinstitute.org/BBBC050 uuid: 4b43bc04-2dd8-4ffa-a568-e3f17f26fdab language: en - authors: - Vebjorn Ljosa - Katherine L. Sokolnicki - Anne E. Carpenter description: Drosophila melanogaster Kc167 cells were stained for DNA (to label nuclei) and actin (a cytoskeletal protein, to show the cell body). Automatic cytometry requires that cells be segmented, i.e., that the pixels belonging to each cell be identified. Because segmenting nuclei and distinguishing foreground from background is comparatively easy for these images, the focus here is on finding the boundaries between adjacent cells. license: CC0-1.0 name: Drosophila Kc167 cells publication_date: '2012-06-28' tags: - AI-ready - exclude from DALIA type: - Data url: https://bbbc.broadinstitute.org/BBBC007 uuid: 1154fd4e-f82a-4179-9d7d-3f3af9c5c117 language: en - authors: - Sabine Taschner-Mandl - Inge M. Ambros - Peter F. Ambros - Klaus Beiske - Allan Hanbury - Wolfgang Doerr - Tamara Weiss - Maria Berneder - Magdalena Ambros - Eva Bozsaky - Florian Kromp - Teresa Zulueta-Coarasa description: Ground-truth annotated fluorescence image dataset for training nuclear segmentation methods license: CC0-1.0 name: An annotated fluorescence image dataset for training nuclear segmentation methods publication_date: '2023-03-07' tags: - AI-ready - exclude from DALIA type: - Data url: https://www.ebi.ac.uk/bioimage-archive/galleries/S-BIAD634-ai.html uuid: 522d95aa-198a-4b0f-8f6a-542dd558f32f - authors: - Krisztian Koos - József Molnár - Lóránd Kelemen - Gábor Tamás - Peter Horvath description: The image set consists of 60 Differential Interference Contrast (DIC) images of Chinese Hamster Ovary (CHO) cells. The images are taken on an Olympus Cell-R microscope with a 20x lens at the time when the cell initiated their attachment to the bottom of the dish. license: CC-BY-3.0 name: Chinese Hamster Ovary Cells publication_date: '2016-07-29' tags: - AI-ready - exclude from DALIA type: - Data url: https://bbbc.broadinstitute.org/BBBC030 uuid: 99bd5620-dab5-48a0-a41f-6cb1e349758f language: en - authors: - Jonas Hartmann - Mie Wong - Elisa Gallo - Darren Gilmour description: 3D zebrafish embryo images with single-cell segmentation and point cloud-based morphometry license: CC-BY-4.0 name: An image-based data-driven analysis of cellular architecture in a developing tissue publication_date: '2022-12-13' tags: - AI-ready - exclude from DALIA type: - Data url: https://www.ebi.ac.uk/bioimage-archive/galleries/S-BIAD599-ai.html uuid: 4a5500d9-d356-426d-b166-9e549b5ec83b - authors: - Yuhan Wang - Martin Weigert - Uwe Schmidt - Stephan Saalfeld - Eugene W. Myers - Tim Wang - Karel Svoboda - Mark Eddison - Greg Fleishman - Shengjin Xu - Fredrick E. Henry - Andrew L. Lemire - Hui Yang - Konrad Rokicki - Cristian Goina - Eugene W Myers - Wyatt Korff - Scott M. Sternson - Paul W. Tillberg description: Accurate segmentation of volumetric fluorescence image data has been a long-standing challenge and it can considerably degrade the accuracy of multiplexed fluorescence in situ hybridization (FISH) analysis. To overcome this challenge, we developed a deep learning-based automatic 3D segmentation algorithm, called Starfinity. It first predicts its cell center probability and its radial distances to the nearest cell borders for each pixel. It then aggregates pixel affinity maps from the densely predicted distances and applies a watershed segmentation on the affinity maps using the thresholded center probability as seeds. license: CC-BY-4.0 name: Ground-truth cell body segmentation used for Starfinity training publication_date: '2021-03-05' tags: - AI-ready - exclude from DALIA type: - Data url: https://janelia.figshare.com/articles/dataset/Ground-truth_cell_body_segmentation_used_for_Starfinity_training/13624268 uuid: 43ec6a6b-5d9f-4733-b5d5-0adcb96ff566 language: en - authors: - Ziming Qiu - Matthew Hartley description: Ultrasound images of mouse embryos with body and brain volume segmentation masks license: CC0-1.0 name: Embryonic mice ultrasound volumes with body and brain volume segmentation masks publication_date: '2023-05-10' tags: - AI-ready - exclude from DALIA type: - Data url: https://www.ebi.ac.uk/bioimage-archive/galleries/S-BIAD686-ai.html uuid: b0c7b1a8-4916-4616-81a3-0fef05dec9dd - authors: - Kay Schneitz - Athul Vijayan - Tejasvinee Mody description: We present computational tools that allow versatile and accurate 3D nuclear segmentation in plant organs, enable the analysis of cell-nucleus geometric relationships, and improve the accuracy of 3D cell segmentation. This biostudies submission includes Arabidopsis ovule model training dataset used in the study. The training dataset is composed of strong and weak nuclei image channels, corresponding ground truth segmentation, cell wall image and associated cell segmentation mentioned in the study. Trained models from the study, a total of 47 trained models are made available from this study. This included 15 initial models, 30 gold models, and 2 platinum models. Models were trained using PlantSeg, Stardist and Cellpose. All image datasets and its segmentation as part of the figures in this study is also available as separate zip files. This includes image dataset from different species and organs as listed below. license: CC0-1.0 name: Go-Nuclear. A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context publication_date: '2024-06-29' tags: - AI-ready - exclude from DALIA type: - Data url: https://www.ebi.ac.uk/bioimage-archive/galleries/ai/analysed-dataset/S-BIAD1026/ uuid: 4baeb657-2d58-4246-8569-91be70eccafa language: en - authors: - Ying Chen - Johannes C. Paetzold - Ali Erturk - Doris Kaltenecker - Mihail Todorov - Harsharan Singh Bhatia - Shan Zhao - Luciano Höher description: This dataset is the training set with annotations for the SELMA3D challenge. The SELMA3D challenge focuses on self-supervised learning for 3D light-sheet microscopy image segmentation. Its objective is to encourage the development of self-supervised learning methods for general segmentation of various structures in 3D light-sheet microscopy images. The dataset comtains 3D image patches of different labeled biological structures in the brain, including blood vessels, c-Fos labeled brain cells involved in neural activity, cell nuclei, and Alzheimers disease plaques. Each patch includes corresponding pixel-wise annotations for the labeled structures. license: CC-BY-4.0 name: 3D light-sheet microscopy data for SELMA3D 2024 challenge - Training subset with annotations publication_date: '2024-06-05' tags: - AI-ready - exclude from DALIA type: - Data url: https://www.ebi.ac.uk/bioimage-archive/galleries/ai/analysed-dataset/S-BIAD1196/ uuid: 1ce4927d-3c9e-4028-be7c-16133be6359a language: en - authors: - David Svoboda - Michal Kozubek - Stanislav Stejskal - Teresa Zulueta-Coarasa description: 'One of the principal challenges in counting or segmenting nuclei is dealing with clustered nuclei. To help assess algorithms performance in this regard, this synthetic image set consists of four subsets with increasing degree of clustering. Each subset is also provided in two different levels of quality: high SNR and low SNR.' license: CC-BY-3.0 name: Synthetic images and segmentation masks simulating HL-60 cell nucleus in 3D publication_date: '2024-11-26' tags: - AI-ready - exclude from DALIA type: - Data url: https://www.ebi.ac.uk/bioimage-archive/galleries/ai/analysed-dataset/S-BIAD1492/ uuid: 92fcd7ed-f37d-43f3-ad19-4395c3101cc8 language: en - authors: - Alain Chen - Liming Wu - Seth Winfree - Kenneth Dunn - Paul Salama - Edward Delp - Teresa Zulueta-Coarasa description: This submission contains a set of 3D microscopy volumes of cell nuclei from different species and tissues that have been manually segmented. We also provide synthetically generated 3D microscopy volumes that can be used for training segmentation methods. license: CC-BY-4.0 name: 3D Ground Truth Annotations of Nuclei in 3D Microscopy Volumes publication_date: '2024-12-20' tags: - AI-ready - exclude from DALIA type: - Data url: https://www.ebi.ac.uk/bioimage-archive/galleries/ai/analysed-dataset/S-BIAD1518/ uuid: ea49e3a2-b0ca-4be9-a357-f6577967b74f language: en - authors: - Neeraj Kumar - Ruchika Verma - Sanuj Sharma - Surabhi Bhargava - Abhishek Vahadane - Amit Sethi description: The dataset for this challenge was obtained by carefully annotating tissue images of several patients with tumors of different organs and who were diagnosed at multiple hospitals. This dataset was created by downloading H&E stained tissue images captured at 40x magnification from TCGA archive. H&E staining is a routine protocol to enhance the contrast of a tissue section and is commonly used for tumor assessment (grading, staging, etc.). Given the diversity of nuclei appearances across multiple organs and patients, and the richness of staining protocols adopted at multiple hospitals, the training datatset will enable the development of robust and generalizable nuclei segmentation techniques that will work right out of the box. license: CC-BY-NC-SA-4.0 name: MoNuSeg Dataset publication_date: '2017-07-01' tags: - AI-ready - exclude from DALIA type: - Data url: https://monuseg.grand-challenge.org/Data/ uuid: 1b76b874-a91d-4e14-ae53-51b47fe6bfa2 language: en - authors: - Ruchika Verma - Neeraj Kumar - Abhijeet Patil - Nikhil Cherian Kurian - Swapnil Rane - Simon Graham description: H&E staining of human tissue sections is a routine and most common protocol used by pathologists to enhance the contrast of tissue sections for tumor assessment (grading, staging, etc.) at multiple microscopic resolutions. Hence, we will provide the annotated dataset of H&E stained digitized tissue images of several patients acquired at multiple hospitals using one of the most common 40x scanner magnification. The annotations will be done with the help of expert pathologists. license: CC-BY-NC-SA-4.0 name: MonuSAC 2020 publication_date: '2021-06-04' tags: - AI-ready - exclude from DALIA type: - Data url: https://monusac-2020.grand-challenge.org/Data/ uuid: dfd8e0f9-c207-4476-b45e-f307e1109316 language: en - authors: - Amirreza Mahbod - Benjamin Bancher - Isabella Ellinger - Deyun Zhang - Syed Nauyan Rashid description: A Dataset for Nuclei Segmentation of Cryosectioned H&E-Stained Histologic Images license: CC-BY-NC-SA-4.0 name: CryoNuSeg publication_date: '2019-12-31' tags: - AI-ready - exclude from DALIA type: - Data url: https://www.kaggle.com/datasets/ipateam/segmentation-of-nuclei-in-cryosectioned-he-images uuid: 6096caf0-c115-4cf6-91d6-4bdae596b7ca - authors: - Amirreza Mahbod - Christine Polak - Katharina Feldmann - Rumsha Khan - Katharina Gelles - Georg Dorffner - Ramona Woitek - Sepideh Hatamikia - Isabella Ellinger description: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Images license: CC-BY-4.0 name: NuInsSeg publication_date: '2024-05-14' tags: - AI-ready - exclude from DALIA type: - Data url: https://www.kaggle.com/datasets/ipateam/nuinsseg uuid: 2b7f1af4-52d3-45a0-a18a-b25e02395ede - authors: - Shenggan Gan - Nicolas Chen description: BCCD Dataset is a small-scale dataset for blood cells detection. license: MIT License name: BCCD Dataset publication_date: '2017-12-07' tags: - AI-ready - exclude from DALIA type: - Data url: https://github.com/Shenggan/BCCD_Dataset uuid: a6b132e3-51b0-499d-8af7-368165449435 - authors: - Naylor Peter Jack - Walter Thomas - Laé Marick - Reyal Fabien description: 'Involves an annotated large number of cells, including normal epithelial and myoepithelial breast cells (localized in ducts and lobules), invasive carcinomatous cells, fibroblasts, endothelial cells, adipocytes, macrophages and inflammatory cells (lymphocytes and plasmocytes). In total, our data set consists of 50 images with a total of 4022 annotated cells, the maximum number of cells in one sample is 293 and the minimum number of cells in one sample is 5, with an average of 80 cells per sample and a high standard deviation of 58. The annotation was performed by three experts: an expert pathologist and two trained research fellows. Each sample was annotated by one of the annotators, checked by another one and in case of disagreement, a consensus was established by discussion among the 3 experts.' license: CC-BY-4.0 name: TNBC publication_date: '2018-02-16' tags: - AI-ready - exclude from DALIA type: - Data url: https://paperswithcode.com/dataset/tnbc uuid: bed0c481-cda6-44b0-893d-b3329efb0c83 language: en - authors: - Carsen Stringer - Tim Wang - Michalis Michaelos - Marius Pachitariu description: This is a cellpose training dataset. Cellpose is a generalist deep learning model for cell segmentation. license: Custom license name: cellpose training data publication_date: '2020-12-14' tags: - AI-ready - exclude from DALIA type: - Data url: https://www.cellpose.org/dataset uuid: 8838da63-b780-4b61-8fce-b67e6b7a66dd - authors: - Marc Aubreville - Frauke Wilm description: Mitosis domain generation. Here you can find code of our own evaluations and a dockered reference algorithm for mitotic figures to use as a template. license: UNLICENSED name: MIDOG 2021 publication_date: '2021-03-16' tags: - AI-ready - exclude from DALIA type: - Data url: https://github.com/DeepMicroscopy/MIDOG uuid: fdabf7eb-1521-4dd8-8b4b-f131488cf086 - authors: - Wenqi Tang - MIC Group description: This repo is the official implementation of our paper "Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides". license: UNLICENSED name: Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides publication_date: '2021-12-12' tags: - AI-ready - exclude from DALIA type: - Data url: https://github.com/bupt-ai-cz/BALNMP uuid: d107577b-be15-41dd-8982-c593cf71fbf8 - authors: - Mohammad Peikari - Sherine Salama - Sharon Nofech-Mozes - Anne L. Martel description: Breast cancer (BC) is the second most commonly diagnosed cancer in the U.S. with more than 250,000 new cases of invasive breast cancers reported in 2017. The majority of women with locally advanced and a subset of patients with operable breast cancer will undergo systemic therapy prior to their surgery (neoadjuvant therapy/ NAT) to reduce the size of tumor(s) and possibly further undergo breast conserving surgery. The Post-NAT-BRCA dataset is a collection of representative sections from breast resections in patients with residual invasive BC following NAT. Histologic sections were prepared and digitized to produce high resolution, microscopic images of treated BC tumors. Also included, are clinical features and expert pathology annotations of tumor cellularity and cell types. The Residual Cancer Burden Index (RCBi), is a clinically validated tool for assessment of response to NAT associated with prognosis. Tumor cellularity is one of the parameters used for calculating the RCBi. In this dataset, tumor cellularity refers to a measure of residual disease after NAT, in the form of proportion of malignant tumor inside the tumor bed region; also annotated. (See MD Anderson RCB Calculator for a detailed description of tumor cellularity.) Malignant, healthy, lymphocyte and other labels were also provided for individual cells to aid development of cell segmentation algorithms. license: CC-BY-3.0 name: Assessment of Residual Breast Cancer Cellularity after Neoadjuvant Chemotherapy using Digital Pathology publication_date: '2017-10-04' tags: - AI-ready - exclude from DALIA type: - Data url: https://www.cancerimagingarchive.net/collection/post-nat-brca/ uuid: e79e935c-5df5-41da-997b-6c949aab3558 language: en - authors: - Mohamed Amgad - Habiba Elfandy - Hagar Hussein - Lamees A Atteya - Mai A T Elsebaie - Lamia S Abo Elnasr - Rokia A Sakr - Hazem S E Salem - Ahmed F Ismail - Anas M Saad - Joumana Ahmed - Maha A T Elsebaie - Mustafijur Rahman - Inas A Ruhban - Nada M Elgazar - Yahya Alagha - Mohamed H Osman - Ahmed M Alhusseiny - Mariam M Khalaf - Abo-Alela F Younes - Ali Abdulkarim - Duaa M Younes - Ahmed M Gadallah - Ahmad M Elkashash - Salma Y Fala - Basma M Zaki - Jonathan Beezley - Deepak R Chittajallu - David Manthey - David A Gutman - Lee A D Cooper description: 'This repo contains the necessary information and download instructions to download the dataset associated with the paper: Amgad M, Elfandy H, ..., Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083. This data can be visualized in a public instance of the Digital Slide Archive at this link. If you click the “eye” image icon in the Annotations panel on the right side of the screen, you will see the results of a collaborative annotation.' license: CC0 name: Breast Cancer Semantic Segmentation (BCSS) dataset publication_date: '2019-11-09' tags: - AI-ready - exclude from DALIA type: - Data url: https://github.com/PathologyDataScience/BCSS uuid: 85264c9d-11f7-47d7-94c1-a8c5d9eb3e06 language: en - authors: - Moore, Josh - Sun, Yi description: 'Slides for the NFDI Tech Talk live streamed to https://www.youtube.com/live/bzfmE29S270 See http://nfdi.de/talks for more information.' license: cc-by-4.0 name: '[NFDI Tech Talk] Cloud Based Image Science' num_downloads: 24 publication_date: '2025-06-02' submission_date: '2025-06-03T11:21:25.871337' url: - https://zenodo.org/records/15575379 - https://doi.org/10.5281/zenodo.15575379 uuid: bc586df5-e0ec-4cf9-9df7-b9ecccb4bae4 file_formats: .pdf tags: - NFDI4Bioimage - include in DALIA authors_with_orcid: - Josh Moore - Yi Sun - authors: - Schmidt, Christian - Mathur, Aastha - Moore, Josh description: 'Presented at ELMI2025   https://www.embl.org/about/info/course-and-conference-office/events/elmi2025/' license: cc-by-4.0 name: '[ELMI2025] Bridging communities with OME-Zarr' num_downloads: 42 publication_date: '2025-06-04' submission_date: '2025-06-03T11:21:26.272302' url: - https://zenodo.org/records/15393592 - https://doi.org/10.5281/zenodo.15393592 uuid: 3dcaa04f-b228-45f0-babb-a6af81b33ba8 file_formats: .pdf tags: - NFDI4Bioimage - include in DALIA authors_with_orcid: - Christian Schmidt - Aastha Mathur - Josh Moore - authors: - Moore, Josh description: 'https://www.ebi.ac.uk/training/events/towards-open-and-standardised-imaging-data-introduction-bio-formats-ome-tiff-and-ome-zarr/ Microscopy and bioimaging technologies are fundamental tools for exploring biological systems, generating large, multidimensional datasets rich in experimental detail. However, the bioimaging community has historically faced major challenges around data handling: vendor-specific proprietary formats, fragmented metadata storage, and increasingly large dataset sizes that outstrip traditional storage and computing solutions. In this webinar, key open technologies developed by the Open Microscopy Environment (OME) to address these challenges were presented. Specifically, the Bio-Formats library for accessing diverse proprietary file formats, the OME-TIFF standard for archival data storage, and the OME-Zarr format for cloud-native, scalable bioimaging workflows were presented.' license: cc-by-4.0 name: 'Towards open and standardised imaging data: an introduction to Bio-Formats, OME-TIFF, and OME-Zarr' num_downloads: 264 publication_date: '2025-05-28' submission_date: '2025-06-03T11:21:26.733860' url: - https://zenodo.org/records/15479606 - https://doi.org/10.5281/zenodo.15479606 uuid: 72cf60c5-830f-4080-82ec-e5307060a76d language: en file_formats: .pdf tags: - NFDI4Bioimage - include in DALIA authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X - authors: - Bustos, Jonatan description: This dataset contains 4 .nd2 image files of pollen grains captured using a Nikon 80i microscope. The files include both the original full-frame images and cropped Regions of Interest (ROIs) extracted from them. All images are in RGB format and include multiple Z-stack layers. license: cc-by-4.0 name: Nd2 does not open in Fiji Bio_formats 8.1.1 (additional files) num_downloads: 11 publication_date: '2025-05-23' submission_date: '2025-06-03T11:22:08.077083' url: - https://zenodo.org/records/15493140 - https://doi.org/10.5281/zenodo.15493140 uuid: e751285d-f2e7-471e-9cd8-2a3967d8a474 language: en file_formats: .nd2 tags: - exclude from DALIA authors_with_orcid: - Jonatan Bustos - authors: - Carlos, Jaramillo description: this file is a .nd2 image of a pollen grain taken with a Nikon 80i.  It is in RGB and it is a stack of hundreds of Z layers license: cc-by-4.0 name: Nd2 does not open in Fiji Bio_formats 8.1.1 num_downloads: 1 publication_date: '2025-06-02' submission_date: '2025-06-03T11:22:15.103885' url: - https://zenodo.org/records/15579371 - https://doi.org/10.5281/zenodo.15579371 uuid: 48befda9-ad2d-4a01-a200-24185f976e76 file_formats: .nd2 tags: - exclude from DALIA authors_with_orcid: - Jaramillo Carlos https://orcid.org/0000-0002-2616-5079 - authors: - Andrey Fedorov - Daniela Schacherer - Dennis Bontempi - Bill Clifford - Vamsi Thiriveedhi - Brianna Major - wlongabaugh - dependabot[bot] - Deepa Krishnaswamy description: Self-guided notebook tutorials to help get started with IDC license: BSD 3-Clause "New" or "Revised" License name: IDC-Tutorials publication_date: '2024-11-28T17:20:53+00:00' submission_date: '2025-06-10T06:50:43.986339' tags: - bio-image analysis - include in DALIA type: GitHub Repository url: https://github.com/ImagingDataCommons/IDC-Tutorials uuid: b50b1a33-9fd0-4aab-9208-a2af56c63d77 - authors: - Swedlow, J.R. - Kankaanpää, P. - Sarkans, U. - et al. name: A global view of standards for open image data formats and repositories description: A comprehensive overview of existing standards for image data formats in biomedicine, including DICOM, OME-TIFF and NIfTI. It discusses standardisation challenges and provides recommendations for improving the interoperability and FAIRness of image data. type: publication url: https://doi.org/10.1038/s41592-021-01113-7 uuid: bea8e15a-37d5-4fb2-8f94-17a779993313 language: en tags: - include in DALIA - authors: - Moore, Josh description: 'Presented at https://www.embl.org/about/info/course-and-conference-office/events/elmi2025/   Abstract For over 20 years, the Open Microscopy Environment (OME) has developed tools and specifications to support bioimaging data sharing. Technologies such as Bio-Formats, OMERO, and OME-TIFF have helped researchers manage the growing size, complexity, and acquisition rates of imaging datasets. However, with increasing mandates for research data management, such as the Nelson memo in the United States, and the shift toward cloud-native workflows, the bioimaging community faces new challenges in ensuring scalable and FAIR data infrastructure. In 2024, following expanding community engagement, the focus of the Next-Generation File Format (NGFF) community was on building consensus around a Request for Comments (RFC) process. This collaborative effort has laid the foundation for future refinements and wider adoption. In parallel, we hosted the “OME2024 NGFF Challenge," bringing together over the course of just four months hundreds of terabytes of data in a first prototype of federated image hosting, showcasing the power of OME-Zarr for handling large-scale, distributed datasets. In 2025, we are set to take a major step toward a stable FAIR solution with OME-Zarr 1.0. This milestone marks a crucial phase towards an international standard, providing an open, cloud-optimized, and scalable solution for handling terabyte- and petabyte-scale imaging data. The 1.0 release will introduce long-awaited transforms, enabling robust support for multimodal datasets, followed by collections and an extensibility mechanism to accommodate evolving scientific needs. These additions emphasize a solid foundation on which future capabilities can be built while providing the stability needed for broader adoption of the format. This presentation will outline the path to 1.0, including community-driven refinements, vendor engagement to ensure complete metadata representation, and alignment with global bioimaging initiatives. As imaging data continues to grow in scale and complexity, consensus-driven evolution of infrastructure will be key to ensuring a truly FAIR future for bioimaging.  ' license: cc-by-4.0 name: '[ELMI2025] The Road to OME-Zarr 1.0' num_downloads: 74 publication_date: '2025-06-05' submission_date: '2025-06-10T11:20:53.889968' url: - https://zenodo.org/records/15597856 - https://doi.org/10.5281/zenodo.15597856 uuid: aa977cf8-3042-4cfe-863f-edd3fec3200a language: en file_formats: .pdf tags: - NFDI4Bioimage - include in DALIA authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X - authors: - Kemmer, Isabel - Euro-BioImaging ERIC description: ' This workshop was held at the ELMI Meeting 2025 in Heidelberg (https://www.embl.org/about/info/course-and-conference-office/events/elmi2025/). Abstract FAIR 101 - Navigating FAIR data from principles to practice Isabel Kemmer, Euro-BioImaging ERIC This workshop will introduce the FAIR principles in the context of bioimaging data. Designed for researchers working across scales and technologies of biological and biomedical imaging, the session will address the unique challenges posed by complex, multidimensional bioimaging datasets. With the aim of providing simple yet impactful steps for a smooth start to the FAIR journey we will explore the features and benefits of FAIR data through interactive exercises and discussions - from metadata annotation and data management planning to repository selection. By the end of the workshop, you will feel more confident in applying the FAIR concepts and be prepared to improve your imaging workflows to make your precious data even more valuable.' license: cc-by-4.0 name: '[ELMI2025] Workshop: FAIR101 - Navigating FAIR data from principles to practice' num_downloads: 12 publication_date: '2025-06-12' submission_date: '2025-06-24T11:22:44.045679' url: - https://zenodo.org/records/15647102 - https://doi.org/10.5281/zenodo.15647102 uuid: 2285c9b6-dc37-4bf4-a62c-55419c510271 language: en file_formats: .pdf tags: - include in DALIA authors_with_orcid: - Isabel Kemmer https://orcid.org/0000-0002-8799-4671 - Euro-BioImaging ERIC - authors: - Schätz, Martin description: 'Presented as a part of: Mexican Bioimaging Workshops 9: Fundamentos de Microscopía “Microscopía en la Salud” Workshop on light microscopyJune 26th to 28th, 2024 Outreach 9June 29th, 2024 Expanding Global Access to BioimagingConnecting the Mexican Bioimaging Community' license: cc-by-4.0 name: 'Navigating the Bioimage Analysis Landscape: Understanding the Community and its Collaborative Dynamics' num_downloads: 133 publication_date: '2024-06-28' submission_date: '2025-06-24T11:23:31.490645' url: - https://zenodo.org/records/12584729 - https://doi.org/10.5281/zenodo.12584729 uuid: fc69b529-2165-4ada-8e55-36dae2730850 language: en file_formats: .pdf * .pptx tags: - include in DALIA authors_with_orcid: - Martin Schätz https://orcid.org/0000-0003-0931-4017 - authors: - Koutentaki, Georgia - Schätz, Martin - Vališ, Jan license: cc-by-4.0 name: WHAT NOT TO DO WHEN CREATING A DATA MANAGEMENT PLAN (DMP) num_downloads: 57 publication_date: '2025-05-14' submission_date: '2025-07-09T05:00:03.804035' url: - https://zenodo.org/records/15402904 - https://doi.org/10.5281/zenodo.15402904 uuid: 4d31048c-5ce3-40ac-90dc-b2755fe19822 file_formats: .pdf tags: - include in DALIA authors_with_orcid: - Georgia Koutentaki https://orcid.org/0000-0002-8099-9963 - Martin Schätz https://orcid.org/0000-0003-0931-4017 - Jan Vališ https://orcid.org/0000-0003-0349-6868 - authors: - Kylies, Dominik - Heil, Hannah S. - Vesga, Arturo G. - Del Rosario, Mario - Schwerk, Maria - Kuehl, Malte - Wong, Milagros N. - Puelles, Victor - Henriques, Ricardo description: 'Here we provide test datasets and a training manual for the parameter optimization with eSRRF.  The training manual will guide users through an eSRRF paramenter optimization routine and quantiative image quality assesment with both, the ImagJ-Plugin NanoJ-eSRRF (Chapter 1) and the python implementation NanoPyx-eSRRF (Chapter 2). By showcasing the optimization routine on three differnt test dataset (Chapter 3), providing intermediate results and expected outcome, the users can eaisily learn how to find the optimal processing parameters for eSRRF processing. Three samples are provided to showcase the eSRRF reconstruction process: 1. Microtubules sample: Set01_DNA-PAINT_Microtubules.tif DNA-PAINT microscopy measurement of immunolabeled microtubules in fixed COS-7 cells, showing 0.121 localizations per frame and µm^2 (data published in Laine and Heil et al.) 108x90 pixels, 500 frames, pixel size: 160 nm  2. Kidney sample: Set02_KidneySDNephrinExM.tif ExM of human kidney biopsies stained with nephrin (data published in Kylies et al.) 150x150 pixels, 200 frames, pixel size: 102 nm  3. Single emitters simulation: Set03_simulation_groundTruth_2p5Sigma - Fluorescence stack_Avg5.tif Simulated individual molecules emitting placed on concentric rings with radii increasing by 220 nm steps. On each ring the molecules are separated by 57.5, 115, 173, 230, 288 and 345 nm, respectively (data published in Laine and Heil et al.) 33x33 pixels, 100 frames, pixel size: 100 nm  4. Test dataset for drift/vibration correction: Set04_ExSRRF_eSRRF_vibration_correction_practice_dataset.tif EsM of human kidney biopsies stained with nephrin (data published in Kylies et al.) 100x100 pixels, 200 frames, pixel size: 102 nm 5. Test dataset for photobleaching: Set05_Photobleaching.tif ExM of 120 nm Nanorulers (data published in Kylies et al.) 150x150 pixels, 75 frames, pixel size: 64 nm   Jupyter-Notebook: ridge_detection.ipynb With this notebook qantitative image analyis of sturctures resolved with ExSRRF can be performed. Such as: calculation of the target structure density.  identifying areas with high inter-ridge spacing by maping the distance to the nearest ridge based on Euclidean distance transform.  measuring the spatial uniformity of the structure of interest by examining the distribution of the local densities and the distances to the nearest ridge.  ' license: cc-by-4.0 name: Expansion and fluctuations-enhanced microscopy for nanoscale molecular profiling of cells and tissues - Data processing manual num_downloads: 156 submission_date: '2025-07-05T21:50:21.910963' url: - https://zenodo.org/records/13897937 - https://doi.org/10.5281/zenodo.13897937 uuid: b712f2da-5847-4b78-8e6e-19006b75cea8 language: en file_formats: .ipynb * .pdf * .tif tags: - include in DALIA authors_with_orcid: - Dominik Kylies https://orcid.org/0009-0005-8801-0354 - Hannah S. Heil https://orcid.org/0000-0003-4279-7022 - Arturo G. Vesga https://orcid.org/0000-0003-1485-9978 - Mario Del Rosario https://orcid.org/0000-0002-0430-1463 - Maria Schwerk https://orcid.org/0000-0002-1185-2343 - Malte Kuehl https://orcid.org/0000-0003-4167-2498 - Milagros N. Wong - Victor Puelles https://orcid.org/0000-0002-7735-5462 - Ricardo Henriques https://orcid.org/0000-0002-2043-5234 - authors: - Weidtkamp-Peters, Stefanie - Boissonnet, Tom - Schmidt, Christian description: 'Presentation slides from an EMBL-EBI Webinar Talk within the webinar series: "How to organise and share my imaging data? - Multimodal data management for marine biologists, environmental scientists and imaging specialists"   Abstract / Description Bioimaging is a pervasive and indispensable methodological approach in the life and biomedical sciences. Due to the development of new technologies and the easier access to compute resources, bioimaging experiments have become a big data discipline, facing the same challenges as other omics technologies within the life sciences. However, to fully exploit the potential of bioimage data, it is necessary to make the data FAIR. In this webinar we will present viable solutions for storing, processing, analysing, and, first and foremost, sharing bioimaging data. We will introduce services provided to the scientific community, that are dealing with various aspects of the bioimage data life cycle such as: - Where to get support for bioimage data management- Local bioimage data management: OMERO and beyond- Annotation of bioimage data: metadata, ontologies, REMBI etc- Linking your image data with experimental protocols and analysis results- Large data living in the cloud: ome.zarr- Publication of bioimage data Who is this course for? This webinar is suitable for marine biologists and environmental scientists collecting samples from the natural environment, generating, visualising, annotating and analysing large, multimodal datasets such as imaging data, and sharing their data by submitting them to public data repositories. The webinar will support you to set up an efficient data flow that is aligned with FAIR principles. This event is part of a webinar series organised by the STANDFLOW project, an initiative supported by EMBL’s Planetary biology Transversal Theme. STANDFLOW is about a collaborative effort towards creating a standardised data management workflow. The project primarily utilises imaging data derived from samples collected through the TREC (Traversing European Coastlines) and the Roscoff Culture Collection. For details on all topics covered in this series and registration information, please visit the following link: How to organise and share my imaging data?: Multimodal data management for marine biologists, and environmental scientists and imaging specialists Outcomes By the end of the webinar you will be able to:  Find resources and support for bioimage data management Get started with bioimage data annotation Identify the dos and don''ts for bioimage data publication   (taken from: https://www.ebi.ac.uk/training/events/journey-fair-bioimage-data/)' license: cc-by-4.0 name: '[Webinar] A journey to FAIR bioimage data' num_downloads: 42 publication_date: '2025-07-03' submission_date: '2025-07-08T11:22:16.769521' url: - https://zenodo.org/records/15796252 - https://doi.org/10.5281/zenodo.15796252 uuid: 80f681e0-94cb-4059-8096-3d396f16e4dc language: en file_formats: .pdf tags: - NFDI4Bioimage - FAIR Principles - Research Data Management - include in DALIA authors_with_orcid: - Stefanie Weidtkamp-Peters https://orcid.org/0000-0001-7734-3771 - Tom Boissonnet https://orcid.org/0000-0002-3328-9467 - Christian Schmidt https://orcid.org/0000-0001-9568-895X - authors: - Haase, Robert description: These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material will develop here and in the corresponding github repository between April and July 2025. license: cc-by-4.0 name: Bio-image Data Science Lectures 2025 @ Uni Leipzig / ScaDS.AI num_downloads: 3351 publication_date: '2025-07-02' submission_date: '2025-07-08T11:22:17.313440' url: - https://zenodo.org/records/15793536 - https://doi.org/10.5281/zenodo.15793536 uuid: a59dab46-98ac-4317-a7cd-c6fca9b3eb64 language: en file_formats: .pdf * .pptx tags: - NFDI4Bioimage - Bioimage Analysis - Artificial Intelligence - exclude from DALIA authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 - authors: - Michele, Bortolomeazzi description: OMERO is the most used research data management system (RDM) in the bioimaging domain, and has been adopted as a centralized RDM solution by several academic and research institutions. A main reason for this is the ability to directly view and annotate images from a web-based interface. However, this feature of OMERO is currently underpowered for the visualization of very large or multimodal datasets. These datasets, are becoming a more and more common foundation for biological and biomedical studies, due to the recent developments in imaging, and sequencing technologies which enabled their application to spatial-omics. In order to begin to provide this multimodal-data capability to OMERO, we developed omero-vitessce (https://github.com/NFDI4BIOIMAGE/omero-vitessce/tree/main), a new OMERO.web plugin for viewing data stored in OMERO with the Vitessce (http://vitessce.io/) multimodal data viewer. omero-vitessce can be installed as an OMERO.web plugin with PiPy (https://pypi.org/project/omero-vitessce/), and allows users to set up interactive visualizations of their images of cells and tissues through interactive plots which are directly linked to the image. This enables the visual exploration of bioimage-analysis results and of multimodal data such as those generated through spatial-omics experiments. The data visualization is highly customizable and can be configured not only through custom configuration files, but also with the graphical interface provided by the plugin, thus making omero-vitessce a highly user-friendly solution for multimodal data viewing. most biological datasets. We plan to extend the interoperability of omero-vitessce with the OME-NGFF and SpatialData file formats to leverage the efficiency of these cloud optimized formats. license: cc-by-4.0 name: Accessible Interactive Spatial-Omics Data Visualizations with Vitessce and OMERO num_downloads: 41 publication_date: '2025-06-30' submission_date: '2025-07-08T11:22:17.726193' url: - https://zenodo.org/records/15771899 - https://doi.org/10.5281/zenodo.15771899 uuid: e41f9a04-555e-42be-ae13-bc37f42da881 language: en file_formats: .pptx tags: - NFDI4Bioimage - OMERO - include in DALIA authors_with_orcid: - Bortolomeazzi Michele https://orcid.org/0000-0001-5805-5774 - authors: - Moore, Josh description: 'Presentation for the BioImagingUK Meeting taking place from 1200 - 1500 BST on Monday 30 June 2025 at mmc2025 https://www.mmc-series.org.uk/meetings-features/bioimaginguk-meeting.html This pre-congress meeting provides an opportunity for the UK Bioimaging community to discuss priorities and strategies in national infrastructure, technology development, training, careers and ways to share knowledge across different disciplines. The session will consist of short talks from members of the BioImagingUK community and industrial/institute collaboration partners to update on progress, new opportunities and initiatives. There will be interactive Q+A sessions to encourage discussion and enable emerging priorities and ideas to be highlighted.' license: cc-by-4.0 name: "Building a National Research Data Infrastructure \nfor Microscopy and BioImage\ \ Analysis" num_downloads: 76 publication_date: '2025-06-30' submission_date: '2025-07-08T11:22:18.112327' url: - https://zenodo.org/records/15756866 - https://doi.org/10.5281/zenodo.15756866 uuid: 1df2f876-ae76-46ea-b32c-b4aafaafba56 language: en file_formats: .pdf tags: - NFDI4Bioimage - include in DALIA authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X - authors: - Haase, Robert description: 'In this talk, I demonstrate potential use-cases for vision-language models (VLM) in bio-image data science, focusing on how to analyse microscopy image data. It covers these use-cases: cell counting bounding-box segmentation image descriptions VLMs guessing which algorithm to use for processing Data analysis code generation Answering github issues  The talk also points at a number of VLM-based open-source tools which start reshaping the scientific bio-image data science domain: bia-bob unprompted git-bob napari-chatgpt bioimage.io chatbot ' license: cc-by-4.0 name: Vision Language Models for Bio-image Data Science num_downloads: 432 publication_date: '2025-06-25' submission_date: '2025-07-08T11:22:18.525415' url: - https://zenodo.org/records/15735577 - https://doi.org/10.5281/zenodo.15735577 uuid: fb0f3cd1-86ff-48e0-b6a1-42dcb16f33ae language: en file_formats: .pdf * .pptx tags: - NFDI4Bioimage - Bioimage Analysis - Artificial Intelligence - include in DALIA authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 - authors: - Euro-BioImaging ERIC description: 'EVOLVE Mentoring Masterclasses Description:This series captures the class guides of the 2025 masterclasses from Euro‑BioImaging''s EVOLVE Mentoring Program. Included Masterclasses: Peter O’Toole – “Entrepreneurship & Leadership in Imaging Core Facilities” Peter O’Toole, President of the Royal Microscopical Society and Director of the Bioscience Technology Facility (University of York), kicks off the series with a deep dive into entrepreneurial leadership. He highlights how to balance science, business, and technology, emphasizing stakeholder engagement, staff investment, cross-training, and using social media to boost visibility and unlock funding. Ilaria Testa – “Interdisciplinary Science, SMART Microscopy & Team Building” Professor Ilaria Testa (SciLifeLab & KTH) reflects on her transition from physics to super-resolution microscopy and team leadership. Her session underscores the power of crossing disciplinary boundaries, mentorship, and innovation in live-cell imaging . Daphna Link‑Sourani – “Leadership, Facility Management & Work‑Life Balance” Dr. Daphna Link‑Sourani (Technion Human MRI Research Center) challenges hierarchical notions of leadership, advocating instead for integrity, empathy, and strategic vision. She draws on her experience establishing an MRI facility to discuss crisis management, user engagement, and balancing career demands. Muriel Mari – “Women in Science: Normalizing, Supporting & Leading"                                                                           Dr. Muriel Mari (Aarhus University) leads a powerful reflection on gender equity in science. Her masterclass goes beyond barriers—focusing on cultural shifts, inclusive leadership, and redefining success. She encourages institutions and individuals alike to move from tokenism to transformative support, and to recognize the diverse paths women take in STEM. Sylvia E. Le Dévédec – “Image Data Management & FAIR Core Facilities”                                                                     Dr. Sylvia Le Dévédec (Leiden University) discusses how to integrate FAIR data principles in imaging core facilities. Drawing on her experience with high-content imaging and Open Science advocacy, she outlines actionable steps toward sustainable, reusable, and accessible data workflows. Why Archive These Sessions?These masterclasses offer invaluable insights for core facility managers, imaging scientists, and team leaders in life sciences. They blend hands-on leadership strategies, technical facility growth advice, and real-world experience—making them essential viewing for professionals and institutions aiming to build sustainable, people-centred imaging infrastructures.' license: cc-by-4.0 name: Masterclasses from the Euro-Bioimaging EVOLVE Mentoring programme 2025 num_downloads: 42 publication_date: '2025-06-26' submission_date: '2025-07-08T11:22:42.307782' url: - https://zenodo.org/records/15747344 - https://doi.org/10.5281/zenodo.15747344 uuid: a5be89ce-be47-4cc3-9685-ff082987afa1 language: en file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Euro-BioImaging ERIC - authors: - Schätz, Martin - Rubešová, Olga - Mareš, Jan - Spark, Alan description: 'The software tool is developed on demand of Radiological Department of Faculty Hospital of Královské Vinohrady, with the aim to provide a tool to estimate the percentage of pneumonia (or COVID-19 presence) in lungs. Paper Estimation of Covid-19 lungs damage based on computer tomography images analysis presenting the tool is available on F1000reserach DOI: 10.12688/f1000research.109020.1. The underlying dataset is published in Zenodo (DOI:10.5281/zenodo.5805939). One of the challenges was to design a tool that would be available without complicated install procedures and would process data in a reasonable time even on office computers. For this reason, 8-bit and 16-bit version of the tool exists. The FIJI software (or ImageJ with Bio-Formats plugin installed) was selected as the best candidate. Examples of use and tutorials are available at GitHub.  The third version includes an intra-variabilty analysis, containing evaluation both for percentage and score metrics. Underlying data:DOI:10.5281/zenodo.5805939The first five datasets are analyzed using this tool, with results and parameters to repeat the analysis in results_csv.csv or results.xlsx. Contributions:Martin SCHÄTZ:       Coding, tool testing, data curation, data set analysisOlga RUBEŠOVÁ:    Code review, tutorial preparation, tool testing, data set analysisJan MAREŠ:             Tool testing, data set analysis, funding acquisitionAlan SPARK:             Tool testing The work was funded by the Ministry of Education, Youth and Sports by grant ‘Development of Advanced Computational Algorithms for evaluating post-surgery rehabilitation’ number LTAIN19007. The work was also supported from the grant of Specific university research – grant No FCHI 2022-001.  ' license: cc-by-4.0 name: ImageJ tool for percentage estimation of pneumonia in lungs num_downloads: 233 publication_date: '2025-07-07' submission_date: '2025-07-08T11:22:49.880643' url: - https://zenodo.org/records/15827771 - https://doi.org/10.5281/zenodo.15827771 uuid: 4bc7c5be-f890-4eb0-8e1e-f21f665dbfca language: en file_formats: .csv * .xlsx * .zip tags: - exclude from DALIA authors_with_orcid: - Martin Schätz https://orcid.org/0000-0003-0931-4017 - Olga Rubešová https://orcid.org/0000-0003-2101-499X - Jan Mareš https://orcid.org/0000-0003-4693-2519 - Alan Spark - authors: - Chu, Wei-Chen description: 'Presentation file used in the EABIAS training event: EABIAS/2025-ImageJ-Micro-Image-Analysis-and-Programming_Taipei (Lesson_04)Video Recording (in Mandarin): https://www.youtube.com/watch?v=uheSMSENnzE' license: cc-by-4.0 name: Interactive Bioimage Analysis Workflow with CLIJ (@EABIAS 2025 training event) num_downloads: 305 publication_date: '2025-03-23' submission_date: '2025-07-09T09:32:53.963703' url: - https://zenodo.org/records/15070246 - https://doi.org/10.5281/zenodo.15070246 uuid: c872ef0f-528d-47e6-ac0b-6033a95590ea language: en file_formats: .pdf * .pptx tags: - include in DALIA authors_with_orcid: - Wei-Chen Chu https://orcid.org/0000-0002-3447-9043 - authors: - Myles Scolnick description: Teaching material for image processing and analysis license: Apache License 2.0 name: image-processing-basics publication_date: '2025-05-27T08:31:14+00:00' submission_date: '2025-07-09T09:44:28.758546' tags: - bio-image analysis - include in DALIA type: GitHub Repository url: https://github.com/fmi-faim/image-processing-basics uuid: 793ab89b-7158-42ce-99b5-3efcfb092f25 - authors: - Stefan Halfpap description: '' name: data_visualization_tutorial publication_date: '2025-04-25T21:00:39+00:00' submission_date: '2025-07-09T10:49:10.643610' tags: - data visualization - include in DALIA type: GitHub Repository url: https://github.com/klauck/data_visualization_tutorial uuid: 61921e84-5324-49c3-8911-6a2f13a8a248 - authors: - Antoine A. Ruzette - dependabot[bot] description: Materials supporting the QuPath workshop at Harvard Medical School. license: Creative Commons Attribution 4.0 International name: qupath-workshop publication_date: '2025-01-16T14:05:02+00:00' submission_date: '2025-07-09T10:49:46.810772' tags: - notebook - slides - collection - include in DALIA type: GitHub Repository url: https://github.com/HMS-IAC/qupath-workshop uuid: edc34d5f-72ce-4735-a781-67ee375c0aa0 - authors: - Haase, Robert description: These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material will develop here and in the corresponding github repository between April and July 2025. license: cc-by-4.0 name: Bio-image Data Science Lectures 2025 @ Uni Leipzig / ScaDS.AI num_downloads: 3876 publication_date: '2025-07-10' submission_date: '2025-07-15T11:23:02.392803' url: - https://zenodo.org/records/15858127 - https://doi.org/10.5281/zenodo.15858127 uuid: 32445e07-b9e2-4bef-b501-17007552a9b1 language: en file_formats: .pdf * .pptx tags: - NFDI4Bioimage - Bioimage Analysis - Artificial Intelligence - exclude from DALIA authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 - authors: - Euro-BioImaging ERIC description: 'EVOLVE Mentoring Masterclasses Description:This series captures the class guides of the 2025 masterclasses from Euro‑BioImaging''s EVOLVE Mentoring Program. Included Masterclasses: Peter O’Toole – “Entrepreneurship & Leadership in Imaging Core Facilities” Peter O’Toole, President of the Royal Microscopical Society and Director of the Bioscience Technology Facility (University of York), kicks off the series with a deep dive into entrepreneurial leadership. He highlights how to balance science, business, and technology, emphasizing stakeholder engagement, staff investment, cross-training, and using social media to boost visibility and unlock funding. Ilaria Testa – “Interdisciplinary Science, SMART Microscopy & Team Building” Professor Ilaria Testa (SciLifeLab & KTH) reflects on her transition from physics to super-resolution microscopy and team leadership. Her session underscores the power of crossing disciplinary boundaries, mentorship, and innovation in live-cell imaging . Daphna Link‑Sourani – “Leadership, Facility Management & Work‑Life Balance” Dr. Daphna Link‑Sourani (Technion Human MRI Research Center) challenges hierarchical notions of leadership, advocating instead for integrity, empathy, and strategic vision. She draws on her experience establishing an MRI facility to discuss crisis management, user engagement, and balancing career demands. Muriel Mari – “Women in Science: Normalizing, Supporting & Leading"                                                                           Dr. Muriel Mari (Aarhus University) leads a powerful reflection on gender equity in science. Her masterclass goes beyond barriers—focusing on cultural shifts, inclusive leadership, and redefining success. She encourages institutions and individuals alike to move from tokenism to transformative support, and to recognize the diverse paths women take in STEM. Sylvia E. Le Dévédec – “Image Data Management & FAIR Core Facilities”                                                                     Dr. Sylvia Le Dévédec (Leiden University) discusses how to integrate FAIR data principles in imaging core facilities. Drawing on her experience with high-content imaging and Open Science advocacy, she outlines actionable steps toward sustainable, reusable, and accessible data workflows. Why Archive These Sessions?These masterclasses offer invaluable insights for core facility managers, imaging scientists, and team leaders in life sciences. They blend hands-on leadership strategies, technical facility growth advice, and real-world experience—making them essential viewing for professionals and institutions aiming to build sustainable, people-centred imaging infrastructures.' license: cc-by-4.0 name: Masterclasses from the Euro-Bioimaging EVOLVE Mentoring programme 2025 num_downloads: 207 publication_date: '2025-06-26' submission_date: '2025-07-15T11:23:36.501044' url: - https://zenodo.org/records/15837532 - https://doi.org/10.5281/zenodo.15837532 uuid: 31f35a4c-5f8a-44ef-9dda-f1d5c143126c language: en file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Euro-BioImaging ERIC - authors: - Burdíková, Zuzana - Švindrych, Zdeněk - Schätz, Martin - Soukup, Jakub - Robison, Pat description: 'The full program is available in the repository (Schedule DMW 2023.rtf) with most of the presentations and all exercises. All exercise files are in ZIP files Data.zip and ThunderSTORM sample data 2023.zip. Date: June 6-7, 2023 Time: 9am - 5pm Location: Dartmouth College, 74 College St, Hanover, Kellogg 200 Sponsor: bioMT (Lunch and coffee breaks) Summary: The BioImage Analysis and Superresolution Microscopy Workshop 2023 took place at Dartmouth College, offering participants a comprehensive learning experience in the field of bioimage analysis and superresolution microscopy. Over the course of two days, researchers, scientists, and students immersed themselves in cutting-edge microscopy techniques and expanded their knowledge and practical skills. Day 1 (Tue, June 6): The workshop began with Zdenek Svindrych providing an overview of microscopy principles, methods, and theoretical foundations. Participants gained insights into image formation in fluorescence microscopes, resolution, and noise. This was followed by an introduction to superresolution microscopy techniques, including Single-Molecule localization Microscopy (STORM, PALM, DNA-PAINT) and Structured Illumination Microscopy (SIM, ISM, Airyscan, SoRa). Zuzana Burdikova then guided attendees through the theoretical aspects of bioimage processing in Fiji, covering image formats, multi-dimensional image analysis, data visualization, and quantitative analysis. Practical sessions allowed participants to apply their knowledge, exploring two-channel colocalization, image filtering, and quantitative measurements in Fiji. Day 2 (Wed, June 7): The second day commenced with a remote presentation by SVI.nl, introducing participants to the Huygens deconvolution software used in widefield, confocal, and superresolution microscopy. Zdenek Svindrych demonstrated the practical applications of ThunderSTORM, an ImageJ plug-in for single molecule localization microscopy (SMLM) data analysis and superresolution imaging. The session included an overview of the ThunderSTORM workflow, covering localization, filtering, rendering, and 3D STORM using the astigmatism method. Jakub Soukup explored advanced noise reduction algorithms such as Noise2Void and StarDist. Martin Schätz discussed data management strategies. In the afternoon, participants engaged in practical sessions. Martin Schätz presented Ilastik, a versatile software for image classification and segmentation. Pat Robison delivered a scientific lecture on the role of detyrosinated microtubules in contracting cardiomyocytes. Zdenek Svindrych, along with other experts, led a hands-on session on customizing Fiji using the ImageJ Macro language. The day concluded with a practical session on the interactive design of GPU-accelerated image data flow graphs in Fiji, guided by Martin Schätz and the workshop team.' license: cc-by-4.0 name: BioImage Analysis and Superresolution Microscopy Workshop 2023 (at Dartmouth College) num_downloads: 805 publication_date: '2023-06-07' submission_date: '2025-07-16T10:41:59.034960' url: - https://zenodo.org/records/8025067 - https://doi.org/10.5281/zenodo.8025067 uuid: f5fbc8f8-e6e8-450c-bc2b-0300669a9cd6 language: en file_formats: .docx * .pdf * .pptx * .rtf * .zip tags: - include in DALIA authors_with_orcid: - Zuzana Burdíková https://orcid.org/0000-0003-1866-8095 - Zdeněk Švindrych https://orcid.org/0000-0001-9197-9813 - Martin Schätz https://orcid.org/0000-0003-0931-4017 - Jakub Soukup https://orcid.org/0000-0002-0459-9394 - Pat Robison - authors: - Schätz, Martin - Azevedo, Maria - Sampaio, Paula description: 'Internal ALM BioImage Analysis Workshop 2023OverviewThe Internal ALM BioImage Analysis Workshop 2023, organized by the Advanced Light Microscopy i3S scientific platform, was a comprehensive 2.5-day internal workshop dedicated to open-source BioImage Analysis. The event combined informative presentations with hands-on sessions, utilizing the EMBL Bioimage Analysis Desktop (BAND) Platform. The used sources for datasets and presentations are in Notes.Workshop ProgramDay 1: Foundations of BioImage AnalysisIntroduction to Research Data Management [00i3S_Data_Management_2023_MP_Slido]: An exploration of data management, naming conventions, and ethical considerations in BioImage Analysis and image manipulation. Referencing content from the NEUBIASAcademy@Home Webinar: "In Defense of Image Data & Analysis Integrity."Interactive Image Data Flow Graphs with CLIJ2 in FIJI [01i3S_Interactive Image Data Flow Graphs]: A hands-on session introducing CLIJ2 in FIJI, with a focus on practical applications. Relevant datasets were explored during the presentation, and are described in notes.Day 2: Advanced Techniques in BioImage AnalysisNoise2Void Denoising with CSBdeep in FIJI [02i3S_IMCF_noise2void_EN] : Exploring the Noise2Void denoising approach using the CSBdeep FIJI plugin, with hands-on examples using data from the juglab/n2v GitHub repository.StarDist for Fluorescence Nuclei Segmentation [03i3S_schatzm_stardist_21] : An introduction to StarDist through the FIJI StarDist plugin, with hands-on experience using the BBBC004 dataset.Introduction to Napari 2023 [04i3S_Introduction to Napari 2023] : A hands-on session introducing Napari and its connection with CLIJ2. The datasets used were detailed in the presentation.Ilastik Pixel Classification and BioImage Model Zoo [05i3S_schatzm_Ilastik_woHandOn_PixObj_2023]: Hands-on exploration of Ilastik with a focus on pixel and object classification. Neural networks from the BioImage Model Zoo were also introduced.BioImage Model Zoo Possibilities [06i3S_BioImageModelZoo-Kreshuk]: An overview of the concept of BioImage Model Zoo and its potential applications.DeepImageJ Plugin with Hands-on on Stardist and Unet NNs [07i3S_deepImageJ-Gomez_de_Mariscal]: Introduction to the deepImageJ plugin, capable of utilizing models from the BioImage Model Zoo. The hands-on session focused on Stardist and Unet Neural Networks.Day 3: Practical Applications and Advanced TrainingHands-on with Ilastik Neural Networks: Utilizing BioImage Model Zoos, participants engaged in hands-on exercises using the Ilastik Multicut method from the AdvanceImageAnalysisEMBO2023 GitHub repository.Hands-on with Cellpose 2.0: Practical sessions included applying pre-trained models with the Simple Visual Cellpose Cheat Sheet. Participants also had the opportunity to train new models using original Cellpose datasets and larger data from BBBC019 and BBBC003 datasets. This workshop would not be possible without the previous excellent work of many people involved in the Network of European BioImage Analysts - NEUBIAS and without my attendance at the EMBO Practical Course Advanced methods in bioimage analysis .' license: cc-by-4.0 name: Internal ALM BioImage Analysis workshop 2023 num_downloads: 367 publication_date: '2023-11-03' submission_date: '2025-07-16T10:41:59.697261' url: - https://zenodo.org/records/10205578 - https://doi.org/10.5281/zenodo.10205578 uuid: d2580d86-408e-4ee5-b0d5-ecb945b0fe14 language: en file_formats: .pdf tags: - include in DALIA authors_with_orcid: - Martin Schätz https://orcid.org/0000-0003-0931-4017 - Maria Azevedo https://orcid.org/0000-0001-7316-8283 - Paula Sampaio https://orcid.org/0000-0003-1148-2159 - authors: - Chu, Wei-Chen description: 'Presentation file used in the  Open source AI Tools for bioimage analysis workshop @ICOB, Academia Sinica, Taiwan (2024)Introduce ilastik, StarDist, Cellpose, Segment Anything Model (SAM), and how to use it briefly. Full video recording (in Chinese) is available on YouTube: https://youtu.be/KqwssouW0G0 This document is part III of the previous document:Chu, W.-C. (2024). Bioimage Analysis with FIJI /ImageJ & Friends workshop (2024) @ICOB, Academia Sinica, Taiwan. Zenodo. https://doi.org/10.5281/zenodo.12803966' license: cc-by-4.0 name: Open source AI Tools for bioimage analysis workshop (2024) @ICOB, Academia Sinica, Taiwan num_downloads: 30 publication_date: '2024-08-09' submission_date: '2025-07-16T10:42:00.208411' url: - https://zenodo.org/records/13284351 - https://doi.org/10.5281/zenodo.13284351 uuid: 9432641f-d43b-451d-8197-5c3f15ecbb53 language: en file_formats: .pptx tags: - Bioimage Analysis - include in DALIA authors_with_orcid: - Wei-Chen Chu https://orcid.org/0000-0002-3447-9043 - authors: - Frisch, Katrin description: Diese FAQ versammeln Fragen, die uns häufig im Zusammenhang mit künstlicher Intelligenz (KI) und guter wissenschaftlicher Praxis (GWP) erreichen. Die Antworten sollen bei der Orientierung in einem schnelllebigen Thema helfen, ohne dabei präskriptiv zu sein. Sie stellen keine offizielle Positionierung des Ombudsman für die Wissenschaft (OfdW) dar, sondern beschreiben den Status Quo und ordnen bereits bestehende Empfehlungen aus Sicht der GWP ein, identifizieren Lücken und verweisen auf weiterführende Literatur. Diese FAQ-Sammlung richtet sich primär an Forschende. Für die Nutzung von KI in der Lehre und in studentischen (Qualifikations-)Arbeiten sind i.d.R. universitäre KI-Richtlinien, angepasste Prüfungsordnungen und Selbstständigkeitserklärungen sowie Entscheidungen individueller Lehrpersonen maßgeblich. Daher werden eventuelle Besonderheiten von KI in der Lehre und in Prüfungsangelegenheiten in diesen FAQ nicht besprochen. license: cc-by-4.0 name: FAQ Künstliche Intelligenz und gute wissenschaftliche Praxis num_downloads: 1940 publication_date: '2024-11-06' submission_date: '2025-07-16T10:42:00.640310' url: - https://zenodo.org/records/14045172 - https://doi.org/10.5281/zenodo.14045172 uuid: cbf2599b-7a81-47a0-993f-395880196b64 language: de file_formats: .pdf tags: - include in DALIA authors_with_orcid: - Katrin Frisch https://orcid.org/0000-0002-6669-5015 - authors: - Michael license: cc-by-4.0 name: DNG in BioFormat opens in wrong resolution num_downloads: 4 publication_date: '2025-07-15' submission_date: '2025-07-22T11:24:17.409487' url: - https://zenodo.org/records/15933943 - https://doi.org/10.5281/zenodo.15933943 uuid: 38e74961-593d-4185-94c2-a4afe3b4f7c4 file_formats: .dng tags: - exclude from DALIA authors_with_orcid: - Michael - authors: - Ahmadi, Mohsen - Wagner, Robert - Bekeschus, Sander - Becker, Markus M. description: This research data management workflow for bioimaging is designed to bridge the gap between image metadata and experimental / process metadata. By linking images and microscopy-related metadata with broader experimental records, the workflow particularly supports the adoption of the FAIR (Findable, Accessible, Interoperable, Reusable) data principles in interdisciplinary fields where bioimaging is used to analyse treated samples requiring multimodal metadata. A Jupyter Notebook guides the user through the metadata level, data handling level, and data processing level and connects various software components in a modular manner. On the metadata level, microscope-specific metadata are captured using the Micro-Meta App and stored as reusable digital assets. Adamant provides a user interface to collect and process schema-based metadata related to the experiment / treatment procedure. Structured imaging and process metadata are attached to the complete experiment description in eLabFTW. On the data handling level, OMERO serves as the central platform for storing and managing microscopy images together with their metadata retrieved from eLabFTW (workflow with ELN) or directly from JSON files (workflow without ELN). On the data processing level, OMERO supports both automated and manual image analysis either directly via the Jupyter Notebook or FIJI. Due to the modularity of the workflow, the integrated tools can be substituted with equivalent systems based on institutional / user requirements. Whether in teaching or research settings, this workflow supports high-throughput, reproducible imaging workflows, ensuring that data, metadata, and analysis steps remain transparent, interoperable, and reusable across diverse bioimage analysis platforms. license: cc-by-4.0 name: Bioimaging workflow based on OMERO, eLabFTW, and Adamant for integrating images with multimodal metadata num_downloads: 86 publication_date: '2025-07-29' submission_date: '2025-08-05T11:24:17.068826' url: - https://zenodo.org/records/16561545 - https://doi.org/10.5281/zenodo.16561545 uuid: de33ce5c-0837-47e0-8758-0507e18e8c01 language: en file_formats: .pdf tags: - NFDI4Bioimage - exclude from DALIA authors_with_orcid: - Mohsen Ahmadi https://orcid.org/0000-0002-7018-0460 - Robert Wagner https://orcid.org/0000-0002-2762-293X - Sander Bekeschus https://orcid.org/0000-0002-8773-8862 - Markus M. Becker https://orcid.org/0000-0001-9324-3236 - authors: - Mathur, Aastha - Euro-BioImaging ERIC description: 'This presentaiton sumarises Euro-BioImaging ERIC services, focussing on their Image Data Services. It briefly presents processes and challenges in image anlaysis service provison and introduces some supporting tools. It also emphasises the roll of community initiatives and networks in providing solutions and support towards Image data management and analysis. This presentaiton was part of the GloBIAS BioImage Analysis Seminar Series. Date of presentation: 2025-07-24' license: cc-by-4.0 name: From bioimaging projects to communities - GloBIAS BIA Seminar Series num_downloads: 6 publication_date: '2025-07-29' submission_date: '2025-08-05T11:24:53.271856' url: - https://zenodo.org/records/16573999 - https://doi.org/10.5281/zenodo.16573999 uuid: 32b4be42-f91a-41e7-913b-03c015834345 language: en file_formats: .pdf tags: - include in DALIA authors_with_orcid: - Aastha Mathur https://orcid.org/0000-0001-9734-9767 - Euro-BioImaging ERIC - authors: - Euro-BioImaging ERIC description: Euro-BioImaging ERIC is the European landmark research infrastructure for biological and biomedical imaging as recognized by the European Strategy Forum on Research Infrastructures (ESFRI). Euro-BioImaging is the gateway to world-class imaging facilities across Europe. This document is the Euro-BioImaging Annual Report for the year 2023. license: cc-by-4.0 name: Euro-BioImaging Annual Report 2023 num_downloads: 17 publication_date: '2024-06-30' submission_date: '2025-08-05T11:24:53.877207' url: - https://zenodo.org/records/16323251 - https://doi.org/10.5281/zenodo.16323251 uuid: 15ec0a79-4a09-4b8e-ac28-1bb2f0201f0b language: en file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Euro-BioImaging ERIC - authors: - Euro-BioImaging ERIC description: Euro-BioImaging ERIC is the European landmark research infrastructure for biological and biomedical imaging as recognized by the European Strategy Forum on Research Infrastructures (ESFRI). Euro-BioImaging is the gateway to world-class imaging facilities across Europe. This document is the Euro-BioImaging Annual Report for the year 2021. license: cc-by-4.0 name: Euro-BioImaging Annual Report 2021 num_downloads: 9 publication_date: '2022-06-30' submission_date: '2025-08-05T11:24:54.474698' url: - https://zenodo.org/records/16357461 - https://doi.org/10.5281/zenodo.16357461 uuid: e0eb742a-fa35-4515-a246-75c09ddab2eb language: en file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Euro-BioImaging ERIC - authors: - Euro-BioImaging ERIC description: Euro-BioImaging ERIC is the European landmark research infrastructure for biological and biomedical imaging as recognized by the European Strategy Forum on Research Infrastructures (ESFRI). Euro-BioImaging is the gateway to world-class imaging facilities across Europe. This document is the Euro-BioImaging Annual Report for the year 2020. license: cc-by-4.0 name: Euro-BioImaging Annual Report 2020 num_downloads: 9 publication_date: '2025-07-23' submission_date: '2025-08-05T11:24:55.090470' url: - https://zenodo.org/records/16357209 - https://doi.org/10.5281/zenodo.16357209 uuid: fb316cd2-88c6-4ef0-96f5-c62440c4ccbf language: en file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Euro-BioImaging ERIC - authors: - David, Romain - Liaskos, Nektarios - Rybina, Arina - Arvanitidis, Christos - Bage, Anne-Sophie - Carvajal-Vallejos, Patricia K. - Das, Sudeep - De Pascalis, Francesca - Dörr, Dorothea - Exter, Katrina - Holub, Petr - Gurwitz, Kim Tamara - Liberante, Fabio - Lieutaud, Philippe - Lister, Allyson - Lopez, Joaquin - Madon, Bénédicte - Massimi, Marzia - Matteoni, Rafaele - Mîrza, Maria - Morgan, Sarah - Oezdemir, Bugra - Panagiotopoulou, Maria - Pavloudi, Christina - P. Melo, Ana M. - Sansone, Susanna-Assunta - Schwalbe, Harald - Serrano-Solano, Beatriz - Sorzano, Carlos Oscar - Urbinati, Emilio - Tang, Jing - Tedds, Jonathan - Saunders, Gary - Ewbank, Jonathan description: 'Preprint in submission process to GigaScience journal Abstract: European Life Science Research Infrastructures (LS-RIs), one of the five major RI Science Clusters in Europe, were established to provide access to cutting-edge technologies to the scientific community. Individually, and collectively as the LS-RI cluster, they contribute to the development of the European Open Science Cloud (EOSC), under the aegis of the EOSC Federation. They are actively involved in the design and implementation of Competence Centres (CCs). These aim to increase the accessibility of domain-specific knowledge and tools, enhance interoperability, facilitate sharing and harmonisation of procedures, and promote Open Science and FAIR (Findable, Accessible, Interoperable, Reusable) practices. In this paper, we report a landscape mapping of the existing resources that formed the basis for the construction of CCs. We describe the possible design of CCs and their articulation with the LS-RIs. We focus on community-based ideas and recommendations to increase the potential of CCs to address long-standing challenges in sustainability, governance, scalability, and interoperability of Open Science within EOSC and the European Research Area (ERA) more generally.This paper provides a description of the nascent LS CCs, built following a survey of needs and services of existing LS-RI communities. When fully implemented, the LS CCs will serve as dynamic hubs to foster innovation, contribute to the EOSC’s future FAIR web of data, and support ongoing developments of the EOSC Federation. They will act as drivers of collaborative and impactful LS research in Europe and beyond. We explore the underlying challenges, and propose solutions, to ensure that the establishment of CCs will add value to the LS RI community, and to the EOSC, in a sustainable way.' license: cc-by-4.0 name: 'Life Science Competence Centres: Open by Design' num_downloads: 276 publication_date: 2025-07 submission_date: '2025-08-05T11:24:56.963094' url: - https://zenodo.org/records/15798751 - https://doi.org/10.5281/zenodo.15798751 uuid: a81a7297-8ec3-414d-a326-5c5549c0b437 language: en file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Romain David https://orcid.org/0000-0003-4073-7456 - Nektarios Liaskos https://orcid.org/0000-0003-0633-8670 - Arina Rybina https://orcid.org/0000-0002-5609-5710 - Christos Arvanitidis https://orcid.org/0000-0002-6924-5255 - Anne-Sophie Bage https://orcid.org/0000-0002-8129-5503 - Patricia K. Carvajal-Vallejos https://orcid.org/0000-0002-4900-4593 - Sudeep Das https://orcid.org/0000-0001-9393-204X - Francesca De Pascalis https://orcid.org/0000-0002-1694-882X - Dorothea Dörr https://orcid.org/0000-0001-8575-7560 - Katrina Exter https://orcid.org/0000-0002-5911-1536 - Petr Holub https://orcid.org/0000-0002-5358-616X - Kim Tamara Gurwitz https://orcid.org/0000-0003-1992-5073 - Fabio Liberante https://orcid.org/0000-0002-0192-5385 - Philippe Lieutaud https://orcid.org/0000-0002-5080-3456 - Allyson Lister https://orcid.org/0000-0002-7702-4495 - Joaquin Lopez https://orcid.org/0000-0001-9697-7710 - Bénédicte Madon https://orcid.org/0000-0001-8608-3895 - Marzia Massimi https://orcid.org/0000-0001-5052-1822 - Rafaele Matteoni https://orcid.org/0000-0002-0314-5948 - Maria Mîrza https://orcid.org/0000-0001-9158-6403 - Sarah Morgan https://orcid.org/0000-0001-9528-8323 - Bugra Oezdemir - Maria Panagiotopoulou https://orcid.org/0000-0002-4221-7254 - Christina Pavloudi https://orcid.org/0000-0001-5106-6067 - Ana M. P. Melo https://orcid.org/0000-0003-0695-3817 - Susanna-Assunta Sansone https://orcid.org/0000-0001-5306-5690 - Harald Schwalbe https://orcid.org/0000-0001-5693-7909 - Beatriz Serrano-Solano https://orcid.org/0000-0002-5862-6132 - Carlos Oscar Sorzano https://orcid.org/0000-0002-9473-283X - Emilio Urbinati https://orcid.org/0009-0000-3629-2584 - Jing Tang https://orcid.org/0000-0001-7480-7710 - Jonathan Tedds https://orcid.org/0000-0003-2829-4584 - Gary Saunders https://orcid.org/0000-0002-7468-0008 - Jonathan Ewbank https://orcid.org/0000-0002-1257-6862 - authors: - Sorensen, Luke - Saito, Ayame - Poon, Sabrina - Noe Han, Myat - Hamnett, Ryan - Neckel, Peter - Humenick, Adam - Mutunduwe, Keith - Glennan, Christie - Mahdavian, Narges - JH Brookes, Simon - M McQuade, Rachel - PP Foong, Jaime - Gómez-de-Mariscal, Estibaliz - Muñoz Barrutia, Arrate - Kaltschmidt, Julia A. - King, Sebastian K. - Haase, Robert - Carbone, Simona - A. Veldhuis, Nicholas - P. Poole, Daniel - Rajasekhar, Pradeep description: ' Reverted to StarDist for neuron segmentation. Used this bugfix for stardist plugin issue. protobuf-java-3.23.4.jar is being shipped as part of GAT update site. Added StarDist models back and removed deepimageJ models for neuron segmentation Updated documentation website to use a stable Fiji download: https://gut-analysis-toolbox.gitbook.io/docs#installation-and-configuration Full Changelog: https://github.com/pr4deepr/GutAnalysisToolbox/compare/v1.0...v1.1' license: bsd-3-clause name: Gut Analysis Toolbox num_downloads: 178 publication_date: '2025-07-24' submission_date: '2025-08-05T11:25:10.020501' url: - https://zenodo.org/records/16396219 - https://doi.org/10.5281/zenodo.16396219 uuid: 335ce5c7-b4f4-4d92-8408-44e378c35b14 language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Luke Sorensen - Ayame Saito - Sabrina Poon - Myat Noe Han https://orcid.org/0000-0003-3028-7359 - Ryan Hamnett https://orcid.org/0000-0002-9118-1585 - Peter Neckel https://orcid.org/0000-0003-1976-0512 - Adam Humenick - Keith Mutunduwe - Christie Glennan - Narges Mahdavian - Simon JH Brookes https://orcid.org/0000-0001-5635-0876 - Rachel M McQuade https://orcid.org/0000-0002-3510-1288 - Jaime PP Foong https://orcid.org/0000-0003-2082-5520 - Estibaliz Gómez-de-Mariscal https://orcid.org/0000-0003-2082-3277 - Arrate Muñoz Barrutia - Julia A. Kaltschmidt - Sebastian K. King https://orcid.org/0000-0001-5396-0265 - Robert Haase https://orcid.org/0000-0001-5949-2327 - Simona Carbone https://orcid.org/0000-0002-4350-6357 - Nicholas A. Veldhuis https://orcid.org/0000-0002-8902-9365 - Daniel P. Poole https://orcid.org/0000-0002-6168-8422 - Pradeep Rajasekhar https://orcid.org/0000-0002-1983-7244 - authors: - Sauteur, Loïc description: 'Related to github issue: https://github.com/ome/bioformats/issues/3517  ' license: cc-by-4.0 name: Nd2 does not open in Fiji Bio_formats 8.1.1 (on Windows) num_downloads: 1 publication_date: '2025-07-31' submission_date: '2025-08-05T11:25:28.907514' url: - https://zenodo.org/records/16628927 - https://doi.org/10.5281/zenodo.16628927 uuid: c2ea9d40-a795-4635-bad0-9ec2e2ca4b35 file_formats: .nd2 tags: - exclude from DALIA authors_with_orcid: - Loïc Sauteur https://orcid.org/0000-0002-9163-0424 - authors: - VP description: 'Solution: https://forum.image.sc/t/weird-representation-of-qptiff-of-fluorescent-sample-in-qupath-v-0-6-0/115165/6?u=zuksmp3 Image in qptiff file format of an immunofluorescence sample acquired on a KF-400-FL slide scanner. Image with three channels: blue, red, green. Weirdly rendered by QuPath v.0.6.0, but correctly displayed in v.0.5.1 and Fiji.' license: cdla-sharing-1.0 name: '[Solved] Sample fluorescence .qptiff file not rendered correctly by QuPath v.0.6.0, correctly by Qupath v.0.5.1' num_downloads: 0 publication_date: '2025-07-29' submission_date: '2025-08-05T11:25:29.542478' url: - https://zenodo.org/records/16569043 - https://doi.org/10.5281/zenodo.16569043 uuid: 807676ab-e93c-48fc-9013-4691646e76af language: en file_formats: .qptiff tags: - exclude from DALIA authors_with_orcid: - VP - authors: - Vellutini, Bruno C. description: 'This tutorial shows how to make cartographic projections of fly embryos using the ImSAnE Toolbox (Heemskerk and Streichan 2015). Instructions: download and open the imsane-tutorial.html file on your browser.' license: gpl-2.0 name: How to make cartographic projections using ImSAnE num_downloads: 38 publication_date: '2022-03-29' submission_date: '2025-08-12T12:09:38.269216' url: - https://zenodo.org/records/7628300 - https://doi.org/10.5281/zenodo.7628300 uuid: ca0d2538-c305-4bee-be70-2cc86ae75d2a language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Bruno C. Vellutini https://orcid.org/0000-0002-0000-9465 - authors: - Vellutini, Bruno C. - Handberg-Thorsager, Mette - Tomancak, Pavel description: 'To generate a high-quality full-length transcriptome for the annelid Platynereis dumerilii, we collected samples from representative developmental stages, from unfertilized eggs to 5 days post-fertilization. Each sample consisted of a bulk mix from 1 to 5 batches of embryos fertilized by different parents. We incubated all batches at 18 degrees Celsius until the desired time point, then collected the embryos into a clean tube and snap-froze them in liquid nitrogen with as little seawater as possible. The samples were stored at -80 degrees Celsius until RNA extraction. We extracted total RNA from the samples using a Trizol protocol. After measuring the RNA concentration with NanoDrop, we created a bulk RNA mix by combining 1 µL from each sample into a new tube. We gave the sample to the Sequencing and Genotyping facility of the Max Planck Institute of Molecular Cell Biology and Genetics, who ran aliquots of this bulk mix through a Bioanalyzer and gel electrophoresis. They found no evidence of RNA degradation. From this sample, they prepared PacBio Iso-Seq libraries using the Express Template Prep Kit 2.0 and sequenced full-length transcripts on a SMRT 8M Cell for 30 hours using a PacBio Sequel II System. They processed the raw movie subreads with SMRT Analysis software, following the Iso-Seq v3 workflow to generate representative circular consensus sequences, demultiplex and remove primers, trim poly(A) tails, and remove concatemers. After transcript clustering and merging, the resulting dataset contained 176,122 polished high-quality isoforms. Using Cogent, we removed redundant isoforms and obtained a dataset with 117,524 transcripts. From this, we generated a dataset containing only the longest isoform for each gene, with 70,003 sequences in total. We calculated descriptive metrics using Transrate. To estimate their completeness, we used BUSCO for metazoa and obtained a score of 85%. Finally, we annotated the longest-isoform dataset using EnTAP. About 85% of the transcripts have a coding sequence. We obtained annotations for 67% of the sequences, while 33% have remained unannotated. Datasets file name file size (zipped) sequences description 0-Pdum_workflow.zip (folder) 3.40 GB - entire pipeline with notebook entries and analyses 1-Pdum_hq_isoforms.zip (fasta) 180.30 MB 176,122 polished high-quality isoforms from CCS 2-Pdum_co_isoforms.zip (fasta) 70.68 MB 117,524 non-redundant polished high-quality isoforms 3-Pdum_co_longest.zip (fasta) 54.85 MB 70,003 longest of non-redundant polished high-quality isoforms 4-Pdum_co_longest_annotations.zip (tsv) 34.37 MB 70,003 (46,635 annotated) annotations for longest-isoform dataset  ' license: cc-by-4.0 name: Platynereis dumerilii full-length transcriptome of developmental stages num_downloads: 74 publication_date: '2024-11-29' submission_date: '2025-08-12T12:09:38.921271' url: - https://zenodo.org/records/14250773 - https://doi.org/10.5281/zenodo.14250773 uuid: 94130a13-90c6-40a3-90e4-f5898a42c1ab language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Bruno C. Vellutini https://orcid.org/0000-0002-0000-9465 - Mette Handberg-Thorsager https://orcid.org/0000-0002-3908-7233 - Pavel Tomancak https://orcid.org/0000-0002-2222-9370 - authors: - Vellutini, Bruno C. - Martín-Durán, José M. - Børve, Aina - Hejnol, Andreas description: 'This repository contains the data and analyses for the manuscript: Vellutini, B. C., Martín-Durán, J. M., Børve, A. & Hejnol, A. Combinatorial Wnt signaling landscape during brachiopod anteroposterior patterning. BMC Biol. 22, 1–23 (2024). https://doi.org/10.1186/s12915-024-01988-w The source is maintained at https://github.com/bruvellu/terebratalia-wnts.' license: cc-by-4.0 name: 'Repository for: Combinatorial Wnt signaling landscape during brachiopod anteroposterior patterning' num_downloads: 41 publication_date: '2024-08-16' submission_date: '2025-08-12T12:09:39.516707' url: - https://zenodo.org/records/13338425 - https://doi.org/10.5281/zenodo.13338425 uuid: fc77ac99-de87-4770-8e36-65ad7586d5d8 language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Bruno C. Vellutini https://orcid.org/0000-0002-0000-9465 - José M. Martín-Durán https://orcid.org/0000-0002-2572-1061 - Aina Børve https://orcid.org/0000-0003-0311-5156 - Andreas Hejnol https://orcid.org/0000-0003-2196-8507 - authors: - Vellutini, Bruno C. - Cuenca, Marina B. - Krishna, Abhijeet - Szałapak, Alicja - Modes, Carl D. - Tomančák, Pavel description: 'This repository contains the imaging data for the laser perturbation experiments of the manuscript: Vellutini BC, Cuenca MB, Krishna A, Szałapak A, Modes CD, Tomančák P. Patterned embryonic invagination evolved in response to mechanical instability. bioRxiv (2023) doi:10.1101/2023.03.30.534554 Please refer to the main repository for more information: https://doi.org/10.5281/zenodo.7781947' license: cc-by-4.0 name: 'Laser perturbation imaging data for: Patterned invagination prevents mechanical instability during gastrulation' num_downloads: 0 publication_date: '2025-07-14' submission_date: '2025-08-12T12:09:40.121020' url: - https://zenodo.org/records/15876646 - https://doi.org/10.5281/zenodo.15876646 uuid: 99dfe37c-7025-4cd7-bf3a-921b04efc14f language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Bruno C. Vellutini https://orcid.org/0000-0002-0000-9465 - Marina B. Cuenca https://orcid.org/0000-0001-6395-7246 - Abhijeet Krishna https://orcid.org/0000-0002-9291-500X - Alicja Szałapak https://orcid.org/0000-0001-9091-7004 - Carl D. Modes https://orcid.org/0000-0001-9940-0730 - Pavel Tomančák https://orcid.org/0000-0002-2222-9370 - authors: - Vellutini, Bruno C. - Cuenca, Marina B. - Krishna, Abhijeet - Szałapak, Alicja - Modes, Carl D. - Tomančák, Pavel description: 'This repository contains the lightsheet and in situ hybridization imaging data for the manuscript: Vellutini BC, Cuenca MB, Krishna A, Szałapak A, Modes CD, Tomančák P. Patterned embryonic invagination evolved in response to mechanical instability. bioRxiv (2023) doi:10.1101/2023.03.30.534554 Please refer to the main repository for more information: https://doi.org/10.5281/zenodo.7781947' license: cc-by-4.0 name: 'Lightsheet and in situ imaging data for: Patterned invagination prevents mechanical instability during gastrulation' num_downloads: 0 publication_date: '2025-07-14' submission_date: '2025-08-12T12:09:40.751909' url: - https://zenodo.org/records/15876638 - https://doi.org/10.5281/zenodo.15876638 uuid: 947a7ad1-b1eb-4080-b3e4-329c15b11be7 language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Bruno C. Vellutini https://orcid.org/0000-0002-0000-9465 - Marina B. Cuenca https://orcid.org/0000-0001-6395-7246 - Abhijeet Krishna https://orcid.org/0000-0002-9291-500X - Alicja Szałapak https://orcid.org/0000-0001-9091-7004 - Carl D. Modes https://orcid.org/0000-0001-9940-0730 - Pavel Tomančák https://orcid.org/0000-0002-2222-9370 - authors: - Krishna, Abhijeet - Szałapak, Alicja - Vellutini, Bruno C. - Cuenca, Marina B. - Tomančák, Pavel - Modes, Carl D. description: 'This repository contains the code and simulations for the manuscript: Vellutini BC, Cuenca MB, Krishna A, Szałapak A, Modes CD, Tomančák P. Patterned embryonic invagination evolved in response to mechanical instability. bioRxiv (2023) doi:10.1101/2023.03.30.534554 Please refer to the main repository for more information: https://doi.org/10.5281/zenodo.7781947' license: cc-by-4.0 name: 'Model and simulations for: Patterned invagination prevents mechanical instability during gastrulation' num_downloads: 36 publication_date: '2025-07-14' submission_date: '2025-08-12T12:09:41.332698' url: - https://zenodo.org/records/15869598 - https://doi.org/10.5281/zenodo.15869598 uuid: faa73146-9a79-42bd-a438-e6e3f750b7c1 language: en file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Abhijeet Krishna https://orcid.org/0000-0002-9291-500X - Alicja Szałapak https://orcid.org/0000-0001-9091-7004 - Bruno C. Vellutini https://orcid.org/0000-0002-0000-9465 - Marina B. Cuenca https://orcid.org/0000-0001-6395-7246 - Pavel Tomančák https://orcid.org/0000-0002-2222-9370 - Carl D. Modes https://orcid.org/0000-0001-9940-0730 - authors: - Vellutini, Bruno C. - Cuenca, Marina B. - Krishna, Abhijeet - Szałapak, Alicja - Modes, Carl D. - Tomančák, Pavel description: 'Presentation matherial from the course "Management of Microscopy Image Data: An Overview of OMERO, BioImage Archive and Image Data Resource" helded in Uni Leipzig on the 04/07/2025. This course is part of the RDM lecture series organized by Dr. Dr. habil. Dagmar Quandt. Link to the event: https://fortbildung.uni-leipzig.de/fortbildung.html?id=2436 M. Massei is funded by the Deutsche Forschungsgemeinschaft (DFG) – project number [NFDI46/1] - 501864659' license: cc-by-4.0 name: '"Management of Microscopy Image Data: An overview of OMERO, BioImage Archive and Image Data Resource" 2025 @ Uni Leipzig' num_downloads: 55 publication_date: '2025-08-15' submission_date: '2025-08-19T11:20:28.232034' url: - https://zenodo.org/records/16880913 - https://doi.org/10.5281/zenodo.16880913 uuid: 58bc7eff-7515-4de1-8950-e1d4c8bfbe58 language: en file_formats: .odp * .pdf * .pptx tags: - include in DALIA authors_with_orcid: - Riccardo Massei https://orcid.org/0000-0003-2104-9519 - authors: - Euro-BioImaging ERIC description: Euro-BioImaging ERIC is the European landmark research infrastructure for biological and biomedical imaging as recognized by the European Strategy Forum on Research Infrastructures (ESFRI). Euro-BioImaging is the gateway to world-class imaging facilities across Europe. This document is the Euro-BioImaging Annual Report for the year 2024. license: cc-by-4.0 name: Euro-BioImaging Annual Report 2024 num_downloads: 21 publication_date: '2025-06-30' submission_date: '2025-08-19T11:21:00.690284' url: - https://zenodo.org/records/16761197 - https://doi.org/10.5281/zenodo.16761197 uuid: 3f3948f3-c810-4543-9c55-2a54e7d1593a language: en file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Euro-BioImaging ERIC - authors: - Marin, Zach description: Two 2000-frame chunks acquired at different times (~40 minutes apart) on a 4Pi widefield, showing some slow sample drift.  license: cc-by-4.0 name: DCIMG dense beads taken in chunks over time num_downloads: 0 publication_date: '2025-08-14' submission_date: '2025-08-19T11:21:28.919627' url: - https://zenodo.org/records/16875377 - https://doi.org/10.5281/zenodo.16875377 uuid: c991078d-3167-4ee4-934c-864dcd48c79b file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Zach Marin https://orcid.org/0000-0001-5341-9911 - authors: - Preußner, Johannes description: When using bioformats the images are not scaled correctly. The problem arises with low magnifications where the lengths in the metadata are given in µm (not in nm). Attached are two pictures. Only with the picture with the ending “Correct_scale_bar” the import is working correctly. One issue might be that the metadata information of the images are stored in iso-8859-1  license: cc-by-4.0 name: Images acquired with Zeiss Sigma 300 - Images with low magnification are corrently not handeled correctly num_downloads: 4 publication_date: '2025-08-07' submission_date: '2025-08-19T11:21:29.423840' url: - https://zenodo.org/records/16760282 - https://doi.org/10.5281/zenodo.16760282 uuid: 46e10b2c-c641-4e0f-991b-617e7ddd647b language: en file_formats: .tif tags: - exclude from DALIA authors_with_orcid: - Johannes Preußner https://orcid.org/0000-0001-8151-8749 - authors: - Moore, Josh description: 'Poster presentation for the abstract "Enabling Peta-Scale Federated Repositories through Cloud-Native Formats: Lessons from a fast-paced challenge in the bioimaging community" submitted to 2nd Conference on Research Data Infrastructure (CoRDI) 2025' license: cc-by-4.0 name: Cloud-Native Formats Enable Federated Repositories at Peta-Scale num_downloads: 31 publication_date: '2025-09-27' submission_date: '2025-08-26T11:21:33.318328' url: - https://zenodo.org/records/16911980 - https://doi.org/10.5281/zenodo.16911980 uuid: 0c912c69-697c-4bd4-b6cb-4419b30609c1 language: en file_formats: .pdf tags: - NFDI4Bioimage - include in DALIA authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X - authors: - Snyder, Erika, Erika Thomas, Erika T. description: 'Hi all,I was referred to this community from the Image.sc Forum original post: https://forum.image.sc/t/imagej-bioformats-importer-incorrectly-reading-metadata/115943 I have an ND2 file, 3 color channels, 2 positions in the well, and 81 timepoints. However, when I open this as I normally would in ImageJ as a hyperstack, the stack interpretation is totally incorrect. It is including my Z-positions as frames in the timelapse. Even when I open the series for the positions independently, images from the other series will appear within it. I am running Bioformats 8.3.0.  I have tried swapping dimensions. That did not work. I have tried creating substacks to parse out one series from the other, this also did not work. The only thing I can think of that is different from before is that I was previously aquiring z-stacks with our MCL nanodrive Piezo, and we had to have that serviced so in the meantime I used the Ti2 eclipse camera drive for z-stack aquisiton. I have opened the metadata to compare aquisitions between the two, and the stack order appears exactly the same, although Bioformats has no problem reading the metadata for aquisitions with the Piezo. I have also opened this file in NIS elements viewer, and all the information for the stacks appears correctly, so I dont think aquisitions is the issue. I have also tried opening this file on multiple computers with multiple versions of imageJ, and the issue persists. Any advice would be greatly appreciated I am panicking a bit because this is a few months worth of data I am suddenly not able to analyze.  Please let me know if there''s anything else needed to help figure this out. ' license: cc-by-4.0 name: ImageJ Bioformats 8.3.0 Importer Incorrectly Reading ND2 Metadata num_downloads: 23 publication_date: '2025-08-21' submission_date: '2025-08-26T11:23:21.174341' url: - https://zenodo.org/records/16921650 - https://doi.org/10.5281/zenodo.16921650 tags: - exclude from DALIA uuid: 740035c1-d1d1-4e71-a6db-a8ecbfe13591 authors_with_orcid: - Erika Snyder https://orcid.org/0009-0005-8850-0687 file_formats: .nd2 - authors: - Barry, David J - Marcotti, Stefania - Salgueiro Torres, Sara - Jones, Martin - Skórkowska, Alicja description: 'Presentation slides associated with the Introduction to Image Analysis workshop run at the Francis Crick Institute on 18-19th August 2025: https://doi.org/10.5281/zenodo.16949737' license: cc-by-sa-4.0 name: Introduction to Image Analysis num_downloads: 123 publication_date: '2025-08-19' submission_date: '2025-08-29T00:13:26.789156' url: - https://zenodo.org/records/16949737 - https://doi.org/10.5281/zenodo.16949737 tags: - include in DALIA uuid: d5a431fc-fcb5-4dbb-a9e3-d2b388c41cdc authors_with_orcid: - David J Barry https://orcid.org/0000-0003-2763-5244 - Stefania Marcotti https://orcid.org/0000-0002-2877-0133 - Sara Salgueiro Torres https://orcid.org/0009-0007-3420-1619 - Martin Jones https://orcid.org/0000-0003-0994-5652 - Alicja Skórkowska https://orcid.org/0000-0002-1167-7610 file_formats: .zip - authors: - Erick Martins Ratamero - dependabot[bot] description: repo for training materials license: MIT License name: training publication_date: '2020-03-09T13:25:54+00:00' submission_date: '2025-08-31T04:14:07.833232' type: GitHub Repository url: https://github.com/erickmartins/training uuid: 63f1c4cf-8558-4163-a4a3-582cf246530e language: en file_formats: .nd2 tags: - exclude from DALIA - authors: - Dave Barry - Stefania Marcotti - Sara Salgueiro Torres - Martin Jones - AlicjaSkorkowska description: '' license: Creative Commons Attribution Share Alike 4.0 International name: introduction-to-image-analysis publication_date: '2025-08-26T14:08:22+00:00' submission_date: '2025-08-29T00:10:58.358044' type: GitHub Repository url: https://github.com/FrancisCrickInstitute/introduction-to-image-analysis tags: - include in DALIA uuid: 958d4854-e2ed-4edb-9f29-fec19ba86502 - authors: - Wetzker, Cornelia description: 'The poster introduces the consortium NFDI4BIOIMAGE and presents tools of research data management in microscopy to increase the FAIRness of data at the Microscopy Conference in Karlsruhe 2025. On site, it is presented in booth 57 for joint introduction of the national research data infrastructure (NFDI) consortia matWERK, FAIRmat and NFDI4BIOIMAGE. C.W. is funded by the German consortium NFDI4BIOIMAGE (Deutsche Forschungsgemeinschaft, grant number NFDI 46/1, project number 501864659).' license: cc-by-4.0 name: Increasing the FAIRness of electron microscopy data in life and material science research num_downloads: 67 publication_date: '2025-08-31' submission_date: '2025-09-02T11:19:39.891576' url: - https://zenodo.org/records/17014253 - https://doi.org/10.5281/zenodo.17014253 uuid: bf1429a9-cdff-4ec5-94f7-0de4d341a88c language: en file_formats: .pdf * .svg tags: - NFDI4Bioimage - include in DALIA authors_with_orcid: - Cornelia Wetzker https://orcid.org/0000-0002-8367-5163 - authors: - Wang license: cc-by-4.0 name: A mihc mrxs example num_downloads: 2 publication_date: '2025-08-27' submission_date: '2025-09-02T11:21:15.704441' url: - https://zenodo.org/records/16962727 - https://doi.org/10.5281/zenodo.16962727 uuid: dd97fc26-76a5-44c6-b88a-23124bef1c6a file_formats: .zip tags: - exclude from DALIA authors_with_orcid: - Wang - authors: - Euro-BioImaging ERIC description: 'Horizon Europe funded EVOLVE Deliverable 6.2 - Landscape analysis of existing training resources for the Nodes This version has not yet been reviewed or approved by the European Commission and is made publicly available for transparency and early community feedback. A final, EC-approved version will be published when available. This document presents a strategic analysis of training resources for Euro-BioImaging Nodes, assessing bothNode-organized and global opportunities. By analyzing Node-organized and externally available trainingcourses, alongside insights from recent surveys and training bursary applications, this report provides afoundation for strengthening the training framework of Euro-BioImagingDelivering high-quality imaging services relies on continuous skill development, particularly as scientificadvancements and technological innovations reshape the imaging landscape. ' license: cc-by-4.0 name: Euro-BioImaging - EVOLVE Deliverable 6.2 - Landscape analysis of existing training resources for the Nodes num_downloads: 31 publication_date: '2025-09-03' submission_date: '2025-09-09T11:20:27.280560' url: - https://zenodo.org/records/17048377 - https://doi.org/10.5281/zenodo.17048377 uuid: 0d297481-ba1d-4543-8bee-a3d988a46bde language: en file_formats: .pdf tags: - include in DALIA authors_with_orcid: - Euro-BioImaging ERIC - authors: - Gladitz, Nils license: cc-by-4.0 name: 'STEDYCON OBF dataset with simulated intensity and complex stacks for bioformats MR #4362' num_downloads: 0 publication_date: '2025-09-02' submission_date: '2025-09-09T11:21:07.957066' url: - https://zenodo.org/records/17039369 - https://doi.org/10.5281/zenodo.17039369 uuid: 1a819753-2201-450e-b90c-e1b33571425d file_formats: .obf tags: - exclude from DALIA authors_with_orcid: - Nils Gladitz - authors: - Virginie - Johannes Hugger description: A tutorial of classical shape analysis methods license: BSD 3-Clause "New" or "Revised" License name: shapeanalysis101 publication_date: '2021-05-17T13:32:40+00:00' submission_date: '2025-09-15T09:38:35.654661' tags: - Bioimage Analysis - include in DALIA type: - GitHub Repository - notebook - collection url: https://github.com/uhlmanngroup/shapeanalysis101 uuid: 2d84fa9e-d2ad-46da-a5d8-34751d15287b - authors: - Virginie description: NextFlow 101 license: BSD 3-Clause "New" or "Revised" License name: nextflow101 publication_date: '2025-08-19T14:51:21+00:00' submission_date: '2025-09-15T09:31:52.468616' tags: - Bioimage Analysis - Workflows - include in DALIA type: - GitHub Repository url: https://github.com/uhlmanngroup/nextflow101 uuid: 63901ab6-b729-44ce-b5c0-2190b18d0517 - authors: - Virginie - Guillaume Witz - Joel Lüthi description: '' license: BSD 3-Clause "New" or "Revised" License name: imagequantification101 publication_date: '2022-08-17T14:55:16+00:00' submission_date: '2025-09-15T09:31:14.189165' tags: - Bioimage Analysis - include in DALIA type: - GitHub Repository - notebook - collection url: https://github.com/uhlmanngroup/imagequantification101 uuid: ac5974d0-5ab8-42b9-bd3d-eb5ff784c1a2 - authors: - Scherle, Michael - Gieschke, Rafael - Mocanu, Isabela - Grüning, Björn - von Suchodoletz, Dirk description: Data and access to it are central to each NFDI consortium. However, moving data around is often impractical—it may be too large, sensitive, or restricted by agreements with, e.g., the funding provider, and copying introduces duplication, versioning issues, and loss of provenance. Rather than bringing data to the researcher, a Desktop-as-a-Service (DaaS) approach can offer researchers interactive, high-performance access in a secure and efficient manner. Driven by the need for seamless workflows and efficient data handling in NFDI4BIOIMAGE, we present a DaaS approach that is broadly applicable across NFDI. It supports diverse use cases, such as standardized virtual training environments for distributed participants (like required in DataPLANT), remote visualization of large-scale HPC datasets, and secure access to sensitive data (BERD)—all without the overhead of local machine setup and maintenance. license: cc-by-4.0 name: Open Source Platform for Scalable Multi-Purpose Virtual Desktop Infrastructures num_downloads: 52 publication_date: '2025-09-12' submission_date: '2025-09-16T11:19:04.625688' url: - https://zenodo.org/records/17103962 - https://doi.org/10.5281/zenodo.17103962 uuid: 41257518-3778-4486-9f5e-dc4e654443a9 language: en file_formats: .pdf tags: - exclude from DALIA authors_with_orcid: - Michael Scherle https://orcid.org/0009-0008-6652-0697 - Rafael Gieschke https://orcid.org/0000-0002-2778-4218 - Isabela Mocanu https://orcid.org/0009-0003-0825-1680 - Björn Grüning https://orcid.org/0000-0002-3079-6586 - Dirk von Suchodoletz https://orcid.org/0000-0002-4382-5104 - authors: - Schwarz, Michael description: 'Raw microscopy image from the NFDI4Bioimage calendar March 2025. The image shows 125x magnified microscopic details of a biofilm formed by Pseudomonas fluorescence on the surface of a liquid culture medium. The culture was inoculated with a cellulose-overexpressing and surface-colonizing mScarlet-tagged wild type and a GFP-tagged mutant that is unable to colonize the surface. The biofilm can collapse over time due to its own mass, so that new strategies have to be developed and thus a life cycle emerges. Image Metadata (using REMBI template): Study   Study description Biofilm formation Study Component   Imaging method Stereo microscopy Biosample   Biological entity Bacteria Organism Pseudomonas fluorescence Specimen   Signal/contrast mechanism Relief, fluorescence Channel 1 - content Relief, grey Channel 1 - biological entity Details of the biofilm in transmitted light Channel 2 - content mScarlet, red Channel 2 - biological entity WT over-expressing cellulose and colonizing the surface Channel 3 - content GFP, green Channel 3 - biological entity ∆wss mutant unable to colonize the surface Image Acquisition   Microscope model Zeiss Axio Zoom V16 Image Data   Magnification 125x Objective PlanNeoFluar Z 1.0x Dimension extents x: 2752, y: 2208 Pixel size description 0.91 µm x 0.91 µm Image area 2500µm x 2500µm Submitted via NFDI4BIOIMAGE    ' license: cc-by-4.0 name: NFDI4BIOIMAGE Calendar March 2025 num_downloads: 24 publication_date: '2025-09-11' submission_date: '2025-09-16T11:19:05.142766' url: - https://zenodo.org/records/17098115 - https://doi.org/10.5281/zenodo.17098115 uuid: f72fe567-6d37-4591-a9cd-ddaa71d9b044 language: en file_formats: .czi * .png tags: - exclude from DALIA authors_with_orcid: - Michael Schwarz https://orcid.org/0009-0002-1414-3291 - authors: - Moore, Josh description: Presented at "International Symposium on Integrative Bioinformatics", Gatersleben Research Conference Series from September 10–12, 2025,  https://meetings.ipk-gatersleben.de/grc-ib2025/ license: cc-by-4.0 name: When Data Doesn't Fit num_downloads: 56 publication_date: '2025-09-11' submission_date: '2025-09-16T11:19:05.612521' url: - https://zenodo.org/records/17087096 - https://doi.org/10.5281/zenodo.17087096 uuid: f65c55eb-beb7-44ea-bb45-b72e77e2b2b5 language: en file_formats: .pdf tags: - include in DALIA authors_with_orcid: - Josh Moore https://orcid.org/0000-0003-4028-811X - authors: - Müntjes, Kira - Schipper, Kerstin description: "Image from the NFDI4BIOIMAGE Calendar December 2025.\nThe microscopic\ \ image shows yeast cells of the fungal model Ustilago maydis that produce single\ \ cell oil at nitrogen-starvation conditions. The genetically engineered cells\ \ are packed with oil droplets that were visualized by BODIPY staining. The study\ \ was conducted in the framework of the BioSC project \"NextVegOil\".\nImage Metadata\ \ (using REMBI template):\n\n\n\n\nStudy\n\n\n\n\nStudy type\n\n\nVisualisation\ \ of microbial oil in the fungus Ustilago maydis\n\n\n\n\nStudy Component\n\n\n\ \n\nImaging method\n\n\nWide field whole organism microscopy\n\n\n\n\nBiosample\n\ \n\n\n\nBiological entity\n\n\nUstilago maydis\n\n\n\n\nOrganism\n\n\nYeast cells\ \ (sporidia)\n\n\n\n\nIdentity\n\n\nUstilago maydis MB215 cyp1Δemt1Δ\ \ (published in https://doi.org/10.1128/AEM.71.6.3033-3040.2005)\n\n\n\n\nIntrinsic\ \ variable\n\n\nGlycolipid production has been ablated by genetic engineering\n\ \n\n\n\nExtrinsic variable\n\n\nBODIPY (4,4-Difluoro-1,3,5,7,8-Pentamethyl-4-Bora-3a,4a-Diaza-s-Indacene\ \ 493/503) staining\n\n\n\n\nExperimental variables \n\n\nCultivation time\n\n\ \n\n\nSpecimen\n\n\n\n\nLocation within biosample\n\n\nOverview image with yeast\ \ cells from liquid culture at nitrogen-starvation condition\n\n\n\n\nPreparation\ \ method\n\n\nLiving cells attached to agarose mounts\n\n\n\n\nSignal/contrast\ \ mechanism\n\n\nDifferential interference contrast and fluorescence\n\n\n\n\n\ Channel 1 - content\n\n\nDIC\n\n\n\n\nChannel 1 - biological entity\n\n\nIntact\ \ yeast cells \n\n\n\n\nChannel 2 - content\n\n\nBODIPY 493/503\n\n\n\n\nChannel\ \ 2 - biological entity\n\n\nIntracellular lipid droplets\n\n\n\n\nImage acquisition\n\ \n\n\n\nInstrument attributes\n\n\nZeiss Axio Observer.Z1; Prime BSI express;\ \ solid-state laser 488 nm\n\n\n\n\nSubmitted via NFDI4BIOIMAGE\n\n\n\n" license: cc-by-4.0 name: NFDI4BIOIMAGE Calendar December 2025 num_downloads: 0 publication_date: '2025-09-15' submission_date: '2025-09-16T11:19:06.253566' url: - https://zenodo.org/records/16993955 - https://doi.org/10.5281/zenodo.16993955 uuid: 2a48d7b4-8f31-4f56-986c-e50069276467 language: en file_formats: .png tags: - exclude from DALIA authors_with_orcid: - Kira Müntjes https://orcid.org/0000-0001-5160-6342 - Kerstin Schipper https://orcid.org/0000-0002-5782-9085 - authors: - Macas, Jadranka description: "Image from the NFDI4BIOIMAGE Calendar November 2025.\nThe image shows\ \ a perivascular accumulation of perivascular B cells, T cells and plasma cells\ \ in a human brain tumor. These structures, also known as tertiary lymphoid structures,\ \ are sites of lymphocyte clonal expansion and plasma cell formation. The study\ \ aims to determine the clinical relevance and immunological function of tertiary\ \ lymphoid structures in human primary brain tumors.\nImage Metadata (using REMBI\ \ template):\n\n\n\n\nStudy\n\n\n\n\nStudy type\n\n\nImmunomonitoring study in\ \ human oncology\n\n\n\n\nStudy Component\n\n\n\n\nImaging method\n\n\nCOMET™\ \ highplex seq-IF staining and scanning system, HORIZON™ Viewer (Lunaphore\ \ Technologies, SA)\n\n\n\n\nBiosample\n\n\n\n\nBiological entity\n\n\nTertiary\ \ lymphoid structure in glioblastoma\n\n\n\n\nOrganism\n\n\nHomo sapiens\n\n\n\ \n\nSpecimen\n\n\n\n\nLocation within biosample\n\n\nTumor (glioblastoma)\n\n\n\ \n\nPreparation method\n\n\nFFPE sample, automatic sequential-IF using COMET™\ \ (Lunaphore Technologies, SA)\n\n\n\n\nSignal/contrast mechanism\n\n\nHORIZON™\ \ Viewer (Lunaphore Technologies, SA)\n\n\n\n\nChannel 1 - content\n\n\nAlexa\ \ Fluor Plus 555, red\n\n\n\n\nChannel 1 - biological entity\n\n\nCD20 - B-cells\n\ \n\n\n\nChannel 2 - content\n\n\nAlexa Fluor Plus 647, green\n\n\n\n\nChannel\ \ 2 - biological entity\n\n\nCD3 - T-cells\n\n\n\n\nChannel 3 - content\n\n\n\ Alexa Fluor Plus 555, white\n\n\n\n\nChannel 3 - biological entity\n\n\nCD163\ \ - anti-inflammatory macrophages (M2-like) \n\n\n\n\nChannel 4 - content\n\n\n\ Alexa Fluor Plus 647, magenta\n\n\n\n\nChannel 4 - biological entity\n\n\nMZB-1\ \ - Marginal zone B and B1 cell-specific protein, MEDA-7 - plasma cells, memory\ \ B-cells\n\n\n\n\nChannel 5 - content\n\n\nAlexa Fluor Plus 647, orange\n\n\n\ \n\nChannel 5 - biological entity\n\n\nNF- Neurofilament - intermediate filaments\ \ around the axons\n\n\n\n\nChannel 6- content\n\n\nAlexa Fluor Plus 555, cyan\n\ \n\n\n\nChannel 6 - biological entity\n\n\nGAP43 - Neuromodulin, neuronal growth-associated\ \ protein 43 - neurons\n\n\n\n\nChannel 7 - content\n\n\nAlexa Fluor Plus 555,\ \ blue\n\n\n\n\nChannel 7 - biological entity\n\n\nvWF - von-Willebrand-Factor\ \ - endothelial cells\n\n\n\n\nImage acquisition\n\n\n\n\nInstrument attributes\n\ \n\nCOMET™ highplex seq-IF staining and scanning system v.1.1.1.0 (Lunaphore\ \ Technologies, SA)\n\n\n\n\nImage acquisition parameters\n\n\nCOMET™ acquisition\ \ software                                                                                                                                                                                           \ \ \n\n\n\n\nImage data\n\n\n\n\nPixel size\n\n\n0.23 µm/pixel\n\n\n\n\n\ Image size\n\n\nWidth 11986 pixels - 2.76 mmHeight 11514 pixels - 2.65 mm\n\n\n\ \n\nPixel bit depth\n\n\n16-bit                                                                                                                                                                                                                                                         \ \                    \n\ \n\n\n\nChannel information\n\n\nDisplayed are 7 markers out of the highplex IF-panel;\ \ number of channels 43 (including autofluorescence)\n\n\n\n\nSubmitted via NFDI4Immuno\n\ \n\n\n" license: cc-by-4.0 name: NFDI4BIOIMAGE Calendar November 2025 num_downloads: 0 publication_date: '2025-09-15' submission_date: '2025-09-16T11:19:06.713444' url: - https://zenodo.org/records/16993649 - https://doi.org/10.5281/zenodo.16993649 uuid: 7810e557-30a4-4654-beec-9952b6eee1c9 language: en file_formats: .png tags: - exclude from DALIA authors_with_orcid: - Jadranka Macas - authors: - Joisten-Rosenthal, Vivien description: 'Image from the NFDI4BIOIMAGE Calendar October 2025. As part of the MibiNet SFB 1535 project (https://www.sfb1535.hhu.de), this lichen was collected in the Northern Eifel region, between Blankenheim and Schmidtheim in Germany. Lichens are among the most successful examples of complex mutualistic symbiosis, where a fungus (mycobiont) forms an association with one or more photosynthetic organisms (photobionts), including green algae and/or cyanobacteria. Based on ITS analysis, the lichen shown has been identified as Peltigera neckeri. Lichens of the genus Peltigera are classified as cyanolichens due to their symbiotic association with a cyanobacterial photobiont of the genus Nostoc. The image shows the lichen''s blue-gray thallus when wet, after its collection on a mossy stone. Research project MibiNet SFB 1535 Project B02 Recording date; time 2023-10-28; 12:21 CEST Location Northern Eifel, between Blankenheim and Schmidtheim, Germany Environmental conditions Cloudy, slightly rainy Temperature 14°C Organism Peltigera neckeri Organism attribute Cyanolichen, foliose Mycobiont Peltigera Photobiont Nostoc Substrate Moss Camera Apple iPhone 12 Objective iPhone 12 back dual wide camera 4.2mm f/1.6 Size 4,032 x 3,024 px Submitted via NFDI4BIOIMAGE ' license: cc-by-4.0 name: NFDI4BIOIMAGE Calendar October 2025 num_downloads: 0 publication_date: '2025-09-15' submission_date: '2025-09-16T11:19:07.221012' url: - https://zenodo.org/records/16993297 - https://doi.org/10.5281/zenodo.16993297 uuid: bc08638e-543d-4b26-b866-bb7f3abc208f language: en file_formats: .heic tags: - exclude from DALIA authors_with_orcid: - Vivien Joisten-Rosenthal https://orcid.org/0009-0000-0645-6891 - authors: - Faber-Zuschratter, Heidi - Stöter, Torsten - Zuschratter, Werner - Hartig, Roland - Wilke, Markus description: "Image from the NFDI4BIOIMAGE Calendar September 2025.\nThe scanning\ \ electron micrograph shows the approach of T-lymphocytes (Jurkat cells; cyan)\ \ to an antigen-presenting B cell (Raji cell; yellow) in the center. The image\ \ was taken as part of the research work of the CRC 854, which focused on molecular\ \ processes that regulate inter- and intracellular communication within the immune\ \ system.\nImage Metadata (using REMBI template):\n\n\n\n\nStudy\n\n\n\n\nStudy\ \ description\n\n\nUltrastructure of the immune synapse\n\n\n\n\nStudy type\n\n\ \nResearch project within DFG CRC 854 (Molecular organisation of cellular communication\ \ within the immune system)\n\n\n\n\nStudy Component\n\n\n\n\nImaging method\n\ \n\nScanning Electron Microscopy\n\n\n\n\nBiosample\n\n\n\n\nBiological entity\n\ \n\nJurkat cell line E6.1 and Raji B cell lymphoma cell line\n\n\n\n\nOrganism\n\ \n\nHomo sapiens\n\n\n\n\nIdentity\n\n\nZ21_A1\n\n\n\n\nSpecimen\n\n\n\n\nPreparation\ \ method\n\n\nCell lines were maintained in RPMI 1640 medium supplemented with\ \ 10% fetal calf serum (FCS; PAN Biotech), stable L-glutamine, penicillin (50\ \ U/ml), and streptomycin (50 mg/ml) (Biochrom) in humidified 5% CO2 at 37°C.\ \ E6.1 cells were mixed at a 1:1 ratio with Raji B cells that had been pulsed\ \ with SEE (bacterial SAG staphylococcal enterotoxin E). After 10 min cells were\ \ plated on poly-L-lysine–covered slides at room temperature for 5 min and\ \ fixed for 10 min in PBS (pH 7.4) containing 1.5% PFA and 0.025% glutaraldehyde.\ \ Cryo-drying by critical point dryer (Leica EM CPD300) followed by sputtering\ \ with gold.\n\n\n\n\nSignal/contrast mechanism\n\n\nDetected secondary electrons\n\ \n\n\n\nChannel 1 - content\n\n\nJurkat cell line E6.1 (artificial color table,\ \ cyan)\n\n\n\n\nChannel 1 - biological entity\n\n\nSurface of Jurkat cells \n\ \n\n\n\nChannel 2 - content\n\n\nRaji B cell lymphoma cell line (artificial color\ \ table, yellow)\n\n\n\n\nChannel 2 - biological entity\n\n\nSurface of a Raji\ \ B cell \n\n\n\n\nImage acquisition\n\n\n\n\nInstrument attributes\n\n\nFEI XL30\ \ FEG ESEM\n\n\n\n\nImage acquisition parameters\n\n\n10 keV, Magnification 6500\ \ x, Scale bar: 2 µm\n\n\n\n\nSubmitted via NFDI4BIOIMAGE\n\n\n\n" license: cc-by-4.0 name: NFDI4BIOIMAGE Calendar September 2025 num_downloads: 0 publication_date: '2025-09-15' submission_date: '2025-09-16T11:19:07.749271' url: - https://zenodo.org/records/16993178 - https://doi.org/10.5281/zenodo.16993178 uuid: 7e52232b-844e-40bd-8e8c-a35ced38be99 language: en file_formats: .png tags: - exclude from DALIA authors_with_orcid: - Heidi Faber-Zuschratter - Torsten Stöter - Werner Zuschratter https://orcid.org/0000-0002-9845-6393 - Roland Hartig https://orcid.org/0000-0002-3706-7458 - Markus Wilke - authors: - Jiang, Haowen - Chalopin, Claire description: 'Image from the NFDI4BIOIMAGE Calendar August 2025. This image illustrates tissue oxygen saturation in the hand, calculated using various computer-assisted methods and based on hyperspectral and multispectral imaging. The purpose of this image is to compare the perfusion parameters (3 and 4) obtained with multispectral cameras delivering relatively less spectral information but capable of real-time imaging against the perfusion parameters (2) obtained with a hyperspectral medical system delivering large spectral information but not capable of real-time imaging. The picture shows that deep learning methods (4) perform better than classical methods (3) that are not based on artificial intelligence. It lays the groundwork for future real-time quantitative assessment of perfusion during organ transplantation surgeries. Image Metadata (using REMBI template): Study Study description Quantification of tissue reperfusion using real-time spectral imaging and deep learning Study type Study on volunteers Study Component Imaging method (1) RGB imaging (2) Hyperspectral imaging (3) and (4) Multispectral imaging Image component description (1) RGB image of the hand under normal perfusion. (2) Perfusion parameter map computed based on hyperspectral imaging (100 spectral bands between 500 and 1000 nm). High perfusion values are represented in red, low perfusion values in blue. (3) Perfusion parameter map computed based on multispectral imaging (31 spectral bands between 460 and 850 nm) and using the spectral bands that are available but that are less than in (2). Therefore, the result in (3) looks very different from the result in (2). (4) Perfusion parameter map computed based on multispectral imaging and using a deep neural network. The result in (4) looks similar to the result in (2). Biosample Biological entity Hand Organism Homo sapiens Image data Image resolution hyperspectral imaging Spatial Resolution: 480*640 pixels Spectral Resolution: 500 nm-1000 nm, 100 bands, 5 nm Image resolution multispectral imaging Spatial Resolution: 1088*2048 pixels Spectral Resolution: 16 bands in 460-600 nm; 15 bands in 600-850 nm Image mode Reflectance Submitted via NFDI4BIOIMAGE ' license: cc-by-4.0 name: NFDI4BIOIMAGE Calendar August 2025 num_downloads: 0 publication_date: '2025-09-15' submission_date: '2025-09-16T11:19:08.206341' url: - https://zenodo.org/records/16993059 - https://doi.org/10.5281/zenodo.16993059 uuid: 427c2a3c-3277-4cdb-9d49-01a9d15db89c language: en file_formats: .png tags: - exclude from DALIA authors_with_orcid: - Haowen Jiang - Claire Chalopin https://orcid.org/0000-0001-9309-7531 - authors: - Lörzing, Pilar - Iliasov, Denis - Schlierf, Michael - Mascher, Thorsten description: "Image from the NFDI4BIOIMAGE Calendar July 2025.\nThe sample was provided\ \ through a collaboration with the group of Thorsten Mascher at TU Dresden. Aim\ \ of this project is to explore the cellular autofluorescence patterns in Streptomyces\ \ using advanced imaging techniques. Streptomyces coelicolor are multicellular,\ \ mycelial bacteria that grow as vegetative hyphae. The use of confocal microscopy\ \ in this project was crucial for optically sectioning these filamentous cells,\ \ enabling the resolution of their cellular autofluorescence patterns with a high\ \ signal-to-noise ratio, which allowed us to visualize the 3D arrangement of the\ \ hyphae.\nImage Metadata (using REMBI template):\n\n\n\n\nStudy\n\n\n\n\nStudy\ \ type\n\n\nCharacterization of the intrinsic autofluorescence in filamentous\ \ actinobacteria\n\n\n\n\nStudy Component\n\n\n\n\nImaging method\n\n\nSpinning\ \ Disk Confocal Microscopy\n\n\n\n\nBiosample\n\n\n\n\nBiological entity\n\n\n\ Hyphae\n\n\n\n\nOrganism\n\n\nStreptomyces coelicolor M600\n\n\n\n\nIntrinsic\ \ variable\n\n\nPlasmid free derivative of the wild type strain\n\n\n\n\nExperimental\ \ variables\n\n\nLive-Cell imaging\n\n\n\n\nSpecimen\n\n\n\n\nPreparation method\n\ \n\nS. coelicolor was grown in maltose-yeast extract-malt extract (MYM) medium\ \ with tap and deionized water (1:1) and supplemented with 0.2 mL R2 trace element\ \ solution per 100 mL. Cultures were inoculated from spore suspension and grown\ \ for 18 hours at 28 °C. 2 µl cell suspension was immobilized on 1%\ \ agarose pads and covered with a cleaned coverslip (1.5H).\n\n\n\n\nChannel 1\ \ - content\n\n\nCellular autofluorescence\n\n\n\n\nChannel 1 - biological entity\n\ \n\nS. coelicolor hyphae\n\n\n\n\nImage acquisition\n\n\n\n\nInstrument attributes\n\ \n\nImaging was performed using a Nikon Ti-E Spinning Disk microscope with 100x\ \ objective and 1.5x tube lens. Fluorescence was excited with a 488 nm laser and\ \ emission light was filtered using a dual band filter 433/530 HC. An Andor Ixon\ \ Ultra 888 EMCCD camera was used for detection. \n\n\n\n\nImage acquisition parameter\n\ \n\nZ-stacks of confocal images with 0.2 µm step size\n\n\n\n\nImage data\n\ \n\n\n\nType\n\n\nMaximum intensity projection of individual z-stacks\n\n\n\n\n\ Format & compression\n\n\nTIFF\n\n\n\n\nDimension extents\n\n\nx: 1024 y:\ \ 1024 z: 28 px\n\n\n\n\nSize description\n\n\nx: 63.12 y: 63.12 z: 5.6 µm\n\ \n\n\n\nPixel/voxel size description\n\n\nx: 86 y: 86 z: 200 nm\n\n\n\n\nSubmitted\ \ via NFDI4BIOIMAGE\n\n\n\n" license: cc-by-4.0 name: NFDI4BIOIMAGE Calendar July 2025 num_downloads: 0 publication_date: '2025-09-15' submission_date: '2025-09-16T11:19:08.740069' url: - https://zenodo.org/records/16992904 - https://doi.org/10.5281/zenodo.16992904 uuid: e61a1d11-1b5b-43f9-9c5b-01eb96f2de05 language: en file_formats: .png tags: - exclude from DALIA authors_with_orcid: - Pilar Lörzing https://orcid.org/0009-0009-2532-9121 - Denis Iliasov https://orcid.org/0009-0004-6533-7410 - Michael Schlierf https://orcid.org/0000-0002-6209-2364 - Thorsten Mascher https://orcid.org/0000-0002-6300-5541 - authors: - Warstat, Kevin description: 'Image from the NFDI4BIOIMAGE Calendar June 2025. This illustration compares two orthomosaics generated from UAV imagery. On the left, a true-color RGB orthomosaic is displayed, accompanied by three smaller orthomosaic images above it, each representing the red, green, and blue bands, vividly colored to highlight their significance. On the right, a corresponding NDVI orthomosaic of the same field is shown, with two images above it illustrating the red and near-infrared bands used as input. All images are processed products from structure from motion modelling. Title Crop spectra Research project PhenoRob (EXC 2070) Recording date 2023-07-11 Location Campus Klein-Altendorf, 53359 Rheinbach, Germany Sensor platform DJI Matrice 600 Pro Sensors Sony alpha 7 mark IV RGB MicaSense RedEdge-MX Dual multispectral camera Submitted via FAIRagro ' license: cc-by-4.0 name: NFDI4BIOIMAGE Calendar June 2025 num_downloads: 0 publication_date: '2025-09-15' submission_date: '2025-09-16T11:19:09.209212' url: - https://zenodo.org/records/16992716 - https://doi.org/10.5281/zenodo.16992716 uuid: c9f20ad0-98a0-4557-af00-835d0842ca7b language: en file_formats: .png tags: - exclude from DALIA authors_with_orcid: - Kevin Warstat https://orcid.org/0009-0002-4195-2406 - authors: - Lück, Stefanie description: "Image from the NFDI4BIOIMAGE Calendar May 2025.\nThe microscopy image\ \ captures the interaction between the barley cv. Golden Promise and the barley\ \ powdery mildew fungus Blumeria graminis f.sp. hordei, observed\ \ 48 hours post-inoculation. The fungus was stained with Coomassie dye, enhancing\ \ its visibility against the barley leaves. The leaves were prepared and fixed\ \ onto slides, followed by scanning with a Zeiss Axio Scan Z.1 microscope scanner\ \ using a 5x objective lens.\nThe upper section of the image displays the hyphal\ \ colonies, which were automatically segmented, highlighting the fungal structures\ \ (black) against the plant tissue (white). The lower section presents a machine\ \ learning-based analysis where a Convolutional Neural Network (CNN) was employed\ \ to predict the fungal structures. Here, the red bounding boxes show the outer\ \ boundaries of detected objects, while the green contours precisely trace the\ \ segmented hyphae, illustrating the effectiveness of the segmentation and prediction\ \ processes.\nImage Metadata (using MIAPPE template):\n\n\n\n\nInvestigation information\n\ \n\n\n\nInvestigation Title\n\n\nAnalysis of Hordeum vulgare cv. Golden promise\ \ infected with Blumeria graminis f. sp. hordei (causative for barley powdery\ \ mildew)\n\n\n\n\nObjective\n\n\nTo study the interaction between Hordeum vulgare\ \ and Blumeria graminis f. sp. hordei using advanced imaging techniques and automated\ \ image analysis.\n\n\n\n\nStudy information\n\n\n\n\nStudy Title\n\n\nMicroscopy\ \ imaging and analysis of barley powdery mildew infection on Hordeum vulgare cv.\ \ Golden promise\n\n\n\n\nStudy Type\n\n\nMicroscopy-based phenotyping experiment\n\ \n\n\n\nStudy Description\n\n\nThe study involves imaging barley leaves inoculated\ \ with Blumeria graminis f. sp. hordei, followed by automated segmentation and\ \ CNN-based prediction of fungal structures.\n\n\n\n\nPlant material\n\n\n\n\n\ Plant Species\n\n\nHordeum vulgare \n\n\n\n\nCultivar\n\n\nGolden promise\n\n\n\ \n\nExperimental design\n\n\n\n\nExperiment Type\n\n\nFungal inoculation and microscopy\ \ imaging\n\n\n\n\nInoculation Details\n\n\nBarley leaves were inoculated with\ \ Blumeria graminis f. sp. hordei.\n\n\n\n\nTime post-inoculation\n\n\n48 hours\n\ \n\n\n\nImaging information\n\n\n\n\nMicroscopy type\n\n\nBrightfield microscopy\n\ \n\n\n\nStaining method\n\n\nCoomassie stain for fungal structures\n\n\n\n\nMicroscope\n\ \n\nZeiss Axio Scan Z.1\n\n\n\n\nObjective Lens\n\n\n5x\n\n\n\n\nImage Format\n\ \n\nZeiss CZI file\n\n\n\n\nImage analysis information\n\n\n\n\nSegmentation method\n\ \n\nAutomated segmentation of hyphal colonies\n\n\n\n\nImage analysis software\n\ \n\nBluVision Micro software\n\n\n\n\nPrediction method\n\n\nConvolutional Neural\ \ Network (CNN) for fungal structure detection\n\n\n\n\nUpper image\n\n\nShows\ \ binary image with hyphal colonies (black) and background (white).\n\n\n\n\n\ Lower image\n\n\nDisplays CNN predictions with red bounding boxes marking detected\ \ objects and green contours outlining segmented hyphae.\n\n\n\n\nSubmitted via\ \ NFDI4Biodiversity\n\n\n\n" license: cc-by-4.0 name: NFDI4BIOIMAGE Calendar May 2025 num_downloads: 0 publication_date: '2025-09-15' submission_date: '2025-09-16T11:19:09.681284' url: - https://zenodo.org/records/16991961 - https://doi.org/10.5281/zenodo.16991961 uuid: 1675e1c8-2037-4b6f-913f-f4ffc7bc45e0 language: en file_formats: .png tags: - exclude from DALIA authors_with_orcid: - Stefanie Lück https://orcid.org/0000-0003-0536-835X - authors: - Zurowietz, Martin - Nattkemper, Tim W. description: 'Image from the NFDI4BIOIMAGE Calendar April 2025. This image shows the BIIGLE image and video annotation tool, which is a web-based software for collaborative research on large imaging datasets [1, 2]. It offers tools for manual and computer-assisted annotation, quality control and the collaboration on custom taxonomies to describe objects. BIIGLE is freely available and can be installed in cloud environments, a local network or on mobile platforms during research expeditions. A public instance can be found at biigle.de. The annotated image shows the coastline of Fernandina Island, Galapagos, which is the habitat of the Galapagos Marine Iguana (Amblyrhynchus cristatus). The image is a large mosaic that was stitched together from many individual images captured by a drone. The green annotations marking the iguanas were machine-generated as part of a feasibility study for the automatic analysis of the data in the project Iguanas from Above [3, 4]. [1] Langenkämper, D., Zurowietz, M., Schoening, T., & Nattkemper, T. W. (2017). BIIGLE 2.0-browsing and annotating large marine image collections. Frontiers in Marine Science, 4, 83. https://doi.org/10.3389/fmars.2017.00083 [2] Zurowietz, M., & Nattkemper, T. W. (2021). Current trends and future directions of large scale image and video annotation: Observations from four years of BIIGLE 2.0. Frontiers in Marine Science, 8, 760036. https://doi.org/10.3389/fmars.2021.760036 [3] Varela-Jaramillo, A., Rivas-Torres, G., Guayasamin, J. M., Steinfartz, S., & MacLeod, A. (2023). A pilot study to estimate the population size of endangered Galápagos marine iguanas using drones. Frontiers in Zoology, 20(1), 4. https://doi.org/10.1186/s12983-022-00478-5 [4] https://iguanasfromabove.com Project Iguanas from Above Location Fernandina Island, Galapagos Organism Amblyrhynchus cristatus Drone model DJI Mavic 2 Pro Camera Hasselblad L1D-20c Size 26,545 × 20,894 px Mosaic algorithm Agisoft Metashape Professional v.1.6 Submitted via NFDI4Biodiversity ' license: cc-by-4.0 name: NFDI4BIOIMAGE Calendar April 2025 num_downloads: 0 publication_date: '2025-09-15' submission_date: '2025-09-16T11:19:10.196132' url: - https://zenodo.org/records/16980661 - https://doi.org/10.5281/zenodo.16980661 uuid: 914de557-986a-428a-b57d-f3686c835595 language: en file_formats: .png tags: - exclude from DALIA authors_with_orcid: - Martin Zurowietz https://orcid.org/0000-0002-7122-2343 - Tim W. Nattkemper https://orcid.org/0000-0002-7986-1158 - authors: - Kutskiy, Oleg description: "Image from the NFDI4BIOIMAGE Calendar February 2025.\n“The Way\ \ of the Cross: Christ collapses under the weight of the cross\" is a recording\ \ from the partially destroyed Transfiguration Cathedral in Odessa, Ukraine. The\ \ image was taken in September 2023 and impressively shows the urgency of photographic\ \ documentation of cultural heritage. It was created as part of the “Documenting\ \ Ukrainian Cultural Heritage Project – Photographic Documentation of War-Threatened\ \ Buildings in Ukraine”.\n\n\n\n\nProject\n\n\nDocumenting Ukrainian Cultural\ \ Heritage – Photographic Documentation of War-Threatened Buildings in Ukraine\n\ \n\n\n\nRecording date\n\n\n2023-09-01\n\n\n\n\nLocation\n\n\nThe Savior Transfiguration\ \ Cathedral, south side choir\nPloshcha Soborna 3, Odessa, Ukraine\n\n\n\n\nDating\n\ \n\n1999/2005\n\n\n\n\nFactual term\n\n\nMural\n\n\n\n\nGenus\n\n\nWall painting\n\ \n\n\n\nStatus\n\n\nPartially destroyed 2023-07-23\n\n\n\n\nImage file number\ \ \n\n\nfmd10034507\n\n\n\n\nTopic\n\n\nIconography: 73D4113 * the third fall\ \ (Christ carrying the cross) \n\n\n\n\nDataset from\n\n\nBildarchiv Foto Marburg\n\ \n\n\n\nAcquisition parameter\n\n\nColor, born digital\n\n\n\n\nSubmitted via\ \ NFDI4Culture\n\n\n\n" license: cc-by-sa-4.0 name: NFDI4BIOIMAGE Calendar February 2025 num_downloads: 0 publication_date: '2025-09-15' submission_date: '2025-09-16T11:19:10.672989' url: - https://zenodo.org/records/16980386 - https://doi.org/10.5281/zenodo.16980386 uuid: 25f9901e-f55b-4d7a-bbf9-3b3edc4a3580 language: en file_formats: .png tags: - exclude from DALIA authors_with_orcid: - Oleg Kutskiy - authors: - Miebach, Lea - Bekeschus, Sander description: 'Image from the NFDI4BIOIMAGE Calendar January 2025. A Heart for Redox Biology: The image of primary bone mesenchymal stromal/stem cells (hBM-MSCs) was captured in a study evaluating the cellular effects of therapeutic oxidation in the context of regenerative medicine. The cells were isolated from an arthroplasty patient cohort in a joint research project between the Center for Orthopaedics at University Medical Center and the group ZIK plasmatis at the Leibniz Institute for Plasma Science and Technology (INP) in Greifswald. You can appreciate the characteristic morphology and complex actin cytoskeleton that is crucial for the cellular function of hBM-MSCs. Can you spot the heart that is formed by the prominent actin protrusions of interconnected cells? Image Metadata (using REMBI template): Study Component Imaging method Spinning-disc confocal mode, epifluorescence Biosample Biological entity Bone marrow-mesenchymal stem cells (BM-MSCs) Organism Homo sapiens Specimen Preparation method Fixation (4% PFA) Signal/contrast mechanism Fluorescence Channel 1 – content 4'',6-Diamidin-2-phenylindol (DAPI; Thermo Fisher, USA), blue Channel 1 – biological entity Nuclei Channel 2 – content MitoSpy Green (Biolegend, USA), green Channel 2 – biological entity Mitochondria Channel 3 – content Flash Phalloidin Red (Biolegend, USA), orange Channel 3 – biological entity Actin Image acquisition Microscope model Operetta CLS (PerkinElmer, USA) Image data Type Raw and processed image in comparison Magnification 20x air objective (NA = 0.8) Excitation Channel 1: 365 nm; Channel 2: 475 nm; Channel 3: 550 nm Detection Channel 1: 465 nm; Channel 2: 525 nm; Channel 3: 610 nm Analysed data Image processing method Algorithm-based, unsupervised image segmentation with Harmony 4.9 analysis software (PerkinElmer, USA) Submitted via NFDI4BIOIMAGE ' license: cc-by-4.0 name: NFDI4BIOIMAGE Calendar January 2025 num_downloads: 0 publication_date: '2025-09-15' submission_date: '2025-09-16T11:19:11.141083' url: - https://zenodo.org/records/16980217 - https://doi.org/10.5281/zenodo.16980217 uuid: 5e5b5c16-257c-4991-a10c-a432c7416a12 language: en file_formats: .png tags: - exclude from DALIA authors_with_orcid: - Lea Miebach https://orcid.org/0000-0002-8773-8862 - Sander Bekeschus https://orcid.org/0000-0001-7411-9299 - authors: - Rademacher, Anne - Huseynov, Alik - Bortolomeazzi, Michele - Wille, Sina Jasmin - Schumacher, Sabrina - Sant, Pooja - Keitel, Denise - Okonechnikov, Konstantin - Ghasemi, David R. - Pajtler, Kristian W. - Mallm, Jan-Philipp - Rippe, Karsten description: 'Image from the NFDI4BIOIMAGE Calendar Cover 2025. The image is a visualization showing the integration of multimodal data including a spinning disk confocal image and gene expression data from a spatial transcriptomic experiment on a human medulloblastoma sample. The microscopy image of the tissue with the nuclei in white has been overlayed with the result of the cell segmentation colored according to the assigned cell type (immune cells: red, stromal cells: violet, brain cells: cyan/blue, tumor cells: green). A subset of transcripts for three genes whose expression varies across the different cell types in the tissue have been represented as colored dots (CD4 (immune cells): red, PTCH1 (tumor cells): green, AQP4 (brain cells): blue). Image Metadata (using REMBI template): Study Study description Comparison of spatial transcriptomics technologies for medulloblastoma cryosection Study type Spatial Transcriptomics (Xenium) on medulloblastoma cryosections Study Component Imaging method Xenium and Spinning disk confocal microscopy Study component description Datasets with raw and processed data from the study "Comparison of spatial transcriptomics technologies for medulloblastoma cryosections" including Xenium and spinning disk confocal microscopy data Biosample Identity MB266 Biological entity Human cerebellum from a patient with Medulloblastoma with extensive nodularity Organism Homo sapiens Specimen Experimental status Patient sample Preparation method 10 µm cryosections were acquired using the cryostar NX50 with a cutting temperature of -15 °C. Tissues were section in 10 µm slices and four samples were placed on one Xenium slide. Subsequently, the tissue was fixed with PFA according to the manufacture´s protocol. Tissues were permeabilized with SDS, incubated in 70% ice cold methanol and washed with PBS. Hybridization of the human generic brain panel with 70 add-on genes (Supplementary Dataset 1) was performed at 50°C in a Bio-Rad C1000 touch cycler for 20 hours. Washing, ligation and amplification steps were carried out according to the manufacturer’s instructions. ROIs were selected according to the tissue area excluding non-tissue covered tiles. Each transcript was imaged in a bright state five times across 60 cycle-channels (15 cycles x 4 channels). After the run on the Xenium analyzer slides were removed and buffer exchanged with PBS-T for further storage at 4°C. Signal/contrast mechanism Fluorescence Channel 1 – content DAPI Channel 1 – biological entity Nuclei (DNA) Image acquisition Instrument attributes Imaging of RNAscope samples and reimaging of Xenium slides by SDCM was conducted on an Andor Dragonfly 505 spinning disk confocal system equipped with a Nikon Ti2-E inverted microscope and a CFI P-Fluor 40X/1.30 oil objective or a Plan Apo 60x/1.40 oil objective. Multicolor images were acquired with the following laser lines 405 nm (DAPI), 488 nm (Alexa 488, eosin), 561 nm (Atto 550), 637 nm (Atto 647) 730nm (Alexa 750). Image acquisition parameters Images were recorded at 16-bit depth and with 1024x1024 pixels dimensions (pixel size: 0.217 µm) using an iXon Ultra 888 EM-CCD camera. The region of interest was selected based on the DAPI signal and 50 z-slices were acquired with a step size of 0.4 µm (20 µm z-range) per field of view (FOV). Tiles were imaged with a 10% overlap to ensure accurate stitching. Image data Type Figure Format & compression PNG Size description 8800x8788+0+0 pixels (Primary image) Pixel/voxel size description 0.217 µm (Primary image) Channel information RGB Image processing method Tiles were imaged with a 10% overlap to ensure accurate stitching. Subsequently, a flatfield-correction was conducted based on the DAPI channel and stitching and registration of the tiles was conducted with Fiji. First, SDCM image stacks were subjected to a maximum intensity projection, followed by flat field and chromatic aberration correction using a custom script. Next, image tiles were stitched using the “Grid/Collection Stitching” plugin. DAPI images from SDCM were registered to MC or Xenium widefield images using “Register Virtual Stack Slices” with Affine feature extraction model and the Elastic bUnwarpJ splines registration model. In case of further staining, images were transformed via Transform Virtual Stack slices employing the transformation file of the DAPI registration. Image Correlation Spatial and temporal alignment The region of interest was selected based on the DAPI signal and 50 z-slices were acquired with a step size of 0.4 µm (20 µm z-range) per field of view (FOV). Tiles were imaged with a 10% overlap to ensure accurate stitching. Subsequently, a flatfield-correction was conducted based on the DAPI channel and stitching and registration of the tiles was conducted with Fiji (https://github.com/RippeLab/MBEN/tree/main/stitching) (https://github.com/RippeLab/MBEN/tree/main/Registration). Related images and relationship MB266-morphology_mip.ome.tif at https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD1093 Analysed data Analysis result type Figure Data used for analysis MB266-transcripts.csv.gz, MB266-transcripts.csv.gz at https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD1093 Analysis method and details Most of the analysis and visualization (including tidyverse, data.table, ggridges R packages) was done in R 4.2.2. Raw data were processed using technology-specific corporate pipelines (custom pipeline was used for MC). For each technology Seurat objects of the sample data and analysis results were created using the Seurat (v. 4.3.0) R package (https://github.com/scOpenLab/spatial_analysis/tree/main) Submitted via NFDI4BIOIMAGE  ' license: cc-by-4.0 name: NFDI4BIOIMAGE Calendar Cover 2025 num_downloads: 0 publication_date: '2025-09-15' submission_date: '2025-09-16T11:19:11.695297' url: - https://zenodo.org/records/16979744 - https://doi.org/10.5281/zenodo.16979744 uuid: b2d615f5-2b9c-41d9-8cb9-ed77335ad69d language: en file_formats: .png tags: - exclude from DALIA authors_with_orcid: - Anne Rademacher https://orcid.org/0000-0003-3054-3121 - Alik Huseynov https://orcid.org/0000-0002-1438-4389 - Michele Bortolomeazzi https://orcid.org/0000-0001-5805-5774 - Sina Jasmin Wille https://orcid.org/0009-0000-1254-7431 - Sabrina Schumacher https://orcid.org/0000-0003-2292-9286 - Pooja Sant https://orcid.org/0000-0001-9301-3211 - Denise Keitel - Konstantin Okonechnikov https://orcid.org/0000-0002-3409-2340 - David R. Ghasemi https://orcid.org/0000-0002-3844-9933 - Kristian W. Pajtler https://orcid.org/0000-0002-3562-6121 - Jan-Philipp Mallm https://orcid.org/0000-0002-7059-4030 - Karsten Rippe https://orcid.org/0000-0001-9951-9395 - authors: - Massei, Riccardo - Serrano-Solano, Beatriz - Fouilloux, Anne - Gruening, Björg - Sun, Yi - Chiang, Diana - Bernt, Matthias - Kostrykin, Leonid description: Imaging is crucial across various scientific disciplines, particularly in life sciences, where it plays a key role in studies ranging from single molecules to whole organisms. However, the complexity and sheer volume of image data present significant challenges. Managing and analyzing this data efficiently requires well-defined image processing tools and analysis pipelines that align with the FAIR principles—ensuring they are findable, accessible, interoperable, and reusable across different domains. In the frame of NFDI4BIOIMAGE1 (the National Research Data Infrastructure focusing on bioimaging in Germany), we want to find viable solutions for storing, processing, analyzing, and sharing bioimaging data. In particular, we want to develop solutions to make findable and machine-readable metadata developing analysis pipelines. In scientific research, such pipelines are crucial for maintaining data integrity, supporting reproducibility, and enabling interdisciplinary collaboration. These tools can be used by different users to retrieve images based on specific attributes as well as support quality control by identifying appropriate metadata. Galaxy, an open-source, web-based platform for data-intensive research, offers a solution by enabling the construction of reproducible pipelines for image analysis2. By integrating popular analysis software like CellProfiler and connecting with cloud services such as OMERO and IDR, Galaxy facilitates the seamless access and management of image data. This capability is particularly valuable in bioimaging, where automated pipelines can streamline the handling of complex metadata, ensuring data integrity and fostering interdisciplinary collaboration. This approach not only increases the efficiency of RDM processes in bioimaging but also contributes to the broader scientific community's efforts to embrace FAIR principles, ultimately advancing scientific discovery and innovation. In the present poster, we showed how to integrate RDM processes and tools in Galaxy. We will showcase how Images can be enriched with metadata (i.e. key-value pairs, tags, raw data, regions of interest) and uploaded to a target OME Remote Objects (OMERO) server using a novel set of OMERO tools developed with Galaxy3. Workflows give the possibility to the user to intuitively fetch images from the local server and perform image analysis (i.e. annotation). Furthermore, we will show the potential integration of eletronic lab books such as eLabFTW4, cloud storage systems (i.e. OneData)5 and interactive norebooks (Jupyter Notebooks) 6 in the Galaxy pipeline. license: cc-by-4.0 name: Development FAIR image analysis workflows and RDM pipelines in Galaxy num_downloads: 44 publication_date: '2025-09-10' submission_date: '2025-09-16T11:19:12.178938' url: - https://zenodo.org/records/17093454 - https://doi.org/10.5281/zenodo.17093454 uuid: a1c75d49-e6d9-423e-ad25-10e3b521aaf0 language: en file_formats: .pdf tags: - NFDI4Bioimage - exclude from DALIA authors_with_orcid: - Riccardo Massei - Beatriz Serrano-Solano - Anne Fouilloux - Björg Gruening - Yi Sun - Diana Chiang - Matthias Bernt - Leonid Kostrykin - authors: - Wagner, Robert - Ahmadi, Mohsen - Waltemath, Dagmar - Yordanova, Kristina - Becker, Markus M. description: Applied plasma research involves several disciplines such as physics, medicine and biology to solve application-oriented problems, often generating large and heterogeneous experimental data sets. The descriptions and metadata describing these interdisciplinary scientific investiga-tions is stored in distributed systems (e.g., physical laboratory notebooks or electronic labora-tory notebooks (ELN) like eLabFTW [1]), and the experimental data are either stored locally within the laboratories or on centralized institutional storage systems. As a result, the collected information often has to be tediously assembled for processing into publications. The workflow represented in Figure 1 addresses this suboptimal situation and promotes the combination of the image database OMERO [2], the ELN system eLabFTW, the research data management tool Adamant [3] and Python scripts for handling microscopy images in plasma life science and plasma medicine [4]. This workflow highlights how the developments from the NFDI4BIOIMAGE consortium can be brought into practical applications by addressing the specific demands of plasma science, where domain-specific metadata is essential for effective data interpretation and reuse. It showcases the benefits of FAIR [5] metadata combining do-main-specific requirements with method-specific solutions. Similar to most imaging workflows, image analysis in plasma research requires metadata from several sections of the experiment. Moreover, the plasma-related metadata are essential for the experimental context and must be included in the analysis, e.g. to describe the influence of plasma on the treated sample. Therefore, the metadata schema Plasma-MDS [6] is adapted to collect plasma-related metadata, such as information on the plasma species having a major impact on the treated samples. Alongside Plasma-MDS, the Recommended Metadata for Bio-logical Images (REMBI) standard [7] is used for the biological metadata such as the sample preparation and treatment procedures. The collection of these metadata is realized using Adamant, which enables the beginner-friendly collection of structured metadata. The tool presents JSON schemas in easy-to-read and easy-to-fill HTML forms, enabling metadata validation. Once completed and validated, the metadata are uploaded directly to eLabFTW using Adamant's workflow functionalities. The images from the treated samples are uploaded to OMERO by OMERO.insight and afterwards automatically annotated via Python scripts. These scripts take previously collected metadata from the related eLabFTW experiments and the microscope description metadata collected by the Micro Meta App [8], which are also stored in eLabFTW. The metadata is categorized and annotated according to the various data organizational levels within OMERO, specifically fo-cusing on project and dataset hierarchies, as well as screens that are composed of plates, which in turn contain wells. Screens resemble microwell plates, commonly used in a variety of biological experiments. The hieraic organization of metadata significantly enhances the ease of reusing images and associated metadata for subsequent processing and analysis. By efficiently distributing and reducing large metadata sets to an acceptable level, while simultaneously eliminating redun-dancies, this approach facilitates straightforward analyses with tools like ImageJ [9] and FIJI [10], thanks to the close association of metadata with the images themselves. In summary, one of the application-specific developments within the NFDI4BIOIMAGE consor-tium is presented, which contributes to the adoption of the FAIR principles in laboratory envi-ronments. Further work will address the integration of ontologies for the semantic description of data and metadata. license: cc-by-4.0 name: Linking of Research (Meta-)data in OMERO to Foster FAIR Data in Plasma Science num_downloads: 53 publication_date: '2025-09-10' submission_date: '2025-09-16T11:19:12.689608' url: - https://zenodo.org/records/17092348 - https://doi.org/10.5281/zenodo.17092348 uuid: 2febfada-4362-466f-872d-fb8b5e2aa6a6 language: en file_formats: .pdf tags: - NFDI4Bioimage - Bioimage Analysis - exclude from DALIA authors_with_orcid: - Robert Wagner https://orcid.org/0000-0002-2762-293X - Mohsen Ahmadi https://orcid.org/0000-0002-7018-0460 - Dagmar Waltemath https://orcid.org/0000-0002-5886-5563 - Kristina Yordanova https://orcid.org/0000-0002-6428-1062 - Markus M. Becker https://orcid.org/0000-0001-9324-3236 - authors: - Sun, Yi - Tischer, Christian - Kelleher, Harry Alexander - Heriche, Jean-Karim description: 'Reproducing computing environments become increasingly challenging in research, especially when compute-intensive scientific workflows require specialised software stacks, specialized hardware (e.g. GPUs), and interactive analysis tools. While traditional high-performance computing (HPC) systems offer scalable resources for batch processing, they don''t easily support interactive workflows. On the other hand, workstations have fixed resources and face workflow deployment challenges because conflicts can occur when multiple tools and dependencies are deployed into the same environment. To address these limitations, we present cloud-based virtual desktop platforms, built on the desktop-as-a-service (DaaS) model, using a containerised, cloud-native approach. Our platforms offer on-demand, customized desktop environments accessible from any web browser, with dynamic allocation of CPU, memory, and GPU resources for efficient utilization of resources. We introduce two types of virtual desktops: BAND, built on top of a Slurm scheduler and BARD, using Kubernetes. In both cases, containerization ensures consistent and reproducible environments across sessions and pre-installed software improves accessibility for researchers. Deployment and system administration are also simplified through the use of orchestration and automation tools. Our virtual desktop platforms are particularly valuable for bioimage analysis, which requires complex workflows involving high interactivity, multiple software and GPU acceleration. By combining containerization and cloud-native services, BAND and BARD offer a scalable and sustainable model for delivering interactive, reproducible research environments.' license: cc-by-4.0 name: Cloud-Based Virtual Desktops for Reproducible Research num_downloads: 59 publication_date: '2025-09-10' submission_date: '2025-09-16T11:19:16.066079' url: - https://zenodo.org/records/17092303 - https://doi.org/10.5281/zenodo.17092303 uuid: fdac071c-3b28-4aac-949d-bd87a51211d1 language: en file_formats: .pdf tags: - NFDI4Bioimage - exclude from DALIA authors_with_orcid: - Yi Sun - Christian Tischer https://orcid.org/0000-0003-4105-1990 - Harry Alexander Kelleher - Jean-Karim Heriche https://orcid.org/0000-0001-6867-9425 - authors: - Haase, Robert description: In this slide deck we learn how to write reproducible bio-image analysis code in Jupyter notebooks. Goal is not just to have code running elsewhere reproducibly, but also enabling others to understand workflows to enable them reproducing the analysis also in their mind and potentially other tools. Additionally we cover how to generate Jupyter notebooks from Napari and using artificial intelligence, namely bia-bob. license: cc-by-4.0 name: Reproducible Bio-Image Analysis using Python, Napari, Jupyter and AI num_downloads: 318 publication_date: '2025-09-09' submission_date: '2025-09-16T11:19:16.556634' url: - https://zenodo.org/records/17085991 - https://doi.org/10.5281/zenodo.17085991 uuid: be614b13-5234-40d7-8038-9bf03fed98a3 language: en file_formats: .pdf * .pptx tags: - NFDI4Bioimage - Bioimage Analysis - include in DALIA authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 - authors: - Thönnißen, Julia - Oliveira, Sarah - Oberstrass, Alexander - Kropp, Jan-Oliver - Gui, Xiao - Schiffer, Christian - Dickscheid, Timo description: 'The rapid development of new imaging technologies across scientific domains–especially high-throughput technologies–results in a growing volume of image datasets in the Tera- to Petabyte scale. Efficient visualization and analysis of such massive image resources is critical but remains challenging due to the sheer size of the data, its continuous growth, and the limitations of conventional software tools to address these problems. Tools for visualization, annotation and analysis of large image data are confronted with the fundamental dilemma of balancing computational efficiency and memory requirements. Many tools are unable to process large datasets due to memory constraints, requiring workarounds like downsampling. On the other hand, solutions that can handle large data efficiently often rely on specialized or even proprietary file formats, limiting interoperability with other software. This reflects diverging requirements: storage favours compression for efficiency, analysis demands fast data access, and visualization requires tiled, multi-resolution representations. Lacking a unified approach for these conflicting needs, the operation of large and dynamically evolving image repositories in practice often requires undesirable data conversions and costly data duplication. In addressing these challenges, the bioimaging community increasingly adheres to the FAIR principles [1] through national and international initiatives [2], [3], [4]. For example, the Open Microscopy Environment (OME) fosters standards such as OME-TIFF [5] and its cloud-native successor OME-NGFF [6]; BioFormats [7] and OMERO [8] facilitate metadata-rich data handling across diverse platforms; and BrAinPI [9] provides web-based visualization of images via Neuroglancer [10]. These tools represent important developments towards more efficient and standardized use of bioimaging data. However, for very large and dynamically growing repositories, it is still not feasible to settle on a single standard for a subset of these tools, in particular in the light of very diverging needs for massively parallel processing on HPC systems. Therefore, converting data to a single target format is often not a practical solution. We propose a concept for a modular image delivery service which acts as a middleware between large image data resources and applications, serving image data from a cloud resource in multiple requested representations on demand. The service allows reading data stored in different input file formats, applying coordinate transformations and filtering operations on-the-fly, and serving the results in a range of different output formats and layouts. Building upon a common framework for reading and transforming data, an extensible set of access points connects the service to client applications: Lightweight REST APIs allow web-based mutli-resolution access (e.g., in common formats such as used in Neuroglancer and OpenSeadragon base viewers); mountable filesystem interfaces enable linking the repository to file-oriented solutions (e.g., OMERO, ImageJ); and programmatic access from customizable software tools (e.g., Napari). To provide compatibility with upcoming image data standards like BIDS [11] and minimize conversion efforts, the service is able to dynamically expose standard-conform views into arbitrarily organized datasets. The proposed approach for reading and transforming data on-the-fly eliminates the need for redundant storage and application-specific conversions of datasets, improving workflow efficiency and sustainability. In summary, we advocate for the development of a flexible and extensible image data service that supports large-scale analysis, dynamic transformations, multi-tool interoperability, and compatibility with community standards for large image datasets. This way it supports the FAIR principles, reduces integration barriers, meets the performance demands of modern imaging research, and still fosters the use of existing community developments.' license: cc-by-4.0 name: A Perspective on FAIR and Scalable Access to Large Image Data num_downloads: 34 publication_date: '2025-08-04' submission_date: '2025-09-16T11:19:19.031121' url: - https://zenodo.org/records/16736220 - https://doi.org/10.5281/zenodo.16736220 uuid: 2f736338-f8e4-413e-bf7a-d1b6f8cd23e8 language: en file_formats: .pdf tags: - include in DALIA authors_with_orcid: - Julia Thönnißen https://orcid.org/0000-0002-5467-871X - Sarah Oliveira https://orcid.org/0000-0002-8050-6305 - Alexander Oberstrass https://orcid.org/0000-0003-0712-034X - Jan-Oliver Kropp https://orcid.org/0000-0002-4770-6612 - Xiao Gui - Christian Schiffer https://orcid.org/0000-0002-2544-843X - Timo Dickscheid https://orcid.org/0000-0002-9051-3701 - authors: - Schätz, Martin - Pérez Koldenkova, Vadim description: Workshop presentation from XXXIV Foro de Investigación en Salud 2025. license: cc-by-4.0 name: Image Analysis in Healthcare - The Current Landscape, Trends, and Collaborative Opportunities num_downloads: 92 publication_date: '2025-09-09' submission_date: '2025-09-16T11:20:35.436895' url: - https://zenodo.org/records/17080219 - https://doi.org/10.5281/zenodo.17080219 uuid: ccae0123-60de-462b-aeb6-dbb64eb4cef6 file_formats: .pdf * .pptx tags: - include in DALIA authors_with_orcid: - Martin Schätz https://orcid.org/0000-0003-0931-4017 - Vadim Pérez Koldenkova https://orcid.org/0000-0001-9118-8800 - authors: - Erin Weisbart description: Contains the survey data collected through the 2024 Bridging Imaging Users to Imaging Analysis Survey and figures/code from preliminary data exploration of the survey results. license: bsd-3-clause name: 'COBA-NIH/2024_Bridging_Imaging_Users_to_Imaging_Analysis_Survey: Survey data with preliminary exploration' num_downloads: 0 publication_date: '2025-09-15' submission_date: '2025-09-16T19:14:02.382718' url: - https://zenodo.org/records/17127544 - https://doi.org/10.5281/zenodo.17127544 tags: - exclude from DALIA uuid: 81370b88-9575-4e72-97f3-9903708f9bc7 authors_with_orcid: - Erin Weisbart file_formats: .zip - authors: - Kevin Yamauchi description: '' license: BSD 3-Clause "New" or "Revised" License name: embo-bia-2025 publication_date: '2025-08-18T09:32:20+00:00' submission_date: '2025-09-16T18:32:42.184908' type: GitHub Repository url: https://github.com/kevinyamauchi/embo-bia-2025 tags: - exclude from DALIA uuid: 7aa3ba41-d1ae-4a2b-9a20-1f08f20b8017 - authors: - Lea Kabjesz - Lea Gihlein - Mara Lampert - FPGro description: 'A collection of workshop materials for exploring prompting techniques by building a Snake game in Cursor. ' license: Creative Commons Attribution 4.0 International name: PygamePrompts publication_date: '2025-08-12T11:36:36+00:00' submission_date: '2025-09-19T12:01:46.533704' tags: - include in DALIA type: GitHub Repository url: https://github.com/kaabl/PygamePrompts uuid: b2c515fb-e34d-4906-b5f8-0fb8ca669763 - authors: - Florian Ingelfinger - Nathan Levy - Can Ergen - Artemy Bakulin - Alexander Becker - Pierre Boyeau - Martin Kim - Diana Ditz - Jan Dirks - Jonas Maaskola - Tobias Wertheimer - Robert Zeiser - Corinne C. Widmer - Ido Amit - Nir Yosef description: CytoVI is a probabilistic generative model that enables statistically rigorous and integrative analysis of antibody-based single cell technologies, outperforming existing methods and enabling key functionalities like cell embeddings and differential protein expression testing, with applications in B cell maturation, non-Hodgkin lymphoma, and diagnostic flow cytometry. license: CC-BY-4.0 name: CytoVI Deep generative modeling of antibody-based single cell technologies publication_date: '2025-09-12' tags: - Artificial Intelligence - Bioinformatics - include in DALIA type: - Preprint url: https://www.biorxiv.org/content/10.1101/2025.09.07.674699v1 language: en uuid: 78a6739e-5068-4d85-bf7d-c3d79e001c26 - authors: - Alberto Díez description: 'Introduction to basic concepts and tools needed to set up Python for bioimage analysis on Windows. ' license: UNKNOWN name: Python for Bioimage Analysis Basic Tools and Setup on Windows publication_date: '2025-09-04' tags: - Bioimage Analysis - include in DALIA type: - Video url: https://youtu.be/tzdFuxF2E3U language: en uuid: ac29d368-d4f0-4b70-b2fd-d65a7b5a025e - authors: - Riccardo Massei - Wibke Busch - Beatriz Serrano-Solano - Matthias Bernt - Stefan Scholz - Elena K. Nicolay - Hannes Bohring - Jan Bumberger description: This study demonstrates the use of Workflow Management Systems (WMS) and the OMERO platform to create reusable semi-automatic workflows for managing high-content screening (HCS) bioimaging data, improving data management efficiency, reducing errors, and providing a blueprint for future HCS data management systems. license: CC-BY-4.0 name: High-content screening (HCS) workflows for FAIR image data management with OMERO publication_date: '2025-05-09' tags: - Bioimage Analysis - OMERO - include in DALIA type: - Publication url: https://www.nature.com/articles/s41598-025-00720-0 language: en uuid: 88fe8efa-7012-4235-bbd0-631cf334043c - authors: - Niraj Kandpal - Peter Zentis - Monica Valencia-Schneider - Astrid Schauss description: 'A tutorial for OMERO and ARC Interoprability and (meta)data exchange. This describes the relevant metadata to transfer from OMERO to ARC and the other way round. ' license: CC-BY-4.0 name: OMERO and ARC Workflow publication_date: '2025-04-15' tags: - Bioimage Analysis - OMERO - include in DALIA type: - Slides url: https://zenodo.org/records/15225616 language: en uuid: 11effefc-ea15-4ff5-b4fe-9760cb90a28b authors_with_orcid: - Niraj Kandpal https://orcid.org/0009-0007-5101-4786 - Peter Zentis https://orcid.org/0000-0002-6999-132X - Monica Valencia-Schneider https://orcid.org/0000-0003-3430-2683 - Astrid Schauss https://orcid.org/0000-0002-6658-2192 file_formats: .pdf - authors: - Josh Moore - Yi Sun description: In this talk, we will be discussing OME-ZARR, a cloud-optimized format for scalable bioimage data management, and BARD, a cloud virtual desktop that provides a seamless way to run resource-intensive applications in the cloud, enabling users to access powerful computing environments from any device with a web browser. license: UNKNOWN name: NFDITalk Cloud based image data science publication_date: '2025-06-02' tags: - Bioimage Analysis - OMERO - exclude from DALIA type: - Video url: https://www.youtube.com/watch?v=bzfmE29S270 language: en uuid: 28cc634c-f16a-4bc4-b731-eb64e048e60d - authors: - Jens Dierkes - Julia Fürst - Tanja Hörner - Sebastian Klammt - Birte Lindstädt - Iris Pigeot - Katja Restel - Carsten Oliver Schmidt - Dagmar Waltemath - Atinkut Zeleke description: This handbook provides an overview of Research Data Management (RDM) and FAIR principles, with a focus on practical implementation and training for master students, doctoral researchers, and professionals in biomedical sciences. The handbook is a living document that will be updated iteratively to address the specific needs and requirements of the target group, with a focus on the NFDI4Health domain. license: CC-BY-4.0 name: Training concepts in research data management and data science with the focus on health research publication_date: '2023-08-22' tags: - FAIR-Principles - Research Data Management - include in DALIA type: - Publication url: https://repository.publisso.de/resource/frl:6453768 language: en uuid: fc75b6f0-f29f-40bb-b76d-6c0e0eb738da - authors: - Fabio Crameri description: Scientific, colour-vision deficiency friendly and perceptually-uniform colour maps that include all readers and significantly reduce visual errors. license: All rights reserved name: Scientific colour maps publication_date: '2023-10-05' tags: - FAIR-Principles - Research Data Management - exclude from DALIA type: - Website url: https://www.fabiocrameri.ch/colourmaps/ language: en uuid: cd1c6e64-73ae-415f-bd3c-a1c407763b46 - authors: - Helena Klara Jambor description: Creating clear and engaging scientific figures is crucial to communicate complex data. In this Comment, I condense principles from design, visual perception and data visualization research in a checklist that can help researchers to improve their data visualization, by focusing on clarity, accessibility and design best practices. license: Copyright name: A checklist for designing and improving the visualization of scientific data publication_date: '2025-06-13' tags: - Copyright - Research Data Management - exclude from DALIA type: - Publication url: https://www.nature.com/articles/s41556-025-01684-z.pdf language: en uuid: effd10a7-6f1a-4f1e-948c-802bf16e6699 - authors: - Booz Allen Hamilton description: This dataset contains a large number of segmented nuclei images. The images were acquired under a variety of conditions and vary in the cell type, magnification, and imaging modality (brightfield vs. fluorescence). The dataset is designed to challenge an algorithms ability to generalize across these variations. license: Restrictive license name: 2018 Data Science Bowl publication_date: '2018-01-16' tags: - Nuclei images - Restrictive license - AI-ready - exclude from DALIA type: - Data url: https://www.kaggle.com/c/data-science-bowl-2018/data language: en uuid: dfafa540-14b7-4e34-896d-5b19c531a0b5 - authors: - Stephan Wienert - Daniel Heim - Kai Saeger - Albrecht Stenzinger - Michael Beil - Peter Hufnagl - Manfred Dietel - Carsten Denkert - Frederick Klauschen description: A novel contour-based approach to cell detection and segmentation has been developed, which uses minimal prior information and detects contours independently of their shape, avoiding a segmentation bias. This approach has been shown to accurately segment a broad range of normal and disease-related morphological features, with high precision and recall rates. license: CC-BY-NC-SA-3.0 name: Detection and Segmentation of Cell Nuclei in Virtual Microscopy Images A Minimum-Model Approach publication_date: '2012-07-11' tags: - Nuclei images - AI-ready - exclude from DALIA type: - Data url: https://www.nature.com/articles/srep00503#Sec16 language: en uuid: 712f20ca-21fc-4cf6-8b06-5349006979c1 - authors: - Naylor Peter Jack - Walter Thomas - Laé Marick - Reyal Fabien description: This dataset has been annonced in our accepted paper "Segmentation of Nuclei in Histopathology Images by deep regression of the distance map" in Transcation on Medical Imaging on the 13th of August. This dataset consists of 50 annotated images, divided into 11 patients. license: CC-BY-4.0 name: Segmentation of Nuclei in Histopathology Images by deep regression of the distance map publication_date: '2018-02-16' tags: - Nuclei images - AI-ready - exclude from DALIA type: - Data url: https://zenodo.org/records/1175282#.WyP61xy-l5E language: en uuid: acee2628-5a74-40fe-98c0-8c3fe3dc73fc file_formats: null - authors: - Haase, Robert description: This slides covers aspects of research data management (RDM) and open science such as the RDM life cylce, FAIR principles, sharing data on Zenodo, rights and duties of scientists in the RDM context. license: cc-by-4.0 name: Research Data Management num_downloads: 41 publication_date: '2025-09-22' submission_date: '2025-09-23T11:19:13.689095' url: - https://zenodo.org/records/17174207 - https://doi.org/10.5281/zenodo.17174207 uuid: b85a3396-ba20-4e22-b4ef-0cf336ff2c9e authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .pdf * .pptx - authors: - Alxneit, Ivo license: cc-by-4.0 name: 'Test data for Bioformats Issue #4366' num_downloads: 2 publication_date: '2025-09-22' submission_date: '2025-09-23T11:22:11.047143' url: - https://zenodo.org/records/17176730 - https://doi.org/10.5281/zenodo.17176730 uuid: a938b445-43ed-49a5-8c34-d7492a165d43 authors_with_orcid: - Ivo Alxneit file_formats: .dm4 - authors: - Haase, Robert description: This slides covers aspects of research data management (RDM) and open science such as the RDM life cylce, FAIR principles, sharing data on Zenodo, rights and duties of scientists in the RDM context. license: cc-by-4.0 name: Research Data Management num_downloads: 120 publication_date: '2025-09-23' submission_date: '2025-09-30T11:19:57.375119' url: - https://zenodo.org/records/17186869 - https://doi.org/10.5281/zenodo.17186869 authors_with_orcid: - Robert Haase https://orcid.org/0000-0001-5949-2327 file_formats: .pdf * .pptx uuid: 7d29de53-2fc0-43bc-a1db-e3726e8ab2c7 - authors: - Stervbo, Ulrik - Brilhaus, Dominik - Vandendorpe, Justine - Fürst, Julia - Weidtkamp-Peters, Stefanie description: 'Presentation at the HHU Core Facility Day 2025 18.09.2025, University of Düsseldorf   NFDI consortia are legally non-independent projects that do not act autonomously towards third parties. The authors of this presentation represent the contributions to these projects as members of their affiliated institutions, which are members of the NFDI e.V.' license: cc-by-4.0 name: NFDI Life Science Consortia – Supporting your journey to FAIR research data num_downloads: 16 publication_date: '2025-10-02' submission_date: '2025-10-07T11:19:38.502429' url: - https://zenodo.org/records/17251110 - https://doi.org/10.5281/zenodo.17251110 file_formats: .pdf authors_with_orcid: - Ulrik Stervbo https://orcid.org/0000-0002-2831-8868 - Dominik Brilhaus https://orcid.org/0000-0001-9021-3197 - Justine Vandendorpe https://orcid.org/0000-0002-9421-8582 - Julia Fürst - Stefanie Weidtkamp-Peters https://orcid.org/0000-0001-7734-3771 uuid: 65c81b9b-87ab-4040-adec-487f7c4e89af - authors: - Instruct-ERIC - Euro-BioImaging ERIC - Institut Pasteur de Montevideo - Institut Pasteur - Universidade de São Paulo - Universidad de la República - Chan Zuckerberg Initiative (United States) - Universidad de la Ciudad de Buenos Aires - Universidade Federal do Rio de Janeiro - Brazilian Center for Research in Energy and Materials description: 'This White Paper outlines a strategic approach to further develop the collaboration among Research Infrastructures (RIs) in the European Union (EU) and the Community of Latin American and Caribbean States (CELAC, for its acronym in Spanish/Portuguese), prioritising long-term planning and commitment, sustainability, open access, and scientific excellence. Building upon existing policy frameworks, particularly the EU-CELAC Working Group on Research Infrastructures, this document proposes concrete mechanisms to strengthen bi-regional cooperation in research and innovation, in the context of shared values and global scientific needs. Research Infrastructures (RIs) are key enablers of scientific progress and innovation, especially in advanced domains such as artificial intelligence, big data, and biomedicine. Their value lies not only in the high performance of their sophisticated equipment, but also in the advanced expertise of RI researchers and technical staff. Global challenges—like pandemics, climate change, and energy transition—require robust and internationally connected RIs supported by stable, long-term institutional commitments. This White Paper focuses on strengthening RI collaboration between European and CELAC countries, particularly in Bioimaging and Structural Biology, which already have established network partnerships through Euro-BioImaging ERIC / Latin America BioImaging, and Instruct-ERIC / Mercosur Structural Biology Center. The document serves as a roadmap for expanding EU–CELAC cooperation, building upon previous collaborative projects, and proposing new mechanisms for sustainable engagement.' license: cc-by-4.0 name: 'Global cooperation in Research Infrastructures: the EU-CELAC case, with focus on bioimaging and structural biology' num_downloads: 197 publication_date: '2025-09-18' submission_date: '2025-10-07T11:20:16.047502' url: - https://zenodo.org/records/17152974 - https://doi.org/10.5281/zenodo.17152974 file_formats: .pdf authors_with_orcid: - Instruct-ERIC - Euro-BioImaging ERIC - Institut Pasteur de Montevideo - Institut Pasteur - Universidade de São Paulo - Universidad de la República - Chan Zuckerberg Initiative (United States) - Universidad de la Ciudad de Buenos Aires - Universidade Federal do Rio de Janeiro - Brazilian Center for Research in Energy and Materials uuid: 8caf58e1-7790-4a8f-8b1a-c87a24573da3 - authors: - Rappe, Anna description: 'Test data, for reference see link below. https://forum.image.sc/t/how-to-open-ims-files-in-fiji-on-mac/116781' license: cc-by-4.0 name: 'Test data for Bioformats Issue #116781' num_downloads: 1 publication_date: '2025-10-03' submission_date: '2025-10-07T11:20:50.206288' url: - https://zenodo.org/records/17257407 - https://doi.org/10.5281/zenodo.17257407 file_formats: .ims authors_with_orcid: - Anna Rappe uuid: 124ab0e1-b9fd-4e3f-bfdb-03b24c7946c2 - authors: - Zobel, Thomas description: 'MetaFold simplifies laboratory data management through easy-to-use templates that create reproducible metadata.It reduces manual steps by automatically generating folder structures and metadata files at multiple locations simultaneously.Thanks to its integration with OMERO and ELNs, microscopy data can be automatically imported based on the existing metadata — saving researchers significant time and effort. Core functions Template-based creation of project folders and metadata forms Multi-user and group-level configuration Secure credential storage and user authentication Direct integrations with elabFTW (ELN) and OMERO (image data management) Project discovery: recursive scan and visualization of existing data Category system: customizable templates by project type ' license: cc-by-4.0 name: MetaFold - Template-Driven Desktop Tool to Sync, Enrich, and Automate Your Research Data num_downloads: 23 publication_date: '2025-10-08' submission_date: '2025-10-21T11:20:17.690525' url: - https://zenodo.org/records/17296688 - https://doi.org/10.5281/zenodo.17296688 uuid: 1c3d6179-5a27-44f7-adc6-542454c0dd54 - authors: - Robert Haase description: 'BioImage Analysis using Python tutorial at EMBO LSM 2025' license: CC-BY-4.0 name: 'embo-lsm-bia-2025' publication_date: '2025-08-03' tags: - Bioimage Analysis - Python - include in DALIA type: - Github Repository url: https://github.com/ScaDS/embo-lsm-bia-2025 language: en - authors: - Mara Lampert description: 'This blog post revolves around reproducibility in bio-image analysis across several popular platforms, including Napari, Fiji, QuPath, Galaxy, CellProfiler and JIPipe.' license: CC-BY-4.0 name: 'From Click to Code: Reproducibility in Bio-image Analysis' publication_date: '2025-08-05' tags: - Bioimage Analysis - include in DALIA type: - Blog Post url: https://focalplane.biologists.com/2025/08/05/from-click-to-code-reproducibility-in-bio-image-analysis/ language: en - authors: - Rahul Pandita description: 'Language models (LLMs) are rapidly becoming essential developer tools. But the metaphors we use to describe them matter. One often used is: "LLMs are the autopilot of coding." As both a developer and a pilot, that metaphor misses the mark on several levels. This blog post pens down why "Copilot" is a far better analogy than "autopilot" when it comes to LLMs and vibe coding.' license: UNKNOWN name: 'Autopilot, Copilot, and Software Developers' publication_date: '2025-10-12' tags: - Artificial Intelligence - include in DALIA type: - Blog Post url: https://rahulpandita.me/blog/2025-10-12-Copilot language: en - authors: - Publications Office of the European Union description: 'Course materials on Open Data Sharing: Introducing and understanding the legal side of open data, Incorporating open data in your applications, Measuring the impact of open data and more.' license: CC-BY-4.0 name: 'Open Data Sharing Course Materials provided by the European Union' publication_date: '2024-01-17' tags: - Research Data Management - Open Data - include in DALIA type: - Website url: https://data.europa.eu/en/academy language: en - authors: - Publications Office of the European Union description: 'Course materials on Open Data Sharing: Introducing and understanding the legal side of open data, Incorporating open data in your applications, Measuring the impact of open data and more.' license: CC-BY-4.0 name: 'Open Data Sharing Course Materials provided by the European Union' publication_date: '2024-01-17' tags: - Research Data Management - Open Data - include in DALIA type: - Website url: https://data.europa.eu/en/academy language: en - authors: - EOSC description: 'All Skills4EOSC training courses are now available in self-paced mode on the Skills4EOSC Moodle platform and on Zenodo, supporting the reuse of learning materials across communities. This includes the courses that were originally delivered in hybrid mode during the project, as part of the Master Trainers’ programme.' license: CC-BY-4.0 name: 'Skills4EOSC Training Courses on Open Data Sharing' publication_date: '2024-01-17' tags: - Research Data Management - Open Data - include in DALIA type: - Website url: https://www.skills4eosc.eu/participate/skills4eosc-training-courses language: en - authors: - Rodrigo Escobar Díaz Guerrero - Jamile Mohammad Jafari - Tobias Meyer-Zedler - Michael Schmitt - Juergen Popp - Thomas Bocklitz description: 'In the interdisciplinary field of microscopy research, managing and integrating large volumes of data stored across disparate platforms remains a major challenge. Data types such as bioimages, experimental records, and spectral information are often maintained in separate repositories, each following different management standards. However, linking these data sources across the research lifecycle is essential to align with the FAIR principles of data management: Findability, Accessibility, Interoperability, and Reusability. Despite this need, there is a notable lack of tools capable of effectively integrating and linking data from heterogeneous sources. To address this gap, we present LEO (Linking Electronic Lab Notebooks with OMERO), a web-based platform designed to create and manage links between distributed data systems. LEO was initially developed to link objects between Electronic Lab Notebooks (ELNs) and OMERO, but its functionality has since been extended through a plugin-based architecture, allowing the integration of additional data sources. This extensibility makes LEO a scalable and flexible solution for a wide range of microscopy research workflows.' license: CC-BY-4.0 name: 'LEO: An Open-Source Platform for Linking OMERO with Lab Notebooks and Heterogeneous Metadata Sources' publication_date: '2025-08-01' tags: - Research Data Management - OMERO - Bioimage Analysis - include in DALIA type: - Publication url: https://arxiv.org/abs/2508.00654 language: en - authors: - Moore, Josh description: 'Presented at the 2025 International OME-NGFF workshop. https://www.biovisioncenter.uzh.ch/en/events/Upcoming-Events/2025-OME-NGFF-workshop.html' license: cc-by-4.0 name: Road to OME-Zarr 1.0 num_downloads: 18 publication_date: '2025-11-10' submission_date: '2025-11-11T11:22:33.461962' url: - https://zenodo.org/records/17579804 - https://doi.org/10.5281/zenodo.17579804 uuid: 112c8828-a4ee-46c9-999e-427a8145212d - authors: - Kabjesz, Lea - Gihlein, Lea - Lampert, Mara Harriet - Haase, Robert description: 'This record contains the poster "Smart Slide Generation: Re-Using Training Materials Through the Power of LLMs" presented by Lea Kabjesz and Lea Gihlein at the All Hands Meeting of the German AI Centers, held on 5th - 6th November 2025 at DFKI Saarbrücken, Germany.    We acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – NFDI 46/1 – 501864659.' license: cc-by-4.0 name: 'Smart Slide Generation: Re-Using Training Materials Through the Power of LLMs' num_downloads: 92 publication_date: '2025-11-06' submission_date: '2025-11-11T11:22:39.019204' url: - https://zenodo.org/records/17541532 - https://doi.org/10.5281/zenodo.17541532 uuid: 67c2eb01-b8af-4809-89ca-5bcbbba62863 - authors: - Wetzker, Cornelia - Fuchs, Vanessa - Wendt, Jens - Ahmadi, Mohsen - Müller, Maximilian E. - Massei, Riccardo description: The consortium NFDI4BIOIMAGE is funded by DFG grant number NFDI 46/1, project number 501864659. license: cc-by-4.0 name: '[RDM4Mic2025] Data stewardship for Bio-Image Research Data Management Support and Training in NFDI4BIOIMAGE' num_downloads: 52 publication_date: '2025-10-29' submission_date: '2025-11-11T11:22:47.679388' url: - https://zenodo.org/records/17472821 - https://doi.org/10.5281/zenodo.17472821 uuid: e43ef56e-258c-4a6a-af97-f0ccb27461c3 - authors: - Beyer, Frauke - Unger, Michaela - Gey, Ronny - Massei, Riccardo - Wetzker, Cornelia - Nicolay, Elena - Lampert, Mara Harriet description: 'The material supports the panel discussion about open data sharing with stories and info from the panelists.   C.W. and R.M. are funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure –NFDI46/1 – 501864659' license: cc-by-4.0 name: 'The Power of Sharing: Open Data in Bio-image Analysis' num_downloads: 86 publication_date: '2025-10-24' submission_date: '2025-11-11T11:22:49.939572' url: - https://zenodo.org/records/17429285 - https://doi.org/10.5281/zenodo.17429285 uuid: adaa7404-8885-4cb0-9cbf-2db959e9be65 - authors: - Kunis, Susanne description: This presentation gives a short overview of the outcome of the I3D:bio project and the ongoing developments in terms of data and metadata in the NFDI4BIOIMAGE consortium. Presented at the RDM4mic meeting in Münster, from Okt. 21-23, 2025 license: cc-by-4.0 name: (Meta)data in Action - Developments in NFDI4BIOIMAGE and I3D:bio num_downloads: 87 publication_date: '2025-10-23' submission_date: '2025-11-11T11:23:05.071201' url: - https://zenodo.org/records/17424911 - https://doi.org/10.5281/zenodo.17424911 uuid: 51260a5b-e7a3-4198-bd37-daf587e2646b - authors: - Zobel, Thomas - Fortmann-Grote, Carsten description: Material for the OMERO Training Workshop at MPI Evolutionary Biology, Dec. 7 & 8 2023 license: cc-by-4.0 name: Howto organize your image data in OMERO num_downloads: 42 publication_date: '2025-10-21' submission_date: '2025-11-11T11:23:07.258414' url: - https://zenodo.org/records/17404133 - https://doi.org/10.5281/zenodo.17404133 uuid: ae0fc896-d4d9-4262-a89f-fb8354cee5ee - authors: - Fortmann-Grote, Carsten description: Presentation in the FIZ-Karlsruhe "NFDICore Playground" Seminar on October 16 2025This work is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – NFDI 46/1 – 501864659 license: cc-by-4.0 name: Knowledge Graphs for NFDI4BIOIMAGE num_downloads: 43 publication_date: '2025-10-16' submission_date: '2025-11-11T11:23:08.814837' url: - https://zenodo.org/records/17372593 - https://doi.org/10.5281/zenodo.17372593 uuid: 1f8fb2c8-b4c3-4f64-acc5-eef53562f66d - authors: - Fortmann-Grote, Carsten - Ullrich, Kristian description: 'Presentation given at the openBIS User Group Meeting on September 22nd 2025   This work is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – NFDI 46/1 – 501864659' license: cc-by-4.0 name: openBIS for Evolutionary Biology num_downloads: 42 publication_date: '2025-09-24' submission_date: '2025-11-11T11:23:30.585626' url: - https://zenodo.org/records/17191504 - https://doi.org/10.5281/zenodo.17191504 uuid: 95c37346-5ccc-44c2-b1d4-c78f5372cf1c - authors: - Pais de Azevedo, Tomás description: Unsupported Imaris file. Exported from Imaris 9.9.1. license: cc-by-4.0 name: IMS image num_downloads: 2 publication_date: '2025-10-21' submission_date: '2025-11-11T11:27:58.639584' url: - https://zenodo.org/records/17406102 - https://doi.org/10.5281/zenodo.17406102 uuid: b1f1ec2a-255d-48b4-bc57-5d63a688e4f7 - authors: - Soltwedel, Johannes Richard description: Slides presented by Johannes Soltwedel at Halfway to I2K virtual conference 2025 license: cc-by-4.0 name: '[Halfway-to-I2K 2025]: Introduction to ome-zarr and S3 cloud storage' num_downloads: 0 publication_date: '2025-11-18' submission_date: '2025-11-18T11:23:41.172852' url: - https://zenodo.org/records/17639350 - https://doi.org/10.5281/zenodo.17639350 - authors: - Moore, Josh description: Invited talk given to Zeiss license: cc-by-4.0 name: OME-Zarr and Next-Generational File Formats (NGFF) num_downloads: 22 publication_date: '2025-07-02' submission_date: '2025-11-18T11:23:41.733350' url: - https://zenodo.org/records/17604591 - https://doi.org/10.5281/zenodo.17604591 - authors: - Gros, Aafke license: cc-by-4.0 name: CellSens vsi file for testing metadata extraction of timeinterval num_downloads: 18 publication_date: '2025-11-12' submission_date: '2025-11-18T11:25:35.234671' url: - https://zenodo.org/records/17590655 - https://doi.org/10.5281/zenodo.17590655 - authors: - Corbat, Agustin Andres description: 'Poster presented at LABI Meeting 2025: From Data to Discovery about the Global BioImage Analysts'' Society.' license: cc-by-4.0 name: 'GloBIAS: Strengthening the Foundations of Bioimage Analysis' num_downloads: 2 publication_date: '2025-11-10' submission_date: '2025-11-18T11:26:19.155859' url: - https://zenodo.org/records/17633744 - https://doi.org/10.5281/zenodo.17633744 - authors: - Corbat, Agustin Andres description: Presentation about the Global BioImage Analysts' Society (GloBIAS) deliver at the LABI Meeting 2025 in Buenos Aires, Argentina. license: cc-by-4.0 name: GloBIAS Presentation at Latin America BioImaging Meeting num_downloads: 2 publication_date: '2025-11-11' submission_date: '2025-11-18T11:26:20.328938' url: - https://zenodo.org/records/17632823 - https://doi.org/10.5281/zenodo.17632823 - authors: - Nguyen, Khanh Xuan - Bernt, Matthias - Schnicke, Thomas - Haase, Robert - Massei, Riccardo description: KNIME and Galaxy are leading workflow platforms widely used for imaging, data analysis, and automation. However, differences in their architectures make translating imaging workflows between them difficult and time- consuming. The lack of automated conversion workflow reuse. Enabling automatic conversion of KNIME imaging workflows into Galaxy could enhance accessibility, streamline image analysis with further Open Source Tools (i.e. OMERO, INTOB), and accelerate imaging-based scientific discovery. Our goal is to develop generative AI strategies for automated KNIME-to- Galaxy workflow translation using large language models (LLM). By learning from test workflows, the system will adapt to platform changes and improve accuracy over time. license: cc-by-4.0 name: Develop an LLM-based framework for translating KNIME workflows into Galaxy Imaging workflows num_downloads: 21 publication_date: '2025-12-01' submission_date: '2025-12-09T11:25:03.971364' url: - https://zenodo.org/records/17775761 - https://doi.org/10.5281/zenodo.17775761 - authors: - Cruz C., Arsenio N. - Pham, Thanh Huy - Zimmer, Collin - Massei, Riccardo - Sun, Yi - Czodrowski, Paul description: Galaxy is an online computational platform used by a global community of thousands of scientists for processing of large-scale data.This collective effort includes the development of the Galaxy software framework, the integration of analysis tools and visualizations, and the operation of public servers that provide access to Galaxy through web browsers (usegalaxy.eu). Galaxy increases the FAIRness of data analysis pipelines by  providing versioned tools and workflows that can be annotated, shared and published. Furthermore, Galaxy has a dedicated interface for image data analysis—imaging.usegalaxy.eu—providing a comprehensive suite of tools and workflows tailored specifically for imaging scientists. Through the implementation of open source CellProfiler tool into the Galaxy framework, we are creating an interactive evaluation environment for the Cell Painting community. This environment can aid the drug discovery process. license: cc-by-4.0 name: From Cells to Morphological Profiles A FAIR cloud processing pipeline using Galaxy num_downloads: 39 publication_date: '2025-12-01' submission_date: '2025-12-09T11:25:05.103328' url: - https://zenodo.org/records/17775957 - https://doi.org/10.5281/zenodo.17775957 - authors: - Haase, Robert description: In this slide-deck we dive into large language models for bioimage data science, focusing on code generation for bio image analysis. The slides also give an outlook on how AI-systems may change data analysis in the future through data analysis code generation. license: cc-by-4.0 name: How LLMs impact BioImage Data Science num_downloads: 57 publication_date: '2025-11-21' submission_date: '2025-12-09T11:25:05.799525' url: - https://zenodo.org/records/17669681 - https://doi.org/10.5281/zenodo.17669681 - authors: - Sun, Yi - Tischer, Christian - "H\xE9rich\xE9, Jean-Karim" description: 'The BAND platform offers cloud based desktops accessible with a web browser so that you can work on your data from anywhere with an internet connection. This project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – NFDI 46/1 – 501864659' license: cc-by-4.0 name: Bioimage ANalysis Desktop Source Code num_downloads: 7 publication_date: '2025-11-21' submission_date: '2025-12-09T11:25:06.895146' url: - https://zenodo.org/records/17671971 - https://doi.org/10.5281/zenodo.17671971 - authors: - Wetzker, Cornelia - Massei, Riccardo - Schlierf, Michael description: 'This poster presents motivations and strategies to share bioimage data in life science research. It presents NFDI4BIOIMAGE as a consortium in research data management in bioimaging and bioimage analysis and its data stewardship team. The consortium NFDI4BIOIMAGE is funded by DFG grant number NFDI 46/1, project number 501864659.  ' license: cc-by-4.0 name: '[SaxFDMTagung2025] Share your Bioimage Data with the World' num_downloads: 56 publication_date: '2025-11-20' submission_date: '2025-12-09T11:25:07.520753' url: - https://zenodo.org/records/17659149 - https://doi.org/10.5281/zenodo.17659149 - authors: - David, Romain - Liaskos, Nektarios - Rybina, Arina - Arvanitidis, Christos - Bage, Anne-Sophie - Carvajal-Vallejos, Patricia K. - Das, Sudeep - De Pascalis, Francesca - "D\xF6rr, Dorothea" - Exter, Katrina - Holub, Petr - Gurwitz, Kim Tamara - Liberante, Fabio - Lieutaud, Philippe - Lister, Allyson - Lopez, Joaquin - "Madon, B\xE9n\xE9dicte" - Massimi, Marzia - Matteoni, Rafaele - "M\xEErza, Maria" - Morgan, Sarah - Oezdemir, Bugra - Panagiotopoulou, Maria - Pavloudi, Christina - P. Melo, Ana M. - Sansone, Susanna-Assunta - Schwalbe, Harald - Serrano-Solano, Beatriz - Sorzano, Carlos Oscar - Urbinati, Emilio - Tang, Jing - Tedds, Jonathan - Saunders, Gary - Ewbank, Jonathan description: 'Preprint in submission process to Briefings in Bioinformatics Abstract: European Life Science Research Infrastructures (LS-RIs), one of the five major RI Science Clusters in Europe, were established to provide access to cutting-edge technologies to the scientific community. Individually, and collectively as the LS-RI cluster, they contribute to the development of the European Open Science Cloud (EOSC), under the aegis of the EOSC Federation. They are actively involved in the design and implementation of Competence Centres (CCs). These aim to increase the accessibility of domain-specific knowledge and tools, enhance interoperability, facilitate sharing and harmonisation of procedures, and promote Open Science and FAIR (Findable, Accessible, Interoperable, Reusable) practices. In this paper, we report a landscape mapping of the existing resources that formed the basis for the construction of CCs. We describe the possible design of CCs and their articulation with the LS-RIs. We focus on community-based ideas and recommendations to increase the potential of CCs to address long-standing challenges in sustainability, governance, scalability, and interoperability of Open Science within EOSC and the European Research Area (ERA) more generally.This paper provides a description of the nascent LS CCs, built following a survey of needs and services of existing LS-RI communities. When fully implemented, the LS CCs will serve as dynamic hubs to foster innovation, contribute to the EOSC’s future FAIR web of data, and support ongoing developments of the EOSC Federation. They will act as drivers of collaborative and impactful LS research in Europe and beyond. We explore the underlying challenges, and propose solutions, to ensure that the establishment of CCs will add value to the LS RI community, and to the EOSC, in a sustainable way.' license: cc-by-4.0 name: 'Life Science Competence Centres: Open by Design' num_downloads: 592 publication_date: 2025-12 submission_date: '2025-12-09T11:26:03.944893' url: - https://zenodo.org/records/17672046 - https://doi.org/10.5281/zenodo.17672046 - authors: - Geibelt, Ellen description: a multi-channel-image with a BF and 5 fluorescence channels acquired with a 20x objective and Axioscan.Z1 (Zeiss slide scanner) license: cc-by-4.0 name: multi-channel-image num_downloads: 6 publication_date: '2025-11-26' submission_date: '2025-12-09T11:26:46.115881' url: - https://zenodo.org/records/17725097 - https://doi.org/10.5281/zenodo.17725097 - authors: - Theart, Rensu - Levet, Florian - Hernandez-Herrera - Leterrier, Christophe - de la Ballina, Laura R description: 'Raw, processed and annotated images associated with the manuscript "Presynaptic Actin Nanostructures: A Reproducibility Case Study". This work is part of an initiative of the Global BioImage Analysts'' Society (GloBIAS) to assess reproducibility of published bioimage analysis workflows. We present our attempt to reproduce Figure 7 of the article Presynapses contain distinct actin nanostructures. J Cell Biol 2 October 2023; 222 (10): e202208110. doi: https://doi.org/10.1083/jcb.202208110. Original images and annotations were kindly provided by their authors.' license: cc-by-4.0 name: 'Sample image data and annotations associated to Presynaptic Actin Nanostructures: A Reproducibility Case Study' num_downloads: 1 publication_date: '2025-12-02' submission_date: '2025-12-09T11:27:12.714197' url: - https://zenodo.org/records/17792816 - https://doi.org/10.5281/zenodo.17792816 - authors: - Wendt, Jens - Bortolomeazzi, Michele description: 'OMERO and Research Data Management Workshop at the Georg-Speyer-Haus in Frankfurt 09-10/12/2025. Colllection of slides and materials for the OMERO and Research Data Management Workshop at the Georg-Speyer-Haus in Frankfurt. funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – NFDI 46/1 – 501864659   Program Dec 9th 2025 – Day 1 (Everyone)  10.00 a.m.: Introduction and Welcome 10.15 a.m.: FAIR data management and OMERO introduction 11.15 a.m.: Data organization in OMERO using Tags 12.00 a.m.: Lunch Break 01.00 p.m.: Metadata annotation in OMERO using Key-Value Pairs 02.00 p.m.: OMERO and image analysis  -access through GUI-based plugins  -access through the API  Macros and python scripts for data analysis and interrogation  AI-models at a glance (e.g., segmentation) 03.30 p.m.: Break 04.00 p.m.: OMERO.figure 04.45 p.m.: A glance at data sharing in public repositories 05.15 p.m.: Time for questions and individual case discussions ~ 05.30 /05.45 p.m.: End of Day 1   Dec. 10th 2025 - Day 2 (IT staff and project management) 09:00 a.m. ~ 10:00 a.m.: OMERO practical Short break - OMERO technical setup: ~ 10:45 a.m. ~ 12:00 a.m.: OMERO and storage ~ 12:00 a.m. ~ 12.30 a.m.: OMERO and HPC  12.30 a.m.: Lunch Break 1.00 p.m. ~ 1.30 p.m.:  User Groups, User handling, pilot user phases to set up OMERO' license: cc-by-4.0 name: OMERO and Research Data Management Workshop at the Georg-Speyer-Haus in Frankfurt num_downloads: 5 publication_date: '2025-12-11' submission_date: '2025-12-16T11:25:49.303069' url: - https://zenodo.org/records/17897525 - https://doi.org/10.5281/zenodo.17897525 - authors: - Wetzker, Cornelia - Schlierf, Michael description: 'Dieses Dokument beschreibt die Notwendigkeit und einen Entwurf eines didaktischen Lehr-Konzeptes für eine Seminarreihe zum Thema Forschungsdatenmanagement für Studierende der Lebenswissenschaften mit Fokus auf biologischer Mikroskopie. Es beschreibt Lernziele, -inhalte und -methoden, um die Grundlagen des Forschungsdatenmanagements, der Nutzung von elektronischen Laborbüchern und dem Bio-Bild-Verwaltungs-Werkzeug OMERO theoretisch und praktisch zu vermitteln. Damit sollen Wissenschaftlern im Zuge ihres Studiums parallel zu ersten experimentellen Forschungsarbeiten frühzeitig die Grundlagen der guten wissenschaftlichen Praxis vermittelt werden.   C.W. wird durch die Deutsche Forschungsgemeinschaft (DFG) gefördert, im Rahmen der Nationalen Forschungsdateninfrastruktur – NFDI 46/1 – 501864659.' license: cc-by-4.0 name: "Didaktisches Konzept zur Einf\xFChrung ins Forschungsdatenmanagement (FDM)\ \ am Beispiel von Elektronischen Laborb\xFCchern (ELNs) und OMERO f\xFCr Bio-Bild-Verwaltung\ \ f\xFCr Studierende der Lebenswissenschaften" num_downloads: 16 publication_date: '2025-12-10' submission_date: '2025-12-16T11:25:50.401687' url: - https://zenodo.org/records/17876813 - https://doi.org/10.5281/zenodo.17876813 - authors: - Vedder, Lucia - Peschel, Paul - Bortolomeazzi, Michele - Schmidt, Christian - "Schr\xF6der, Katrin" description: 'Veranstaltungsformat: Präsenz- und Online-Veranstaltung  Veranstaltungsort: Hybrid event at University of Kassel (FB 10) Veranstalter: Universitätsbibliothek Forschungsdaten-Service NFDI4BIOIMAGE FAIRagro RTG Multiscale Clocks Zielgruppen: Promovierende/Doktorand:innen Absolvent:innen Wissenschaftliche Mitarbeitende Gruppenleitungen Postdoktorand:innen/Postdocs Mitarbeitende der Universität Kassel Workshop: Image Data Management in the Biological Sciences and Agriculture Research Do you work with image data and want to optimize your data management workflows? This workshop provides an overview of the key challenges associated with managing image data and presents available support services. You will learn about useful tools for image data management and how Bioimaging Research Data Management (RDM) can be integrated into your everyday practice. This workshop will not only provide knowledge in imaging data management but also create an opportunity to get to know each other, network, and exchange ideas. For the best learning outcome, we recommend in-person attendance.  Content Module 1:  General Introduction to Imaging Research Data Management 1.00 pm - 1.50 pm: for all audiences, incl. non-bioimaging stakeholders Module 2:  NFDI4BIOIMAGE: Tools and Software for Bioimage Data Management FAIRagro: Observation data: From ground robots to UAVs 2.10 pm - 3.00 pm: for researchers at all career levels and tool providers Module 3: NFDI4BIOIMAGE: Bioimaging RDM in the everyday-practice FAIRagro: Satellite and grid data: the view from above 3.10 pm - 4.00 pm: for researchers at all career levels   It is possible to attend individual modules, and your choices can be made during the registration process. ' license: cc-by-4.0 name: '[Workshop] Image Data Management in the Biological Sciences and Agriculture Research' num_downloads: 54 publication_date: '2025-12-02' submission_date: '2025-12-16T11:25:54.086699' url: - https://zenodo.org/records/17787401 - https://doi.org/10.5281/zenodo.17787401 - authors: - Boissonnet, Tom - Massei, Riccardo description: 'This record contains the presentations given at the 4th BioHackathon Germany (1–5 Dec 2025, Walsrode) by de.NBI / ELIXIR-DE, introducing the project on "Integrating Data Science Capabilities within the OMERO Data Management Ecosystem" and summarizing its results. We acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – NFDI 46/1 – 501864659.' license: cc-by-4.0 name: de.NBI hackathon 2025 - Project 7 introduction and report presentations num_downloads: 31 publication_date: '2025-12-08' submission_date: '2025-12-16T11:26:05.625356' url: - https://zenodo.org/records/17856704 - https://doi.org/10.5281/zenodo.17856704 - authors: - Soltwedel, Johannes Richard description: Introductory slides for top-level understanding of ome-zarr, cloud storage (S3) of ome-zarr data and usecases and nature of upcaming RFC5-style transformations. license: cc-by-4.0 name: Introduction to OME-ZARR, S3 and transformations num_downloads: 21 publication_date: '2025-12-09' submission_date: '2025-12-16T11:29:42.879922' url: - https://zenodo.org/records/17898202 - https://doi.org/10.5281/zenodo.17898202 - authors: - Dayer, Andrew J license: cc-by-4.0 name: 'Test files for Nikon NEF distorted image #4389' num_downloads: 1 publication_date: '2025-12-12' submission_date: '2025-12-16T11:35:49.229001' url: - https://zenodo.org/records/17914642 - https://doi.org/10.5281/zenodo.17914642 - authors: - Kumar, Neeraj - Verma, Ruchika - Sharma, Sanuj - Bhargava, Surabhi - Vahadane, Abhishek - Sethi, Amit description: 'The dataset for this challenge was obtained by carefully annotating tissue images of several patients with tumors of different organs and who were diagnosed at multiple hospitals. This dataset was created by downloading H&E stained tissue images captured at 40x magnification from TCGA archive. H&E staining is a routine protocol to enhance the contrast of a tissue section and is commonly used for tumor assessment (grading, staging, etc.). Given the diversity of nuclei appearances across multiple organs and patients, and the richness of staining protocols adopted at multiple hospitals, the training datatset will enable the development of robust and generalizable nuclei segmentation techniques that will work right out of the box.' license: CC-BY-NC-SA-4.0 name: A dataset for generalized nuclear segmentation for computational pathology publication_date: '2017-03-06' tags: - AI-ready - exclude from DALIA type: - Data url: https://huggingface.co/datasets/RationAI/MoNuSeg language: en