--- title: "About this training" description: "Who this training is for, why open science matters for Superfund research, how to use the lessons, technical implementation, and version history." type: Guide tags: - About - Open science - Superfund Research Program - Training generated: by: "claude/fable-5-1" at: "2026-09-12T02:00:00Z" sources: - id: dust-2025-about resource: "https://github.com/tyson-swetnam/dust-2025/blob/29027dbda9ca29a123a68d8b4e2ae5dc198f7193/docs/about.md" title: "DUST 2025 Open Science Training: About This Training" author: "human:tswetnam" last_modified: "2025-10-14T14:53:34-07:00" - id: foss-contributing resource: "https://github.com/UNM-CARC/foss/blob/d1b13dc37e48b34b294fe21bfbab11b23875b4b1/docs/about/contributing.md" title: "FOSS (UNM CARC edition): Contributing" author: "team:unm-carc" last_modified: "2026-09-11T07:41:50-06:00" - id: foss-ai-agents resource: "https://github.com/UNM-CARC/foss/blob/d1b13dc37e48b34b294fe21bfbab11b23875b4b1/docs/about/ai-agents.md" title: "FOSS (UNM CARC edition): For AI agents" author: "team:unm-carc" last_modified: "2026-09-11T07:41:50-06:00" status: stable --- # About this training ## Overview DUST 2026: Open Science Training is an educational resource for trainees of three NIEHS Superfund Research Program (SRP) centers in the Southwest and Texas: - **[University of Arizona DUST Center](https://superfund.arizona.edu/){target=_blank}** - "Hazardous Dust in Drylands – Exposure, Health Impacts, and Mitigation", studying arsenic exposure, mine tailings, phytoremediation, and lung injury in Arizona-Sonora mining communities. - **[UNM METALS Center](https://hsc.unm.edu/pharmacy/research/areas/metals/){target=_blank}** - "Metal Exposure and Toxicity Assessment on Tribal Lands in the Southwest", studying uranium and metal mixtures from abandoned mines in partnership with the Pueblo of Laguna and Navajo Nation communities. - **[Texas A&M Superfund Research Center](https://superfund.tamu.edu/){target=_blank}** - "Comprehensive tools and models for addressing exposure to mixtures during environmental emergency-related contamination events", studying chemical-mixture exposure after weather-related and human-caused emergencies, with Houston-area community partners. The Arizona and New Mexico centers share a focus on inhaled mine dust and on communities living with legacy contamination; the Texas center brings disaster research response and exposure to complex mixtures. All three face the same open-science, data-management, and AI questions. The three lessons equip environmental health researchers with essential skills for conducting modern, transparent, and reproducible science in the context of mine waste contamination, toxicology, and environmental remediation research. These materials were first written in 2025 for the University of Arizona DUST Center. We are now based at the [UNM Center for Advanced Research Computing](https://carc.unm.edu/){target=_blank}, and the 2026 edition is written for trainees at all three centers, with examples from Arizona and New Mexico side by side. ## Why Open Science Matters for Superfund Research As researchers studying hazardous waste sites, arsenic and uranium exposure, and environmental health impacts, open science practices are critical for: - **Community Impact** - Sharing findings transparently with communities affected by mine tailings and abandoned uranium mines - **Reproducibility** - Ensuring toxicology and exposure studies can be validated and built upon - **Collaboration** - Facilitating multi-institutional research on complex environmental health problems - **Compliance** - Meeting NIH data management and sharing requirements and the zero-embargo public access policy in force since July 2025 - **Environmental Justice** - Making research accessible to policymakers and affected populations - **Indigenous Data Sovereignty** - Respecting the CARE Principles and tribal review when data involve Navajo Nation, Pueblo of Laguna, or other tribal partners - **Scientific Integrity** - Documenting methods for studies involving hazardous materials and vulnerable populations ## Training Philosophy ### Learning by Doing Each lesson balances conceptual understanding with hands-on activities. Skills are best developed through practice, reflection, and application to real-world scenarios. ### Accessibility First Open science should be accessible to all researchers, regardless of technical background, career stage, or institutional resources. These materials are: - Free and openly licensed - Self-paced with clear structure - Jargon-free where possible, with explanations where not - Practical and immediately applicable ### Continuous Improvement This training is a living resource. We welcome feedback, suggestions, and contributions from the community. Open an [issue on GitHub](https://github.com/UNM-CARC/dust-2026/issues){target=_blank} or submit a pull request to help improve these materials. ## Who Created This? This training was developed by synthesizing materials from multiple open science initiatives: - **DUST 2025** - The first edition of this training, written for the University of Arizona DUST Center - **CyVerse FOSS** - Foundational Open Science Skills program, including the UNM CARC edition - **NCEMS Pre-Summit Training** - Open science training for the NCEMS community - **Intro to GPT Workshop** - AI, prompt engineering, and agentic AI fundamentals - **Awesome Open Science** - Curated resources for open science tools See the [Credits and attribution](credits.md) page for detailed attribution. ## How to Use This Training ### For Individual Learners Work through the three lessons sequentially at your own pace. Each in-person lesson takes approximately 50 minutes and includes: - Clear learning objectives - Core concepts with examples - Hands-on activities for practice - Self-assessment questions - Additional resources for deeper learning 1. [Lesson 1: Foundations of Open Science](../lessons/01-open-science.md), then its [self-paced homework](../lessons/01-open-science-self-paced.md) 2. [Lesson 2: Modern Data Management](../lessons/02-data-management.md), then its [self-paced homework](../lessons/02-data-management-self-paced.md) 3. [Lesson 3: Ethics and Artificial Intelligence](../lessons/03-ai-ethics.md), then its [self-paced homework](../lessons/03-ai-ethics-self-paced.md) Each lesson comes in two parts. The lecture page is what an instructor covers in 50 minutes; the homework page holds the full material in twelve modules, each ending in a checkpoint question, and takes about 90 to 120 minutes. If you are learning alone, do both. You can also hand either page to an AI assistant and take it as a lecture, a tutorial, or a quiz: see [Learn with an AI tutor](ai-tutor.md). Set aside dedicated time for each lesson and complete the activities to maximize learning. The [Additional resources](resources.md) page collects further reading. ### For Instructors These materials can be used for: - **Workshops** - Three 50-minute lessons or a half-day intensive; assign each homework page before or after its session - **Course modules** - Integrate into methods courses or research seminars - **Lab training** - Onboard new lab members to open science practices - **Professional development** - Departmental or institutional training programs All materials are licensed CC BY 4.0, allowing you to adapt and remix as needed for your context. !!! tip "Teaching Tips" - Teach from the lecture page and assign the self-paced page as homework; the lecture is deliberately a summary - Encourage discussion during activities - Adapt examples to your discipline and to your center's field sites - Share your own experiences with open science - Create space for questions and concerns - Follow up with resources specific to your field ### For Research Groups Use these lessons to: - Establish shared practices and standards for SRP research projects - Create data management protocols for environmental samples, biomarkers, and exposure data - Develop ethical guidelines for AI use in environmental health research - Build open science culture across toxicology, remediation, and epidemiology teams - Prepare for NIH data management and sharing and public access requirements - Document protocols for handling sensitive location data from contaminated sites and tribal lands Consider working through lessons together as a group, discussing how to apply concepts to mine waste studies, phytoremediation experiments, uranium and metal-mixture toxicology, and community-engaged health research. ## Technical Implementation This website is built with: - **[Zensical](https://zensical.org){target=_blank}** - Static site generator for the Markdown source - **[Open Knowledge Format (OKF) v0.2](https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/main/okf/SPEC.md){target=_blank}** - Every page carries YAML frontmatter with provenance and lifecycle fields, so the `docs/` tree is a machine-readable knowledge bundle - **[llms.txt](https://llmstxt.org){target=_blank}** - A linked outline and a full-corpus text file for AI agents - **GitHub Pages** - Free hosting, deployed automatically by GitHub Actions If you are an AI agent or are wiring one up, see [For AI agents](ai-agents.md) for the endpoints and trust signals. The entire source is available on [GitHub](https://github.com/UNM-CARC/dust-2026){target=_blank}: view the source markdown files, propose improvements or corrections, fork the repository to create your own version, or learn how to build similar documentation sites. ## Accessibility We aim for WCAG 2.2 level AA: semantic structure for screen readers, keyboard operation throughout, visible focus, sufficient contrast in light and dark mode, alternative text and text descriptions for figures, plain-language summaries and glossaries, no audio-only or video-only content, reduced motion on request, and machine-readable accessibility metadata so AI assistants can adapt a lesson for blind, deaf, or multilingual learners. The [Accessibility](accessibility.md) page describes all of this, its known limitations, and how to report a barrier. ## Privacy This website: - Does not ask for or store personal information - Does not use authentication or accounts - Uses Google Analytics for aggregate usage statistics - Does not place tracking cookies (beyond analytics) - Is hosted on GitHub Pages (subject to GitHub's privacy policy) ## License All content is licensed under the [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/){target=_blank}. You are free to **share** (copy and redistribute in any medium or format) and **adapt** (remix, transform, and build upon the material), provided you give appropriate credit, indicate changes, and apply no additional legal or technological restrictions. ## Contact For questions, suggestions, or issues: - Open an [issue on GitHub](https://github.com/UNM-CARC/dust-2026/issues){target=_blank} - Email: [tswetnam@unm.edu](mailto:tswetnam@unm.edu) ## Version History **Version 2.1** (September 2026) - Every lesson split into a 50-minute in-person lecture and a self-paced homework page with twelve modules and checkpoints - Gold Standard Science: the nine tenets mapped to open-science practices, the 2025 agency implementation plans, the September 2026 annual reports, and the debate - Learn with an AI tutor: lesson metadata (`lesson:` block, schema.org LearningResource) and prompts for lecture, tutor, and interactive modes - Accessibility statement, figure text descriptions, glossaries, plain-language summaries, focus and reduced-motion styles **Version 2.0** (September 2026) - Joint examples for the University of Arizona DUST Center and the UNM METALS Center - 2026 US public-access and publication-cost policy landscape - Updated article processing charges - openRxiv and arXiv as independent nonprofit preprint servers - Data rescue and CARE Principles emphasis - Agentic AI in the ethics lesson - Repaired links and updated tools - Rebuilt on Zensical with OKF v0.2 frontmatter and llms.txt for AI agents **Version 1.0** (January 2025) - Initial release with three complete lessons - Open Science foundations - Data management best practices - AI ethics and responsible use Future versions will incorporate community feedback and evolving best practices.

Adapted from [DUST 2025](https://github.com/tyson-swetnam/dust-2025/blob/29027dbda9ca29a123a68d8b4e2ae5dc198f7193/docs/about.md){target=_blank} (last source update 2025-10-14), CC BY 4.0. Spotted a problem? [Open an issue](https://github.com/UNM-CARC/dust-2026/issues){target=_blank}.