# Open_Data

Recopilación de información sobre Open Data. Links, libros, blogs y otra información interesante.

Este fichero es copia de uno alojado en Github, en este [repositorio](https://github.com/santiagomota/Open_Data) y que se actualiza periódicamente. 

Se ha incluido otra copia en [Kaggle](https://www.kaggle.com/code/santiagomota/open-data-links/).

Y se aloja en las webs [Github](https://santiagomota.github.io/Open_Data/) y [Netlify](https://open-data-pages.netlify.app/).


<p align="center">
  <a href="https://github.com/santiagomota/Open_Data">
    <img src="./docs/figs/Open_Data_Github.png" width="30%"><br>
    https://github.com/santiagomota/Open_Data
  </a>
</p>

<p align="center">
  <a href="https://santiagomota.github.io/Open_Data/">
    <img src="./docs/figs/Open_Data_Web_Github.png" width="30%"><br>
    https://santiagomota.github.io/Open_Data/
  </a>
</p>

<p align="center">
  <a href="https://open-data-pages.netlify.app/">
    <img src="./docs/figs/Open_Data_Web_Netlify.png" width="30%"><br>
    https://open-data-pages.netlify.app/
  </a>
</p>


## Fuentes de datos abiertos y APIs

- [20 Awesome Websites For Collecting Big Data](https://datafloq.com/read/20-awesome-websites-for-collecting-big-data/2737?utm_source=Datafloq%20newsletter&utm_campaign=979b1fada5-EMAIL_CAMPAIGN_2017_03_13&utm_medium=email&utm_term=0_655692fdfd-979b1fada5-90449429)
- [25 Open Datasets for Deep Learning Every Data Scientist Must Work With](https://www.analyticsvidhya.com/blog/2018/03/comprehensive-collection-deep-learning-datasets/)
- [25 Satellite Maps To See Earth in New Ways](https://gisgeography.com/satellite-maps/)
- [30 Amazing (And Free) Big Data And AI Public Data Sources For 2018](https://www.linkedin.com/pulse/30-amazing-free-big-data-ai-public-sources-2018-bernard-marr/?trackingId=nkTXcNLieYPDBqZuB3KIsw%3D%3D&lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3B9KuSD9KfQ6ie%2BALso3gwvw%3D%3D&licu=urn%3Ali%3Acontrol%3Ad_flagship3_feed-object)
- [46 museos y bibliotecas que han digitalizado todo su conocimiento y lo ofrecen gratis en internet](http://www.xataka.com/otros/46-museos-y-bibliotecas-que-han-digitalizado-todo-su-conocimiento-humano)
- [AENA - Estadísticas de tráfico aéreo](https://www.aena.es/es/estadisticas/inicio.html)
- [Agencia Tributaria. Estadísticas](https://sede.agenciatributaria.gob.es/Sede/estadisticas.html)
- [AI for Copernicus - a data repository by CALLISTO](https://github.com/Agri-Hub/Callisto-Dataset-Collection)
- [AI4SmallFarms: A Data Set for Crop Field Delineation in Southeast Asian Smallholder Farms](https://phys-techsciences.datastations.nl/dataset.xhtml?persistentId=doi:10.17026/dans-xy6-ngg6)
- [AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification](https://captain-whu.github.io/AID/)
- [Alaska Satellite Facility](https://asf.alaska.edu/getstarted/)
- [Amazon product data 2014](http://jmcauley.ucsd.edu/data/amazon/)
- [Amazon product data 2018](https://nijianmo.github.io/amazon/index.html)
- [Análisis de 1.100 millones de trayectos de taxis y uber en NYC](https://github.com/toddwschneider/nyc-taxi-data)
- [Android Earthquake Alerts: A global system for early warning](https://research.google/blog/android-earthquake-alerts-a-global-system-for-early-warning/)
- [API de Facebook](https://developers.facebook.com/docs/graph-api)
- [API de GitHub](https://developer.github.com/v3/)
- [Argo Floats](https://argo.ucsd.edu/) - Global ocean observations of temperature, salinity, and pressure.
- [API TomTom. Tráfico en ciudades](http://developer.tomtom.com/products/onlinenavigation/onlinetraffic/onlinetrafficflow)
- [Armed Conflict Location & Event Data Project (ACLED)](https://acleddata.com/)
- [ASTER Global DEM (GDEM)](https://lpdaac.usgs.gov/products/astgtmv003/) - ASTER Global Digital Elevation Model 1 arc second
- [ArcticDEM](https://www.pgc.umn.edu/data/arcticdem/) - High-resolution DEM for the Arctic region
- [Awesome Geospatial](https://github.com/sacridini/Awesome-Geospatial)
- [Awesome Public Datasets 1](https://github.com/dipanjanS/awesome-public-datasets)
- [Awesome Public Datasets 2](https://github.com/awesomedata/awesome-public-datasets)
- [Awesome Sentinel. Copernicus Sentinel Satellites resources](https://github.com/Fernerkundung/awesome-sentinel)
- [awesome-gee-community-datasets](https://github.com/samapriya/awesome-gee-community-datasets)
- [AWS Data Exchange](https://docs.aws.amazon.com/data-exchange/)
- [AWS Datasets](https://registry.opendata.aws/)
- [AWS Open Data Geo](https://github.com/opengeos/aws-open-data-geo)
- [AWS Open Data](https://github.com/opengeos/aws-open-data)
- [Ayuntamiento de Madrid. Censo de locales, sus actividades y terrazas de hostelería y restauración](https://datos.gob.es/es/catalogo/l01280796-censo-de-locales-sus-actividades-y-terrazas-de-hosteleria-y-restauracion-historico1)
- [Berkeley Earth](https://berkeleyearth.org/data/) - Global land temperature and air pollution datasets.
- [Blog. 100 recursos sobre Big Data y Data Science](https://www.todobi.com/mas-de-100-recursos-sobre-big-data-y/)
- [British Ordnance Survey Data Hub](https://osdatahub.os.uk/)
- [BUILDING OUTLINE EXTRACTION OF ENSCHEDE, THE NETHERLANDS USING AERIAL IMAGES AND DIGITAL SURFACE MODELS](https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:257588)
- [CaixaBank Research](https://www.caixabankresearch.com/es)
- [CGIAR-CSI SRTM](https://csidotinfo.wordpress.com/data/srtm-90m-digital-elevation-database-v4-1/) - SRTM 90m Digital Elevation Database v4.1
- [Canada Open Government Portal](https://open.canada.ca/data/en/dataset?q=education)
- [Center for Applied Internet Data Analysis](https://www.caida.org/data/overview/)
- [Center for Disease Control](https://wonder.cdc.gov/)
- [CHIRPS: Rainfall Estimates from Rain Gauge and Satellite Observations](https://www.chc.ucsb.edu/data/chirps) - High-resolution precipitation data.
- [CIS. Centro de Investigaciones Sociológicas](https://www.cis.es/inicio)
- [Climate Data Online](https://www.ncdc.noaa.gov/cdo-web/)
- [Climate Change Knowledge Porta](https://climateknowledgeportal.worldbank.org/) - Country-specific climate risks, data, and projections.
- [Climate TRACE](https://climatetrace.org/data)
- [Cómo los datos abiertos pueden ayudar en la crisis de los refugiados](https://datos.gob.es/es/blog/como-los-datos-abiertos-pueden-ayudar-en-la-crisis-de-los-refugiados?utm_source=newsletter&utm_medium=email&utm_campaign=Datos-en-tiempo-real-open-access-y-mucho-ms-en-datosgobes)
- [Copernicus Atmosphere Monitoring Service (CAMS) Global Near-Real-Time](https://developers.google.com/earth-engine/datasets/catalog/ECMWF_CAMS_NRT)
- [Copernicus Open Access Hub](https://scihub.copernicus.eu/dhus/#/home)
- [Copernicus DEM](https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model) - European Digital Elevation Model (EU-DEM)
- [Copernicus Marine Environment Monitoring Service (CMEMS)](https://marine.copernicus.eu/) - Ocean monitoring for sea surface temperature, sea level, and salinity.
- [CRAN Task View OpenData](https://github.com/ropensci/opendata)
- [Crimen en UK](https://data.police.uk/)
- [DANS Data Station Physical and Technical Sciences](https://phys-techsciences.datastations.nl/)
- [Data Derived from OpenStreetMap for Download](https://osmdata.openstreetmap.de/)
- [Data Is Plural](https://www.data-is-plural.com/)
- [Data Kicks](https://data-kicks.com/index.php/blog/)
- [Data on CO2 and Greenhouse Gas Emissions by Our World in Data](https://github.com/owid/co2-data/tree/master)
- [Data World](https://data.world/)
- [Datasets de ejemplo de IBM Watson Analytics](https://www.ibm.com/communities/analytics/watson-analytics-blog/guide-to-sample-datasets/)
- [Datasets de Quandl](https://www.quandl.com/search?query=)
- [Dataset4EO](https://github.com/EarthNets/Dataset4EO)
- [Datos abiertos Ayuntamiento de Valencia](https://www.valencia.es/cas/ayuntamiento/gobierno-abierto)
- [Datos abiertos de la Generalitat de Cataluña](http://dadesobertes.gencat.cat/es/)
- [Datos abiertos de la Unión Europea](https://data.europa.eu/es)
- [Datos abiertos de Santander](http://datos.santander.es/)
- [Datos abiertos del Ayuntamiento de Madrid](http://datos.madrid.es/)
- [Datos Abiertos del Consorcio Regional de Transportes de Madrid](https://datos.crtm.es/)
- [Datos abiertos del gobierno de España](http://datos.gob.es/)
- [Datos abiertos Junta de Andalucía](http://www.juntadeandalucia.es/datosabiertos/portal.html)
- [Datos de la Eurocopa 2024](https://github.com/Jelagmil/Euro2024_data)
- [Datos de todos los vuelos en USA entre 1987 y 2008 (datos originales)](http://stat-computing.org/dataexpo/2009/the-data.html)
- [Datos de todos los vuelos en USA entre 1987 y 2008 (otra fuente y ejemplos de uso en H2O). 120G](https://github.com/h2oai/h2o-2/wiki/Hacking-Airline-DataSet-with-H2O)
- [Datos estadísticos DGT](https://sedeapl.dgt.gob.es/WEB_IEST_CONSULTA/)
- [Datosclima. Base de datos meteo](http://datosclima.es/Aemet2013/DescargaDatos.html)
- [DH Network](http://opendhn.dhnetwork.opendata.arcgis.com/)
- [Digital Earth Africa (DE Africa) Map](https://www.digitalearthafrica.org/platform-resources/platform)
- [Dirección General de Tráfico (DGT)](https://sedeapl.dgt.gob.es/WEB_IEST_CONSULTA/inicio.faces)
- [Dynamic World V1 Land Use](https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1) 
- [EarthEnv-DEM90 digital elevation model](https://www.earthenv.org/DEM) - Global DEM created from multiple datasets
- [EarthView dataset](https://huggingface.co/datasets/satellogic/EarthView)
- [ECMWF ERA5](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5) - Hourly reanalysis climate data (temperature, precipitation, wind, etc.).
- [EM-DAT - The international disaster database](https://www.emdat.be/)
- [EDGAR - Emissions Database for Global Atmospheric Research](https://edgar.jrc.ec.europa.eu/emissions_data_and_maps)
- [EnMAP. The German Spaceborne Imaging Spectrometer Mission](https://www.enmap.org/)
- [El planeta Tierra en AWS](https://aws.amazon.com/es/earth/)
- [ERA DATASET. Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos](https://lcmou.github.io/ERA_Dataset/)
- [ERA5 Daily Aggregates - Latest Climate Reanalysis Produced by ECMWF / Copernicus Climate Change Service](https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_DAILY)
- [ESA OpenSR - Robust, accountable super-resolution for Sentinel-2 and beyond](https://isp.uv.es/opensr/)
- [ESA Third Party Missions (TPM)](https://earth.esa.int/eogateway/missions/third-party-missions)
- [ESA WorldCover 2021. Global land cover product at 10 m for 2021 based on Sentinel-1 and 2 data](https://worldcover2021.esa.int/)
- [España. Estadísticas de mercado de trabajo](https://www.mites.gob.es/es/estadisticas/mercado_trabajo/index.htm)
- [España. Inmigración. Estadísticas](https://www.inclusion.gob.es/web/opi/estadisticas)
- [España. Seguridad Social. Estadísticas](https://www.seg-social.es/wps/portal/wss/internet/EstadisticasPresupuestosEstudios/Estadisticas)
- [Esri Open Data Hub](https://hub.arcgis.com/search)
- [European Banking Authority (EBA)](https://www.eba.europa.eu/risk-and-data-analysis)
- [European Data Portal](https://www.europeandataportal.eu/)
- [European Forest Fire Information System (EFFIS)](https://forest-fire.emergency.copernicus.eu/)
- [FAO Map Catalog](https://data.apps.fao.org/catalog/)
- [FAO's Global Information System on Water and Agriculture](https://www.fao.org/aquastat/en/geospatial-information/wapor)
- [FBREF - Estadísticas e Historia del Fútbol](https://fbref.com/es/)
- [Fields of The World (FTW)](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world/description/)
- [Fivethirtyeight](https://data.fivethirtyeight.com/)
- [FLUXNET](https://fluxnet.org/) - Data from flux towers for carbon, water, and energy exchange monitoring.
- [Fondo Monetario Internacional](http://www.imf.org/en/data)
- [Free GIS Data](http://freegisdata.rtwilson.com/)
- [Freshwater Ecoregions of the World](https://www.worldwildlife.org/pages/freshwater-ecoregions-of-the-world--2)
- [Fuentes de datos espaciales (Diva-GIS)](https://diva-gis.org/)
- [Functional Map of the World (fMoW) Dataset](https://github.com/fMoW/dataset)
- [Gapminder](https://www.gapminder.org/data/)
- [gee-community-catalog](https://gee-community-catalog.org/)
- [geoBoundaries](https://www.geoboundaries.org/)
- [geodata.state.gov](https://geodata.state.gov/geonetwork/srv/spa/catalog.search#/home)
- [GEBCO (General Bathymetric Chart of the Oceans)](https://www.gebco.net/) - Bathymetric DEM for ocean floors
- [Geonames Cities with population > 5000](https://documentation-resources.opendatasoft.com/explore/dataset/doc-geonames-cities-5000/table/)
- [Geoportal Registradores](https://geoportal.registradores.org/)
- [Geospatial Data Catalogs](https://github.com/opengeos/geospatial-data-catalogs)
- [Geospatial Data Abstraction Library (GDAL) links](https://gdal.org/en/stable/) - Provides links to raster datasets from various organizations.
- [GHSL - Global Human Settlement Layer](https://human-settlement.emergency.copernicus.eu/download.php?ds=bu)
- [Global Forest Change 2000-2023](https://storage.googleapis.com/earthenginepartners-hansen/GFC-2023-v1.11/download.html)
- [Global Flood Database v1 (2000-2018)](https://developers.google.com/earth-engine/datasets/catalog/GLOBAL_FLOOD_DB_MODIS_EVENTS_V1)
- [Global Health Observatory (GHO) API](https://www.who.int/data/gho/info/gho-odata-api)
- GLOPOP-S. A global dataset of 7 billion individuals with socio-economic characteristics (sintetic) [Data](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KJC3RH) [Github](https://github.com/VU-IVM/GLOPOP-S) [Paper](https://www.nature.com/articles/s41597-024-03864-2)
- [Global Historical Climatology Network (GHCN)](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily) - Weather station data for precipitation, temperature, and more.
- [Global Land Cover Facility](https://www.un-spider.org/links-and-resources/data-sources/global-land-cover-facility-university-maryland-nasa-gofc-gold) - Land cover and vegetation datasets.
- [Global Wildfire Information System (GWIS)](https://gwis.jrc.ec.europa.eu/)
- [Gobierno Estados Unidos](http://www.data.gov/)
- [Google Books Ngram Viewe](http://storage.googleapis.com/books/ngrams/books/datasetsv2.html)
- [Google Cloud Vision API](https://cloud.google.com/vision/)
- [Google Datset Search](https://datasetsearch.research.google.com/)
- [Google Earth Engine Catalog](https://github.com/opengeos/Earth-Engine-Catalog)
- [Google finanzas](http://www.google.com/finance/)
- [Google Open Buildings](https://sites.research.google/gr/open-buildings/)
- [Google Patents Public Data](https://console.cloud.google.com/marketplace/product/google_patents_public_datasets/google-patents-public-data)
- [Google Public Data](https://www.google.com/publicdata/directory)
- [Google-Microsoft-OSM Open Buildings - combined by VIDA](https://beta.source.coop/repositories/vida/google-microsoft-osm-open-buildings/description/)
- [Helsinki Open Data](http://www.hri.fi/en/)
- [Hugging Face Datasets](https://huggingface.co/datasets)
- [HydroRIVERS](https://www.hydrosheds.org/products/hydrorivers)
- [Hyperspectral: Over 50 Tanager Radiance Datasets](https://www.planet.com/pulse/unleash-the-power-of-hyperspectral-over-50-tanager-radiance-datasets-now-available-on-planet-s/)
- [Idealista ux&tech](https://www.idealista.com/labs/blog/)
- [idealista18 - 2018 real estate listings in Spain. 3 cities](https://github.com/paezha/idealista18)
- [ImageNet database](http://www.image-net.org/)
- [Infraestructura de Datos Espaciales de España](https://idee.es/web/idee/inicio)
- [Infraestructura de Datos Espaciales de la Comunidad de Madrid](http://www.madrid.org/cartografia/idem/html/web/index.htm)
- [IPUMS GIS Boundary Files](https://international.ipums.org/international/gis.shtml)
- [ISCGM Global Map](https://globalmaps.github.io/)
- [ISIMIP3b bias-adjusted atmospheric climate input data](https://data.isimip.org/datasets/24cb1007-3c96-4b59-a0dc-42d94a8cff8c/)
- [JAXA’s Global ALOS 3D World (AW3D30)](https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm) - ALOS Global Digital Surface Model "ALOS World 3D - 30m (AW3D30)"
- [Kaggle datasets](https://www.kaggle.com/datasets)
- [Kaggle Weekly Kernels Award Winner Announcements](https://www.kaggle.com/general/37924#post354114)
- [Land Information New Zealand (LINZ) Data Service](https://data.linz.govt.nz/)
- [Legacy Aircraft Noise and Performance (ANP) data](https://www.easa.europa.eu/en/domains/environment/policy-support-and-research/aircraft-noise-and-performance-anp-data/anp-legacy-data)
- [LinkedIn - Data for Impact](https://economicgraph.linkedin.com/data-for-impact)
- [Lista de algunos datatsets dentro de paquetes de R](https://vincentarelbundock.github.io/Rdatasets/datasets.html)
- [M3LEO: A Multi-Modal Multi-Label Earth Observation Dataset](https://huggingface.co/M3LEO)
- [Mapas de Open Street Maps](http://download.geofabrik.de/)
- [Marine Regions](https://marineregions.org/downloads.php)
- [Marine Cadastre (AIS)](https://hub.marinecadastre.gov/)
- [Mendeley Data](https://data.mendeley.com/)
- [Microsoft - A Planetary Computer for a Sustainable Future](https://planetarycomputer.microsoft.com/)
- [Microsoft Cognitive Services](https://www.microsoft.com/cognitive-services/)
- [Microsoft Research Open Data](https://msropendata.com/)
- [More datasets for teaching data science: The expanded dslabs package](https://simplystatistics.org/posts/2019-07-19-more-datasets-for-teaching-data-science-the-expanded-dslabs-package/)
- [Multi-Temporal Crop Classification with HLS Imagery across CONUS](https://beta.source.coop/repositories/clarkcga/multi-temporal-crop-classification/description/)
- [Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification](https://github.com/danfenghong/ISPRS_S2FL)
- [Naciones Unidas. Datos detallados de comercio global](https://comtradeplus.un.org/)
- [NAIP: National Agriculture Imagery Program](https://developers.google.com/earth-engine/datasets/catalog/USDA_NAIP_DOQQ)
- [NASA Common Metadata Repository (CMR) SpatioTemporal Asset Catalog (STAC)](https://github.com/opengeos/aws-open-data-stac)
- [NASA Earth Observations (NEO)](https://neo.gsfc.nasa.gov/)
- [NASA](https://nssdc.gsfc.nasa.gov/)
- NASA Fire Information for Resource Management System (FIRMS) [Link1](https://firms.modaps.eosdis.nasa.gov/) [Link2](https://www.earthdata.nasa.gov/data/tools/firms) - Near real-time data on wildfires from MODIS and VIIRS satellites.
- [NASA Earthdata](https://earthdata.nasa.gov/) - Shuttle Radar Topography Mission (SRTM)
- [NASA POWER (Prediction of Worldwide Energy Resources)](https://power.larc.nasa.gov/) - Provides global weather and solar radiation data for energy, agriculture, and environmental sectors.
- [NASDAQ](https://indexes.nasdaqomx.com/Index/History/NQASPA8600AUD)
- [National Historical Geographic Information System (NHGIS)](https://www.nhgis.org/)
- [National Map (USGS)](https://www.usgs.gov/programs/national-geospatial-program/national-map) - National Elevation Dataset (NED), LiDAR, and more
- [Natural Earth Data](https://www.naturalearthdata.com/downloads/) - Raster data for relief and shaded relief imagery.
- [Natural Earth](http://www.naturalearthdata.com/)
- [Nature Scientific Data](https://www.nature.com/sdata/)
- [NHS Digital](digital.nhs.uk/data-and-information/statistical-publications-open-data-and-data-products)
- [NHSR datasets](https://github.com/nhs-r-community/NHSRdatasets)
- [NLP Datasets](https://github.com/niderhoff/nlp-datasets/blob/master/README.md)
- [NOAA Daily Global Historical Climatology Network - Kaggle dataset](https://www.kaggle.com/noaa/ghcn-d)
- [NOAA. Agencia de meteo. USA.](http://www.nesdis.noaa.gov/index.html)
- [NOAA Global Forecast System (GFS)](https://www.ncei.noaa.gov/) - Weather forecasts for temperature, precipitation, and wind.
- [OCDE Data](https://www.oecd.org/en/data.html)
- [One versus One - European football statistics](https://one-versus-one.com/en)
- [Openaerialmap](https://openaerialmap.org/) - Aerial imagery collected by individuals and organizations.
- [Open Africa dataset](https://open.africa/dataset)
- [Open Data Barometer](https://opendatabarometer.org/?_year=2017&indicator=ODB)
- [Open data EMT](http://opendata.emtmadrid.es/)
- [Open Data Inception. 1.600 portales abiertos](http://wwwhatsnew.com/2016/03/19/open-data-inception-recopilacion-de-1600-portales-de-datos-abiertos/?utm_content=buffer4e4d4&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer)
- [Open Data Renfe](http://data.renfe.com/)
- [Open Data Sources Database](https://anthonyhuntley.com/data-science-databases/#DataSourceDatabase)
- [Open High-Resolution Satellite Imagery: The WorldStrat Dataset -- With Application to Super-Resolution](https://arxiv.org/abs/2207.06418)
- [Open Topography](https://opentopography.org/) - Various high-resolution DEM datasets from LiDAR and other sources
- [Open Trade Statistics](https://tradestatistics.io/)
- [openaddresses](https://openaddresses.io/)
- [OpenCelliD - Open Database of Cell Towers](https://www.opencellid.org/downloads.php)
- [Opendata del CERN](http://opendata.cern.ch/)  **Error**
- [Opendatasoft](https://documentation-resources.opendatasoft.com/explore/?sort=modified)
- [openflights.org/](https://openflights.org/)
- [OpenGEOS data](https://github.com/opengeos/data)
- [OpenWeatherMap](https://openweathermap.org/api)
- [OSM Landuse](https://osmlanduse.org/)
- OSM-Building-Classification [Data](https://osf.io/utgae/) [Code](https://github.com/gmuggs/OSM-Building-Classification) [Paper](https://www.nature.com/articles/s41597-024-04046-w) - Classification of 67,705,475 buildings across the United States into residential and non-residential
- [Overture - Fused-partitioned](https://beta.source.coop/repositories/fused/overture/description/)
- [Overture Maps](https://github.com/OvertureMaps/data)
- [Paquete de R 'datasets'](http://stat.ethz.ch/R-manual/R-patched/library/datasets/html/00Index.html)
- [Paquete pasra acceder al API del Instituto de Canario de Estadística](https://github.com/rOpenSpain/istacbaser)
- [Pew Research Center](https://www.pewresearch.org/download-datasets/)
- [Planet SkySat Public Ortho Imagery, Multispectral](https://developers.google.com/earth-engine/datasets/catalog/SKYSAT_GEN-A_PUBLIC_ORTHO_MULTISPECTRAL)
- [Propublica](https://www.propublica.org/data/)
- [RapidAI4EO: A Corpus of Dense Time Series Satellite Imagery](https://beta.source.coop/repositories/planet/rapidai4eo/description/)
- [Rdatasets](https://vincentarelbundock.github.io/Rdatasets/articles/data.html)
- [Recopilación de datasets de BigML](https://blog.bigml.com/list-of-public-data-sources-fit-for-machine-learning/)
- [Red Eléctrica Española (REE) - API](https://www.ree.es/es/apidatos)
- [Red Natura 2000](https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/rednatura2000_descargas.html)
- [Reddit datasets](https://www.reddit.com/r/datasets/)
- [rspatialdata is a collection of data sources and tutorials on visualising spatial data using R](https://rspatialdata.github.io/)
- [SARDet-100K: large-scale multi-class SAR object detection dataset](https://eod-grss-ieee.com/dataset-detail/U1dJZE1BY1RwclAvOFFJQmlKR1Btdz09)
- [Satélite Landsat](https://aws.amazon.com/public-data-sets/landsat/)
- [Satellite Embedding (Google)](https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_SATELLITE_EMBEDDING_V1_ANNUAL?hl=es-419#description)
- [Satellite imagery datasets containing ships](https://github.com/jasonmanesis/Satellite-Imagery-Datasets-Containing-Ships)
- [SEN12MS-CR. 22,218 patch triplets of corresponding Sentinel-1 dual-pol SAR data, Sentinel-2 multi-spectral images, and cloud-covered Sentinel-2 multi-spectral images](https://mediatum.ub.tum.de/1554803)
- [Sen2Like](https://docs.openeo.cloud/usecases/ard/sen2like/#_1-sen2like-for-rgb)
- [SEN2NAIP - Remote sensing dataset designed to support conventional and reference-based SR model training](https://huggingface.co/datasets/isp-uv-es/SEN2NAIP)
- [Sentinel Hub NoR Sponsored Accounts and Data Collections](https://www.sentinel-hub.com/Network-of-Resources/)
- [Sentinel Satellite Data](https://browser.dataspace.copernicus.eu)
- [Sentinel-5P](https://developers.google.com/earth-engine/datasets/catalog/sentinel-5p)
- [Sentinel-2 data set for the delineation of agricultural field boundaries in Flevoland, The Netherlands](https://phys-techsciences.datastations.nl/dataset.xhtml?persistentId=doi:10.17026/dans-x8d-p6zm)
- [Síntesis de Indicadores e Informes Macroeconómicos](https://portal.mineco.gob.es/es-es/economiayempresa/EconomiaInformesMacro/Paginas/EconomiaInformesMacro.aspx)
- [SkyFi Geospatial Hub](https://skyfi.com/)
- [SkySat missions](https://earth.esa.int/eogateway/missions/skysat)
- [Social Science Data Lab](https://github.com/SocialScienceDataLab/)
- Socioeconomic Data and Applications Center (SEDAC)[Link1](https://sedac.ciesin.columbia.edu/data/collection/gpw-v4/sets/browse) y [Link2](https://earthdata.nasa.gov/centers/sedac-daac)
- [Soil Map of the World FAO/UNESCO](https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/)
- [Some datasets for teaching data science](https://simplystatistics.org/posts/2018-01-22-the-dslabs-package-provides-datasets-for-teaching-data-science/)
- [Source Cooperative Featured Repositories](https://beta.source.coop/)
- [Spatial H3 Hub](https://spatial-h3-hub.foursquare.com/)
- [STAC Index SpatioTemporal Asset Catalog (STAC)](https://github.com/opengeos/stac-index-catalogs)
- [StatsBomb sports data](https://statsbomb.com/what-we-do/hub/free-data/)
- [Tanager Core Imagery](https://www.planet.com/data/stac/browser/tanager-core-imagery/catalog.json?.language=es)
- [TanDEM-X 90m DEM (DLR)](https://download.geoservice.dlr.de/TDM90/) - Global DEM generated from radar data
- [Teaching of Statistics in the Health Sciences](https://causeweb.org/tshs/)
- [Tematicas.org Recopilación de series e índices](https://tematicas.org/)
- [Terra Populus](https://terra.ipums.org/)
- [Terraclimate](https://www.climatologylab.org/terraclimate.html) - Monthly climate and hydrology data at a global scale.
- [The Big Bad NLP Database](https://quantumstat.com/nlp-dataset-library/)
- [The Government Finance Database](https://willamette.edu/mba/research-impact/public-datasets/index.html)
- [The SpaceNet Datasets](https://spacenet.ai/datasets/)
- [The World Bank Open Knowledge Repository](https://openknowledge.worldbank.org)
- [The world’s economic database](https://db.nomics.world/)
- [TidyRainbow](https://github.com/r-lgbtq/tidyrainbow)
- [TidyTuesday](https://github.com/rfordatascience/tidytuesday)
- [Tráfico en el Reino Unido](https://webarchive.nationalarchives.gov.uk/ukgwa/*/http://www.dft.gov.uk/traffic-counts/)
- [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/datasets)
- [UC Merced Land Use Dataset](http://weegee.vision.ucmerced.edu/datasets/landuse.html)
- [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/)
- [UK Data Service](https://ukdataservice.ac.uk/)
- [UK Office for National Statistics](https://www.ons.gov.uk/)
- [UK Open Data](https://data.gov.uk/search)
- [UK Open Geography Portal](https://geoportal.statistics.gov.uk/)
- [Ultimos datos de Open Street Map. Spain](https://download.geofabrik.de/europe/spain.html)
- [Umbra Open Data Tracker](https://github.com/bellingcat/umbra-open-data-tracker)
- [Una recopilación de APIs públicas](https://github.com/toddmotto/public-apis)
- [Una recopilación de datasets públicos](https://github.com/caesar0301/awesome-public-datasets)
- [Understat](https://understat.com/)
- [UNEP Environmental Data Explorer](https://www.unep.org/publications-data)
- [United Nations Platform for Space-based Information for Disaster Management and Emergency Response (un-spider.org) data sources](https://un-spider.org/links-and-resources/data-sources)
- [United Nations World Urbanization Prospects](https://population.un.org/wup/)
- [Universidad de Harvard](https://dataverse.harvard.edu/)
- [US Homeland Infrastructure Foundation-Level Data](https://hifld-geoplatform.hub.arcgis.com/)
- [USGS 3DEP LiDAR Point Clouds](https://registry.opendata.aws/usgs-lidar/)
- [USGS Earth Explorer](https://earthexplorer.usgs.gov/) - SRTM, ASTER GDEM, ALOS, and more
- [Viewfinder Panoramas](https://viewfinderpanoramas.org/) - High-quality DEM for remote regions
- [WHU-RS19 is a set of satellite images exported from Google Earth](https://paperswithcode.com/dataset/whu-rs19)
- [Wyvern Open Data Program](https://wyvern.space/open-data/)
- [World Economic Forum](https://www.weforum.org/publications/)
- WorldCereal open global harmonized reference data repository [Data]](https://zenodo.org/records/7609500) [Github](https://github.com/WorldCereal/worldcereal-classification)
- [Worldpop - Open Spatial Demographic Data](https://www.worldpop.org/) y [Worldpop Hub](https://hub.worldpop.org/)
- [Yelp Dataset](https://business.yelp.com/data/resources/open-dataset/)
- [Zhu Lab - Data Science in Earth Observation](https://github.com/zhu-xlab)
- Amazon AWS: [este](http://aws.amazon.com/es/datasets/) y [este](https://aws.amazon.com/es/public-data-sets/)
- EarthNets for Earth Observation [Page](https://earthnets.nicepage.io/) [Github](https://github.com/EarthNets)
- Facebook Neural-Code-Search-Evaluation-Dataset [dataset]](https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset) y [noticia](https://venturebeat.com/2019/10/03/facebook-open-sources-data-set-for-code-search-ai-benchmark/)
- HREA: High Resolution Electricity Access. [Universidad de Michigan](https://hrea.isr.umich.edu/index.html) y [Microsoft](https://planetarycomputer.microsoft.com/dataset/hrea#overview)
- IPUMS provides census and survey data from around the world [Web](https://www.ipums.org/) y [paquete ipumsr](https://tech.popdata.org/ipumsr/)
- Maxar Open Data: [Aquí](https://github.com/opengeos/maxar-open-data) y [aquí](https://radiantearth.github.io/stac-browser/#/external/maxar-opendata.s3.amazonaws.com/events/catalog.json?.language=es)
- MIT [1](http://web.mit.edu/towtank/www/vivdr/datasets.html) y [2](https://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/datasets/)
- Natural Earth Vector. [Github](https://github.com/nvkelso/natural-earth-vector) y [Web](https://www.naturalearthdata.com/)
- Open Charge Map. Global Open Data registry of electric vehicle charging locations. [Export](https://github.com/openchargemap/ocm-export) y [Ejemplo](https://tech.marksblogg.com/open-charge-map-global-ev-charging-point-dataset.html)
- SSL4EO-S12 dataset. Large-scale multimodal multitemporal dataset for unsupervised/self-supervised pre-training in Earth observation [Paper](https://arxiv.org/abs/2211.07044) [Github](https://github.com/zhu-xlab/SSL4EO-S12)
- World Bank Open Data [1](https://data.worldbank.org/) y [2](https://datacatalog.worldbank.org/)


## Otras referencias interesantes

- [100 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning](https://www.kdnuggets.com/2016/03/100-active-blogs-analytics-big-data-science-machine-learning.html#.VvqjkSV5Tio.linkedin)
- [100 Free Tutorials for Learning R](https://www.listendata.com/p/r-programming-tutorials.html)
- [16 Cursos](https://www.analyticsvidhya.com/blog/2016/10/16-new-must-watch-tutorials-courses-on-machine-learning/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29)
- [A dive into R Markdown](http://cfss.uchicago.edu/program_rmarkdown.html)
- [A ggplot2 Tutorial for Beautiful Plotting in R](https://cedricscherer.netlify.app/2019/08/05/a-ggplot2-tutorial-for-beautiful-plotting-in-r/)
- [AiTLAS: Benchmark Arena -- Open-source benchmark suite for evaluating deep learning approaches for image classification in Earth Observation (EO)](https://github.com/biasvariancelabs/aitlas-arena)
- [An Introduction to Statistical Learning - Web R & Python](https://www.statlearning.com/)
- [ArcGIS to R spatial cheat sheet](http://www.seascapemodels.org/data/ArcGIS_to_R_Spatial_CheatSheet.pdf)
- [Awesome Data Science](https://github.com/academic/awesome-datascience)
- [Awesome R](https://github.com/qinwf/awesome-R)
- [BigEarthNet A Large-Scale Sentinel Benchmark Archive](https://bigearth.net/)
- [Bivariate Choropleth Maps: A How-to Guide](https://www.joshuastevens.net/cartography/make-a-bivariate-choropleth-map/)
- [blogdown: Creating Websites with R Markdown](https://bookdown.org/yihui/blogdown/)
- [Blogs con github](http://jmcglone.com/guides/github-pages/) y  [Blogs con github y RStudio](http://andysouth.github.io/blog-setup/)
- [CAMIS - A PHUSE DVOST Working Group](https://psiaims.github.io/CAMIS/). The repository below provides examples of statistical methodology in different software and languages, along with a comparison of the results obtained and description of any discrepancies.
- [Chuleta de expresiones regulares](https://github.com/rstudio/cheatsheets/blob/main/regex.pdf)
- [Chuleta general de R](https://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf)
- [Codificación de caracteres](https://www.joelonsoftware.com/2003/10/08/the-absolute-minimum-every-software-developer-absolutely-positively-must-know-about-unicode-and-character-sets-no-excuses/)
- [Common Probability Distributions: The Data Scientist’s Crib Sheet](https://blog.cloudera.com/blog/2015/12/common-probability-distributions-the-data-scientists-crib-sheet/?utm_content=buffer49e9f&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer)
- [Cómo crear una API en Python](https://anderfernandez.com/blog/como-crear-api-en-python/)
- [Computer vision](https://github.com/kjw0612/awesome-deep-vision)
- [Computerworld - Paquetes de R interesantes](https://www.computerworld.com/article/1375862/great-r-packages-for-data-import-wrangling-visualization.html)
- [Curso Caltech. Learning from data](https://work.caltech.edu/telecourse.html)
- [Cursos para aprender más sobre R](https://datos.gob.es/es/noticia/cursos-para-aprender-mas-sobre-r)
- [Data Science Blogs](https://github.com/rushter/data-science-blogs)
- [Data Science Cheatsheets](https://github.com/FavioVazquez/ds-cheatsheets)
- [Data Science Collected Resources](https://github.com/tirthajyoti/Data-science-best-resources)
- [Data Science Resources](https://github.com/jonathan-bower/DataScienceResources)
- [Data Scientist Roadmap](https://github.com/MrMimic/data-scientist-roadmap)
- [Data Viz Catalogue](https://graphica.app/catalogue)
- [Dataviz Project](https://datavizproject.com/)
- [Dealing with Regular Expressions](http://uc-r.github.io/regex)
- [Ejemplos de Shiny](http://zevross.com/blog/2016/04/19/r-powered-web-applications-with-shiny-a-tutorial-and-cheat-sheet-with-40-example-apps/)
- [Estadística con R](https://www.cienciadedatos.net/estadistica-con-r.html)
- [EUMETSAT science studies](https://www.eumetsat.int/science-studies)
- [Feature Engineering for Machine Learning](https://trainindata.medium.com/feature-engineering-for-machine-learning-a-comprehensive-overview-a7ad04c896f8)
- [Financial-Times / chart-doctor](https://github.com/Financial-Times/chart-doctor/tree/main/visual-vocabulary)
- [Formatos a medida para R Markdown](http://www.r-bloggers.com/r-markdown-custom-formats/)
- [Free R Reading Material](https://committedtotape.shinyapps.io/freeR/)
- [From Data to Viz](https://www.data-to-viz.com/)
- [Galerias de graficos](http://www.r-graph-gallery.com/)
- [Ggplot](http://socviz.co/)
- [GIS and mapping](https://nowosad.github.io/SIGR2021/workshop1/workshop1_jn.html#1)
- [GIS formats](https://atlas.co/formats/)
- [Glosario de Machine Learning de Google](https://developers.google.com/machine-learning/glossary/)
- [Google Dataset Search](datasetsearch.research.google.com)
- [Google Rules of Machine Learning: Best Practices for ML Engineering](http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf)
- [Google's best practices in machine learning](https://developers.google.com/machine-learning/guides/rules-of-ml/)
- [HDRIs Images (HDRIs)](https://polyhaven.com/hdris))
- [HOT - Drone Tasking Manager](https://github.com/hotosm/Drone-TM)
- [htmlwidgets for R - gallery](http://gallery.htmlwidgets.org/)
- [IDEAtlas. Developing AI-based methods to map and characterize informal settlements from Earth Observation data](https://ideatlas.eu/)
- [Información de Rmarkdown en R Studio](http://rmarkdown.rstudio.com/)
- [Information is Beautiful Awards](https://www.informationisbeautifulawards.com/)
- [Information is beautiful](https://informationisbeautiful.net/)
- [Information is Beautiful](informationisbeautiful.net/data)
- [Interactive 4D LiDAR Segmentation](https://ilya-fradlin.github.io/Interactive4D/)
- [Investigative Journalism with Satellite Images](https://bourgoing.com/en/linvestigation-par-satellite/)
- [Kaggle Winning Solutions](http://kagglesolutions.com/)
- [Microsoft Presidio - Data Protection and De-identification SDK](https://microsoft.github.io/presidio/)
- [Naming files](https://speakerd.s3.amazonaws.com/presentations/5e4b07f0d9a94f8e9a29b902bad6ed0b/naming-slides.pdf)
- [Otra lista de recursos variados en Github](https://github.com/Shujian2015/FreeML)
- [overpass turbo - Herremaienta de filtrado para OSM](https://overpass-turbo.eu/)
- [Pandoc User’s Guide](http://pandoc.org/MANUAL.html#templates)
- [Periodic Table Of Visualization Methods](https://www.visual-literacy.org/periodic_table/periodic_table.html)
- [Plataforma H2O](https://github.com/h2oai)
- [Practical Introduction to Web Scraping in R](https://blog.rsquaredacademy.com/web-scraping/)
- [R Code – Best practices](https://www.r-bloggers.com/r-code-best-practices/)
- [R Coding Style Guide](https://irudnyts.github.io//r-coding-style-guide/)
- [R Data Science Tutorials](https://github.com/ujjwalkarn/DataScienceR)
- [R for Water Resources Data Science](https://www.r4wrds.com/)
- [R Learning Path: From beginner to expert in R in 7 steps](https://www.kdnuggets.com/2016/03/datacamp-r-learning-path-7-steps.html)
- [R Markdown cheatsheet](https://raw.githubusercontent.com/rstudio/cheatsheets/main/rmarkdown.pdf)
- [R Markdown referencia](https://www.rstudio.com/wp-content/uploads/2015/03/rmarkdown-reference.pdf)
- [R package primer](https://kbroman.org/pkg_primer/)
- [R Universe search](https://r-universe.dev/search)
- [RDocumentation](https://www.rdocumentation.org/)
- [Regular Expression Language - Quick Reference](https://docs.microsoft.com/en-us/dotnet/standard/base-types/regular-expression-language-quick-reference)
- [Regular Expressions Every R programmer Should Know](https://www.r-bloggers.com/regular-expressions-every-r-programmer-should-know/)
- [Remote Sensing for OSINT](https://bellingcat.github.io/RS4OSINT/)
- [Remote sensing image retrieval](https://github.com/IBM/remote-sensing-image-retrieval)
- [RMarkdown Driven Development (RmdDD)](https://emilyriederer.netlify.app/post/rmarkdown-driven-development/)
- [rseek.org - rstats search engine](https://rseek.org/)
- [Rstudio cheatsheets](https://www.rstudio.com/resources/cheatsheets/?utm_content=buffer1b56a&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer)
- [Simplifying the ROC and AUC metrics](https://towardsdatascience.com/understanding-the-roc-and-auc-curves-a05b68550b69)
- [Soporte técnico de RStudio](https://support.posit.co/hc/en-us)
- [Study finds 94% of business spreadsheets have critical errors](https://phys.org/news/2024-08-business-spreadsheets-critical-errors.html)
- [Template para documentos científicos con Rmarkdown](http://www.petrkeil.com/?p=2401)
- [The Chartmaker Directory](chartmaker.visualisingdata.com)
- [The Data Visualisation Catalogue](https://datavizcatalogue.com/)
- [The R Graph Gallery](https://r-graph-gallery.com/)
- [The State of Naming Conventions in R](https://journal.r-project.org/archive/2012-2/RJournal_2012-2_Baaaath.pdf)
- [The TimeViz Browser 2.0](https://browser.timeviz.net/)
- [Tipos de licencias de software](https://choosealicense.com/licenses/)
- [Tipos de licencias open data (minicurso de data.europa.edu)](https://data.europa.eu/en/academy/open-data-licensing)
- [Tutorials for learning R](https://www.r-bloggers.com/how-to-learn-r-2/)
- [UK government using R to modernize reporting of official statistics](https://www.r-bloggers.com/uk-government-using-r-to-modernize-reporting-of-official-statistics/)
- [Usar git](https://try.github.io/levels/1/challenges/1)
- [useR! Machine Learning Tutorial](https://github.com/ledell/useR-machine-learning-tutorial)
- Using Geospatial Data in R [Post](https://www.r-bloggers.com/2021/06/using-geospatial-data-in-r/) & [Github](https://github.com/SocialScienceDataLab/MZES_SSDL_Georeferenced_Survey_Data)
- [Utilizando Sweave y Knitr](https://support.posit.co/hc/en-us/articles/200552056-Using-Sweave-and-knitr)
- [Writing an R package from scratch](https://hilaryparker.com/2014/04/29/writing-an-r-package-from-scratch/)
- Global Fishing Watch. AI and satellite imagery to reveal the expanding footprint of human activity at sea. [Post](https://globalfishingwatch.org/press-release/new-research-harnesses-ai-and-satellite-imagery-to-reveal-the-expanding-footprint-of-human-activity-at-sea/?utm_source=GFW+subscribers&utm_campaign=9363c93195-EMAIL_CAMPAIGN_JAN_2024_CURRENT_ENGLISH&utm_medium=email&utm_term=0_-9363c93195-%5BLIST_EMAIL_ID%5D). [Github](https://github.com/GlobalFishingWatch/paper-industrial-activity/tree/main). [Train data](https://figshare.com/articles/journal_contribution/Satellite_mapping_reveals_extensive_industrial_activity_at_sea_-_training_data/24309469). [Analysis data](https://figshare.com/articles/journal_contribution/Satellite_mapping_reveals_extensive_industrial_activity_at_sea_-_analysis_data/24309475) and [Vessel detection from Sentinel-1 SAR](https://globalfishingwatch.org/data-download/datasets/public-sar-vessel-detections:v20231026)
- Legalidad Web sraping: [Is Web Scraping Legal? : The Definitive Guide (2024 update)](https://prowebscraper.com/blog/is-web-scraping-legal/) y [Web Scraping: ¿legal o ilegal?](https://ecija.com/web-scraping-legal-ilegal/)
- Pautas para dar formato al código programando en R: [Google](https://google.github.io/styleguide/Rguide.xml), [Hadley Wickham (RStudio)](http://adv-r.had.co.nz/Style.html) y [Coding Club](https://ourcodingclub.github.io/2017/04/25/etiquette.html#syntax)
- Sistemas de Coordenadas. [Aqui](https://rspatial.org/spatial/rst/6-crs.html) y [aqui](https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/OverviewCoordinateReferenceSystems.pdf)
- Statistical Learning de Stanford with R [Curso](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning-r), [Libro](https://hastie.su.domains/ElemStatLearn/), [Código](https://github.com/khanhnamle1994/statistical-learning) y [Transparencias](https://github.com/khanhnamle1994/statistical-learning/tree/master/Lecture-Slides)


## Libros

- [10 Free Must-Read Books for Machine Learning and Data Science](https://www.kdnuggets.com/2017/04/10-free-must-read-books-machine-learning-data-science.html?utm_content=bufferc386f&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer)
- [Advanced R](https://adv-r.hadley.nz/index.html)
- [Advanced Statistical Modeling in R](https://zia207.quarto.pub/advanced-statistical-modeling-in-r/02-00-00-advance-modeling-r.html)
- [Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA](https://becarioprecario.bitbucket.io/spde-gitbook/)
- [AI With R](https://air.albert-rapp.de/)
- [An Introduction to R](https://intro2r.com/)
- [An Introduction to Spatial Data Analysis and Visualisation in R](https://www.spatialanalysisonline.com/An%20Introduction%20to%20Spatial%20Data%20Analysis%20in%20R.pdf)
- [An R companion to Statistics: data analysis and modelling](https://mspeekenbrink.github.io/sdam-r-companion/index.html)
- [Análisis de datos y algoritmos de predicción con R](http://rafalab.dfci.harvard.edu/dslibro/)
- [Análisis espacial con R](https://publicaciones.ciga.unam.mx/index.php/ec/catalog/book/306)
- [Applied Data Science for Credit Risk](https://github.com/andrija-djurovic/adsfcr)
- [Aprendiendo R sin morir en el intento](https://aprendiendo-r-intro.netlify.app/)
- [Aprendizaje Estadístico con R](https://rubenfcasal.github.io/aprendizaje_estadistico/index.html)
- [Bayesian inference with INLA](https://becarioprecario.bitbucket.io/inla-gitbook/index.html)
- [BBC Visual and Data Journalism cookbook for R graphics](https://bbc.github.io/rcookbook/)
- [Big Book of R](https://www.bigbookofr.com/index.html)
- [Bioinformática Estadística. Análisis estadístico de datos Ómicos](https://www.uv.es/ayala/docencia/tami/tami13.pdf)
- [Biological Data Science with R](https://bdsr.stephenturner.us/)
- [Building reproducible analytical pipelines with R](https://raps-with-r.dev/)
- [Command Line Basics for R Users](https://bash-intro.rsquaredacademy.com/)
- [Creating APIs in R with Plumber](https://www.rplumber.io/docs/index.html)
- [Data Analysis and Prediction Algorithms with R](http://rafalab.dfci.harvard.edu/dsbook/)
- [Data Management in Large-Scale Education Research](https://datamgmtinedresearch.com/)
- [Data Science in Education Using R](https://datascienceineducation.com/)
- [Data Skills for Reproducible Science](https://psyteachr.github.io/msc-data-skills/)
- [Data Visualization with R](https://rkabacoff.github.io/datavis/)
- [Databases using R by RStudio](https://db.rstudio.com/getting-started/)
- [Dendrometria](https://gitlab.com/Puletti/dendrometria_libro)
- [Deep Learning and Scientific Computing with R torch](https://skeydan.github.io/Deep-Learning-and-Scientific-Computing-with-R-torch/)
- [Deep Learning](https://srdas.github.io/DLBook/)
- [Econometrics with the Tidyverse](https://colleen.quarto.pub/the-tidy-econometrics-workbook/)
- [Efficient R programming](https://csgillespie.github.io/efficientR/)
- [Efficient Machine Learning with R](https://emlwr.org/)
- [Elegant and informative maps with tmap](https://r-tmap.github.io/tmap-book/)
- [Engineering Production-Grade Shiny Apps](https://engineering-shiny.org/)
- [Estadística básica](https://www.uv.es/ayala/docencia/nmr/nmr13.pdf)
- [Estilometría, análisis de textos en R para filólogos](http://www.aic.uva.es/cuentapalabras/presentacion.html)
- [Exploring Complex Survey Data Analysis Using R](https://tidy-survey-r.github.io/tidy-survey-book/)
- [Exploratory Data Analysis with R - Roger D. Peng](https://bookdown.org/rdpeng/exdata/)
- [Forecasting: Principles and Practice](https://otexts.com/fpp3/)
- [Forecasting: Principles and Practice, the Pythonic Way](https://otexts.com/fpppy/)
- [Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM)](https://openforecast.org/adam/)
- [Feature Engineering A-Z](https://feaz-book.com/)
- [Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny](http://www.paulamoraga.com/book-geospatial/)
- [Handbook of Graphs and Networks in People Analytics With Examples in R and Python](https://ona-book.org/)
- [Handbook of Regression Modeling in People Analytics](https://peopleanalytics-regression-book.org/)
- [Handling Strings with R](http://www.gastonsanchez.com/r4strings/)
- [Hands-On Data Visualization](https://handsondataviz.org/)
- [Hands-On Machine Learning with R](https://bradleyboehmke.github.io/HOML/)
- [Hands-On Programming with R](https://rstudio-education.github.io/hopr/)
- [Happy Git and GitHub for the useR](https://happygitwithr.com/)
- [Herramientas para usar modelos de lenguaje de gran escala (LLM) en R](https://luisdva.github.io/llmsr-book/es/index.es.html)
- [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/)
- [Introducción a R](https://cran.r-project.org/doc/contrib/R-intro-1.1.0-espanol.1.pdf)
- [Introduction to Econometrics with R](https://www.econometrics-with-r.org/)
- [Introduction to Probability for Data Science](https://probability4datascience.com/index.html)
- [Introduction to urban accessibility: a practical guide in R](https://github.com/ipeaGIT/intro_access_book)
- [JavaScript for R](https://book.javascript-for-r.com/)
- [Large Language Model tools for R](https://luisdva.github.io/llmsr-book/)
- [Learning Statistics with R](https://learningstatisticswithr.com/)
- [Libro Vivo de Ciencia de Datos](https://librovivodecienciadedatos.ai/)
- [Linear Algebra for Data Science](https://shainarace.github.io/LinearAlgebra/index.html)
- [Model to Meaning](https://marginaleffects.com/)
- [Modern R with the tidyverse](https://b-rodrigues.github.io/modern_R/)
- [NASA Earthdata Cloud Cookbook](https://nasa-openscapes.github.io/earthdata-cloud-cookbook/)
- [Officeverse R & Office](https://ardata-fr.github.io/officeverse/index.html)
- [Open Source Technology in Clinical Data Analysis](https://phuse-org.github.io/OSTCDA/)
- [Outstanding User Interfaces with Shiny](https://unleash-shiny.rinterface.com/)
- [Predictive Soil Mapping with R](https://soilmapper.org/)
- [Probabilidad básica](https://www.uv.es/ayala/docencia/probabilidad/prob.pdf)
- [Quantitative Politics with R](http://qpolr.com/)
- [R Advanced Spatial Lessons](https://bbest.github.io/R-adv-spatial-lessons/)
- [R for Data Analysis](https://trevorfrench.github.io/R-for-Data-Analysis/)
- [R for data science: tidyverse and beyond](https://bookdown.org/Maxine/r4ds/)
- [R for everyone](https://www.jaredlander.com/r-for-everyone/)
- [R for Health Data Science](https://argoshare.is.ed.ac.uk/healthyr_book/)
- [R Graphics Cookbook](https://r-graphics.org/index.html)
- [R in action](https://www.manning.com/books/r-in-action-second-edition)
- [R intro](https://cran.r-project.org/doc/manuals/R-intro.pdf)
- [R Markdown Cookbook](https://bookdown.org/yihui/rmarkdown-cookbook/)
- [R Markdown: The Definitive Guide](https://bookdown.org/yihui/rmarkdown/)
- [R Notes for Professionals](https://books.goalkicker.com/RBook/)
- [R Packages](https://r-pkgs.org/)
- [R para principiantes](https://cran.r-project.org/doc/contrib/rdebuts_es.pdf)
- [R para profesionales de los datos: una introducción](https://datanalytics.com/libro_r/)
- [R Programming for Data Science. Roger D. Peng.](https://leanpub.com/rprogramming)
- [R Programming for Data Science](https://www.cs.upc.edu/~robert/teaching/estadistica/rprogramming.pdf)
- [R4JournalismBook](https://smach.github.io/R4JournalismBook/)
- [rstudio4edu](https://rstudio4edu.github.io/rstudio4edu-book/)
- [Simulación Estadística con R](https://rubenfcasal.github.io/simbook/)
- [Spatial Analysis With R](http://gis.humboldt.edu/OLM/r/Spatial%20Analysis%20With%20R.pdf)
- [Spatial Data Science with applications in R](https://r-spatial.org/book/)
- [Spatial Data Science](https://keen-swartz-3146c4.netlify.app/)
- [Spatial Microsimulation with R](https://spatial-microsim-book.robinlovelace.net/index.html)
- [Spatial Modelling for Data Scientists](https://gdsl-ul.github.io/san/)
- [Spatial Statistics for Data Science: Theory and Practice with R](https://www.paulamoraga.com/book-spatial/index.html)
- [Statistical Inference via Data Science](https://moderndive.com/index.html)
- [Supervised Machine Learning for Text Analysis in R](https://smltar.com/)
- [Technical Foundations of Informatics](https://info201.github.io/)
- [Text Mining with R](https://www.tidytextmining.com/)
- [The 20 Best Data Science Books Available online in 2020](https://www.ubuntupit.com/best-data-science-books-available-online/)
- [The Art of Data Science](https://bookdown.org/rdpeng/artofdatascience/)
- [The caret Package](http://topepo.github.io/caret/index.html)
- [The Epidemiologist R Handbook](https://epirhandbook.com/en/)
- [The R Book](https://www.cs.upc.edu/~robert/teaching/estadistica/TheRBook.pdf)
- [The Rust Programming Language](https://doc.rust-lang.org/book/title-page.html)
- [The Shiny AWS Book](https://business-science.github.io/shiny-production-with-aws-book/)
- [Think Bayes 2e](https://github.com/AllenDowney/ThinkBayes2)
- [Tidy Finance with R](https://tidy-finance.org/)
- [Tidy Finance](https://www.tidy-finance.org/)
- [Todos los libros en bookdown](https://bookdown.org/home/archive/)
- [Twitter for Scientists](https://t4scientists.com/)
- [What They Forgot to Teach You About R](https://whattheyforgot.org/)
- [YaRrr! The Pirate’s Guide to R](https://bookdown.org/ndphillips/YaRrr/)
- Applied Statistics with R [Libro](https://daviddalpiaz.github.io/appliedstats/) y [Código](https://github.com/daviddalpiaz/appliedstats)
- Data Science Live Book [Libro](https://livebook.datascienceheroes.com/) y [Código](https://github.com/pablo14/data-science-live-book)
- Fundamentals of Data Visualization [Libro](https://clauswilke.com/dataviz/) y [Código](https://github.com/clauswilke/dataviz)
- Geocomputation with R [Libro](https://geocompr.robinlovelace.net/) y [Código](https://github.com/Robinlovelace/geocompr/)
- Introduction to Data Science [Libro](https://rafalab.github.io/dsbook/) y [Código](https://github.com/rafalab/dsbook)
- Mastering Apache Spark with R [Libro](https://therinspark.com/intro.html) y [Código](https://github.com/r-spark/the-r-in-spark)
- R for Data Science. [Inglés](https://r4ds.hadley.nz/) y [Castellano](https://es.r4ds.hadley.nz/)
- R for Statistical Learning [Libro](https://daviddalpiaz.github.io/r4sl/) y [Código](https://github.com/daviddalpiaz/r4sl)
- sits: Satellite Image Time Series Analysis on Earth Observation Data Cubes [Libro](https://e-sensing.github.io/sitsbook/index.html) y [Kaggle](https://www.kaggle.com/esensing/code)


## Revisar los links

Dentro del repositorio, se ha creado un archivo [`revisar_links.R`](revisar_links.R) para revisar si los links son válidos. Para que sea mas fácil su uso, recopila los links del repositorio público de [Open Data](https://github.com/santiagomota/Open_Data) en el fichero [README](https://raw.githubusercontent.com/santiagomota/Open_Data/master/README.md), pero el código se puede modificar con la variable `repo_url`.