{"id":42939712,"url":"https://github.com/santiagomota/open_data","last_synced_at":"2026-01-30T20:03:51.365Z","repository":{"id":39380571,"uuid":"113558383","full_name":"santiagomota/Open_Data","owner":"santiagomota","description":"Cosas relacionadas con el Open Data","archived":false,"fork":false,"pushed_at":"2025-11-13T15:44:53.000Z","size":59852,"stargazers_count":15,"open_issues_count":0,"forks_count":6,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-11-13T17:26:02.256Z","etag":null,"topics":["data","datascience","libros","open-data","r"],"latest_commit_sha":null,"homepage":"","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/santiagomota.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2017-12-08T09:43:01.000Z","updated_at":"2025-11-13T15:44:58.000Z","dependencies_parsed_at":"2023-11-20T10:36:43.249Z","dependency_job_id":"afed0666-179f-43ff-a845-8a15864d6e29","html_url":"https://github.com/santiagomota/Open_Data","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"purl":"pkg:github/santiagomota/Open_Data","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/santiagomota%2FOpen_Data","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/santiagomota%2FOpen_Data/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/santiagomota%2FOpen_Data/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/santiagomota%2FOpen_Data/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/santiagomota","download_url":"https://codeload.github.com/santiagomota/Open_Data/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/santiagomota%2FOpen_Data/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28918235,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-30T19:10:10.838Z","status":"ssl_error","status_checked_at":"2026-01-30T19:06:40.573Z","response_time":66,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data","datascience","libros","open-data","r"],"created_at":"2026-01-30T20:03:29.191Z","updated_at":"2026-01-30T20:03:51.355Z","avatar_url":"https://github.com/santiagomota.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Open_Data\n\nRecopilación de información sobre Open Data. Links, libros, blogs y otra información interesante.\n\nEste fichero es copia de uno alojado en Github, en este [repositorio](https://github.com/santiagomota/Open_Data) y que se actualiza periódicamente. \n\nSe ha incluido otra copia en [Kaggle](https://www.kaggle.com/code/santiagomota/open-data-links/).\n\nY se aloja en las webs [Github](https://santiagomota.github.io/Open_Data/) y [Netlify](https://open-data-pages.netlify.app/).\n\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/santiagomota/Open_Data\"\u003e\n    \u003cimg src=\"./docs/figs/Open_Data_Github.png\" width=\"30%\"\u003e\u003cbr\u003e\n    https://github.com/santiagomota/Open_Data\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://santiagomota.github.io/Open_Data/\"\u003e\n    \u003cimg src=\"./docs/figs/Open_Data_Web_Github.png\" width=\"30%\"\u003e\u003cbr\u003e\n    https://santiagomota.github.io/Open_Data/\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://open-data-pages.netlify.app/\"\u003e\n    \u003cimg src=\"./docs/figs/Open_Data_Web_Netlify.png\" width=\"30%\"\u003e\u003cbr\u003e\n    https://open-data-pages.netlify.app/\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\n## Fuentes de datos abiertos y APIs\n\n- [20 Awesome Websites For Collecting Big Data](https://datafloq.com/read/20-awesome-websites-for-collecting-big-data/2737?utm_source=Datafloq%20newsletter\u0026utm_campaign=979b1fada5-EMAIL_CAMPAIGN_2017_03_13\u0026utm_medium=email\u0026utm_term=0_655692fdfd-979b1fada5-90449429)\n- [25 Open Datasets for Deep Learning Every Data Scientist Must Work With](https://www.analyticsvidhya.com/blog/2018/03/comprehensive-collection-deep-learning-datasets/)\n- [25 Satellite Maps To See Earth in New Ways](https://gisgeography.com/satellite-maps/)\n- [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\u0026lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3B9KuSD9KfQ6ie%2BALso3gwvw%3D%3D\u0026licu=urn%3Ali%3Acontrol%3Ad_flagship3_feed-object)\n- [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)\n- [AENA - Estadísticas de tráfico aéreo](https://www.aena.es/es/estadisticas/inicio.html)\n- [Agencia Tributaria. Estadísticas](https://sede.agenciatributaria.gob.es/Sede/estadisticas.html)\n- [AI for Copernicus - a data repository by CALLISTO](https://github.com/Agri-Hub/Callisto-Dataset-Collection)\n- [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)\n- [AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification](https://captain-whu.github.io/AID/)\n- [Alaska Satellite Facility](https://asf.alaska.edu/getstarted/)\n- [Amazon product data 2014](http://jmcauley.ucsd.edu/data/amazon/)\n- [Amazon product data 2018](https://nijianmo.github.io/amazon/index.html)\n- [Análisis de 1.100 millones de trayectos de taxis y uber en NYC](https://github.com/toddwschneider/nyc-taxi-data)\n- [Android Earthquake Alerts: A global system for early warning](https://research.google/blog/android-earthquake-alerts-a-global-system-for-early-warning/)\n- [API de Facebook](https://developers.facebook.com/docs/graph-api)\n- [API de GitHub](https://developer.github.com/v3/)\n- [Argo Floats](https://argo.ucsd.edu/) - Global ocean observations of temperature, salinity, and pressure.\n- [API TomTom. Tráfico en ciudades](http://developer.tomtom.com/products/onlinenavigation/onlinetraffic/onlinetrafficflow)\n- [Armed Conflict Location \u0026 Event Data Project (ACLED)](https://acleddata.com/)\n- [ASTER Global DEM (GDEM)](https://lpdaac.usgs.gov/products/astgtmv003/) - ASTER Global Digital Elevation Model 1 arc second\n- [ArcticDEM](https://www.pgc.umn.edu/data/arcticdem/) - High-resolution DEM for the Arctic region\n- [Awesome Geospatial](https://github.com/sacridini/Awesome-Geospatial)\n- [Awesome Public Datasets 1](https://github.com/dipanjanS/awesome-public-datasets)\n- [Awesome Public Datasets 2](https://github.com/awesomedata/awesome-public-datasets)\n- [Awesome Sentinel. Copernicus Sentinel Satellites resources](https://github.com/Fernerkundung/awesome-sentinel)\n- [awesome-gee-community-datasets](https://github.com/samapriya/awesome-gee-community-datasets)\n- [AWS Data Exchange](https://docs.aws.amazon.com/data-exchange/)\n- [AWS Datasets](https://registry.opendata.aws/)\n- [AWS Open Data Geo](https://github.com/opengeos/aws-open-data-geo)\n- [AWS Open Data](https://github.com/opengeos/aws-open-data)\n- [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)\n- [Berkeley Earth](https://berkeleyearth.org/data/) - Global land temperature and air pollution datasets.\n- [Blog. 100 recursos sobre Big Data y Data Science](https://www.todobi.com/mas-de-100-recursos-sobre-big-data-y/)\n- [British Ordnance Survey Data Hub](https://osdatahub.os.uk/)\n- [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)\n- [CaixaBank Research](https://www.caixabankresearch.com/es)\n- [CGIAR-CSI SRTM](https://csidotinfo.wordpress.com/data/srtm-90m-digital-elevation-database-v4-1/) - SRTM 90m Digital Elevation Database v4.1\n- [Canada Open Government Portal](https://open.canada.ca/data/en/dataset?q=education)\n- [CEMS-Flood flood inundation maps](https://confluence.ecmwf.int/display/CEMS/CEMS-Flood+flood+inundation+maps)\n- [CEMS Early Warning Data Store](https://ewds.climate.copernicus.eu/)\n- [Center for Applied Internet Data Analysis](https://www.caida.org/data/overview/)\n- [Center for Disease Control](https://wonder.cdc.gov/)\n- [CHIRPS: Rainfall Estimates from Rain Gauge and Satellite Observations](https://www.chc.ucsb.edu/data/chirps) - High-resolution precipitation data.\n- [CIS. Centro de Investigaciones Sociológicas](https://www.cis.es/inicio)\n- [Climate Data Online](https://www.ncdc.noaa.gov/cdo-web/)\n- [Climate Change Knowledge Porta](https://climateknowledgeportal.worldbank.org/) - Country-specific climate risks, data, and projections.\n- [Climate TRACE](https://climatetrace.org/data)\n- [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\u0026utm_medium=email\u0026utm_campaign=Datos-en-tiempo-real-open-access-y-mucho-ms-en-datosgobes)\n- [Copernicus Atmosphere Monitoring Service (CAMS) Global Near-Real-Time](https://developers.google.com/earth-engine/datasets/catalog/ECMWF_CAMS_NRT)\n- [Copernicus Open Access Hub](https://scihub.copernicus.eu/dhus/#/home)\n- [Copernicus DEM](https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model) - European Digital Elevation Model (EU-DEM)\n- [Copernicus Marine Environment Monitoring Service (CMEMS)](https://marine.copernicus.eu/) - Ocean monitoring for sea surface temperature, sea level, and salinity.\n- [Copernicus Emergency Management Service - CEMS](https://confluence.ecmwf.int/display/CEMS/Copernicus+Emergency+Management+Service+-+CEMS+Home)\n- [Copernicus Emergency Management Service](https://emergency.copernicus.eu/)\n- [CRAN Task View OpenData](https://github.com/ropensci/opendata)\n- [Crimen en UK](https://data.police.uk/)\n- [DANS Data Station Physical and Technical Sciences](https://phys-techsciences.datastations.nl/)\n- [Data Derived from OpenStreetMap for Download](https://osmdata.openstreetmap.de/)\n- [Data Is Plural](https://www.data-is-plural.com/)\n- [Data Kicks](https://data-kicks.com/index.php/blog/)\n- [Data on CO2 and Greenhouse Gas Emissions by Our World in Data](https://github.com/owid/co2-data/tree/master)\n- [Data World](https://data.world/)\n- [Datasets de ejemplo de IBM Watson Analytics](https://www.ibm.com/communities/analytics/watson-analytics-blog/guide-to-sample-datasets/)\n- [Datasets de Quandl](https://www.quandl.com/search?query=)\n- [Dataset4EO](https://github.com/EarthNets/Dataset4EO)\n- [Datos abiertos Ayuntamiento de Valencia](https://www.valencia.es/cas/ayuntamiento/gobierno-abierto)\n- [Datos abiertos de la Generalitat de Cataluña](http://dadesobertes.gencat.cat/es/)\n- [Datos abiertos de la Unión Europea](https://data.europa.eu/es)\n- [Datos abiertos de Santander](http://datos.santander.es/)\n- [Datos abiertos del Ayuntamiento de Madrid](http://datos.madrid.es/)\n- [Datos Abiertos del Consorcio Regional de Transportes de Madrid](https://datos.crtm.es/)\n- [Datos abiertos del gobierno de España](http://datos.gob.es/)\n- [Datos abiertos Junta de Andalucía](http://www.juntadeandalucia.es/datosabiertos/portal.html)\n- [Datos de la Eurocopa 2024](https://github.com/Jelagmil/Euro2024_data)\n- [Datos de todos los vuelos en USA entre 1987 y 2008 (datos originales)](http://stat-computing.org/dataexpo/2009/the-data.html)\n- [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)\n- [Datos estadísticos DGT](https://sedeapl.dgt.gob.es/WEB_IEST_CONSULTA/)\n- [Datosclima. Base de datos meteo](http://datosclima.es/Aemet2013/DescargaDatos.html)\n- [DH Network](http://opendhn.dhnetwork.opendata.arcgis.com/)\n- [Digital Earth Africa (DE Africa) Map](https://www.digitalearthafrica.org/platform-resources/platform)\n- [Dirección General de Tráfico (DGT)](https://sedeapl.dgt.gob.es/WEB_IEST_CONSULTA/inicio.faces)\n- [Dynamic World V1 Land Use](https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1) \n- [EarthEnv-DEM90 digital elevation model](https://www.earthenv.org/DEM) - Global DEM created from multiple datasets\n- [EarthView dataset](https://huggingface.co/datasets/satellogic/EarthView)\n- [ECMWF ERA5](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5) - Hourly reanalysis climate data (temperature, precipitation, wind, etc.).\n- [EM-DAT - The international disaster database](https://www.emdat.be/)\n- [EDGAR - Emissions Database for Global Atmospheric Research](https://edgar.jrc.ec.europa.eu/emissions_data_and_maps)\n- [EnMAP. The German Spaceborne Imaging Spectrometer Mission](https://www.enmap.org/)\n- [El planeta Tierra en AWS](https://aws.amazon.com/es/earth/)\n- [ERA DATASET. Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos](https://lcmou.github.io/ERA_Dataset/)\n- [ERA5 Daily Aggregates - Latest Climate Reanalysis Produced by ECMWF / Copernicus Climate Change Service](https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_DAILY)\n- [ESA OpenSR - Robust, accountable super-resolution for Sentinel-2 and beyond](https://isp.uv.es/opensr/)\n- [ESA Third Party Missions (TPM)](https://earth.esa.int/eogateway/missions/third-party-missions)\n- [ESA WorldCover 2021. Global land cover product at 10 m for 2021 based on Sentinel-1 and 2 data](https://worldcover2021.esa.int/)\n- [España. Estadísticas de mercado de trabajo](https://www.mites.gob.es/es/estadisticas/mercado_trabajo/index.htm)\n- [España. Inmigración. Estadísticas](https://www.inclusion.gob.es/web/opi/estadisticas)\n- [España. Seguridad Social. Estadísticas](https://www.seg-social.es/wps/portal/wss/internet/EstadisticasPresupuestosEstudios/Estadisticas)\n- [Esri Open Data Hub](https://hub.arcgis.com/search)\n- [European Banking Authority (EBA)](https://www.eba.europa.eu/risk-and-data-analysis)\n- [European Data Portal](https://www.europeandataportal.eu/)\n- [European Forest Fire Information System (EFFIS)](https://forest-fire.emergency.copernicus.eu/)\n- [FAO Map Catalog](https://data.apps.fao.org/catalog/)\n- [FAO's Global Information System on Water and Agriculture](https://www.fao.org/aquastat/en/geospatial-information/wapor)\n- [FBREF - Estadísticas e Historia del Fútbol](https://fbref.com/es/)\n- [Fields of The World (FTW)](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world/description/)\n- [Fivethirtyeight](https://data.fivethirtyeight.com/)\n- [FLUXNET](https://fluxnet.org/) - Data from flux towers for carbon, water, and energy exchange monitoring.\n- [Fondo Monetario Internacional](http://www.imf.org/en/data)\n- [Free GIS Data](http://freegisdata.rtwilson.com/)\n- [Freshwater Ecoregions of the World](https://www.worldwildlife.org/pages/freshwater-ecoregions-of-the-world--2)\n- [Fuentes de datos espaciales (Diva-GIS)](https://diva-gis.org/)\n- [Functional Map of the World (fMoW) Dataset](https://github.com/fMoW/dataset)\n- [Future Population Projections](https://hub.worldpop.org/doi/10.5258/SOTON/WP00849)\n- [Gapminder](https://www.gapminder.org/data/)\n- [gee-community-catalog](https://gee-community-catalog.org/)\n- [geoBoundaries](https://www.geoboundaries.org/)\n- [geodata.state.gov](https://geodata.state.gov/geonetwork/srv/spa/catalog.search#/home)\n- [GEBCO (General Bathymetric Chart of the Oceans)](https://www.gebco.net/) - Bathymetric DEM for ocean floors\n- [Geonames Cities with population \u003e 5000](https://documentation-resources.opendatasoft.com/explore/dataset/doc-geonames-cities-5000/table/)\n- [Geoportal Registradores](https://geoportal.registradores.org/)\n- [Geospatial Data Catalogs](https://github.com/opengeos/geospatial-data-catalogs)\n- [Geospatial Data Abstraction Library (GDAL) links](https://gdal.org/en/stable/) - Provides links to raster datasets from various organizations.\n- [GHSL - Global Human Settlement Layer](https://human-settlement.emergency.copernicus.eu/download.php?ds=bu)\n- [Global Forest Change 2000-2023](https://storage.googleapis.com/earthenginepartners-hansen/GFC-2023-v1.11/download.html)\n- [Global Flood Database v1 (2000-2018)](https://developers.google.com/earth-engine/datasets/catalog/GLOBAL_FLOOD_DB_MODIS_EVENTS_V1)\n- [Global Health Observatory (GHO) API](https://www.who.int/data/gho/info/gho-odata-api)\n- 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)\n- [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.\n- [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.\n- [Global Wildfire Information System (GWIS)](https://gwis.jrc.ec.europa.eu/)\n- [Gobierno Estados Unidos](http://www.data.gov/)\n- [Google Books Ngram Viewe](http://storage.googleapis.com/books/ngrams/books/datasetsv2.html)\n- [Google Cloud Vision API](https://cloud.google.com/vision/)\n- [Google Datset Search](https://datasetsearch.research.google.com/)\n- [Google Earth Engine Catalog](https://github.com/opengeos/Earth-Engine-Catalog)\n- [Google finanzas](http://www.google.com/finance/)\n- [Google Open Buildings](https://sites.research.google/gr/open-buildings/)\n- [Google Patents Public Data](https://console.cloud.google.com/marketplace/product/google_patents_public_datasets/google-patents-public-data)\n- [Google Public Data](https://www.google.com/publicdata/directory)\n- [Google-Microsoft-OSM Open Buildings - combined by VIDA](https://beta.source.coop/repositories/vida/google-microsoft-osm-open-buildings/description/)\n- [Helsinki Open Data](http://www.hri.fi/en/)\n- [Hugging Face Datasets](https://huggingface.co/datasets)\n- [HydroRIVERS](https://www.hydrosheds.org/products/hydrorivers)\n- [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/)\n- [Idealista ux\u0026tech](https://www.idealista.com/labs/blog/)\n- [idealista18 - 2018 real estate listings in Spain. 3 cities](https://github.com/paezha/idealista18)\n- [ImageNet database](http://www.image-net.org/)\n- [Infraestructura de Datos Espaciales de España](https://idee.es/web/idee/inicio)\n- [Infraestructura de Datos Espaciales de la Comunidad de Madrid](http://www.madrid.org/cartografia/idem/html/web/index.htm)\n- [IPUMS GIS Boundary Files](https://international.ipums.org/international/gis.shtml)\n- [ISCGM Global Map](https://globalmaps.github.io/)\n- [ISIMIP3b bias-adjusted atmospheric climate input data](https://data.isimip.org/datasets/24cb1007-3c96-4b59-a0dc-42d94a8cff8c/)\n- [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)\"\n- [Kaggle datasets](https://www.kaggle.com/datasets)\n- [Kaggle Weekly Kernels Award Winner Announcements](https://www.kaggle.com/general/37924#post354114)\n- [Land Information New Zealand (LINZ) Data Service](https://data.linz.govt.nz/)\n- [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)\n- [LinkedIn - Data for Impact](https://economicgraph.linkedin.com/data-for-impact)\n- [Lista de algunos datatsets dentro de paquetes de R](https://vincentarelbundock.github.io/Rdatasets/datasets.html)\n- [M3LEO: A Multi-Modal Multi-Label Earth Observation Dataset](https://huggingface.co/M3LEO)\n- [Mapas de Open Street Maps](http://download.geofabrik.de/)\n- [Marine Regions](https://marineregions.org/downloads.php)\n- [Marine Cadastre (AIS)](https://hub.marinecadastre.gov/)\n- [Mendeley Data](https://data.mendeley.com/)\n- [Microsoft - A Planetary Computer for a Sustainable Future](https://planetarycomputer.microsoft.com/)\n- [Microsoft Cognitive Services](https://www.microsoft.com/cognitive-services/)\n- [Microsoft Research Open Data](https://msropendata.com/)\n- [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/)\n- [Multi-Temporal Crop Classification with HLS Imagery across CONUS](https://beta.source.coop/repositories/clarkcga/multi-temporal-crop-classification/description/)\n- [Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification](https://github.com/danfenghong/ISPRS_S2FL)\n- [Naciones Unidas. Datos detallados de comercio global](https://comtradeplus.un.org/)\n- [NAIP: National Agriculture Imagery Program](https://developers.google.com/earth-engine/datasets/catalog/USDA_NAIP_DOQQ)\n- [NASA Common Metadata Repository (CMR) SpatioTemporal Asset Catalog (STAC)](https://github.com/opengeos/aws-open-data-stac)\n- [NASA Earth Observations (NEO)](https://neo.gsfc.nasa.gov/)\n- [NASA](https://nssdc.gsfc.nasa.gov/)\n- 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.\n- [NASA Earthdata](https://earthdata.nasa.gov/) - Shuttle Radar Topography Mission (SRTM)\n- [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.\n- [NASDAQ](https://indexes.nasdaqomx.com/Index/History/NQASPA8600AUD)\n- [National Historical Geographic Information System (NHGIS)](https://www.nhgis.org/)\n- [National Map (USGS)](https://www.usgs.gov/programs/national-geospatial-program/national-map) - National Elevation Dataset (NED), LiDAR, and more\n- [Natural Earth Data](https://www.naturalearthdata.com/downloads/) - Raster data for relief and shaded relief imagery.\n- [Natural Earth](http://www.naturalearthdata.com/)\n- [Nature Scientific Data](https://www.nature.com/sdata/)\n- [NHS Digital](digital.nhs.uk/data-and-information/statistical-publications-open-data-and-data-products)\n- [NHSR datasets](https://github.com/nhs-r-community/NHSRdatasets)\n- [NLP Datasets](https://github.com/niderhoff/nlp-datasets/blob/master/README.md)\n- [NOAA Daily Global Historical Climatology Network - Kaggle dataset](https://www.kaggle.com/noaa/ghcn-d)\n- [NOAA. Agencia de meteo. USA.](http://www.nesdis.noaa.gov/index.html)\n- [NOAA Global Forecast System (GFS)](https://www.ncei.noaa.gov/) - Weather forecasts for temperature, precipitation, and wind.\n- [OCDE Data](https://www.oecd.org/en/data.html)\n- [One versus One - European football statistics](https://one-versus-one.com/en)\n- [Openaerialmap](https://openaerialmap.org/) - Aerial imagery collected by individuals and organizations.\n- [Open Africa dataset](https://open.africa/dataset)\n- [Open Data Barometer](https://opendatabarometer.org/?_year=2017\u0026indicator=ODB)\n- [Open data EMT](http://opendata.emtmadrid.es/)\n- [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\u0026utm_medium=social\u0026utm_source=linkedin.com\u0026utm_campaign=buffer)\n- [Open Data Renfe](http://data.renfe.com/)\n- [Open Data Sources Database](https://anthonyhuntley.com/data-science-databases/#DataSourceDatabase)\n- [Open High-Resolution Satellite Imagery: The WorldStrat Dataset -- With Application to Super-Resolution](https://arxiv.org/abs/2207.06418)\n- [Open Topography](https://opentopography.org/) - Various high-resolution DEM datasets from LiDAR and other sources\n- [Open Trade Statistics](https://tradestatistics.io/)\n- [openaddresses](https://openaddresses.io/)\n- [OpenBuildingMap](https://git.gfz-potsdam.de/globaldynamicexposure/openbuildingmap)\n- [OpenCelliD - Open Database of Cell Towers](https://www.opencellid.org/downloads.php)\n- [Opendata del CERN](http://opendata.cern.ch/)  **Error**\n- [Opendatasoft](https://documentation-resources.opendatasoft.com/explore/?sort=modified)\n- [openflights.org/](https://openflights.org/)\n- [OpenGEOS data](https://github.com/opengeos/data)\n- [OpenWeatherMap](https://openweathermap.org/api)\n- [OSM Landuse](https://osmlanduse.org/)\n- 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\n- [Overture - Fused-partitioned](https://beta.source.coop/repositories/fused/overture/description/)\n- [Overture Maps](https://github.com/OvertureMaps/data)\n- [Paquete de R 'datasets'](http://stat.ethz.ch/R-manual/R-patched/library/datasets/html/00Index.html)\n- [Paquete pasra acceder al API del Instituto de Canario de Estadística](https://github.com/rOpenSpain/istacbaser)\n- [Pew Research Center](https://www.pewresearch.org/download-datasets/)\n- [Planet SkySat Public Ortho Imagery, Multispectral](https://developers.google.com/earth-engine/datasets/catalog/SKYSAT_GEN-A_PUBLIC_ORTHO_MULTISPECTRAL)\n- [Propublica](https://www.propublica.org/data/)\n- [RapidAI4EO: A Corpus of Dense Time Series Satellite Imagery](https://beta.source.coop/repositories/planet/rapidai4eo/description/)\n- [Rdatasets](https://vincentarelbundock.github.io/Rdatasets/articles/data.html)\n- [Recopilación de datasets de BigML](https://blog.bigml.com/list-of-public-data-sources-fit-for-machine-learning/)\n- [Red Eléctrica Española (REE) - API](https://www.ree.es/es/apidatos)\n- [Red Natura 2000](https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/rednatura2000_descargas.html)\n- [Reddit datasets](https://www.reddit.com/r/datasets/)\n- [rspatialdata is a collection of data sources and tutorials on visualising spatial data using R](https://rspatialdata.github.io/)\n- [SARDet-100K: large-scale multi-class SAR object detection dataset](https://eod-grss-ieee.com/dataset-detail/U1dJZE1BY1RwclAvOFFJQmlKR1Btdz09)\n- [Satélite Landsat](https://aws.amazon.com/public-data-sets/landsat/)\n- [Satellite Embedding (Google)](https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_SATELLITE_EMBEDDING_V1_ANNUAL?hl=es-419#description)\n- [Satellite imagery datasets containing ships](https://github.com/jasonmanesis/Satellite-Imagery-Datasets-Containing-Ships)\n- [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)\n- [Sen2Like](https://docs.openeo.cloud/usecases/ard/sen2like/#_1-sen2like-for-rgb)\n- [SEN2NAIP - Remote sensing dataset designed to support conventional and reference-based SR model training](https://huggingface.co/datasets/isp-uv-es/SEN2NAIP)\n- [Sentinel Hub NoR Sponsored Accounts and Data Collections](https://www.sentinel-hub.com/Network-of-Resources/)\n- [Sentinel Satellite Data](https://browser.dataspace.copernicus.eu)\n- [Sentinel-5P](https://developers.google.com/earth-engine/datasets/catalog/sentinel-5p)\n- [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)\n- [Síntesis de Indicadores e Informes Macroeconómicos](https://portal.mineco.gob.es/es-es/economiayempresa/EconomiaInformesMacro/Paginas/EconomiaInformesMacro.aspx)\n- [SkyFi Geospatial Hub](https://skyfi.com/)\n- [SkySat missions](https://earth.esa.int/eogateway/missions/skysat)\n- [Social Science Data Lab](https://github.com/SocialScienceDataLab/)\n- 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)\n- [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/)\n- [Some datasets for teaching data science](https://simplystatistics.org/posts/2018-01-22-the-dslabs-package-provides-datasets-for-teaching-data-science/)\n- [Source Cooperative Featured Repositories](https://beta.source.coop/)\n- [Spatial H3 Hub](https://spatial-h3-hub.foursquare.com/)\n- [STAC Index SpatioTemporal Asset Catalog (STAC)](https://github.com/opengeos/stac-index-catalogs)\n- [StatsBomb sports data](https://statsbomb.com/what-we-do/hub/free-data/)\n- [Tanager Core Imagery](https://www.planet.com/data/stac/browser/tanager-core-imagery/catalog.json?.language=es)\n- [TanDEM-X 90m DEM (DLR)](https://download.geoservice.dlr.de/TDM90/) - Global DEM generated from radar data\n- [Teaching of Statistics in the Health Sciences](https://causeweb.org/tshs/)\n- [Tematicas.org Recopilación de series e índices](https://tematicas.org/)\n- [Terra Populus](https://terra.ipums.org/)\n- [Terraclimate](https://www.climatologylab.org/terraclimate.html) - Monthly climate and hydrology data at a global scale.\n- [The Big Bad NLP Database](https://quantumstat.com/nlp-dataset-library/)\n- [The Government Finance Database](https://willamette.edu/mba/research-impact/public-datasets/index.html)\n- [The SpaceNet Datasets](https://spacenet.ai/datasets/)\n- [The World Bank Open Knowledge Repository](https://openknowledge.worldbank.org)\n- [The world’s economic database](https://db.nomics.world/)\n- [TidyRainbow](https://github.com/r-lgbtq/tidyrainbow)\n- [TidyTuesday](https://github.com/rfordatascience/tidytuesday)\n- [Tráfico en el Reino Unido](https://webarchive.nationalarchives.gov.uk/ukgwa/*/http://www.dft.gov.uk/traffic-counts/)\n- [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/datasets)\n- [UC Merced Land Use Dataset](http://weegee.vision.ucmerced.edu/datasets/landuse.html)\n- [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/)\n- [UK Data Service](https://ukdataservice.ac.uk/)\n- [UK Office for National Statistics](https://www.ons.gov.uk/)\n- [UK Open Data](https://data.gov.uk/search)\n- [UK Open Geography Portal](https://geoportal.statistics.gov.uk/)\n- [Ultimos datos de Open Street Map. Spain](https://download.geofabrik.de/europe/spain.html)\n- [Umbra Open Data Tracker](https://github.com/bellingcat/umbra-open-data-tracker)\n- [Una recopilación de APIs públicas](https://github.com/toddmotto/public-apis)\n- [Una recopilación de datasets públicos](https://github.com/caesar0301/awesome-public-datasets)\n- [Understat](https://understat.com/)\n- [UNEP Environmental Data Explorer](https://www.unep.org/publications-data)\n- [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)\n- [United Nations World Urbanization Prospects](https://population.un.org/wup/)\n- [Universidad de Harvard](https://dataverse.harvard.edu/)\n- [US Homeland Infrastructure Foundation-Level Data](https://hifld-geoplatform.hub.arcgis.com/)\n- [USGS 3DEP LiDAR Point Clouds](https://registry.opendata.aws/usgs-lidar/)\n- [USGS Earth Explorer](https://earthexplorer.usgs.gov/) - SRTM, ASTER GDEM, ALOS, and more\n- [Viewfinder Panoramas](https://viewfinderpanoramas.org/) - High-quality DEM for remote regions\n- [WHU-RS19 is a set of satellite images exported from Google Earth](https://paperswithcode.com/dataset/whu-rs19)\n- [Wyvern Open Data Program](https://wyvern.space/open-data/)\n- [World Economic Forum](https://www.weforum.org/publications/)\n- WorldCereal open global harmonized reference data repository [Data]](https://zenodo.org/records/7609500) [Github](https://github.com/WorldCereal/worldcereal-classification)\n- [Worldpop - Open Spatial Demographic Data](https://www.worldpop.org/) y [Worldpop Hub](https://hub.worldpop.org/)\n- [Yelp Dataset](https://business.yelp.com/data/resources/open-dataset/)\n- [Zhu Lab - Data Science in Earth Observation](https://github.com/zhu-xlab)\n- Amazon AWS: [este](http://aws.amazon.com/es/datasets/) y [este](https://aws.amazon.com/es/public-data-sets/)\n- EarthNets for Earth Observation [Page](https://earthnets.nicepage.io/) [Github](https://github.com/EarthNets)\n- 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/)\n- HREA: High Resolution Electricity Access. [Universidad de Michigan](https://hrea.isr.umich.edu/index.html) y [Microsoft](https://planetarycomputer.microsoft.com/dataset/hrea#overview)\n- IPUMS provides census and survey data from around the world [Web](https://www.ipums.org/) y [paquete ipumsr](https://tech.popdata.org/ipumsr/)\n- 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)\n- 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/)\n- Natural Earth Vector. [Github](https://github.com/nvkelso/natural-earth-vector) y [Web](https://www.naturalearthdata.com/)\n- 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)\n- 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)\n- World Bank Open Data [1](https://data.worldbank.org/) y [2](https://datacatalog.worldbank.org/)\n\n\n## Otras referencias interesantes\n\n- [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)\n- [100 Free Tutorials for Learning R](https://www.listendata.com/p/r-programming-tutorials.html)\n- [16 Cursos](https://www.analyticsvidhya.com/blog/2016/10/16-new-must-watch-tutorials-courses-on-machine-learning/?utm_source=feedburner\u0026utm_medium=email\u0026utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29)\n- [A dive into R Markdown](http://cfss.uchicago.edu/program_rmarkdown.html)\n- [A ggplot2 Tutorial for Beautiful Plotting in R](https://cedricscherer.netlify.app/2019/08/05/a-ggplot2-tutorial-for-beautiful-plotting-in-r/)\n- [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)\n- [An Introduction to Statistical Learning - Web R \u0026 Python](https://www.statlearning.com/)\n- [ArcGIS to R spatial cheat sheet](http://www.seascapemodels.org/data/ArcGIS_to_R_Spatial_CheatSheet.pdf)\n- [Awesome Data Science](https://github.com/academic/awesome-datascience)\n- [Awesome R](https://github.com/qinwf/awesome-R)\n- [BigEarthNet A Large-Scale Sentinel Benchmark Archive](https://bigearth.net/)\n- [Bivariate Choropleth Maps: A How-to Guide](https://www.joshuastevens.net/cartography/make-a-bivariate-choropleth-map/)\n- [blogdown: Creating Websites with R Markdown](https://bookdown.org/yihui/blogdown/)\n- [Blogs con github](http://jmcglone.com/guides/github-pages/) y  [Blogs con github y RStudio](http://andysouth.github.io/blog-setup/)\n- [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.\n- [Chuleta de expresiones regulares](https://github.com/rstudio/cheatsheets/blob/main/regex.pdf)\n- [Chuleta general de R](https://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf)\n- [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/)\n- [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\u0026utm_medium=social\u0026utm_source=facebook.com\u0026utm_campaign=buffer)\n- [Cómo crear una API en Python](https://anderfernandez.com/blog/como-crear-api-en-python/)\n- [Computer vision](https://github.com/kjw0612/awesome-deep-vision)\n- [Computerworld - Paquetes de R interesantes](https://www.computerworld.com/article/1375862/great-r-packages-for-data-import-wrangling-visualization.html)\n- [Curso Caltech. Learning from data](https://work.caltech.edu/telecourse.html)\n- [Cursos para aprender más sobre R](https://datos.gob.es/es/noticia/cursos-para-aprender-mas-sobre-r)\n- [Data Science Blogs](https://github.com/rushter/data-science-blogs)\n- [Data Science Cheatsheets](https://github.com/FavioVazquez/ds-cheatsheets)\n- [Data Science Collected Resources](https://github.com/tirthajyoti/Data-science-best-resources)\n- [Data Science Resources](https://github.com/jonathan-bower/DataScienceResources)\n- [Data Scientist Roadmap](https://github.com/MrMimic/data-scientist-roadmap)\n- [Data Viz Catalogue](https://graphica.app/catalogue)\n- [Dataviz Project](https://datavizproject.com/)\n- [Dealing with Regular Expressions](http://uc-r.github.io/regex)\n- [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/)\n- [Estadística con R](https://www.cienciadedatos.net/estadistica-con-r.html)\n- [EUMETSAT science studies](https://www.eumetsat.int/science-studies)\n- [Feature Engineering for Machine Learning](https://trainindata.medium.com/feature-engineering-for-machine-learning-a-comprehensive-overview-a7ad04c896f8)\n- [Financial-Times / chart-doctor](https://github.com/Financial-Times/chart-doctor/tree/main/visual-vocabulary)\n- [Formatos a medida para R Markdown](http://www.r-bloggers.com/r-markdown-custom-formats/)\n- [Free R Reading Material](https://committedtotape.shinyapps.io/freeR/)\n- [From Data to Viz](https://www.data-to-viz.com/)\n- [Galerias de graficos](http://www.r-graph-gallery.com/)\n- [Ggplot](http://socviz.co/)\n- [GIS and mapping](https://nowosad.github.io/SIGR2021/workshop1/workshop1_jn.html#1)\n- [GIS formats](https://atlas.co/formats/)\n- [Glosario de Machine Learning de Google](https://developers.google.com/machine-learning/glossary/)\n- [Google Dataset Search](datasetsearch.research.google.com)\n- [Google Rules of Machine Learning: Best Practices for ML Engineering](http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf)\n- [Google's best practices in machine learning](https://developers.google.com/machine-learning/guides/rules-of-ml/)\n- [HDRIs Images (HDRIs)](https://polyhaven.com/hdris))\n- [HOT - Drone Tasking Manager](https://github.com/hotosm/Drone-TM)\n- [htmlwidgets for R - gallery](http://gallery.htmlwidgets.org/)\n- [IDEAtlas. Developing AI-based methods to map and characterize informal settlements from Earth Observation data](https://ideatlas.eu/)\n- [Información de Rmarkdown en R Studio](http://rmarkdown.rstudio.com/)\n- [Information is Beautiful Awards](https://www.informationisbeautifulawards.com/)\n- [Information is beautiful](https://informationisbeautiful.net/)\n- [Information is Beautiful](informationisbeautiful.net/data)\n- [Interactive 4D LiDAR Segmentation](https://ilya-fradlin.github.io/Interactive4D/)\n- [Investigative Journalism with Satellite Images](https://bourgoing.com/en/linvestigation-par-satellite/)\n- [Kaggle Winning Solutions](http://kagglesolutions.com/)\n- [Microsoft Presidio - Data Protection and De-identification SDK](https://microsoft.github.io/presidio/)\n- [Naming files](https://speakerd.s3.amazonaws.com/presentations/5e4b07f0d9a94f8e9a29b902bad6ed0b/naming-slides.pdf)\n- [Otra lista de recursos variados en Github](https://github.com/Shujian2015/FreeML)\n- [overpass turbo - Herremaienta de filtrado para OSM](https://overpass-turbo.eu/)\n- [Pandoc User’s Guide](http://pandoc.org/MANUAL.html#templates)\n- [Periodic Table Of Visualization Methods](https://www.visual-literacy.org/periodic_table/periodic_table.html)\n- [Plataforma H2O](https://github.com/h2oai)\n- [Practical Introduction to Web Scraping in R](https://blog.rsquaredacademy.com/web-scraping/)\n- [R Code – Best practices](https://www.r-bloggers.com/r-code-best-practices/)\n- [R Coding Style Guide](https://irudnyts.github.io//r-coding-style-guide/)\n- [R Data Science Tutorials](https://github.com/ujjwalkarn/DataScienceR)\n- [R for Water Resources Data Science](https://www.r4wrds.com/)\n- [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)\n- [R Markdown cheatsheet](https://raw.githubusercontent.com/rstudio/cheatsheets/main/rmarkdown.pdf)\n- [R Markdown referencia](https://www.rstudio.com/wp-content/uploads/2015/03/rmarkdown-reference.pdf)\n- [R package primer](https://kbroman.org/pkg_primer/)\n- [R Universe search](https://r-universe.dev/search)\n- [RDocumentation](https://www.rdocumentation.org/)\n- [Regular Expression Language - Quick Reference](https://docs.microsoft.com/en-us/dotnet/standard/base-types/regular-expression-language-quick-reference)\n- [Regular Expressions Every R programmer Should Know](https://www.r-bloggers.com/regular-expressions-every-r-programmer-should-know/)\n- [Remote Sensing for OSINT](https://bellingcat.github.io/RS4OSINT/)\n- [Remote sensing image retrieval](https://github.com/IBM/remote-sensing-image-retrieval)\n- [RMarkdown Driven Development (RmdDD)](https://emilyriederer.netlify.app/post/rmarkdown-driven-development/)\n- [rseek.org - rstats search engine](https://rseek.org/)\n- [Rstudio cheatsheets](https://www.rstudio.com/resources/cheatsheets/?utm_content=buffer1b56a\u0026utm_medium=social\u0026utm_source=twitter.com\u0026utm_campaign=buffer)\n- [Simplifying the ROC and AUC metrics](https://towardsdatascience.com/understanding-the-roc-and-auc-curves-a05b68550b69)\n- [Soporte técnico de RStudio](https://support.posit.co/hc/en-us)\n- [Study finds 94% of business spreadsheets have critical errors](https://phys.org/news/2024-08-business-spreadsheets-critical-errors.html)\n- [Template para documentos científicos con Rmarkdown](http://www.petrkeil.com/?p=2401)\n- [The Chartmaker Directory](chartmaker.visualisingdata.com)\n- [The Data Visualisation Catalogue](https://datavizcatalogue.com/)\n- [The R Graph Gallery](https://r-graph-gallery.com/)\n- [The State of Naming Conventions in R](https://journal.r-project.org/archive/2012-2/RJournal_2012-2_Baaaath.pdf)\n- [The TimeViz Browser 2.0](https://browser.timeviz.net/)\n- [Tipos de licencias de software](https://choosealicense.com/licenses/)\n- [Tipos de licencias open data (minicurso de data.europa.edu)](https://data.europa.eu/en/academy/open-data-licensing)\n- [Tutorials for learning R](https://www.r-bloggers.com/how-to-learn-r-2/)\n- [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/)\n- [Usar git](https://try.github.io/levels/1/challenges/1)\n- [useR! Machine Learning Tutorial](https://github.com/ledell/useR-machine-learning-tutorial)\n- Using Geospatial Data in R [Post](https://www.r-bloggers.com/2021/06/using-geospatial-data-in-r/) \u0026 [Github](https://github.com/SocialScienceDataLab/MZES_SSDL_Georeferenced_Survey_Data)\n- [Utilizando Sweave y Knitr](https://support.posit.co/hc/en-us/articles/200552056-Using-Sweave-and-knitr)\n- [Writing an R package from scratch](https://hilaryparker.com/2014/04/29/writing-an-r-package-from-scratch/)\n- 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\u0026utm_campaign=9363c93195-EMAIL_CAMPAIGN_JAN_2024_CURRENT_ENGLISH\u0026utm_medium=email\u0026utm_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)\n- 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/)\n- 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)\n- Sistemas de Coordenadas. [Aqui](https://rspatial.org/spatial/rst/6-crs.html) y [aqui](https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/OverviewCoordinateReferenceSystems.pdf)\n- 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)\n\n\n## Libros\n\n- [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\u0026utm_medium=social\u0026utm_source=twitter.com\u0026utm_campaign=buffer)\n- [Advanced R](https://adv-r.hadley.nz/index.html)\n- [Advanced Statistical Modeling in R](https://zia207.quarto.pub/advanced-statistical-modeling-in-r/02-00-00-advance-modeling-r.html)\n- [Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA](https://becarioprecario.bitbucket.io/spde-gitbook/)\n- [AI With R](https://air.albert-rapp.de/)\n- [An Introduction to R](https://intro2r.com/)\n- [An Introduction to Spatial Data Analysis and Visualisation in R](https://www.spatialanalysisonline.com/An%20Introduction%20to%20Spatial%20Data%20Analysis%20in%20R.pdf)\n- [An R companion to Statistics: data analysis and modelling](https://mspeekenbrink.github.io/sdam-r-companion/index.html)\n- [Análisis de datos y algoritmos de predicción con R](http://rafalab.dfci.harvard.edu/dslibro/)\n- [Análisis espacial con R](https://publicaciones.ciga.unam.mx/index.php/ec/catalog/book/306)\n- [Applied Data Science for Credit Risk](https://github.com/andrija-djurovic/adsfcr)\n- [Aprendiendo R sin morir en el intento](https://aprendiendo-r-intro.netlify.app/)\n- [Aprendizaje Estadístico con R](https://rubenfcasal.github.io/aprendizaje_estadistico/index.html)\n- [Bayesian inference with INLA](https://becarioprecario.bitbucket.io/inla-gitbook/index.html)\n- [BBC Visual and Data Journalism cookbook for R graphics](https://bbc.github.io/rcookbook/)\n- [Big Book of R](https://www.bigbookofr.com/index.html)\n- [Bioinformática Estadística. Análisis estadístico de datos Ómicos](https://www.uv.es/ayala/docencia/tami/tami13.pdf)\n- [Biological Data Science with R](https://bdsr.stephenturner.us/)\n- [Building reproducible analytical pipelines with R](https://raps-with-r.dev/)\n- [Command Line Basics for R Users](https://bash-intro.rsquaredacademy.com/)\n- [Creating APIs in R with Plumber](https://www.rplumber.io/docs/index.html)\n- [Data Analysis and Prediction Algorithms with R](http://rafalab.dfci.harvard.edu/dsbook/)\n- [Data Management in Large-Scale Education Research](https://datamgmtinedresearch.com/)\n- [Data Science in Education Using R](https://datascienceineducation.com/)\n- [Data Skills for Reproducible Science](https://psyteachr.github.io/msc-data-skills/)\n- [Data Visualization with R](https://rkabacoff.github.io/datavis/)\n- [Databases using R by RStudio](https://db.rstudio.com/getting-started/)\n- [Dendrometria](https://gitlab.com/Puletti/dendrometria_libro)\n- [Deep Learning and Scientific Computing with R torch](https://skeydan.github.io/Deep-Learning-and-Scientific-Computing-with-R-torch/)\n- [Deep Learning](https://srdas.github.io/DLBook/)\n- [Econometrics with the Tidyverse](https://colleen.quarto.pub/the-tidy-econometrics-workbook/)\n- [Efficient R programming](https://csgillespie.github.io/efficientR/)\n- [Efficient Machine Learning with R](https://emlwr.org/)\n- [Elegant and informative maps with tmap](https://r-tmap.github.io/tmap-book/)\n- [Engineering Production-Grade Shiny Apps](https://engineering-shiny.org/)\n- [Estadística básica](https://www.uv.es/ayala/docencia/nmr/nmr13.pdf)\n- [Estilometría, análisis de textos en R para filólogos](http://www.aic.uva.es/cuentapalabras/presentacion.html)\n- [Exploring Complex Survey Data Analysis Using R](https://tidy-survey-r.github.io/tidy-survey-book/)\n- [Exploratory Data Analysis with R - Roger D. Peng](https://bookdown.org/rdpeng/exdata/)\n- [Forecasting: Principles and Practice](https://otexts.com/fpp3/)\n- [Forecasting: Principles and Practice, the Pythonic Way](https://otexts.com/fpppy/)\n- [Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM)](https://openforecast.org/adam/)\n- [Feature Engineering A-Z](https://feaz-book.com/)\n- [Geospatial Data Carpentry for Urbanism](https://carpentries-incubator.github.io/r-geospatial-urban/)\n- [Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny](http://www.paulamoraga.com/book-geospatial/)\n- [Handbook of Graphs and Networks in People Analytics With Examples in R and Python](https://ona-book.org/)\n- [Handbook of Regression Modeling in People Analytics](https://peopleanalytics-regression-book.org/)\n- [Handling Strings with R](http://www.gastonsanchez.com/r4strings/)\n- [Hands-On Data Visualization](https://handsondataviz.org/)\n- [Hands-On Machine Learning with R](https://bradleyboehmke.github.io/HOML/)\n- [Hands-On Programming with R](https://rstudio-education.github.io/hopr/)\n- [Happy Git and GitHub for the useR](https://happygitwithr.com/)\n- [Herramientas para usar modelos de lenguaje de gran escala (LLM) en R](https://luisdva.github.io/llmsr-book/es/index.es.html)\n- [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/)\n- [Introducción a R](https://cran.r-project.org/doc/contrib/R-intro-1.1.0-espanol.1.pdf)\n- [Introduction to Econometrics with R](https://www.econometrics-with-r.org/)\n- [Introduction to Probability for Data Science](https://probability4datascience.com/index.html)\n- [Introduction to urban accessibility: a practical guide in R](https://github.com/ipeaGIT/intro_access_book)\n- [JavaScript for R](https://book.javascript-for-r.com/)\n- [Large Language Model tools for R](https://luisdva.github.io/llmsr-book/)\n- [Learning Statistics with R](https://learningstatisticswithr.com/)\n- [Libro Vivo de Ciencia de Datos](https://librovivodecienciadedatos.ai/)\n- [Linear Algebra for Data Science](https://shainarace.github.io/LinearAlgebra/index.html)\n- [Model to Meaning](https://marginaleffects.com/)\n- [Modern R with the tidyverse](https://b-rodrigues.github.io/modern_R/)\n- [NASA Earthdata Cloud Cookbook](https://nasa-openscapes.github.io/earthdata-cloud-cookbook/)\n- [Officeverse R \u0026 Office](https://ardata-fr.github.io/officeverse/index.html)\n- [Open Source Technology in Clinical Data Analysis](https://phuse-org.github.io/OSTCDA/)\n- [Outstanding User Interfaces with Shiny](https://unleash-shiny.rinterface.com/)\n- [Predictive Soil Mapping with R](https://soilmapper.org/)\n- [Probabilidad básica](https://www.uv.es/ayala/docencia/probabilidad/prob.pdf)\n- [Quantitative Politics with R](http://qpolr.com/)\n- [R Advanced Spatial Lessons](https://bbest.github.io/R-adv-spatial-lessons/)\n- [R for Data Analysis](https://trevorfrench.github.io/R-for-Data-Analysis/)\n- [R for data science: tidyverse and beyond](https://bookdown.org/Maxine/r4ds/)\n- [R for everyone](https://www.jaredlander.com/r-for-everyone/)\n- [R for Health Data Science](https://argoshare.is.ed.ac.uk/healthyr_book/)\n- [R Graphics Cookbook](https://r-graphics.org/index.html)\n- [R in action](https://www.manning.com/books/r-in-action-second-edition)\n- [R intro](https://cran.r-project.org/doc/manuals/R-intro.pdf)\n- [R Markdown Cookbook](https://bookdown.org/yihui/rmarkdown-cookbook/)\n- [R Markdown: The Definitive Guide](https://bookdown.org/yihui/rmarkdown/)\n- [R Notes for Professionals](https://books.goalkicker.com/RBook/)\n- [R Packages](https://r-pkgs.org/)\n- [R para principiantes](https://cran.r-project.org/doc/contrib/rdebuts_es.pdf)\n- [R para profesionales de los datos: una introducción](https://datanalytics.com/libro_r/)\n- [R Programming for Data Science. Roger D. Peng.](https://leanpub.com/rprogramming)\n- [R Programming for Data Science](https://www.cs.upc.edu/~robert/teaching/estadistica/rprogramming.pdf)\n- [R4JournalismBook](https://smach.github.io/R4JournalismBook/)\n- [rstudio4edu](https://rstudio4edu.github.io/rstudio4edu-book/)\n- [Simulación Estadística con R](https://rubenfcasal.github.io/simbook/)\n- [Spatial Analysis With R](http://gis.humboldt.edu/OLM/r/Spatial%20Analysis%20With%20R.pdf)\n- [Spatial Data Management with DuckDB](https://duckdb.gishub.org/)\n- [Spatial Data Science with applications in R](https://r-spatial.org/book/)\n- [Spatial Data Science](https://keen-swartz-3146c4.netlify.app/)\n- [Spatial Microsimulation with R](https://spatial-microsim-book.robinlovelace.net/index.html)\n- [Spatial Modelling for Data Scientists](https://gdsl-ul.github.io/san/)\n- [Spatial Statistics for Data Science: Theory and Practice with R](https://www.paulamoraga.com/book-spatial/index.html)\n- [Statistical Inference via Data Science](https://moderndive.com/index.html)\n- [Supervised Machine Learning for Text Analysis in R](https://smltar.com/)\n- [Technical Foundations of Informatics](https://info201.github.io/)\n- [Text Mining with R](https://www.tidytextmining.com/)\n- [The 20 Best Data Science Books Available online in 2020](https://www.ubuntupit.com/best-data-science-books-available-online/)\n- [The Art of Data Science](https://bookdown.org/rdpeng/artofdatascience/)\n- [The caret Package](http://topepo.github.io/caret/index.html)\n- [The Epidemiologist R Handbook](https://epirhandbook.com/en/)\n- [The R Book](https://www.cs.upc.edu/~robert/teaching/estadistica/TheRBook.pdf)\n- [The Rust Programming Language](https://doc.rust-lang.org/book/title-page.html)\n- [The Shiny AWS Book](https://business-science.github.io/shiny-production-with-aws-book/)\n- [Think Bayes 2e](https://github.com/AllenDowney/ThinkBayes2)\n- [Tidy Finance with R](https://tidy-finance.org/)\n- [Tidy Finance](https://www.tidy-finance.org/)\n- [Todos los libros en bookdown](https://bookdown.org/home/archive/)\n- [Twitter for Scientists](https://t4scientists.com/)\n- [What They Forgot to Teach You About R](https://whattheyforgot.org/)\n- [YaRrr! The Pirate’s Guide to R](https://bookdown.org/ndphillips/YaRrr/)\n- Applied Statistics with R [Libro](https://daviddalpiaz.github.io/appliedstats/) y [Código](https://github.com/daviddalpiaz/appliedstats)\n- Data Science Live Book [Libro](https://livebook.datascienceheroes.com/) y [Código](https://github.com/pablo14/data-science-live-book)\n- Fundamentals of Data Visualization [Libro](https://clauswilke.com/dataviz/) y [Código](https://github.com/clauswilke/dataviz)\n- Geocomputation with R [Libro](https://geocompr.robinlovelace.net/) y [Código](https://github.com/Robinlovelace/geocompr/)\n- Introduction to Data Science [Libro](https://rafalab.github.io/dsbook/) y [Código](https://github.com/rafalab/dsbook)\n- Mastering Apache Spark with R [Libro](https://therinspark.com/intro.html) y [Código](https://github.com/r-spark/the-r-in-spark)\n- R for Data Science. [Inglés](https://r4ds.hadley.nz/) y [Castellano](https://es.r4ds.hadley.nz/)\n- R for Statistical Learning [Libro](https://daviddalpiaz.github.io/r4sl/) y [Código](https://github.com/daviddalpiaz/r4sl)\n- 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)\n\n\n## Revisar los links\n\nDentro 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`.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsantiagomota%2Fopen_data","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsantiagomota%2Fopen_data","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsantiagomota%2Fopen_data/lists"}