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85 results for “georeferencing”
Sentinel2GlobalLULC: A dataset of Sentinel-2 georeferenced RGB imagery annotated for global land use/land cover mapping with deep learning (License CC BY 4.0)
<p>Sentinel2GlobalLULC is a deep learning-ready dataset of RGB images from the Sentinel-2 satellites designed for global land use and land cover (LULC) mapping. Sentinel2GlobalLULC v2.1 contains 194,877 images in GeoTiff and JPEG format corresponding to 29 broad LULC classes. Each image has 224 x 224 pixels at 10 m spatial resolution and was produced by assigning the 25th percentile of all available observations in the Sentinel-2 collection between June 2015 and October 2020 in order to remove atmospheric effects (i.e., clouds, aerosols, shadows, snow, etc.). A spatial purity value was assigned to each image based on the consensus across 15 different global LULC products available in Google Earth Engine (GEE). </p> <p> </p> <p>Our dataset is structured into 3 main zip-compressed folders, an Excel file with a dictionary for class names and descriptive statistics per LULC class, and a python script to convert RGB GeoTiff images into JPEG format. The first folder called "Sentinel2LULC_GeoTiff.zip" contains 29 zip-compressed subfolders where each one corresponds to a specific LULC class with hundreds to thousands of GeoTiff Sentinel-2 RGB images. The second folder called "Sentinel2LULC_JPEG.zip" contains 29 zip-compressed subfolders with a JPEG formatted version of the same images provided in the first main folder. The third folder called "Sentinel2LULC_CSV.zip" includes 29 zip-compressed CSV files with as many rows as provided images and with 12 columns containing the following metadata (this same metadata is provided in the image filenames): </p> <ul> <li>Land Cover Class ID: is the identification number of each LULC class</li> <li>Land Cover Class Short Name: is the short name of each LULC class</li> <li>Image ID: is the identification number of each image within its corresponding LULC class </li> <li>Pixel purity Value: is the spatial purity of each pixel for its corresponding LULC class calculated as the spatial consensus across up to 15 land-cover products </li> <li>GHM Value: is the spatial average of the Global Human Modification index (gHM) for each image</li> <li>Latitude: is the latitude of the center point of each image</li> <li>Longitude: is the longitude of the center point of each image</li> <li>Country Code: is the Alpha-2 country code of each image as described in the ISO 3166 international standard. To understand the country codes, we recommend the user to visit the following website where they present the Alpha-2 code for each country as described in the ISO 3166 international standard:https: //www.iban.com/country-codes</li> <li>Administrative Department Level1: is the administrative level 1 name to which each image belongs</li> <li>Administrative Department Level2: is the administrative level 2 name to which each image belongs</li> <li>Locality: is the name of the locality to which each image belongs</li> <li>Number of S2 images : is the number of found instances in the corresponding Sentinel-2 image collection between June 2015 and October 2020, when compositing and exporting its corresponding image tile</li> </ul> <p>For seven LULC classes, we could not export from GEE all images that fulfilled a spatial purity of 100% since there were millions of them. In this case, we exported a stratified random sample of 14,000 images and provided an additional CSV file with the images actually contained in our dataset. That is, for these seven LULC classes, we provide these 2 CSV files:</p> <ul> <li>A CSV file that contains all exported images for this class </li> <li>A CSV file that contains all images available for this class at spatial purity of 100%, both the ones exported and the ones not exported, in case the user wants to export them. These CSV filenames end with "including_non_downloaded_images".</li> </ul> <p>To clearly state the geographical coverage of images available in this dataset, we included in the version v2.1, a compressed folder called "Geographic_Representativeness.zip". This zip-compressed folder contains a csv file for each LULC class that provides the complete list of countries represented in that class. Each csv file has two columns, the first one gives the country code and the second one gives the number of images provided in that country for that LULC class. In addition to these 29 csv files, we provided another csv file that maps each ISO Alpha-2 country code to its original full country name.</p> <p>© <a href="https://doi.org/10.5281/zenodo.5055632">Sentinel2GlobalLULC Dataset </a>by Yassir Benhammou, Domingo Alcaraz-Segura, Emilio Guirado, Rohaifa Khaldi, Boujemâa Achchab, Francisco Herrera & Siham Tabik is marked with Attribution 4.0 International (CC-BY 4.0)</p>
Georeferenced real estate data for Addis Ababa
<p>This dataset contains georeferenced real estate data for Addis Ababa, used to test the gradient predictions of the monocentric city model. The data includes property address (longitude, latitude), price (rent), size, and other relevant attributes collected from 2017 to 2024.</p> <p>The <code>raw.zip</code> file contains the raw data for each provider, untouched. </p>
GeoDAR-TopoCat: Drainage topology and catchment database (TopoCat) for Georeferenced global Dams And Reservoirs (GeoDAR)
<p><strong>Contact</strong>: Md Safat Sikder (msikder@ksu.edu), Jida Wang (jidawang@ksu.edu; gdbruins@ucla.edu)</p> <p> </p> <p><strong>Data description</strong></p> <p>This data can be considered a supplement to the Georeferenced global Dams And Reservoirs (GeoDAR) dataset (doi:10.5281/zenodo.6163413). </p> <p>Here in GeoDAR-TopoCat, the method of TopoCat (doi:10.5281/zenodo.7420810) has been applied on GeoDAR reservoirs in order to construct the drainage topology and catchments for global reservoirs.</p> <p>To avoid ambiguity, please refer to this version of GeoDAR-TopoCat as “<strong>GeoDAR-TopoCat v1.1-1.0</strong>”, where “1.1” specifies the version of GeoDAR reservoirs, whose drainage topology and catchments are constructed using the method in version “1.0” of TopoCat.</p> <p> </p> <p><strong>Relevant datasets</strong></p> <ul> <li>The original GeoDAR v1.1 dataset without topology can be accessed here: doi:10.5281/zenodo.6163413.</li> <li>The TopoCat v1.0 dataset, originally developed based on HydroLAKES v1.0, can be accessed here: doi:10.5281/zenodo.7420810.</li> </ul> <p> </p> <p><strong>Attribute description</strong></p> <p>Description of the attributes of GeoDAR-TopoCat is the same as those of TopoCat v1.0. The unique ID of each GeoDAR reservoir is specified in “id_v11” (consistent with the GeoDAR dataset). Please refer to the attributes of TopoCat and GeoDAR for more details.</p> <p> </p> <p><strong>Data and code availability</strong></p> <p>All datasets are available under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license (<a href="https://creativecommons.org/licenses/by/4.0">https://creativecommons.org/licenses/by/4.0</a>).</p> <p>Please refer to GeoDAR and TopoCat datasets for other details and disclaimers.</p> <p> </p> <p><strong>Citation</strong></p> <p>We request anyone who uses GeoDAR-TopoCat to cite <strong>both GeoDAR and TopoCat papers</strong>:</p> <p>Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, M. S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs database for bridging attributes and geolocations. Earth System Science Data, 14, 1869-1899, 2022, <a href="https://doi.org/10.5194/essd-14-1869-2022">https://doi.org/10.5194/essd-14-1869-2022</a>.</p> <p>Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Song, C., Ding, M., Crétaux, J.-F., and Pavelsky, T. M., 2023. Lake-TopoCat: A global lake drainage topology and catchment dataset. Earth System Science Data Discussion, in review, <a href="https://doi.org/10.5194/essd-2022-433">https://doi.org/10.5194/essd-2022-433</a>.</p>
Dataset of Georeferenced Dams in South America (DDSA) v1.0.2
<p><strong>Recommended citation</strong></p> <p>Paredes-Beltran, B., Sordo-Ward, A., and Garrote, L.: Dataset of Georeferenced Dams in South America (DDSA), Earth Syst. Sci. Data, 13, 213–229, https://doi.org/10.5194/essd-13-213-2021, 2021.</p> <p><strong>Updated version 1.0.2:</strong></p> <p>We present version 1.0.2 to the DDSA database, the improvements made to version 1.0.1 are described below:</p> <ol> <li>Supplementary table 1: Future Dams in South America has been updated and now 574 future projected dams in South America, 61 under construction for 2020 and 513 planned projects for the future.</li> </ol> <p><strong>Updates made in version 1.0.1:</strong></p> <p>Version 1.0.1 to the DDSA database, includes improvements made to version 1.0.0, which are described below:</p> <ol> <li>New hydrological information attributes have been included: <ol> <li>Aridity index</li> <li>Residence time</li> <li>Degree of regulation.</li> </ol> </li> <li>A shapefile of watersheds for each dam has been included.</li> <li> <ol> </ol> Supplementary table 1: Future Dams in South America has been included.</li> </ol> <p><strong>Use of the dataset</strong></p> <p>Before using the dataset, please notify us (be.paredes@alumnos.upm.es; be.paredes@uta.edu.ec) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.</p> <p>When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using this dataset.</p> <p><strong>Description</strong></p> <p>Dams and their reservoirs generate major impacts on society and the environment. In general, its relevance relies on facilitating the management of water resources for anthropogenic purposes. However, dams could also generate many potential adverse impacts related to safety, ecology or biodiversity. These factors, and the additional effects that climate change could cause in these infrastructures and their surrounding environment, highlight the importance of dams and the necessity for their continuous monitoring and study. There are several studies examining dams both at regional and global scale, however, those that include the South America region focus mainly on the most renowned basins (primarily the Amazon basin), most likely due to the lack of records on the rest of the basins of the region. For this reason, a consistent database of georeferenced dams located in South America is presented: Dataset of georeferenced dams in South America DDSA. It contains 1,010 entries of dams with a combined reservoir volume of 1,017 cubic kilometres and it is presented in form of a list describing a total of 24 attributes that include the dams name, characteristics, purposes and georeferenced location. Also, hydrological information on the dams’ catchments is also included: catchment area, mean precipitation, mean near-surface temperature, mean potential evapotranspiration, mean runoff, catchment population, catchment equipped area for irrigation, aridity index, residence time and degree of regulation. Information was obtained from public records, governments records, existing international databases and from extensive internet research. Each register was validated individually and geolocated using public access online map browsers and then, hydrological and additional information was derived from a hydrological model computed using the HydroSHEDS dataset. With this database, we expect to contribute to the development of new research in this region.</p> <p><strong>Content</strong></p> <p>The files included in the Dataset of georeferenced dams in South America DDSA are:</p> <ul> <li><strong>1.</strong> Dam Information</li> <li><strong>2.1.</strong> Dam Hydrological Information - Catchment Area</li> <li><strong>2.2.</strong> Dam Hydrological Information - Catchment Mean Monthly Near Surface Temperature</li> <li><strong>2.3. </strong>Dam Hydrological Information - Catchment Mean Monthly Precipitation</li> <li><strong>2.4.</strong> Dam Hydrological Information - Catchment Mean Monthly Potential Evapotranspiration</li> <li><strong>2.5. </strong>Dam Hydrological Information - Catchment Mean Monthly Runoff</li> <li><strong>2.6.</strong> Dam Hydrological Information - Catchment Population</li> <li><strong>2.7. </strong>Dam Hydrological Information - Catchment Eqquiped Area for Irrigation</li> <li><strong>2.8. </strong>Dam Hydrological Information - Aridity Index</li> <li><strong>2.9. </strong>Dam Hydrological Information - Residence Time</li> <li><strong>2.10. </strong>Dam Hydrological Information - Degree of Regulation</li> <li><strong>3.</strong> Dataset Attribute Description</li> <li><strong>4.</strong> Dataset Data Source</li> <li><strong>5.</strong> Dataset in KMZ format </li> <li><strong>6.</strong> Dataset in SHAPEFILE format (dams)</li> <li><strong>7. </strong>Dataset in SHAPEFILE format (dams catchments)</li> <li><strong>8. </strong>Supplementary Table 1: Future Dams in South America v1.01</li> </ul>
Georeferenced Data of the case studies: Sai, Kajbar, Difoinarti
<p>Georeferenced Data of the three case studies: Sai (24 February 2017), Kajbar (3 March 2017) and Difoinarti (4 March 2017 & 19 September 2017).</p> <p>Available in geojson, kml and gpx.</p> <p>See also an interpretation here: https://umap.openstreetmap.fr/en/map/walking-on-fire-perambulatory-fieldwork-and-shared_836451#16/20.7619/30.3284</p> <p> </p>
Georeferenced data for the study Environmental suitability throughout the late Quaternary explains population genetic diversity
<p>Data filtered from GBIF (datasetKey: 50c9509d-22c7-4a22-a47d-8c48425ef4a7) Contains 150 records of the <i>Sciurus aberti </i>squirrel filtered in latitudinal windows of 5 degrees from 20 to 45 degrees N. </p>
Bibliometric and spatially georeferenced datasets of beach research in Mexico from 1993 to 2023
<p><span>These datasets resulted from a systematic review of published investigations on Mexican sandy beaches from 1993 to 2023. The literature search was performed on late December 2023 using three bibliographic repositories: Scopus, Web of Science and Redalyc. In the first dataset all records were standardized following the format of the Scopus database, including the following bibliographic metrics: authors, title, publication year, source, citations, authors' affiliations, and keywords. </span></p> <p><span>The second dataset includes the georeferenced beach location according to information provided in the articles listed in the first dataset. On this regard, the dataset includes the following fields: citation of the literature source; name of the beach as provided in the literature sources; latitude and longitude in grades, minutes and seconds using the geodetic datum WGS84; the research theme; and the research subtheme.</span></p>
Georeferenced and cropped "63k Maps of Burma"
<p>Georeferenced (to WGS1984) and cropped set of about 820 historic maps of Burma at a scale of 1 inch per mile (63,360) covering about 75% of the country. Those topographic maps, originally produced and published by the Great Trigonometrical Survey of India between 1899 and 1946, have been scanned and shared with the public as part of the "Old Survey Of India Maps” Community under a CC BY 4.0 International Licence. Many of these maps are reprints of earlier maps produced before the war. Most mapsheets are early editions (edition 1 or edition 2).</p> <p>Each of the 820 map sheet scans was georeferenced using the Latitude-Longitude corner coordinates in Everest 1830 projection. Those map sheets were cropped, keeping only the map area - to allow a seamless mosaic without the mapframe overlapping adjacent map sheets when several map sheets are put together in a GIS. Those cropped map sheets were projected from Everest 1830 to WGS1984 (EPSG4326) - standard GPS - projection to make them easier to use and combine with other GIS data.</p> <p>Those map sheets can be loaded directly in any GIS such as QGIS or ESRI ArcGIS as well as Google Earth.</p> <ul> <li>The mm_OI_JBv2024 folder contains the cropped end georeferenced map sheets in jpg-format as well as accompagning georeference and metadata incl.<br> <ul> <li>The mm_OI_JBv2024_kmlLinks contains kml files to easily load the mapsheets into Google Earth</li> <li>The mm_historicOI_EPSG4326.gdb contains an ESRI mosaic dataset to easily load all mapsheets into ArcGIS</li> </ul> </li> <li>The mm_OI_JBv2024_scanMaps folder contains the uncropped original map scans (renamed though) in jpg-format.</li> <li>The mm_topoOI_JBv7_masterlist.xlsx is a masterlist cataloguing all map sheets for easier use and matching them with the original source files as shared as part of the "Old Survey Of India Maps" (e.g. to identify new mapsheets should new maps be released)</li> <li>The indexMaps folder contains small scale index maps to locate the map sheets using their map sheet Grid-Letter-nomenclature</li> </ul> <p>All georeferenced map scans are based on maps shared by John Brown via Zenodo</p> <ul> <li><a href="../records/8040798">https://zenodo.org/records/8040798</a> (63k Maps of Burma, version 7, Published June 14, 2023)</li> <li><a href="../records/10463372">https://zenodo.org/records/10463372</a> (63k Maps of Burma--additional 1--20240105, version 1, Published January 5, 2024)</li> </ul> <p>The file naming convention is to first give the <strong><em>number</em></strong> of the 4 degree x 4 degree block followed by the <strong><em>letter (A to P)</em></strong> of the sixteen 1 degree x 1 degree blocks in each 4 degree block eg. 38 D, and this is followed by a <strong><em>number</em></strong> from 1 to 16 to indicate the number of the map in the 1 degree block. </p> <p>This <strong><em>Number Letter Number</em></strong> designation is followed by the <strong>map series type</strong> either OI (contains a LCC grid) or OILatLon (only has a Lat-Lon grid), followed by the <strong>edition and year of the edition</strong>, followed by the <strong><em>date of publication/print</em></strong>. If the information is not available an "X" (for edition) or "0000" (for an unknown year) is used. A best-guess approach was used if the edition and print year and version information was ambiguous.</p> <p>The files as shared via the "<a href="https://zenodo.org/records/11661876">Old Survey Of India Maps</a>" have been renamed to standardize the file naming, sometimes correcting them and to make them unique in the case several editions of the same map sheet were available. </p> <p>A topographical index produced by the Survey of India is provided to assist the viewer in selecting a particular map of interest.</p>
Georeferenced and cataloged dataset built from Nimuendajú's Ethnohistorical Map
<p>This is a publicly available georeferenced ethnohistoric dataset based on Nimuendajú (2017). These data were first used in Barsanetti and Ferreira (2024). We have made this folder available to the wider public for use in academic, not-for-profit research. If you use these data in one of your papers, please cite the data (DOI: 10.5281/zenodo.12772630) and also the following references (or newer versions of them): </p> <p>Nimuendajú, Curt (2017). Mapa Etno-Histórico do Brasil e Regiões Adjacentes. Brasília: Instituto do Patrimônio Histórico e Artístico Nacional, Instituto Brasileiro de Geografia e Estatística.</p> <p>Barsanetti, Bruno, and Alípio Ferreira (2024). “Historical Indigenous Extinctions and Modern Deforestation in the Amazon.” Working paper.</p> <p>Please refer to Barsanetti and Ferreira (2024) for a detailed explanation of the dataset and preparation and a discussion of its strengths and limitations. Please read the "Read Me" file for details on the files, which are in the compressed folder "Compressed Data". We appreciate the research assistance of Matheus Dutra, Alan Gayger, Lilian Kingston Freitas, and Alexandre Portugal. Please contact us by email at bruno.barsanetti@fgv.br for any inquiries. This version of the dataset was published on July 18, 2024.</p> <p>Bruno Barsanetti, EPGE Brazilian School of Economics and Finance<br>Alipio Ferreira, Southern Methodist University</p>
Mapa georreferenciado de las zonas básicas de salud de CANARIAS. Año 2017. España. || Georeferenced map of primary care area of reference in the CANARY ISLANDS. Year 2017. Spain.
<p><strong>// ES::Español</strong><br> Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma de CANARIAS, España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria.</p> <p>Los mapas están proyectados en el sistema de referencia de coordenadas geográficas.</p> <p><strong>// EN::English</strong><br> Geographical information and maps of the primary care settings of the Autonomous Community of CANARIAS, Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area.</p> <p>Maps are projected in the geographic coordinates reference system.</p>
Mapa georreferenciado de las zonas básicas de salud de MADRID. Año 2017. España. || Georeferenced map of primary care area of reference in MADRID. Year 2017. Spain.
<p><strong>// ES::Español</strong><br> Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma de <strong>MADRID</strong>, España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria.</p> <p>Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p><strong>// EN::English</strong><br> Geographical information and maps of the primary care settings of the Autonomous Community of <strong>MADRID</strong>, Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area.</p> <p>Maps are projected in the geographic coordinates reference system.</p>
ES: Mapa georreferenciado de las zonas básicas de salud de CANTABRIA. Año 2017. España. || EN: Georeferenced map of primary care area of reference in CANTABRIA . Year 2017. Spain.
<p>ES: Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria. Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN: Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area. Maps are projected in the geographic coordinates reference system.</p>
ES: Mapa georreferenciado de las zonas básicas de salud de CASTILLA LEON. Año 2017. España. || EN: Georeferenced map of primary care area of reference in CASTILLA LEON . Year 2017. Spain.
<p>ES: Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria. Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN: Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area. Maps are projected in the geographic coordinates reference system.</p>
ES: Mapa georreferenciado de las zonas básicas de salud de CATALUÑA. Año 2017. España. || EN: Georeferenced map of primary care area of reference in CATALUÑA. Year 2017. Spain.
<p>ES: Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria. Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN: Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area. Maps are projected in the geographic coordinates reference system.</p>
ES: Mapa georreferenciado de las zonas básicas de salud de CASTILLA LA MANCHA. Año 2017. España. || EN: Georeferenced map of primary care area of reference in CASTILLA LA MANCHA. Year 2017. Spain.
<p>ES: Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria. Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN: Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area. Maps are projected in the geographic coordinates reference system.</p>
ES: Mapa georreferenciado de las zonas básicas de salud de ARAGON. Año 2017. España. || EN: Georeferenced map of primary care area of reference in ARAGON. Year 2017. Spain.
<p>ES:Español<br> Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria.</p> <p>Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN:English<br> Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area.</p> <p>Maps are projected in the geographic coordinates reference system.</p>
ES: Mapa georreferenciado de las zonas básicas de salud de BALEARES. Año 2017. España. || EN: Georeferenced map of primary care area of reference in BALEARES. Year 2017. Spain.
<p>ES:Español<br> Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria.</p> <p>Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN:English<br> Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area.</p> <p>Maps are projected in the geographic coordinates reference system.</p>
ES: Mapa georreferenciado de las zonas básicas de salud de ASTURIAS. Año 2017. España. || EN: Georeferenced map of primary care area of reference in ASTURIAS. Year 2017. Spain.
<p> ES::Español<br> Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria.</p> <p>Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN:English<br> Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area.</p> <p>Maps are projected in the geographic coordinates reference system.</p>
ES: Mapa georreferenciado de las zonas básicas de salud de GALICIA. Año 2017. España. || EN: Georeferenced map of primary care area of reference in GALICIA . Year 2017. Spain.
<p>ES: Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria. Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN: Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area. Maps are projected in the geographic coordinates reference system.</p>
ES: Mapa georreferenciado de las zonas básicas de salud de ANDALUCIA. Año 2017. España || EN: Georeferenced map of primary care area of reference in ANDALUCIA. Year 2017. Spain.
<p>ES:Español<br> Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria.</p> <p>Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN:English<br> Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area.</p> <p>Maps are projected in the geographic coordinates reference system.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.