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436 results for “250”
Tree-covered and intact forest landscapes BC1000, 1995, 2000, 2005, 2010, 2013, 2016 at 250 m
<p>Based on the <a href="http://www.unep-wcmc.org/resources-and-data/generalised-original-and-current-forest">UNEP historic forest cover map</a>, ESA land cover time series and <a href="http://www.intactforests.org/data.ifl.html">intact forest landscape (IFL 2000, 2013 and 2016) data</a>. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/LDN">here</a></strong>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>ldg = theme: land degradation,</li> <li>forest.cover = variable: forest / tree cover,</li> <li>esacci.ifl = determination method: combination of ESA land cover and IFL maps,</li> <li>c = factor,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>1995 = time reference: year 1995,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>clay.wfraction = variable: sand weight fraction,</li> <li>usda.3a1a1a = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Soil pH in H2O at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Soil pH in H2O in × 10 at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>ph.h2o = variable: soil pH in H2O,</li> <li>usda.4c1a2a = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Soil texture classes (USDA system) for 6 soil depths (0, 10, 30, 60, 100 and 200 cm) at 250 m
<p>Soil texture classes (USDA system) for 6 standard soil depths (0, 10, 30, 60, 100 and 200 cm) at 250 m. Derived from predicted soil texture fractions using the soiltexture package in R. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>texture.class = variable: soil texture class,</li> <li>usda = determination method: USDA texture triangle,</li> <li>c = factor,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Soil organic carbon content in x 5 g / kg at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Soil organic carbon content in × 5 g / kg (to convert to % divide by 2) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. The maps are provided using Byte type to significantly reduce file size. Predicted from a global compilation of soil points. Also available for download: soil organic stock maps in in kg / m<sup>2</sup> (<a href="https://doi.org/10.5281/zenodo.1475453">https://doi.org/10.5281/zenodo.1475453</a>) and bulk density maps in kg / m<sup>3</sup> (<a href="https://doi.org/10.5281/zenodo.1475970">https://doi.org/10.5281/zenodo.1475970</a>). Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>organic.carbon = variable: soil organic carbon content in x 5 g / kg,</li> <li>usda.6a1c = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950–2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>sand.wfraction = variable: sand weight fraction,</li> <li>usda.3a1a1a = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Soil bulk density (fine earth) 10 x kg / m-cubic at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Soil bulk density (fine earth) 10 x kg / m<sup>3</sup> at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>bulkdens.fineearth = variable: soil bulk density,</li> <li>usda.4a1h = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Soil organic carbon stock in kg/m2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution
<p>Soil organic carbon stock in kg/m<sup>2</sup> for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution. To convert to t/ha multiply by 10. Derived using soil organic carbon content (<a href="https://doi.org/10.5281/zenodo.1475457">https://doi.org/10.5281/zenodo.1475457</a>), bulk density (<a href="https://doi.org/10.5281/zenodo.1475970">https://doi.org/10.5281/zenodo.1475970</a>) and coarse fragments (<a href="https://doi.org/10.5281/zenodo.2525681">https://doi.org/10.5281/zenodo.2525681</a>), predicted from point data at 6 standard depths. Depth to bed rock has been ignored, hence total stocks might be about 10–15% lower then reported. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>organic.carbon.stock = variable: soil organic carbon stock in kg/m2,</li> <li>msa.kgm2 = determination method: derived from organic carbon content, bulk density and coarse fragments,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..10cm = vertical reference: 0-10 cm layer below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Predicted USDA soil orders at 250 m (probabilities)
<p>Distribution of the USDA orders (12) based on machine learning predictions of great groups (<a href="https://doi.org/10.5281/zenodo.1476844">https://doi.org/10.5281/zenodo.1476844</a>) from global compilation of soil profiles. To learn more about soil orders and great groups please refer to the <a href="https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download/?cid=stelprdb1247203.pdf">Illustrated Guide to Soil Taxonomy - NRCS - USDA</a>. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/tree/master/soil">here</a></strong>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>order = variable: USDA order,</li> <li>usda.histosols = determination method: USDA soil taxonomy class Histosols,</li> <li>p = probability,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: soil surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Predicted USDA soil suborders at 250 m (probabilities)
<p>Distribution of the USDA suborders based on machine learning predictions of great groups (<a href="https://doi.org/10.5281/zenodo.1476844">https://doi.org/10.5281/zenodo.1476844</a>) from global compilation of soil profiles. To learn more about soil suborders and great groups please refer to the <a href="https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download/?cid=stelprdb1247203.pdf">Illustrated Guide to Soil Taxonomy - NRCS - USDA</a>. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">https://gitlab.com/openlandmap/global-layers/-/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>suborder = variable: USDA suborder,</li> <li>usda.ustolls = determination method: USDA soil taxonomy class Ustolls,</li> <li>p = probability,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: soil surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) at 250 m monthly for period 2014-2019 based on COPERNICUS land products
<p>Long-term monthly Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) median value at 250 m based on the time-series of <a href="https://land.copernicus.eu/global/products/fapar">COPERNICUS FAPAR</a>. Derived using the data.table package and quantile function in R. Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/Copernicus_vito"><strong>here</strong></a>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the LandGIS maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>veg = theme: vegetation,</li> <li>fapar = Fraction of Absorbed Photosynthetically Active Radiation,</li> <li>proba.v.oct = determination method: PROBA-V products, month October,</li> <li>d = median value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2014..2019 = time reference: from 2014 to 2019,</li> <li>v1.0 = version number: 1.0,</li> </ul>
Predicted USDA soil great groups at 250 m (probabilities)
<p>Distribution of the USDA soil great groups based on machine learning predictions from global compilation of soil profiles (>350,000 training points). To learn more about soil great groups please refer to the <a href="https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download/?cid=stelprdb1247203.pdf">Illustrated Guide to Soil Taxonomy - NRCS - USDA</a>. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>A back-up copy of all predictions (>65GB) can be downloaded from: <a href="http://gofile.me/6J25n/mQ3cHOOMr">http://gofile.me/6J25n/mQ3cHOOMr</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>grtgroup = variable: USDA great group,</li> <li>usda.argiustolls = determination method: USDA soil taxonomy class Argiustolls,</li> <li>p = probability,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: soil surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Protected planet (protected areas), forests and intact forest landscapes at 100 m, 250 m to 1 km resolution
<p><a href="https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA">Protected planet</a> (protected areas; version Oct 2024) and <a href="https://intactforests.org/data.ifl.html">intact forest landscapes</a> (2000, 2013, 2016 and 2020) rasterized to 100 m, 250 m and 1 km resolutions. The aggregated map contains all pixels that are either protected or intacts. To use these resources please refer to original data producers:</p> <ul> <li>Defourny, P., Lamarche, C., Bontemps, S., De Maet, T., Van Bogaert, E., Moreau, I., Brockmann, C., Boettcher, M., Kirches, G., Wevers, J., Santoro, M., Ramoino, F., & Arino, O. (2017). Land Cover Climate Change Initiative - Product User Guide v2. Issue 2.0. <a href="http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf">http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf</a></li> <li>Olsson, E., Albrecht, R., & Golden Kroner, R.E. (2021). PADDDtracker Data Release Version 2.1: Technical Notes. Conservation International, Arlington, VA. DOI: 10.5281/zenodo.4749615.</li> <li>Potapov, P., Hansen, M. C., Laestadius L., Turubanova S., Yaroshenko A., Thies C., Smith W., Zhuravleva I., Komarova A., Minnemeyer S., Esipova E. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. <a href="http://advances.sciencemag.org/content/3/1/e1600821">Science Advances, 2017; 3:e1600821</a></li> <li>UNEP-WCMC and IUCN (2024), Protected Planet: The World Database on Protected Areas (WDPA) [Online], October 2024, Cambridge, UK: UNEP-WCMC and IUCN. Available at: <a title="Visit Protected Planet" href="http://protectedplanet.net/" target="_blank" rel="noopener">www.protectedplanet.net</a>.</li> </ul> <p>The time-series of forest areas (<strong>forest.areas_esa.cci_p</strong>) are based on the <a href="https://climate.esa.int/en/odp/#/project/land-cover">ESA CCI Land Cover time-series</a> (2000–2022) 300-m resolution data; also available at 1-km resolution based on "average" resampling. Two maps (<strong>forest.cover.sum_esa.cci_p_250m</strong> and <strong>forest.cover.diff_esa.cci_p_250m</strong>) show long term cumulative forest cover and difference in forest cover for 2022 vs 2000.</p> <p>The protected planet areas and intact forest landscapes were rasterized using:</p> <pre><code>## https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA for(j in 0:2){ system(paste0('gdal_rasterize -ot Byte -a_nodata 0 -burn 100 -where "IUCN_CAT LIKE \'I%\'" /data/CCI_LandCover/WDPA_Oct2024_Public_shp_', j, '/WDPA_Oct2024_Public_shp-polygons.shp WDPA_Oct2024_Public_shp_', j, '_1km.tif -tr 0.008333333 0.008333333 -te -180 -65.00208 180 87.37 -co COMPRESS=DEFLATE -a_srs EPSG:4326')) } s = sds(rast("WDPA_Oct2024_Public_shp_ALL_0_1km.tif"), rast("WDPA_Oct2024_Public_shp_ALL_1_1km.tif"), rast("WDPA_Oct2024_Public_shp_ALL_2_1km.tif")) dg.x = app(s, fun=max, na.rm=TRUE, cores = 32) dg.x0 = terra::ifel(is.na(dg.x), 0, dg.x, filename="protected.areas_wdpa.all_p_1km_s_2023_2024_go_epsg4326_v20241025.tif", wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT2S'), overwrite=TRUE) ## https://intactforests.org/data.ifl.html for(j in c(2000,2013,2016,2020)){ system(paste0('gdal_rasterize -ot Byte -a_nodata 0 -burn 100 -l \"ifl_', j, '\" /mnt/lacus/raw/protectedplanet/ifl_', j, '.shp intact.forest_gfw_p_1km_s_', j, '0101_', j, '1231_go_epsg4326_v20241025.tif -tr 0.008333333 0.008333333 -te -180 -65.00208 180 87.37 -co COMPRESS=DEFLATE -a_srs EPSG:4326')) } ## Combination IFL & WPDA b = sds(rast("protected.areas_wdpa.all_p_1km_s_2023_2024_go_epsg4326_v20241025.tif"), rast("intact.forest_gfw_p_1km_s_20200101_20201231_go_epsg4326_v20241025.tif")) bg.x = app(b, fun=max, na.rm=TRUE, cores = 32) bg.x0 = terra::ifel(is.na(bg.x), 0, bg.x, filename="protected.intact.areas_wdpa.ifl_p_1km_s_2020_2024_go_epsg4326_v20241025.tif", wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT2S'), overwrite=TRUE)</code></pre>
Japanese "Abe" Tweets from 2019-02-10 to 2020-10-07 (15,407,811 tweets and 114,231,250 retweets)
<p><strong>Abstract</strong> (our paper)</p> <p>To examine conservative–liberal differences in the extent to which partisan tweets reach less partisan moderate users in a nonwestern context, we analyzed a network of retweets about former Japanese Prime Minister Shinzo Abe. The analyses consistently demonstrated that partisan tweets originating from the conservative cluster reach a wider range of moderate users than those from the liberal cluster. Network analyses revealed that while the conservative and the liberal clusters’ internal structures were similar, the conservative cluster reciprocated the follows from moderate accounts at a higher rate than the liberal cluster. In addition, moderate accounts reciprocated the conservative cluster’s following at a higher rate than they did for the liberal cluster. The analysis of tweet content showed no difference in the frequency of hashtag use between conservatives and liberals, but there were differences in the use of emotion words and linguistic expressions. In particular, emotion words related to the propagation of messages, such as those expressing “dislike”, were used more frequently by conservatives, while the use of adjectives by conservatives was closer to that of moderate users, indicating that conservative tweets are more palatable for moderate users than liberal tweets.</p> <p><strong>Data</strong></p> <p>Abe.tsv.gz:<br> The first column is the tweet id, the second column is the tweet id of the retweet source, and the third column is the date and time (JST) when the tweet was posted.<br> This data was collected by giving the query "安倍 OR アベ" to the Twitter Search API. Therefore, most of the tweets are Japanese tweets. The second column is empty if the tweet is not a retweet.</p> <p><strong>Publication</strong></p> <p>This data set was created for our study. If you make use of this data set, please cite:<br> Mitsuo Yoshida, Takeshi Sakaki, Tetsuro Kobayashi, Fujio Toriumi. Japanese conservative messages propagate to moderate users better than their liberal counterparts on Twitter. <em>Scientific Reports</em>. vol.11, article no.19224, 2021.<br> <a href="https://doi.org/10.1038/s41598-021-98349-2">https://doi.org/10.1038/s41598-021-98349-2</a></p>
ESA Cryo-TEMPO - Northern hemisphere land/ocean flag and distance to coast at resolution of 250 m.
<p>Land/Ocean flag nd distance to coast at high spatial resolution (250m) in the northern hemisphere. The land/ocean flag is computed from merged Open Street Map and Natural Earth land polygons. The shapefiles were rasterized and reprojected to northern hemisphere using gdal. All land mass with the exception of Greenland are based on Open Street Map. Distance to coast was computed with gdal (gdal_proximity.py). </p> <p>The file format is netCDF-4 and the datafile contains two variables (land_ocean_flag & distance_to_coast). The coordinate reference system of the variables is defined by EPSG:6931 (WGS 84 / NSIDC EASE-Grid 2.0 North) and the bounds of the data set are supplied as xc and yc variables in the data file. </p> <p>The file is used in the ESA CryoSat-2 Thematic Products (Cryo-TEMPO) Polar Ocean and Sea Ice products in the northern hemisphere. </p> <p> </p>
Global cropland extent (fractions) annual 2000-2022 at 250 m and 1 km
<p>Global cropland extent annual for 2000-2022 based on the <a href="https://glad.umd.edu/dataset/croplands">Potapov et al. (2021)</a>. Cropland defined as: land used for annual and perennial herbaceous crops for human consumption, forage (including hay), and biofuel. Perennial woody crops, permanent pastures, and shifting cultivation are excluded from the definition. The original 30-m resolution data (0/1 values) was interpolated from time-series 2003, 2007, 2011, 2015, 2019 to annual values 2000 to 2022 using linear interpolation. All values shown are in principle fractions 0-100%. The 30-m and 100-m resoluton images (COGs) are too large for Zenodo but you can access them from URLs in the filenames_openlandmap_cropland.txt file. See for example (drop the URL in QGIS):</p> <ul> <li>https://s3.eu-central-1.wasabisys.com/openlandmap/layers30m/cropland_glad.potapov.et.al_p_30m_s_20030101_20031231_go_epsg.4326_v20240624.tif (3.2GB)</li> <li>https://s3.eu-central-1.wasabisys.com/openlandmap/layers100m/cropland_glad.potapov.et.al_p_100m_s_20030101_20031231_go_epsg.4326_v20240624.tif (2.3GB)</li> </ul> <p><strong>Disclaimer</strong>: linear interpolation has limited accuracy and is basically only used to gap-fill the missing years. The remaining missing values in the maps can be ALL consider to be 0 value for cropland. A more detailed up-to-date cropland map of the world is provided by <a href="https://doi.org/10.5194/essd-15-5491-2023">van Tricht et al., (2023)</a>, however only single year (2021) has been mapped at 10-m resolution within the <a href="../doi/10.5281/zenodo.7875104">WorldCereal project</a>.</p> <p>The temporal interpolation was implemented using terra package ii.e. using the following fuction:</p> <pre><code>library(terra) y.l = c(2003, 2007, 2011, 2015, 2019) out.years = 2000:2022 i = parallel::mclapply(y.l, function(x){system(paste0('gdal_translate Global_cropland_', x, '.vrt Global_cropland_', x, '.tif -co TILED=YES -co BIGTIFF=YES -co COMPRESS=DEFLATE -co ZLEVEL=9 -co BLOCKXSIZE=1024 -co BLOCKYSIZE=1024 -co NUM_THREADS=8 -co SPARSE_OK=TRUE -a_nodata 255 -scale 0 1 0 100 -ot Byte'))}, mc.cores = length(y.l)) ## land mask at 1 deg (100x100km) ---- x = parallel::mclapply(y.l, function(x){system(paste0("gdal_translate Global_cropland_", x, ".tif Global_cropland_", x, "_1d.tif -tr 1 1 -r average -co BIGTIFF=YES -ot Byte -co NUM_THREADS=10"))}, mc.cores = length(y.l)) ## 2 hrs g1 = terra::rast(paste0("Global_cropland_", y.l, "_1d.tif")) gs = sum(g1, na.rm=TRUE) plot(gs) gs.p <- as.polygons(gs, values = TRUE, extent=FALSE, dissolve=FALSE, na.rm=TRUE) ## Input layers: r = terra::rast(paste0("Global_cropland_", y.l, ".tif")) int.mc = function(r, tile, y.l, out.years=2000:2022){ bb = paste(as.vector(ext(tile)), collapse = ".") if(any(!file.exists(paste0("./tmp/", out.years, "/Global_cropland_", out.years, "_", bb, ".tif")))){ r.t = terra::crop(r, ext(tile)) ## each tile is 16M pixels r.x = as.data.frame(r.t, xy=TRUE, na.rm=FALSE) rs = rowSums(r.x[,-c(1:2)], na.rm=TRUE) ## if sum is == 0 means no cropland throughout the time-series sel = which(rs>0) ## extract complete values: r.x0 = r.x[sel,-c(1:2)] r.x0[is.na(r.x0)] = 0 ## interpolate between values: t1s = as.data.frame(t(apply(r.x0, 1, function(y){ try( approx(y.l, as.vector(y), xout=out.years, rule=2)$y ) }))) ## write to GeoTIFFs t1s$x <- r.x$x[sel]; t1s$y <- r.x$y[sel] ## convert to RasterLayer: r.x = rast(t1s[,c("x","y",paste0("V", 1:length(out.years)))], type="xyz", crs="+proj=longlat +datum=WGS84 +no_defs") for(j in 1:length(out.years)){ writeRaster(r.x[[j]], filename=paste0("./tmp/", out.years[j], "/Global_cropland_", out.years[j], "_", bb, ".tif"), gdal=c("COMPRESS=DEFLATE"), datatype='INT1U', NAflag=0, overwrite=FALSE) } } } ## test it: #int.mc(r, tile=gs.p[1000], y.l) ## run in parallel ---- ## takes 12 hrs... 1TB RAM i = parallel::mclapply(sample(1:length(gs.p)), function(x){try( int.mc(r, tile=gs.p[x], y.l) )}, mc.cores = 70) </code></pre>
Trento 1911 - Building 250
<u>Coordinates</u>: N/A <br><u>Length</u>: 22.28 m<br><u>Width</u>: 10.65 m<br><u>Height</u>: 25.99 m<br><u>Points</u>: 16 <br><u>Vertices</u>: 84 <br><u>Primitives</u>: 28 <br><br><u>Main Files:</u><br><table><tbody><tr><th>Filename</th><th>.glb</th><th>.xml</th><th>.obj</th></tr><tr><td><a href="https://zenodo.org/api/records/12695933/files/building_250.obj/content">building_250.obj</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250.obj/content">Link</a></td></tr><tr><td><a href="https://zenodo.org/api/records/12695933/files/building_250.glb/content">building_250.glb</a></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250.glb/content">Link</a></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12695933/files/11569780_metsmods.xml/content">11569780_metsmods.xml</a></td><td></td><td><a href="https://zenodo.org/api/records/12695933/files/11569780_metsmods.xml/content">Link</a></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12695933/files/11569780_edm.xml/content">11569780_edm.xml</a></td><td></td><td><a href="https://zenodo.org/api/records/12695933/files/11569780_edm.xml/content">Link</a></td><td></td></tr></tbody></table><br><br><u>Thumbnails:</u><br><table><tbody><tr><th>Perspective</th><th>1000x1000</th><th>512x512</th><th>256x256</th><th>128x128</th></tr><tr><td>Perspective 1</td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_1.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_1_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_1_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_1_128x128.png/content">Link</a></td></tr><tr><td>Perspective 2</td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_2.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_2_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_2_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_2_128x128.png/content">Link</a></td></tr><tr><td>Perspective 3</td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_3.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_3_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_3_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_3_128x128.png/content">Link</a></td></tr><tr><td>Perspective 4</td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_4.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_4_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_4_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_4_128x128.png/content">Link</a></td></tr><tr><td>Perspective Top</td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_top.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_top_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_top_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695933/files/building_250_perspective_top_128x128.png/content">Link</a></td></tr></tbody></table><br><br><br><u>Changelog</u>: <br> - v<a href="https://doi.org/10.5281/zenodo.12538347">0.0.2</a>: Thumbnails added, Description updated with Link Tables.<br> - v<a href="https://doi.org/10.5281/zenodo.12695933">0.0.3</a>: Added XMLs for Europeana Data Model (EDM) and MetsMods.<br>
Sion 1760 - Building 250 (LoD-1)
<u>Coordinates</u>: N/A <br><u>Length</u>: 7.53 m<br><u>Width</u>: 12.45 m<br><u>Height</u>: 7.29 m<br><u>Vertices</u>: 24 <br><u>Primitives</u>: 12 <br><br> The Length, Width, Height, Vertices and Primitives listed above have been derived directly from the OBJ file.<br><br><u>Main Files:</u><br><table><tbody><tr><th>Filename</th><th>.mtl</th><th>.glb</th><th>.xml</th><th>.obj</th></tr><tr><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002.obj/content">0___LoD1__id-250_002.obj</a></td><td></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002.obj/content">Link</a></td></tr><tr><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002.glb/content">0___LoD1__id-250_002.glb</a></td><td></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002.glb/content">Link</a></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002.mtl/content">0___LoD1__id-250_002.mtl</a></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002.mtl/content">Link</a></td><td></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12695079/files/11251660_metsmods.xml/content">11251660_metsmods.xml</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12695079/files/11251660_metsmods.xml/content">Link</a></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12695079/files/11251660_edm.xml/content">11251660_edm.xml</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12695079/files/11251660_edm.xml/content">Link</a></td><td></td></tr></tbody></table><br><br><u>Thumbnails:</u><br><table><tbody><tr><th>Perspective</th><th>1000x1000</th><th>512x512</th><th>256x256</th><th>128x128</th></tr><tr><td>Perspective 1</td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_1.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_1_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_1_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_1_128x128.png/content">Link</a></td></tr><tr><td>Perspective 2</td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_2.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_2_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_2_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_2_128x128.png/content">Link</a></td></tr><tr><td>Perspective 3</td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_3.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_3_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_3_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_3_128x128.png/content">Link</a></td></tr><tr><td>Perspective 4</td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_4.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_4_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_4_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_4_128x128.png/content">Link</a></td></tr><tr><td>Perspective Top</td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_top.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_top_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_top_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695079/files/0___LoD1__id-250_002_perspective_top_128x128.png/content">Link</a></td></tr></tbody></table><br><br><br><u>Changelog</u>: <br> - v<a href="https://doi.org/10.5281/zenodo.11492087">0.0.2</a>: Thumbnails added, Description updated with Link Tables.<br> - v<a href="https://doi.org/10.5281/zenodo.12695079">0.0.3</a>: Added XMLs for Europeana Data Model (EDM) and MetsMods.<br>
Trento 1851 - Building 250
<u>Coordinates</u>: N/A <br><u>Length</u>: 10.47 m<br><u>Width</u>: 14.29 m<br><u>Height</u>: 6.61 m<br><u>Points</u>: 8 <br><u>Vertices</u>: 36 <br><u>Primitives</u>: 12 <br><br><u>Main Files:</u><br><table><tbody><tr><th>Filename</th><th>.glb</th><th>.xml</th><th>.obj</th></tr><tr><td><a href="https://zenodo.org/api/records/12694143/files/building_250.obj/content">building_250.obj</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250.obj/content">Link</a></td></tr><tr><td><a href="https://zenodo.org/api/records/12694143/files/building_250.glb/content">building_250.glb</a></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250.glb/content">Link</a></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12694143/files/11564182_edm.xml/content">11564182_edm.xml</a></td><td></td><td><a href="https://zenodo.org/api/records/12694143/files/11564182_edm.xml/content">Link</a></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12694143/files/11564182_metsmods.xml/content">11564182_metsmods.xml</a></td><td></td><td><a href="https://zenodo.org/api/records/12694143/files/11564182_metsmods.xml/content">Link</a></td><td></td></tr></tbody></table><br><br><u>Thumbnails:</u><br><table><tbody><tr><th>Perspective</th><th>1000x1000</th><th>512x512</th><th>256x256</th><th>128x128</th></tr><tr><td>Perspective 1</td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_1.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_1_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_1_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_1_128x128.png/content">Link</a></td></tr><tr><td>Perspective 2</td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_2.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_2_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_2_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_2_128x128.png/content">Link</a></td></tr><tr><td>Perspective 3</td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_3.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_3_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_3_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_3_128x128.png/content">Link</a></td></tr><tr><td>Perspective 4</td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_4.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_4_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_4_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_4_128x128.png/content">Link</a></td></tr><tr><td>Perspective Top</td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_top.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_top_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_top_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694143/files/building_250_perspective_top_128x128.png/content">Link</a></td></tr></tbody></table><br><br><br><u>Changelog</u>: <br> - v<a href="https://doi.org/10.5281/zenodo.12530844">0.0.2</a>: Thumbnails added, Description updated with Link Tables.<br> - v<a href="https://doi.org/10.5281/zenodo.12694143">0.0.3</a>: Added XMLs for Europeana Data Model (EDM) and MetsMods.<br>
Sion 1640 - Building 250 (LoD-1)
<u>Coordinates</u>: N/A <br><u>Length</u>: 19.74 m<br><u>Width</u>: 17.8 m<br><u>Height</u>: 4.06 m<br><u>Vertices</u>: 24 <br><u>Primitives</u>: 12 <br><br> The Length, Width, Height, Vertices and Primitives listed above have been derived directly from the OBJ file.<br><br><u>Main Files:</u><br><table><tbody><tr><th>Filename</th><th>.mtl</th><th>.glb</th><th>.xml</th><th>.obj</th></tr><tr><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250.mtl/content">0___LoD1__id-250.mtl</a></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250.mtl/content">Link</a></td><td></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250.obj/content">0___LoD1__id-250.obj</a></td><td></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250.obj/content">Link</a></td></tr><tr><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250.glb/content">0___LoD1__id-250.glb</a></td><td></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250.glb/content">Link</a></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12693901/files/11250200_edm.xml/content">11250200_edm.xml</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12693901/files/11250200_edm.xml/content">Link</a></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12693901/files/11250200_metsmods.xml/content">11250200_metsmods.xml</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12693901/files/11250200_metsmods.xml/content">Link</a></td><td></td></tr></tbody></table><br><br><u>Thumbnails:</u><br><table><tbody><tr><th>Perspective</th><th>1000x1000</th><th>512x512</th><th>256x256</th><th>128x128</th></tr><tr><td>Perspective 1</td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_1.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_1_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_1_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_1_128x128.png/content">Link</a></td></tr><tr><td>Perspective 2</td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_2.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_2_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_2_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_2_128x128.png/content">Link</a></td></tr><tr><td>Perspective 3</td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_3.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_3_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_3_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_3_128x128.png/content">Link</a></td></tr><tr><td>Perspective 4</td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_4.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_4_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_4_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_4_128x128.png/content">Link</a></td></tr><tr><td>Perspective Top</td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_top.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_top_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_top_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12693901/files/0___LoD1__id-250_perspective_top_128x128.png/content">Link</a></td></tr></tbody></table><br><br><br><u>Changelog</u>: <br> - v<a href="https://doi.org/10.5281/zenodo.11488653">0.0.2</a>: Thumbnails added, Description updated with Link Tables.<br> - v<a href="https://doi.org/10.5281/zenodo.12693901">0.0.3</a>: Added XMLs for Europeana Data Model (EDM) and MetsMods.<br>
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.