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708 results for “Global dataset”
GRDC-Caravan: extending the original dataset with data from the Global Runoff Data Centre
<p>Large-sample datasets are essential in hydrological science to support modelling studies and global assessments. This dataset is an extension to <em>Caravan</em>, a global community dataset of meteorological forcing data, catchment attributes, and discharge data for catchments around the world (Kratzert et al. 2023).</p> <p>The extension includes a subset of those hydrological discharge data and station-based watersheds from the Global Runoff Data Centre (GRDC), which are covered by an open data policy (Attribution 4.0 International; CC BY 4.0). In total, the dataset covers stations from 5356 catchments and 25 countries worldwide with a time series record from 1950 – 2023.</p> <p>GRDC is an international data centre operating under the auspices of the World Meteorological Organization (WMO) at the German Federal Institute of Hydrology (BfG). Established in 1988, it holds the most substantive collection of quality assured river discharge data worldwide. Primary providers of river discharge data and associated metadata are the National Hydrological and Hydro-Meteorological Services of WMO Member States.</p> <p>Reference:</p> <p>Kratzert, F., Nearing, G., Addor, N. et al. Caravan - A global community dataset for large-sample hydrology. Sci Data 10, 61 (2023). <a href="https://doi.org/10.1038/s41597-023-01975-w">https://doi.org/10.1038/s41597-023-01975-w</a></p> <p><strong>Update:</strong></p> <p>With version 0.2 a bug has been fixed that affected the time series of four bands of all GRDC gauges in the GRDC extension. The affected bands were total_precipitation, surface_net_solar_radiation, surface_net_thermal_radiation and potential_evaporation, i.e. all features that are accumulated over the day, as per definition of ERA5-Land.<br>For details look at https://github.com/kratzert/Caravan/issues/26.</p> <p>Version 0.3: Data description file added.<br><br>Version 0.4: Added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) in the meteorological forcing data and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND. Also added all PET-related climated indices derived with the Penman-Monteith PET band (suffix "_FAO_PM") and renamed the old PET-related indices accordingly (suffix "_ERA5_LAND").<br><br>Version 0.5: License overview of the respective countries has been added.<br>Dataset description has been modified and improved.<br><br>Version 0.6: The attribute tables are sorted alphabetically. Minor inconsistencies in the data description file have been corrected.</p> <p> </p> <p><strong>Dataset structure:</strong></p> <p>The dataset is provided in the following two file formats:<br>1. caravan-grdc-extension-csv.zip: provides the time series data as comma-separated text files (CSV) (downloadable as 8.8 GB zip archive)<br>2. caravan-grdc-extension-nc.zip: provides the time series data in the Network Common Data Form (NetCDF) (downloadable as 7.6 GB zip archive)</p> <p><strong>The data in the versions 0.1-0.3 are identical. Version 0.4 added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND.</strong></p> <p>Further details of the structure of the dataset are described in the data description file.</p>
Projected Global Area Equipped for Irrigation Datasets during 2020-2100 under SSP scenarios
<h1><strong>1. Background</strong></h1> <p>Accurately predicting the global area equipped for irrigation in the future is crucial for providing essential datasets relevant to fields such as earth system simulation, agricultural water resource management, climate change adaptation, and environmental conservation. However, the predictive datasets of the area equipped for irrigation are still lacking. To address this gap, we provide the <strong>Projected Global Area Equipped for Irrigation Datasets (PGAEID)</strong>, which provide spatially explicit estimates of Area Equipped for Irrigation (AEI) from 2020 to 2100 under three Shared Socioeconomic Pathway (SSP) scenarios: <strong>SSP1</strong> (sustainable development), <strong>SSP2</strong> (intermediate development), and <strong>SSP3</strong> (regional rivalry), <strong>SSP4</strong> (unequal development), and <strong>SSP5</strong> (fossil-fueled development).</p> <h1><strong>2. Methodology</strong></h1> <h3><strong>2.1 Ensemble Machine Learning (EML) Framework</strong></h3> <ul> <li><strong>Algorithms</strong>: Integrated six machine learning models: <ul> <li>Multiple Linear Regression (MLR)</li> <li>Decision Trees (DT)</li> <li>Autoregressive Integrated Moving Average (ARIMA)</li> <li>Multi-Layer Perceptron (MLP)</li> <li>Radial Basis Function (RBF)</li> <li>Random Forests (RF)</li> </ul> </li> <li><strong>Training Data</strong>: Historical national irrigation records (FAO AQUASTAT, 1961–2015).</li> <li><strong>Validation Metrics</strong>: <ul> <li>Nash-Sutcliffe Efficiency (NSE): <strong>0.9</strong><strong>8</strong></li> <li>Kling-Gupta Efficiency (KGE): <strong>0.</strong><strong>97</strong></li> <li>Mean Absolute Percentage Error (MAPE): <strong>1</strong><strong>.</strong><strong>7%</strong></li> </ul> </li> </ul> <h3><strong>2.2 Spatial Downscaling</strong></h3> <ul> <li><strong>Baseline</strong>: FAO 2005 irrigation data combined with GMIA2005 gridded agricultural intensity maps.</li> <li><strong>Dynamic Projection</strong>: Annual change rates applied to 5′ × 5′ grids under SSP-specific socioeconomic drivers.</li> </ul> <h1><strong>3. Dataset Overview</strong></h1> <h3><strong>3.1 Key Features</strong></h3> <ul> <li><strong>Temporal Coverage</strong>: 2020–2100 (10-year intervals).</li> <li><strong>Spatial Resolution</strong>: 5-arcminute (≈10 km at the equator).</li> <li><strong>Scenarios</strong>: SSP1, SSP2, SSP3, SSP4, SSP5.</li> <li><strong>Variables</strong>: area equipped for irrigation (10<sup>3</sup> ha/year).</li> </ul> <h3><strong>3.2 Dataset Structure</strong></h3> <p>The dataset is provided as a compressed archive (PGAEID_Ver3.0.rar), containing:</p> <p>1.<strong>Global_Area_Equipped_for_Irrigation_GeoTiff</strong><strong>/</strong></p> <ul> <li><strong>Subfolders</strong>: <ul> <li>SSP1</li> <li>SSP2</li> <li>SSP3</li> <li>SSP4</li> <li>SSP5</li> </ul> </li> <li><strong>File Format</strong>: GeoTIFF (45 files total).</li> <li><strong>Naming Convention</strong>:<br>AEI_[SSP]_[Year].tif <ul> <li>Example: AEI_SSP1_2020.tif</li> </ul> </li> </ul> <p>2. <strong>National & Regional_AEI</strong><strong>/</strong></p> <ul> <li><strong>Shapefiles</strong>: National/regional area equipped for irrigation for 26 prediction units (2020–2100).</li> <li><strong>Excel File</strong>: Global Area Equipped for Irrigation (2020-2100).xlsx.</li> </ul> <p>3. <strong>Technical Annex.docx</strong></p> <ul> <li>Detailed methodology, validation, and workflow documentation.</li> </ul> <h1><strong>4. Applications</strong></h1> <p>This dataset supports:</p> <ul> <li><strong>Earth System Simulation: </strong>Supporting irrigation parameterization in global climate and hydrological models.</li> <li><strong>Water Resource Management: </strong>Assisting decision-makers in sustainable irrigation planning.</li> <li><strong>Climate Change Adaptation: </strong>Providing insights into how irrigation practices evolve under different socioeconomic pathways.</li> <li><strong>Environmental Conservation: </strong>Assessing the impact of irrigation on regional ecosystems.</li> </ul> <p><strong>Note:</strong></p> <p>Global aggregated totals of area equipped for irrigation derived from the 26 prediction units (country/regional scale) may exhibit minor discrepancies compared to sums calculated from the 5-arcminute gridded data (≈10 km resolution). Such differences stem from variations in spatial aggregation methods, file formats (vector vs. raster), and underlying data processing frameworks. Users may select the dataset best aligned with their analytical objectives:</p> <ul> <li>The <strong>country/region-based data (26 units)</strong> is recommended for national-scale analyses or policy evaluations requiring administrative boundaries.</li> <li>The <strong>5-arcminute gridded data</strong> is preferable for spatially explicit modeling or subnational assessments.</li> </ul> <p>Both datasets maintain equivalent quality and methodological rigor; the choice depends on the desired spatial granularity and application context.</p>
Projected Global Fertilizers Consumption Datasets during 2020-2100 under SSP scenarios
<h1><strong>1. Background</strong></h1> <p>Accurate projections of future global fertilizer consumption are critical for advancing research in earth system modeling, agricultural sustainability, and fertilizer industry planning. However, existing datasets often lack long-term temporal coverage and high spatial resolution. To address this gap, we present the <strong>Projected Global Fertilizers Consumption Datasets (PGFCD)</strong>, which provide spatially explicit estimates of nitrogen (N), phosphorus (P), and potassium (K) fertilizer consumption from 2020 to 2100 under three Shared Socioeconomic Pathway (SSP) scenarios: <strong>SSP1</strong> (sustainable development), <strong>SSP2</strong> (intermediate development), and <strong>SSP3</strong> (regional rivalry), <strong>SSP4</strong> (unequal development), and <strong>SSP5</strong> (fossil-fueled development).</p> <p> </p> <h1><strong>2. Methodology</strong></h1> <h2><strong>2.1 Ensemble Machine Learning (EML) Framework</strong></h2> <ul> <li><strong>Algorithms</strong>: Integrated six machine learning models: <ul> <li>Multiple Linear Regression (MLR)</li> <li>Decision Trees (DT)</li> <li>Autoregressive Integrated Moving Average (ARIMA)</li> <li>Multi-Layer Perceptron (MLP)</li> <li>Radial Basis Function (RBF)</li> <li>Random Forests (RF)</li> </ul> </li> <li><strong>Training Data</strong>: Historical national/regional fertilizer consumption (FAOSTAT, 1961–2015).</li> <li><strong>Validation Metrics</strong>: <ul> <li>Nash-Sutcliffe Efficiency (NSE): <strong>0.93</strong></li> <li>Kling-Gupta Efficiency (KGE): <strong>0.89</strong></li> <li>Mean Absolute Percentage Error (MAPE): <strong>10.97%</strong></li> </ul> </li> </ul> <h2><strong>2.2 Spatial Downscaling</strong></h2> <ul> <li><strong>Baseline</strong>: FAO 2000 fertilizer data combined with gridded nutrient application maps for major crops in 2000.</li> <li><strong>Dynamic Projection</strong>: Annual change rates applied to 5′ × 5′ grids under SSP-specific socioeconomic drivers.</li> </ul> <p> </p> <h1><strong>3. Dataset Overview</strong></h1> <h2><strong>3.1 Key Features</strong></h2> <ul> <li><strong>Temporal Coverage</strong>: 2020–2100 (10-year intervals).</li> <li><strong>Spatial Resolution</strong>: 5-arcminute (≈10 km at the equator).</li> <li><strong>Scenarios</strong>: SSP1, SSP2, SSP3, SSP4, SSP5. </li> <li><strong>Variables</strong>: N, P, and K fertilizer consumption (tonnes/year).</li> </ul> <h2><strong>3.2 Dataset Structure</strong></h2> <p>The dataset is provided as a compressed archive (PGFCD_Ver6.0.rar), containing:</p> <p><strong>1. Fertilization_Consumption_GeoTiff</strong><strong>/</strong></p> <ul> <li><strong>Subfolders</strong>: <ul> <li>N_fer/: Nitrogen fertilizer projections</li> <li>P_fer/: Phosphorus fertilizer projections</li> <li>K_fer/: Potassium fertilizer projections</li> </ul> </li> <li><strong>File Format</strong>: GeoTIFF (135 files total). <ul> <li><strong>Naming Convention</strong>:<br>[FertilizerType]_fer_con_[SSP]_[Year].tif <ul> <li>Example: K_fer_con_SSP1_2020.tif</li> </ul> </li> </ul> </li> </ul> <p><strong>2. Country_region_based/</strong></p> <ul> <li><strong>Shapefiles</strong>: National/regional fertilizer consumption for 26 prediction units (2020–2100).</li> <li><strong>Excel File</strong>: Global Fertilizer Consumption (2020-2100).xlsx.</li> </ul> <p><strong>3. Technical Annex.docx</strong></p> <ul> <li>Detailed methodology, validation, and workflow documentation.</li> </ul> <p> </p> <h1><strong>4. Applications</strong></h1> <p>This dataset supports:</p> <ul> <li><strong>Earth System Modeling</strong>: Improved parameterization of fertilization impacts on biogeochemical cycles.</li> <li><strong>Agricultural Policy</strong>: Scenario-based planning for sustainable fertilizer use.</li> <li><strong>Industry Strategy</strong>: Long-term market analysis under diverse socioeconomic pathways.</li> </ul> <p> </p> <h1><strong>Note:</strong></h1> <p>Global aggregated totals of fertilizer consumption derived from the 26 prediction units (country/regional scale) may exhibit minor discrepancies compared to sums calculated from the 5-arcminute gridded data (≈10 km resolution). Such differences stem from variations in spatial aggregation methods, file formats (vector vs. raster), and underlying data processing frameworks. Users may select the dataset best aligned with their analytical objectives:</p> <ul> <li>The <strong>country/region-based data (26 units)</strong> is recommended for national-scale analyses or policy evaluations requiring administrative boundaries.</li> <li>The <strong>5-arcminute gridded data</strong> is preferable for spatially explicit modeling or subnational assessments.</li> </ul> <p>Both datasets maintain equivalent quality and methodological rigor; the choice depends on the desired spatial granularity and application context.</p> <p>We are profoundly indebted to <strong>Dr. Andreas Gericke</strong> at Section II 2.3 Protection of the Seas and Polar Regions, German Environment Agency, and <strong>Dr. Veronika Schlosser</strong> at Chair of Sustainability Assessment of Food and Agricultural Systems, Technical University of Munich for their diligent review and insightful feedback on the previously submitted data. Their expertise has enabled us to thoroughly correct the identified inaccuracies, strengthening the integrity of our research.</p> <p>We hold <strong>Dr. Andreas Gericke</strong> and <strong>Dr. Veronika Schlosser</strong> in the highest esteem and sincerely apologize for any oversights that may have marred our work.</p>
Global urban and rural settlement dataset from 2000 to 2020
<p>Data Update (v2.1): Added WGS84 coordinate-referenced datasets with longitude/latitude gridded partitions for localized access.</p>
Global Pasture Watch - Grassland reference samples based on visual interpretation of VHR imagery and harmonized datasets (2000–2024)
<p>Reference point samples used in the production of the <a href="https://doi.org/10.5281/zenodo.13890401">global maps of annual grassland class and extent for 2000—2022</a><strong> </strong>within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Wath</a> initiative. </p> <p>The reference samples (estabilished by Feature Space Coverage Sampling-FSCS) comprises <strong>2.3M points</strong> visually classified (<em>using Very High Resolution imagery</em>) in:</p> <ol> <li><strong>Cultivated grassland,</strong></li> <li><strong>Natural/semi-natural grassland</strong></li> <li><strong>Other land cover</strong></li> </ol> <p>The file <code>gpw_grassland_fscs.vi.vhr_tile.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> aggregates the samples by visual interpretation units ( 1x1 km) and includes the follow collumns:</p> <ul> <li>cluster_id: Cluster id defined by k-means (FSCS),</li> <li>cluster_distance: Distance from the sample tile to center of the cluster (FSCS),</li> <li>cluster_size: Size of cluster (strata) defined by the FSCS,</li> <li>priority: Priority used by the visual interpretation,</li> <li>tile_id: Sample tile id,</li> <li>imagery: VHR reference images used by the visual interpretation,</li> <li>min_year: Minimum of year covered by the reference samples,</li> <li>max_year: Maximum of year covered by the reference samples,</li> <li>n_years: Number of years covered by the reference samples,</li> <li>n_samples_c1: Number of reference samples for "Cultivated grass" (1),</li> <li>n_samples_c2: Number of reference samples for "Natural / Semi-natural grass" (2),</li> <li>n_samples_c3: Number of reference samples for "Open Shrubland" (2),</li> <li>n_samples_c4: Number of reference samples for "Not grass" (3),</li> <li>n_samples_all: Total number of reference samples,</li> </ul> <p>The file <code>gpw_grassland_fscs.vi.vhr_point.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> provides individual points (with 60-m spatial support) and include the follow collumns:</p> <ul> <li>sample_id: Sample id deribed by MD5 Hash of columns x, y, imagery and year,</li> <li>x: Longitude in WGS84 (EPSG:4326),</li> <li>y: Latitude in WGS84 (EPSG:4326),</li> <li>vi_tile_id: 1-km tile id,</li> <li>tile_id: GLAD tile id (1x1 degree)</li> <li>imagery: VHR Reference image used by the visual interpretation (Google; Bing; Interpolated),</li> <li>ref_date: Reference date of GPW samples (based on VHR image) and of other existing datasets,</li> <li>year: Reference year of GPW samples (based on VHR image) and of other existing datasets,</li> <li>class: Class id (1: Cultivated grassland; 2: Natural/semi-natural grassland; 3: Open shrubland; 4: Other land cover) ,</li> <li>class_label: Class labels (Cultivated grassland; Natural/semi-natural grassland; Open shrubland; Other land cover) ,</li> <li>dataset_name: Existing dataset names (CGLS-LC, EuroCrops, GeoWiki, GeoWiki-feedback, LCMap-Conus, LUCAS, MapBiomas, WorldCereal, GPW) <br>dataset_class: Original land cover class provided by the maintainer of existing dataset</li> <li>esa_worldcover_2020: Land cover class labels extracted from ESA WorldCover 2020,</li> <li>glad_glcluc_yyyy: Land cover class labels extracted from UMD GLAD GLCLUC for the reference date,</li> <li>glc_fcs30d_yyyy: Land cover class labels extracted from GLC_FCS30D for the reference date,</li> <li>gpw_fscs_cluster: K-Means output ranging from 0—9999 according to Feature Space Coverage Sampling (FSCS),</li> <li>ml_cv_group: spatial block CV group (based on vi_tile_id),</li> <li>ml_type: specify if the sample was used for (1) training or (2) calibration.</li> </ul> <p>The file <code>gpw_grassland_fscs.vi.vhr_grid.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> provides the grid samples (with 10-m spatial support) and include the follow collumns:</p> <ul> <li>tile_id: 1-km tile id,</li> <li>bing_class: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) defined using as reference Bing Maps Images,</li> <li>bing_image_start_date: Start date of the Bing Maps Images used in the visual interpretation,</li> <li>bing_image_end_date: End date of the Bing Maps Images used in the visual interpretation,</li> <li>google_class: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) defined using as reference Google Maps Images,</li> <li>google_image_start_date: Start date of the Google Maps Images used in the visual interpretation,</li> <li>google_image_end_date: End date of the Google Maps Images used in the visual interpretation,</li> <li>missing_image_date: No images available,</li> <li>same_image_bing_google: Images from the same date available in Google and Bing Maps.</li> </ul> <p>The dataset was produced through the <a href="https://plugins.qgis.org/plugins/qgis-fgi-plugin/">QGIS plugin Fast Grid Inspection</a>.</p> <h3>Related resources</h3> <ul> <li><strong>Maps of dominant grassland:</strong><br><a href="https://zenodo.org/records/13890400">2000-2002</a> <a href="https://zenodo.org/records/13890402">2003-2005</a> <a href="https://zenodo.org/records/13890404">2006-2008</a> <a href="https://zenodo.org/records/13890408">2009-2011</a> <a href="https://zenodo.org/records/13890410">2012-2014</a> <a href="https://zenodo.org/records/13890412">2015-2017</a> <a href="https://zenodo.org/records/13890414">2018-2020</a> <a href="https://zenodo.org/records/13890416">2021-2022</a></li> <li><strong>Probability maps of cultivated grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Probability maps of natural/semi-natural grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Grassland reference samples based on VHR imagery (2000–2022):</strong><br><a href="https://doi.org/10.5281/zenodo.11281157">GeoPackage files</a></li> <li><strong>Global machine learning models (Random Forest):</strong><br><a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li><strong>Reference sampling design derived by FSCV:</strong><br><a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li><strong>Harmonized reference samples based on existing LULC dataset:</strong><br><a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li><strong>Source code for reproducibility:<br></strong><a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a><strong><br></strong></li> <li><strong>Mapping feedback tool:</strong><br><a href="https://geo-wiki.org">GeoWiki</a></li> <li><strong>Data catalogues:</strong><br><a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> <h3>Support</h3> <p>For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a></p>
A 20-year (1998-2017) global sea surface dimethyl sulfide gridded dataset with daily resolution
<p>This dataset contains (1) the matched and binned data used for constructing an artificial neural network (ANN) ensemble model to simulate the sea surface concentration of dimethyl sulfide (DMS); (2) the simulated global daily sea surface concentrations of DMS ranging from 1998 to 2017 by ANN model and the calculated total transfer velocities (Kt) and sea-to-air fluxes. The input variables of this ANN ensemble model include chlorophyll <em>a</em>, sea surface temperature (SST), mixed layer depth (MLD), nitrate, phosphate, silicate, dissolved oxygen (DO), downward short-wave radiation (DSWF), and sea surface salinity (SSS). The spatial resolution of the simulated dataset is 1°×1°. The units of DMS concentration, Kt, and flux are nmol L–1, m d–1, and μmol S m–2 d–1, respectively.</p> <p><strong>Update Note</strong></p> <ol> <li>In Version 4.0 and earlier versions, the sea ice cover data (from the OISST dataset) used to calculate Kt and DMS flux contained certain time periods with completely missing values, which were incorrectly replaced with zeros. This led to a significant overestimation of Kt and DMS flux in polar regions where sea ice coverage exists. The missing data periods include: November 27–28, 2011; January 7–9, 2016; April 18 to June 30, 2016; and January 7 to February 28, 2017. This issue was resolved beginning with Version 5.0 through the use of updated sea ice data.</li> <li>Compared to Version 5.0, the current version introduces a correction to a bug in the DMS concentration simulation. When input data were missing—primarily in polar regions—the DMS concentration should have been flagged as missing. However, it was previously assigned a value of 1.6031 nM in Version 5.0. In the current version, these values are now replaced with -999 to indicate missing data.</li> </ol>
A global gridded CO2 flux dataset inferred from OCO-2 retrievals using the GONGGA inversion system (v2025)
<p><strong>Data Description</strong></p> <p>Here we provide a global monthly CO2 flux dataset at 1° × 1° spatial resolution for the period 2014.9-2024.12. The dataset is generated using the GONGGA (Global ObservatioN-based system for monitoring Greenhouse GAs) inversion system by assimilating OCO-2 (Observing Carbon Observatory 2) v11.2r column CO2 retrievals that scaled to the WMO X2019 standard. The dataset contains fluxes from biosphere (Net Ecosystem Exchange, NEE) (both prior and posterior), ocean (both prior and posterior), biomass burning emissions and fossil fuel emissions.</p> <p>We also provide the posterior model simulated values corresponding to all measurements contained in the lastest release of NOAA’s ObsPack database (obspack_co2_1_GLOBALVIEWplus_v10.1_2024-11-13 and obspack_co2_1_NRT_v10.1_2025-02-07).</p> <p><strong>Change from v2024</strong></p> <ul> <li>Assimilation of OCO-2 v11.2r retrievals</li> <li>Update of prior fluxes</li> </ul> <p><strong>Data version specification</strong></p> <p>v202x.ori refers to original GONGGA flux data with 3-hourly time resolution and 2° latitude × 2.5° longitude spatial resolution, v202x refers to GONGGA flux data resampled to monthly time resolution and 1° latitude × 1° longitude spatial resolution for facilitating comparisons with other GCP inversion results.</p> <p><strong>Article citation</strong></p> <p>Jin, Z., Wang, T., Zhang, H., Wang, Y., Ding, J., Tian, X., Constraint of satellite CO2 retrieval on the global carbon cycle from a Chinese atmospheric inversion system. Science China Earth Sciences, 2023, 66: 609-618, doi: 10.1007/s11430-022-1036-7.</p> <p>Jin, Z., Tian, X., Wang, Y., Zhang, H., Zhao, M., Wang, T., Ding, J., and Piao, S.: A global surface CO2 flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system, Earth System Science Data, 2024, 16: 2857-2876, doi: 10.5194/essd-16-2857-2024.</p>
GLAB-VOD: Global L-band AI-Based Vegetation Optical Depth Dataset Based on Machine Learning and Remote Sensing
<p>GLAB VOD is a Global L-band Ai-Based vegetation optical depth dataset with 18-day temporal and 25 km spatial resolution, covering 2002 to 2020. The dataset is created using a neural network with SMOS-SMAP-INRAE-BORDEAUX (SMOSMAP-IB) VOD product as a target (over 2015-2020) and brightness temperatures (TB) from the SMOS, AMSR-E, and AMSR-2 spaceborne missions alongside with a novel soil moisture dataset (CASM) as inputs. The GLAB-VOD dataset was created using a recently developed methodology previously used to create a long-term consistent soil moisture dataset CASM, adapted to the VOD retrievals. First, the TB and VOD signals were divided into fixed seasonal cycle and residuals, where the residual part of the signal contains sub-seasonal periodic signals, trends, extremes, and noise. Then, a multi-staged neural network training scheme was used to achieve internally consistent predictions by merging data from different sources without introducing biases or compromising data distribution. A side-product of this project is GLAB TB - a global long-term brightness temperature dataset that matches SMOS TB quality and spawns back to 2002. GLAB TB has daily temporal resolution and 25 km spatial resolution. </p>
Global Surface Ozone Concentration Dataset 1990-2017 Mapped at Fine Resolution through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output
<p>This global surface ozone concentration dataset corresponds to the data developed in this paper:</p> <p>DeLang, M. N., J. S. Becker, K.-L. Chang, M. L. Serre, O. R. Cooper, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, S. Cleland, E. Collins, M. Brauer, and J. J. West (2021) Mapping yearly fine resolution global surface ozone through the Bayesian Maximum Entropy data fusion of observations and model output for 1990-2017, <em>Environmental Science & Technology</em>, 55, 4389-4398, doi: 10.1021/acs.est.0c07742.</p> <p>Ozone concentrations are estimated as described in the paper, with output shown for the Ozone Season Daily Maximum 8-hr metric (OSDMA8) for each year between 1990 and 2017, at 0.1 degree spatial resolution. Ozone is estimated through data fusion of output from several global models, with observations of ozone collected by TOAR. The data fusion involves application of the M3Fusion method to create a multi-model composite of several global models, followed by BME data fusion, as described in the paper. </p> <p>The *.nc file contains the latitude, longitude, ozone concentration estimate, and estimated variance for each 0.1 x 0.1 degree grid cell.</p> <p>Please contact Jason West (jasonwest@unc.edu) with questions about the dataset. We'd like to hear from you to know how you're using the data!</p> <p> </p> <p> </p>
Global soil type dataset for WRF-ARW model, based on HWSD version 2
<p>Global soil type dataset, based on HWSD ("Harmonized World Soil Database", version 2.0), suitable for meteorological model WRF-ARW.</p> <ul> <li>spatial resolution: 30 arc seconds by 30 arc seconds (about 1km)</li> <li>original data (HWSD 2.0) <ul> <li><a href="https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_RASTER.zip">https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_RASTER.zip</a></li> <li><a href="https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_DB.zip">https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_DB.zip</a></li> <li>https://gaez.fao.org/pages/hwsd</li> <li>documentation: Nachtergaele, Freddy, et al. Harmonized world soil database version 2.0. Food and Agriculture Organization of the United Nations, 2023. https://www.fao.org/3/cc3823en/cc3823en.pdf</li> </ul> </li> <li>the original 7 soil layers (0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm, 80–100 cm, 100–150 cm and 150–200 cm) have been remapped to the 2 layers required by WRF (topsoil 0-30 cm, botsoil 30-200 cm)</li> <li>the original Soil Mapping Units (SMU) have been remapped to the 16 soil categories used by WRF: <ul> <li>the depth-weighted averages of the content of clay, silt and sand lead to 12 texture-based categories (Sand, Loamy sand, Sandy loam, Silt loam, Silt, Loam, Sandy clay loam, Silty clay loam, Clay loam, Sandy clay, Silty clay, Clay), as defined by USDA;</li> <li>category "Organic material" is assigned where the average content of organic carbon exceeds the threshold of 25%;</li> <li>where the content of clay, silt and sand is not defined, HWSD special categories are mapped to the WRF last 3 categories, as follows: <ul> <li>"Water bodies" to "Water", </li> <li>"Rock outcrops" and "Rocky sublayers" to "Bedrock", </li> <li>"Land ice and glaciers", "Dunes/shifting sands", "Salt flats", and "Other" to "Other"</li> </ul> </li> </ul> </li> </ul> <p>The dataset is provided in three ways:</p> <ol> <li>two global files (SoilType_depth<T>to<B>cm.tif), one for each layer; format is GeoTIFF, compatible with <a href="https://github.com/openwfm/convert_geotiff" target="_blank" rel="noopener"><em>convert_geotiff</em></a>, a commandline utility for converting data from GeoTIFF to geogrid format used by WRF;</li> <li>16 tiles, 8 for each layer, each covering 90 degrees by 90 degrees (SoilType_depth<T>to<B>cm_lon<W>to<E>deg_lat<S>to<N>deg.tif); format is GeoTIFF;</li> <li>two compressed folders, hwsd_toplayer.zip and hwsd_bottomlayer.zip, each including 648 tiles in binary format and an "index" ASCII file, following the Geogrid data format and naming convention, as described <a href="https://www2.mmm.ucar.edu/wrf/users/tutorial/presentation_pdfs/202101/duda_wps_advanced.pdf">here</a>.</li> </ol> <p>Soil categories are coded as follows</p> <table> <tbody> <tr> <td><strong>code</strong></td> <td><strong>category</strong></td> </tr> <tr> <td>1</td> <td>sand</td> </tr> <tr> <td>2</td> <td>loamy sand</td> </tr> <tr> <td>3</td> <td>sandy loam</td> </tr> <tr> <td>4</td> <td>silt loam</td> </tr> <tr> <td>5</td> <td>silt</td> </tr> <tr> <td>6</td> <td>loam</td> </tr> <tr> <td>7</td> <td>sandy clay loam</td> </tr> <tr> <td>8</td> <td>silty clay loam</td> </tr> <tr> <td>9</td> <td>clay loam</td> </tr> <tr> <td>10</td> <td>sandy clay</td> </tr> <tr> <td>11</td> <td>silty clay</td> </tr> <tr> <td>12</td> <td>clay</td> </tr> <tr> <td>13</td> <td>organic material</td> </tr> <tr> <td>14</td> <td>water</td> </tr> <tr> <td>15</td> <td>bedrock</td> </tr> <tr> <td>16</td> <td>other</td> </tr> </tbody> </table> <p> </p>
Global River Discharge Reanalysis dataset (GRDR)
<p>The Global River Discharge Reanalysis dataset (GRDR) is a global river discharge product that contains daily flows in ~2.9 million vectorized river reaches for 1984-2018.</p> <p>For more details about GRDR, please refer to the following publication: Feng D. and Gleason C.J., 2024, More flow upstream and less flow downstream: The changing form and function of global rivers, <em>Science</em></p>
Source Data and ambient ozone dataset generated in "Substantially underestimated global health risks of current ozone pollution"
<p>Existing assessments might have underappreciated ozone-related health impacts worldwide. Here our study assesses current global ozone pollution using the high-resolution (0.05°) estimation from a geo-ensemble learning model, with key focuses on population exposure and all-cause mortality burden. Our model demonstrates strong performance, achieving a mean bias of less than -1.5 parts per billion against in-situ measurements. We estimate that 66.2% of the global population is exposed to excess ozone for short term (> 30 days per year), and 94.2% suffers from long-term exposure. Furthermore, severe ozone exposure levels are observed in Cropland areas, particularly over Asia. Importantly, the all-cause ozone-attributable deaths significantly surpass previous recognition from specific diseases worldwide. Notably, mid-latitude Asia (30°N) and the western United States show high mortality burden, contributing substantially to global ozone-attributable deaths. Our study highlights current significant global ozone-related health risks and may benefit the ozone-exposed population in the future.</p>
Global monthly sectoral water withdrawal and allocation datasets (QUAlloc, water use and allocation model) at 10 km spatial resolution
<p>Output data of water withdrawals and water allocation per water source from the sectoral water use and allocation model (QUAlloc).</p> <p>Dataset properties:</p> <ul> <li>spatial resolution: 10 km (global-scale)</li> <li>temporal resolution: monthly time-step</li> <li>period: 1980 - 2019</li> <li>units: m3/month</li> </ul> <p>Output datasets:<br> <data_type>_<sector_name>_allocated_to_<source_type>_monthlyTot_1980_2019.nc</p> <ul> <li><data_type><br> <ul> <li>"withdrawal": refers to the water that is withdrawn at a water source level to satisfy the demands within an allocation zone</li> <li>"demand": refers to the withdrawn water that is supplied to each location (cell) where there are demands to satisfy</li> </ul> </li> <li><sector_name> <ul> <li>"domestic"</li> <li>"irrigation"</li> <li>"livestock"</li> <li>"manufacture"</li> <li>"thermoelectric"</li> </ul> </li> <li><source_type> <ul> <li>"renewable_surfacewater": refers to water obtained from the surface water system components (e.g., direct runoff, base flow, interflow, etc.)</li> <li>"renewable_groundwater": refers to water obtained from aquifers that are recharged by percolation from the upper soil layers</li> <li>"nonrenewable_groundwater": refers to water obtained from aquifers not replenished on a human time scale</li> </ul> </li> </ul> <p>The sectoral water use and allocation model used, QUAlloc, can be found at: https://github.com/SustainableWaterSystems/QUAlloc.</p>
Global Surface Ozone Concentration Dataset 1990-2017 Generated by Bayesian Maximum Entropy Data Fusion With RAMP Bias Correction
<p>This dataset reports estimates of surface ozone concentration at fine spatial resolution for 1990 to 2017, at 0.5 degree horizontal resolution. Also reported is the variance. Estimates correspond to this paper:</p> <p><span>Becker, J. S.</span><span>, DeLang, M. N., K.-L. Chang, M. L. Serre, O. R. Cooper, <u>H. Wang</u>, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, M. Brauer, and <span>J. J. West</span> (2023) Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration, <em>Elementa Science of the Anthropocene</em>, 11: 1, doi: 10.1525/elementa.2022.00025.</span></p> <p>The dataset reports estimates of surface ozone for the OSDMA8 metric (the 6-month ozone-season average of the daily maximum 8-hr concentration), estimated through a data fusion of ozone observations from the Tropospheric Ozone Assessment Report (TOAR) database, and output from multiple global atmospheric models. Estimates are created in each year by a combination of M3Fusion to create a multi-model composite, Regional Air Quality Model Performance (RAMP) regional and nonlinear bias correction, and Bayesian Maximum Entropy (BME) data fusion in space and time. The estimates here are the final results using a weighted RAMP bias correction. </p>
PEATGRIDS: Mapping global peat thickness and carbon stock via digital soil mapping approach, dataset
<p>PEATGRIDS: a dataset containing the first peat thickness and carbon stock maps estimated over peatlands area across the globe at ~1 km x ~1 km resolution. Carbon stock was calculated across all depths of the predicted peat thickness, multiplied by peat bulk density (BD) and carbon content (CC) across five depths: 0-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. Mapping effort was performed using quantile random forest regression based on remotely sensed data and environmental covariates, including topography, climate, soil properties, and land cover. The maps cover areas potentially as peatlands according to the UNEP's global peatland map obtained from the <a title="Global Peat Database" href="https://greifswaldmoor.de/global-peatland-database-en.html" target="_blank" rel="noopener">Global Peat Database</a>. We may update this dataset in the future, please consider using the latest version. </p> <p>Note: This version (2.0.1) clarifies the metric units for carbon stock per area in the previous version (2.0). </p>
A 1 km global cropland dataset from 10000 BCE to 2100 CE
<p>This dataset is the 1 km global cropland dataset from 10000 BCE to 2100 CE. It contains a total of 131 global cropland maps at 1 km resolution from past to future. The time-step intervals are 1000 years for 10000 BCE-1 CE, 100 years for 1 CE-1700 CE, and 10 years for 1700 CE-2100 CE. After 2010 CE, eight future SSP-RCP scenarios are provided. The map values indicate the proportion of cropland within 1×1 km grid cell.</p> <p>This dataset can also be viewed online at <a href="https://cbw.users.earthengine.app/view/globalcroplanddataset">https://cbw.users.earthengine.app/view/globalcroplanddataset</a></p> <p><strong>Citations:</strong></p> <p>When using this dataset, please cite both the dataset and the following data description article:</p> <p><em>Cao, B., Yu, L., Li, X., Chen, M., Li, X., Hao, P., and Gong, P.: A 1 km global cropland dataset from 10 000 BCE to 2100 CE, Earth Syst. Sci. Data, 13, 5403–5421, https://doi.org/10.5194/essd-13-5403-2021, 2021. </em></p>
Global Reservoir Evaporation Dataset
<p>This dataset contains the monthly evaporation rate and volumes for 7242 reservoirs from March 1984 to December 2016 across the world. The evaporation rate was calculated using the three datasets viz. (1) TerraClimate; (2) ERA5; (3) Princeton Global Forcings. The surface area of these reservoirs is obtained from the Global reservoir surface area dataset (GRSAD). The detailed descriptions for this dataset are presented in Tian et al (2021,2022). The basic information of the global reservoirs was provided by the Global Reservoir and Dam Database (GRanD).<br> When using the data, please cite the following references:<br> Tian, W., Liu, X., Wang, K., Bai, P., Liu, C., & Liang, X. (2022). Estimation of Global Reservoir Evaporation Losses. Journal of Hydrology, 127524.<br> Tian, W., Liu, X., Wang, K., Bai, P., & Liu, C. (2021). Estimation of reservoir evaporation losses for China. Journal of Hydrology, 596, 126142.</p>
Copernicus Digital Elevation Model (DEM) for Europe at 3 arc seconds (ca. 90 meter) resolution derived from Copernicus Global 30 meter DEM dataset
<p>Overview:<br> The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.</p> <p>The Copernicus DEM for Europe at 3 arcsec (0:00:03 = 0.00083333333 ~ 90 meter) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).</p> <p>Processing steps:<br> The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in <a href="https://gdal.org/drivers/raster/vrt.html">VRT</a> format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized:</p> <p><code>gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt </code></p> <p>In order to reduce the spatial resolution to 3 arc seconds, weighted resampling was performed in GRASS GIS (using <code>r.resamp.stats -w</code> and the pixel values were scaled with 1000 (storing the pixels as integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using <code>r.relief</code>, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.</p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 3 arc seconds (approx. 90 m)</p> <p>Pixel values:<br> meters * 1000 (scaled to Integer; example: value 23220 = 23.220 m a.s.l.)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)</p> <p>Original dataset license:<br> <a href="https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf">https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
Copernicus Digital Elevation Model (DEM) for Europe at 30 arc seconds (ca. 1000 meter) resolution derived from Copernicus Global 30 meter DEM dataset
<p>Overview:<br> The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.</p> <p>The Copernicus DEM for Europe at 30 arcsec (0:00:30 = 0.0083333333 ~ 1000 meter) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).</p> <p>Processing steps:<br> The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in <a href="https://gdal.org/drivers/raster/vrt.html">VRT</a> format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized:</p> <p><code>gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt </code></p> <p>In order to reduce the spatial resolution to 30 arc seconds, weighted resampling was performed in GRASS GIS (using <code>r.resamp.stats -w</code> and the pixel values were scaled with 1000 (storing the pixels as integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using <code>r.relief</code>, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.</p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 30 arc seconds (approx. 1000 m)</p> <p>Pixel values:<br> meters * 1000 (scaled to Integer; example: value 23220 = 23.220 m a.s.l.)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)</p> <p>Original dataset license:<br> <a href="https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf">https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p> </p>
Copernicus Digital Elevation Model (DEM) for Europe at 1000 meter resolution (EU-LAEA) derived from Copernicus Global 30 meter DEM dataset
<p>Overview:<br> The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.</p> <p>The Copernicus DEM for Europe at 1000 meter resolution (EU-LAEA projection) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).</p> <p>Processing steps:<br> The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in <a href="https://gdal.org/drivers/raster/vrt.html">VRT</a> format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized:</p> <p><code>gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt </code></p> <p>In order to reproject the data to EU-LAEA projection while reducing the spatial resolution to 1000 m, bilinear resampling was performed in GRASS GIS (using <code>r.proj</code> and the pixel values were scaled with 1000 (storing the pixels as Integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using <code>r.relief</code>, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.</p> <p>Projection + EPSG code:<br> ETRS89-extended / LAEA Europe (EPSG: 3035)</p> <p>Spatial extent:<br> north: 6874000<br> south: -485000<br> west: 869000<br> east: 8712000</p> <p>Spatial resolution:<br> 1000 m</p> <p>Pixel values:<br> meters * 1000 (scaled to Integer; example: value 23220 = 23.220 m a.s.l.)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.proj; r.relief)</p> <p>Original dataset license:<br> <a href="https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf">https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</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.