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387 results for “global map”
Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)
<h2><strong>Sub-dataset: Dominant grassland class, 2000-2002</strong></h2> <h2>Description</h2> <p>Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least <strong>30% of dry or wet low vegetation</strong>, dominated by grasses and forbs (less than 3 meters) and a:</p> <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> <p>The grassland extent is classified into two classes:</p> <ul> <li><strong>Cultivated grassland</strong>: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li><strong>Natural/semi-natural grassland</strong>: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li><strong>Open shrubland (v2-beta): </strong>Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> <p>The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including:</p> <ul> <li><strong>Probabilities</strong> of cultivated grassland (values range from 0–100),</li> <li><strong>Probabilities</strong> of natural/semi-natural grassland (values range from 0–100), and</li> <li><strong>Probabilities</strong> of open shrubland (values range from 0–100), and</li> <li><strong>Dominant</strong> class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> <p>All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention:</p> <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>) and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li><strong>Maps of dominant grassland:</strong><br><a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li><strong>Probability maps of cultivated grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (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">2000-2024 (All URLs)</a></li> <li> <div><strong>Grassland reference samples based on VHR imagery (2000–2024):</strong><br><a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </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> <p><strong>Support</strong></p> <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>
Map data of historical global estimates of soil respiration
<p>The map data of global soil respiration converted to NetCDF format.</p><p>All open access available estimates were collated.</p><p>Shoji Hashimoto, Akihiko Ito, Kazuya Nishina (2023) "Divergent data-driven estimates of global soil respiration". Communications Earth & Environment, 4 Article number: 460</p><p><a href="https://doi.org/10.1038/s43247-023-01136-2 ">https://doi.org/10.1038/s43247-023-01136-2</a> </p><p>Refer to Table 1 for the study ID and data source or the attributions of the NetCDF file. </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>
Mapping the global distribution of C4 vegetation using observations and optimality theory
<p>This dataset includes annual C4 vegetation distribution and its uncertainty from 2001 to 2019. We also provide the distribution of C4 natural grasses and C4 crops during the same period, as well as the code and interim dataset to generate the main figures. Please refer to manuscript for more details:</p> <p>Luo, X., Zhou, H., Satriawan, T.W., Tian, J., Zhao, R., Keenan, T.F., Griffith, D. M., Sitch, S. Smith, N.G. & Still, C.J. (2024). Mapping the global distribution of C4 vegetation using observations and optimality theory. <em>Nature Communications.</em> https://doi.org/10.1038/s41467-024-45606-3.</p> <p><strong>Update (Nov 2023): </strong>we have updated the observational constraint from a linear model to a non-linear model - logistic curve, to better depict how C4 photosynthetic advantage translates into C4 grass coverage changes (C4_distribution_NUS_v2.2.nc).</p> <p><strong>Update (August 2023): </strong>we corrected the issue caused by a bias in the remote sensing grassland base map, and released the version 2 of the C4 vmap (C4_distribution_NUS_v2.nc).</p> <p><strong>Update (June 2023): </strong>we noticed there is a critical issue in the version 1 of our C4 map, due to the quality of remote sensing grassland base map used. We are now working on providing a new version (V2) in the next few months (Jun 2023).</p>
Parsimonious machine learning for the global mapping of aboveground biomass density
<p>This repository hosts data and code presented in the article "Parsimonious machine learning for the global mapping of aboveground biomass potential". The repository contains a compressed file containing all the code needed to reproduce the methodology that we developed and to analyse its results. We did not upload all the temporary and intermediate data files that are created during the execution of the method. We rather uploaded "milestone" data, i.e. final results or important intermediate ones. This includes the final training dataset, model calibration data, the final trained model, the global data for prediction, the final global map of potential aboveground biomass density (AGBD) at present times (raster files at 1km2 and 10km2 resolution), maps depicting regions where climatic conditions are outside of the training range of positive AGBD instances and maps depicting world regions without trees. </p> <p><strong>Files:</strong></p> <p><a href="../api/records/11580414/draft/files/code.zip/content" target="_blank" rel="noopener noreferrer">code.zip</a> : Compressed directory with all the code needed to reproduce the methodology presented in the manuscript. Contains a README file. Also contains temporary data generated in the process, the training dataset, the trained model, and model calibration data.</p> <p><a href="../api/records/11580414/draft/files/potential_AGBD_Mgha_1km2_contemporary_climate.tif/content" target="_blank" rel="noopener noreferrer">potential_AGBD_Mgha_1km_present_climate_1980_2010.tif</a> : the predicted global potential AGBD under contemporary climate conditions and at a resolution of 1 squared kilometer.</p> <p><a href="../api/records/11580414/draft/files/potential_AGBD_Mgha_1km2_contemporary_climate.tif/content" target="_blank" rel="noopener noreferrer">potential_AGBD_Mgha_10km_</a><a href="../api/records/11580414/draft/files/potential_AGBD_Mgha_1km2_contemporary_climate.tif/content" target="_blank" rel="noopener noreferrer">present_climate_1980_2010.tif</a> : the predicted global potential AGBD under contemporary climate conditions downsampled at a resolution of 10 squared kilometers.</p> <p><a href="../api/records/11580414/draft/files/potential_AGBD_Mgha_1km2_contemporary_climate.tif/content" target="_blank" rel="noopener noreferrer">potential_AGBD_Mgha_10km_model_difference.tif</a> : the difference between our prediction of potential AGBD and the prediction from a complex state-of-the-art model from Walker et al. (2022). </p> <p><a href="../api/records/11580414/draft/files/potential_AGBD_Mgha_1km2_contemporary_climate.tif/content" target="_blank" rel="noopener noreferrer">potential_AGB_Mg_1km_</a><a href="../api/records/11580414/draft/files/potential_AGBD_Mgha_1km2_contemporary_climate.tif/content" target="_blank" rel="noopener noreferrer">present_climate_1980_2010.tif</a> : the predicted global potential pixel-level AGB under contemporary climate conditions downsampled at a resolution of 1 squared kilometers.</p> <p><a href="../api/records/11580414/draft/files/number_predictors_out_of_range.zip/content" target="_blank" rel="noopener noreferrer">number_predictors_out_of_range.zip</a> : tiled maps representing the number of climatic predictors outside of the training range before including 0 AGBD instances in the training dataset. </p> <p><a href="../api/records/11580414/draft/files/tree_absence_map.zip/content" target="_blank" rel="noopener noreferrer">tree_absence_map.zip</a> : tiled maps representing world regions without trees. Based on Crowther et al. (2015) (https://elischolar.library.yale.edu/yale_fes_data/1/).</p> <p><a href="../api/records/11580414/draft/files/potential_agbd_Mgha_climate_envelope.pkl/content" target="_blank" rel="noopener noreferrer">inference_pipeline_potential_agbd_Mgha_climate.pkl</a> : Calibrated model for the prediction of potential AGBD given bioclimatic conditions. </p> <p><a href="../api/records/11580414/draft/files/predictors_data_global.zip/content" target="_blank" rel="noopener noreferrer">predictors_data_global.zip</a> : Global predictors data to apply the model on.</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>
Teaser Local Climate Zone maps extracted from the global map of Local Climate Zones.
<p>Local Climate Zone teaser maps for the 15 largest functional urban areas stratified by urban ecoregion. The data is extracted from the global 100 m spatial resolution LCZ map, and is used to plot Figures 5, 6 and 7 of Demuzere et al. (2022a). The full global LCZ map is available via Demuzere et al. (2022b).<br> <br> See readme.txt for more details.</p> <p><em>Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., Vliet, J. van, & Bechtel, B. (2022a). A global map of Local Climate Zones to support earth system modelling and urban scale environmental science. Earth Syst. Sci. Data Discuss.<br> Demuzere M, Kittner J, Martilli A, et al. Global map of Local Climate Zones. Zenodo. 2022b. doi:10.5281/zenodo.6364594</em></p>
Second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'
<p>This is the second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'.</p> <p>The paper was published in Geophysical Research Letters. We provide the data that has been smoothed by moving filter and not. The data can be loaded by the <em>raster </em>package in <em>R.</em> Note that the unit is ppmv.</p> <p>Please note that both of these files must be in the same directory to open in <em>R</em> properly<em>.</em></p> <p>Please get in touch with the authors if you have any issues, email: xliu21@smail.nju.edu.cn or wanghk@nju.edu.cn</p>
Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data
<p><strong>Overview:</strong> The Global Inundation Extent from Multi-Satellites (GIEMS; Prigent et al. 2007, Papa et al. 2010) downscaled at 15 arc-second (GIEMS-D15; Fluet-Chouinard et al. 2015) was produced through the downscaling of the GIEMS database (natively at 0.25°). The downscaling procedure predicts the location of surface water cover with an inundation ranking surface generated by bagged decision trees. The decision trees were trained on binary presence/absence of wetland in the GLC2000 global land cover map (Bartholomé & Belward 2005) and used 13 topographic and hydrographic predictors derived from the SRTM-derived HydroSHEDS database (Lehner, Verdin & Jarvis 2008). The downscaling technique to three temporal aggregation of the GIEMS dataset representing three states of land surface inundation extents: mean annual minimum (MA<sub>Min</sub>; total area, 6.5 × 106 km<sup>2</sup>), mean annual maximum (MA<sub>Max</sub>; 12.1 × 106 km<sup>2</sup>), and long-term maximum (LT<sub>Max</sub>; 17.3 × 106 km<sup>2</sup>). The area of MAMin and MAMax from GIEMS were supplemented with the minimum area value from lakes, river and reservoirs from GLWD (Lehner & Döll 2004; classes 1,2,3). LTMax was corrected as the mean area from 3-year rolling maximum from GIEMS and the total wetland area from GLWD (classes 1-12). The accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates. Yet, a comparison against independent regional wetland maps showed adequate agreement over large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than originally offered by GIEMS, allowing for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems.</p> <p><strong>Projection:</strong> WGS84 (EPSG:4326)</p> <p><strong>Geographic extent:</strong></p> <ul> <li>Longitude: -180° to 180°</li> <li>Latitude: -56° to 84°</li> </ul> <p><strong>Spatial resolution: </strong>15 arc-second (500m at equator)</p> <p><strong>Legend</strong> (for discrete pixel values):</p> <ul> <li>0 = Upland</li> <li>1 = Mean Annual Minimum (MA<sub>Min</sub>)</li> <li>2 = Mean Annual Maximum (MA<sub>Max</sub>)</li> <li>3 = Long Term Maximum (LT<sub>Max</sub>)</li> </ul>
Global map of elastic thickness on Venus
<p>This map is Figure 14 from Anderson, F. S., and S. E. Smrekar (2006), Global mapping of crustal and lithospheric thickness on Venus, J. Geophys. Res., 111, E08006, doi:10.1029/2004JE002395. The location labels have been removed. Estimates of elastic thickness have an error of ±10 to 15 km. Caveats in Anderson and Smrekar (2006) should be carefully understood prior to use. No value of elastic thickness was obtained in areas in white. </p>
Daily maps for global daylight length at 30 arc seconds resolution (2022)
<p>Overview:<br> Daily maps for global daylight length, calculated for the year 2022.</p> <p>Processing steps:<br> For each day within the year 2022, the photoperiod (sunshine hours on flat terrain) are calculated using the SOLPOS algorithm developed by the National Renewable Energy Laboratory (NREL), USA. Resultant values have been converted from hours to minutes.</p> <p>File naming scheme (DDD = day within year) (min is abbreviation for minute):<br> <code>daylight_min_2022_DDD.tif</code></p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 90<br> south: -90<br> west: -180<br> east: 180</p> <p>Spatial resolution:<br> 30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br> Daily</p> <p>Pixel values:<br> unit: minutes</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.2.0</p> <p>License: CC-BY-SA 4.0</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: National Renewable Energy Laboratory (NREL): SOLPOS 2.0 sun position algorithm (https://www.nrel.gov/grid/solar-resource/solpos.html)</p>
Global map of Martian fluvial systems
<p>This dataset represents an update of previous global maps of Martian fluvial systems. We included all the valleys longer than 20 km and mapped them as vector-based polylines within the QGIS software, using the more recent and, to date, the best resolution THEMIS (Thermal Emission Imaging Spectrometer) daytime IR mosaic (100 m/pixel). In addition, we used, where necessary (for small-scale systems and valleys with high erosion), CTX (Contex Camera) data, with a resolution up to 6 m/pixel. The imagery data were coupled with the MOLA (Mars Orbiter Laser Altimeter Mosaic) mosaic which has a spatial resolution of 463 m/pixel. At low latitudes, we used an equidistant cylindrical projection, while at high latitudes, we used sinusoidal and polar stereographic projections to represent and analyze the data. Topographic information and data of higher image quality (new THEMIS mosaic plus CTX data) than those of previous manual maps, allowed us to identify new structures and more tributaries for a large number of systems. An attribute table is associated to our dataset including useful information such as coordinates, total length and an approximative maximum age indication for each system. The latter has been obtained coupling our map with the global geologic map of Tanaka et al. (2014) which represents, to date, the most accurate dating of the planet surface.</p> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite this DOI:<br> 10.5281/zenodo.1051038<br> <br> Please also cite these works: </p> <p>Alemanno et al.: 2018, Global Map of Martian Fluvial Systems: Age and Total Eroded Volume Estimations, Earth and Space Science Journal, 5, 560-577, doi:<a href="https://doi.org/10.1029/2018EA000362">https://doi.org/10.1029/2018EA000362</a><br> Orofino et al.: 2018, Estimate of the water flow duration in large Martian fluvial systems. Planetary and Space Science Journal, 163, 83-96. doi: <a href="https://doi.org/10.1016/j.pss.2018.06.001">10.1016/j.pss.2018.06.001</a><br> Alemanno G.: 2018, Study of the fluvial activity on Mars through mapping, sediment transport modelling and spectroscopic analyses. PhD dissertation thesis, <a href="https://arxiv.org/abs/1805.02208">arXiv:1805.02208</a> [astro-ph.EP].</p>
DEM Intercomparison eXercise (DEMIX) - Maps of completeness criteria scores for global DEMs
<h2>Introduction</h2> <p>This introduction gives a brief overview of the context in which the dataset has been produced. Readers curious about the detailed standards and procedures described in this section are encouraged to open the resources linked to this dataset.</p> <h3>The Digital Elevation Model Intercomparison eXercise (DEMIX)</h3> <p>This work is part of the Digital Elevation Model Intercomparison eXercise (DEMIX), initiated by the <a href="https://ceos.org/ourwork/workinggroups/wgcv/current-activites/#:~:text=DEMIX%3A%20Digital%20Elevation%20Model%20Intercomparison,elevation%20model%20for%20their%20application.">Committee on Earth Observation Satellites (CEOS)</a>. This initiative aims at "<a href="https://isprs-archives.copernicus.org/articles/XLIII-B4-2021/395/2021/">providing harmonised terminology and methods, as well as practical guidelines and results allowing the intercomparison of continental or global Digital Elevation Models (DEM)</a>" (Strobl et al., 2021). Several publications have defined the framework of DEMIX, from <a href="https://doi.org/10.3390/rs13183581">the terminology and definitions</a> (Guth et al., 2021) to the <a href="https://doi.org/10.1109/TGRS.2024.3368015">DEM ranking methods</a> (Bielski et al., 2024). An additional methodology paper has been publicated regarding the assessment of <a href="https://doi.org/10.3390/ijgi13030096">planimetric displacements between DEMs</a> (Riazanoff et al., 2024), which are a common source of biases in DEM comparisons.</p> <h3>The DEMIX grid</h3> <p>Studies performed within the DEMIX framework rely on the <a href="../records/7504791">DEMIX grid</a> (Guth et al., 2023), a geodetic grid (EPSG:4326) dividing the world in areas of approximately 10x10km. These standard areas are called DEMIX tiles, and can be precisely located thanks to their identifier.</p> <h3>Criteria and scores</h3> <p>Within DEMIX, several criteria have been defined to assess the quality of DEMs. These criteria take as input a DEM and a DEMIX tile, and provide as output the score of the DEM for this specific tile. Repeating this process over several DEMs and DEMIX tiles of interest allow for a comparison of scores, leading to a ranking of DEMs. <a href="https://doi.org/10.1109/TGRS.2024.3368015">DEMIX rankings are based on the Randomized Complete Block Design (RCBD)</a> (Bielski et al., 2024).</p> <h2>This dataset</h2> <p>This dataset is composed of global maps of one map per (DEM, criterion) tuple. Each GeoTIFF map can be superimposed with the <a href="../records/7504791">DEMIX grid</a> (Guth et al., 2023) in a GIS (tested in QGIS 3.16).</p> <h3>Completeness criteria</h3> <p>The completeness criteria have originally been defined by Peter Strobl. A brief description of each criterion is given in the next table. Please see the column "Original document" and files of this repository for the complete definitions.</p> <table> <tbody> <tr> <td><strong>Criterion</strong></td> <td><strong>Description</strong></td> <td><strong>Requirements</strong></td> <td><strong> Original document</strong></td> </tr> <tr> <td>A01 - Product fractional cover</td> <td>Fraction of a DEMIX tile <strong>covered</strong> by the DEM product</td> <td>None</td> <td>See document "DEMIX_CDD-A01_20211103.docx"</td> </tr> <tr> <td>A02 - Valid data fraction</td> <td>Fraction of a DEMIX tile <strong>covered</strong> by <strong>valid </strong>pixels of the DEM product</td> <td>"No data" or "void" value in metadata</td> <td>See document "DEMIX_CDD-A02_20211103.docx"</td> </tr> <tr> <td>A03 - Primary data fraction</td> <td>Fraction of a DEMIX tile <strong>covered </strong>by <strong>valid </strong>pixels generated from the <strong>main source of data</strong> of the DEM product</td> <td>"No data" or "void" value in metadata + source data/editing mask</td> <td>See document "DEMIX_CDD-A03_20211103.docx"</td> </tr> <tr> <td>A04 - Valid land fraction</td> <td>Fraction of a DEMIX tile <strong>covered </strong>by <strong>valid </strong>pixels of <strong>land </strong>of the DEM product</td> <td>"No data" or "void" value in metadata + water body mask</td> <td>See document "DEMIX_CDD-A04_20211103.docx"</td> </tr> <tr> <td>A05 - Primary land fraction</td> <td>Fraction of a DEMIX tile <strong>covered </strong>by <strong>valid </strong>pixels of <strong>land </strong>generated from the <strong>main source of data </strong>of the DEM product</td> <td>"No data" or "void" value in metadata + water body mask + source data/editing mask</td> <td>See document "DEMIX_CDD-A05_20211103.docx"</td> </tr> </tbody> </table> <h3>DEMs and ancillary data</h3> <p>The following DEM products and ancillary layers have been used to generate the dataset.</p> <table> <tbody> <tr> <td><strong>Identifier</strong></td> <td><strong>Used layers</strong></td> <td><strong>Data access</strong></td> </tr> <tr> <td> <p>ASTGTM v003</p> </td> <td>ASTER GDEM elevations (dem.tif) + editing / source masks (num.tif)</td> <td><a href="https://lpdaac.usgs.gov/products/astgtmv003/">https://lpdaac.usgs.gov/products/astgtmv003/</a></td> </tr> <tr> <td> <p>ASTWBD v001</p> </td> <td>ASTER GDEM water body mask (att.tif)</td> <td><a href="https://lpdaac.usgs.gov/products/astwbdv001/">https://lpdaac.usgs.gov/products/astwbdv001/</a></td> </tr> <tr> <td> <p>AW3D30 v2003</p> </td> <td>ALOS World 3D elevations (DSM.tif) + editing / source / water body masks (MSK.tif)</td> <td><a href="https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm">https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm</a></td> </tr> <tr> <td>COP-DEM_GLO-30-DGED v2019_1</td> <td>Copernicus DEM GLO-30 elevations (DEM.tif) + editing (EDM.tif) + source (SRC.tif) + water body (WBM.tif) masks</td> <td><a href="https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model">https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model</a></td> </tr> <tr> <td>COP-DEM_GLO-90-DGED v2019_1</td> <td>Copernicus DEM GLO-90 elevations (DEM.tif) + editing (EDM.tif) + source (SRC.tif) + water body (WBM.tif) masks</td> <td><a href="https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model">https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model</a></td> </tr> <tr> <td> <p>NASADEM_HGT v001</p> </td> <td>NASADEM elevations (.hgt) + editing / source (.num) + water body (.swb) masks</td> <td><a href="https://lpdaac.usgs.gov/products/nasadem_hgtv001/">https://lpdaac.usgs.gov/products/nasadem_hgtv001/</a></td> </tr> <tr> <td> <p>SRTMGL1 v003</p> </td> <td>SRTMGL1 elevations (.hgt)</td> <td><a href="https://lpdaac.usgs.gov/products/srtmgl1v003/">https://lpdaac.usgs.gov/products/srtmgl1v003/</a></td> </tr> <tr> <td> <p>SRTMGL1N v003</p> </td> <td>SRTMGL1 editing / source / water body masks (.num)</td> <td><a href="https://lpdaac.usgs.gov/products/srtmgl1nv003/">https://lpdaac.usgs.gov/products/srtmgl1nv003/</a></td> </tr> </tbody> </table> <h3>Computation of scores</h3> <p>For each DEMIX tile and DEM, each "fractional cover" has been computed using the following procedure:</p> <ol> <li><strong>Crop DEMIX tile layers</strong> - The tiles of each DEM layer (elevations, editing, sources and water bodies) are cropped to the extent of the DEMIX tile.</li> <li><strong>Compute standardized layers</strong><strong> </strong>- Given the cropped DEM layers, four standardized layers are produced, which are: <ul> <li>Heights layer - Containing the heights of the DEM</li> <li>Land/water mask layer - Indicating whether DEM pixels are land or water: <ul> <li>0 = NO_DATA</li> <li>1 = BACKGROUND</li> <li>2 = INVALID</li> <li>3 = WATER</li> <li>4 = LAND</li> </ul> </li> <li>Source mask layer - Indicating the source data of DEM heights (or "edited" value): <ul> <li>0 = NO_DATA</li> <li>1 = BACKGROUND</li> <li>2 = INVALID</li> <li>3 = PRIMARY_DATA</li> <li>4 = EXTERNAL_DATA</li> <li>5 = EDITED</li> </ul> </li> <li>Valid mask layer - Indicating if the DEM pixels are valid or not: <ul> <li>0 = NO_DATA</li> <li>1 = BACKGROUND</li> <li>2 = INVALID</li> <li>3 = VALID</li> </ul> </li> </ul> </li> <li><strong>Retrieve pixel number N</strong><em><strong> </strong>-<strong> </strong></em>The total pixel number N is computed for one of the layers (all layers have the same number of pixels).</li> <li><strong>Retrieve criterion pixel number C </strong>-<strong> </strong>The criterion pixel number C is computed based on the standard layers, more precisely: <ul> <li>A01 - Product fractional cover - Number of pixels of <strong>valid mask layer equal to 1, 2 or 3</strong></li> <li>A02 - Valid data fraction - Number of pixels of <strong>valid mask layer equal to 3</strong></li> <li>A03 - Primary data fraction - Number of pixels of <strong>source mask layer equal to 3</strong></li> <li>A04 - Valid land fraction - Number of pixels of <strong>land/water mask layer equal to 4</strong></li> <li>A05 - Primary land fraction - Number of pixels of <strong>source mask layer equal to 3</strong> and<strong> land/water mask layer equal to 4</strong></li> </ul> </li> <li><strong>Compute the final score S</strong><strong> </strong>- The final score S is expressed as the following percentage: <strong>S = ceil(C/N*100)</strong></li> </ol> <h2>Known issues</h2> <p>The "SRTMGL1N v003" is known to have "tile repeating issues", where part of the data is wrongly flagged as water. This issue has been reported with no particular response from the providers of the DEM (see <a href="https://forum.earthdata.nasa.gov/viewtopic.php?t=2752">https://forum.earthdata.nasa.gov/viewtopic.php?t=2752</a>).</p> <p><strong>References:</strong></p> <ul> <li>Guth, P.L.; Strobl, P.; Gross, K.; Riazanoff, S. <em>DEMIX 10k Tile Data Set (1.0)</em> [Data set]. Zenodo 2023. <a href="https://doi.org/10.5281/zenodo.7504791" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7504791</a></li> <li>Guth, P.L.; Van Niekerk, A.; Grohmann, C.H.; Muller, J.-P.; Hawker, L.; Florinsky, I.V.; Gesch, D.; Reuter, H.I.; Herrera-Cruz, V.; Riazanoff, S.; López-Vázquez, C.; Carabajal, C.C.; Albinet, C.; Strobl, P. <em>Digital Elevation Models: Terminology and Definitions</em>. Remote Sens. 2021, 13, 3581. <a href="https://doi.org/10.3390/rs13183581">https://doi.org/10.3390/rs13183581</a></li> <li>Riazanoff, S.; Corseaux, A.; Albinet, C.; Strobl, P.A.; López-Vázquez, C.; Guth, P.L.; Tadono, T. <em>Best BiCubic Method to Compute the Planimetric Misregistration between Images with Sub-Pixel Accuracy: Application to Digital Elevation Models</em>. <em>ISPRS Int. J. Geo-Inf.</em> 2024, <em>13</em>, 96. <a href="https://doi.org/10.3390/ijgi13030096">https://doi.org/10.3390/ijgi13030096</a></li> <li>Bielski, C.; López-Vázquez, C.; Grohmann, C.H.; Guth, P.L.; Hawker, L.; Gesch, D.; Trevisani, S.; Herrera-Cruz, V.; Riazanoff, S.; Corseaux, A.; Reuter, H.I.; Strobl, P.A.; <em>Novel Approach for Ranking DEMs: Copernicus DEM Improves One Arc Second Open Global Topography</em> in <em>IEEE Transactions on Geoscience and Remote Sensing</em>, vol. 62, pp. 1-22, 2024, Art no. 4503922. <a href="https://doi.org/10.1109/TGRS.2024.3368015">https://doi.org/10.1109/TGRS.2024.3368015</a></li> <li>Strobl, P.A.; Bielski, C.; Guth, P.L.; Grohmann, C.H.; Muller, J.P.; López-Vázquez, C.; Gesch, D.B.; Amatulli, G.; Riazanoff, S.; Carabajal, C. The Digital Elevation Model Intercomparison eXperiment DEMIX, a community based approach at global DEM benchmarking. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2021, XLIII-B4-2021, 395–400. <a href="https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021">https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021</a></li> </ul>
SHIFT: A DEM-Based Spatial Heterogeneity Improved Mapping of Global Geomorphic Floodplains
<h2>Description</h2> <p><strong>SHIFT</strong> (Spatial Heterogeneity Improved Floodplain by Terrain analysis) is a 90-m resolution global geomorphic floodplain map based on terrain analysis. It takes MERIT-Hydro as the terrain input and Floodplain Hydraulic Geometry (FHG) as the thresholding scheme, with the scaling parameters estimated by a stepwise framework that both respects the power law and approximates the spatial extent of hydrodynamic modeling. SHIFT effectively captures the global patterns of the geomorphic floodplains, with better regional details than existing data.</p> <h2>Data Structure</h2> <p>We provide 2 resolutions of data for different needs.</p> <ul> <li><strong>SHIFT_v3_90m</strong>: The original SHIFT data derived from MERIT-Hydro, with lakes and reservoirs removed. The resolution is 0.000833333333333 degrees under geographic coordinate system (EPSG:4326), approximately 90 meters at the equator. Pixels with value 1 are floodplains, 2 are lakes and reservoirs and 0 are non-floodplains, with empty values set as 255 (denoting pixels not within any watersheds under the threshold of 1000 km2).</li> <li><strong>SHIFT_v3_1km</strong>: The resampled SHIFT data with lakes and reservoirs marked. The resolution is 0.00833333333333 degrees under geographic coordinate system (EPSG:4326), approximately 1 km at the equator. Pixels with value 1 are floodplains, 2 are lakes and reservoirs and 0 are non-floodplains, with empty values set as 255 (denoting pixels not within any watersheds under the threshold of 1000 km2).</li> </ul> <p>Also, we provide our derived spatially-varying parameters in all Level-3 basins to support future studies. Parameters are provided in a shapefile, with 'a' denotes the proportional parameter and 'b' denotes the exponent. We aggregated MERIT-Basins based on its spatial relationship with basins from Level-3 HydroBASINS, ensuring that the centroid of a MERIT-Basin falls within the corresponding boundary. This approach accounts for slight differences in boundaries due to the use of different terrain data, preventing confusion in hydrological representation.</p> <p>For more details, please refer to:</p> <ul> <li>Zheng, K., Lin, P., and Yin, Z.: SHIFT: a spatial-heterogeneity improvement in DEM-based mapping of global geomorphic floodplains, Earth Syst. Sci. Data, 16, 3873–3891, <a href="https://doi.org/10.5194/essd-16-3873-2024" rel="noopener">https://doi.org/10.5194/essd-16-3873-2024</a>, 2024.</li> </ul> <h2>Development Log</h2> <ol> <li><strong>Changes in v3 compared to v2:</strong> <ol> <li> <p><strong>Inclusion of Missing Level-3 Basin:</strong> We have added a previously missing Level-3 basin (PFAF ID: 242) that covers an area in Eastern Europe, specifically from Warsaw to Minsk. This omission was due to a technical problem that has now been resolved. Data are now still available in two resolutions: 90-meter and 1-kilometer.</p> </li> <li> <p><strong>Updated Parameters</strong>: Along with the new boundaries, updated parameters are provided in the shapefile.</p> </li> <li> <p><strong>Re-estimated Global Floodplain Area</strong>: Based on the new data, we have re-estimated the global total floodplain area from 9.9 × 10^6 km² to 9.92 × 10^6 km². This area still represents approximately 6.6% of the total land mass.</p> </li> </ol> </li> <li><strong>Changes in v2 compared to v1:</strong> <ol> <li><strong>Parameter 'b' Estimation:</strong> We modified the technical details of parameter 'b' estimation, specifically the binning parameter, adding a constraining mechanism to handle data noise. This resulted in stabler estimates for large basins and a clearer pattern of global residual uncertainty.</li> <li><strong>Target Function for Parameter 'a':</strong> We changed our target function to balance information from both datasets, using Fleiss’s Kappa (FK) and a penalty term to reduce bias.</li> </ol> </li> </ol> <h2>Contacts</h2> <ul> <li>Kaihao Zheng, <a href="mailto:Mostaly@pku.edu.cn" target="_blank" rel="noopener">Mostaly@pku.edu.cn</a></li> <li>Peirong Lin, <a href="mailto:peironglinlin@pku.edu.cn" target="_blank" rel="noopener">peironglinlin@pku.edu.cn</a></li> </ul> <p> </p>
Global topsoil SOC stock from 1981 to 2018 estimated by combining process-based model and space-for-time digital soil mapping
<p>This dataset include the topsoil (0-30cm) soil organic carbon (SOC) stocks in mineral soils under major land classes (forest, grassland, shrub land, savannas, cropland, cropland/natural vegetation mosaic, and sparely vegetated land) from 1981 to 2018. The long-time series of SOC stocks were estimated by using a space-for-time digital soil mapping (DSMst) model where the RothC-simulated SOC stocks were incorporated as one of the dynamic covariates of the DSMst model.</p> <p>The detail information on the products were given below:</p> <p>Name: DSMst-RothC 5-km global topsoil SOC stock products</p> <p>Period: 1981-2018</p> <p>Spatial resolution: 0.041666667 degree</p> <p>Temporal resolution: 1 year</p> <p>CRS: geographic latitude/longitude (EPSG:4326 - WGS 84 – Geographic)</p> <p>Extent: -180°, -90°: 180°, 90°</p> <p>Data format: GeoTIFF</p> <p>Compression: LZW</p> <p>Data type: Float32</p> <p>Unit: t C ha<sup>-1</sup></p>
A map of global peatland extent created using machine learning (Peat-ML)
<p>Map of global peatland extent estimated by machine learning. The download includes both a netcdf file version and a GeoTIFF (as a zip archive)</p> <p>Abstract from associated paper:</p> <p>Peatlands store large amounts of soil carbon and freshwater, constituting an important component of the global carbon and<br> hydrologic cycles. Accurate information on the global extent and distribution of peatlands is presently lacking but is needed<br> by Earth System Models (ESMs) to simulate the effects of climate change on the global carbon and hydrologic balance. Here,<br> we present Peat-ML, a spatially continuous global map of peatland fractional coverage generated using machine learning<br> techniques suitable for use as a prescribed geophysical field in an ESM. Inputs to our statistical model follow drivers of<br> peatland formation and include spatially distributed climate, geomorphological and soil data, along with remotely-sensed<br> vegetation indices. Available maps of peatland fractional coverage for 14 relatively extensive regions were used along with<br> mapped ecoregions of non-peatland areas to train the statistical model. In addition to qualititative comparisons to other maps<br> in the literature, we estimated model error in two ways. The first estimate used the training data in a blocked leave-one-out<br> cross-validation strategy designed to minimize the influence of spatial autocorrelation. That approach yielded an average r<sup>2</sup><br> of 0.73 with a root mean squared error and mean bias error of 9.11% and -0.36%, respectively. Our second error estimate<br> was generated by comparing Peat-ML against a high-quality, extensively ground-truthed map generated by Ducks Unlimited<br> Canada for the Canadian Boreal Plains region. This comparison suggests our map to be of comparable quality to mapping<br> products generated through more traditional approaches, at least for boreal peatlands.</p>
Global distribution map of Rhenish stoneware during the 16th to 18th century
<p>The dataset provides a distribution map of Rhenish stonewares between the 16th and 18th century. The data was collected from published archaeological data (print and online) available to the author. According the published information the pottery was classified to different wares (Cologne, Frechen, Siegburg, Raeren, Westerwald). Values are given for individual sherd numbers. If no information was given in the publication, the value is set to "1". Bibligraphic reference is given by author - date. Full bibliographic reference can be found in the pdf-file.</p> <p>The csv-file contains next to location name, bibliographic reference and pottery counts values for longitude and latitude. The coordinate reference is WGS 84 - EPSG:4326.</p>
ELABORATION OF THE ITALIAN PORTION OF THE GLOBAL SOIL ORGANIC CARBON MAP (GSOCMAP)
<p>The Global Soil Organic Carbon map (GSOCmap) published by the Food and Agriculture Organization<br> constitutes a baseline estimation of soil organic carbon stock (CS, ton ha–1) from 0 to 30 cm, on a grid at 30 arc-seconds<br> resolution (approximately 1 x 1 km). It has been produced for the Italian territory by the Italian Soil Partnership (ISP): a<br> national hub of institutions dealing with soils, either academic/research institutions, and regional soil services (RSS). The<br> RSS are the main soil data owners in Italy and play a central role in the elaboration of policies for soil management. The<br> RSS adhering to the ISP are: Calabria, Campania, Emilia Romagna, Friuli Venezia Giulia, Liguria, Lombardia, Marche,<br> Piemonte, Puglia, Sicilia, Toscana, and Veneto. A national soil database is maintained by the Consiglio per la Ricerca e<br> l'Analisi dell'Economia Agraria (CREA). The RSS contributed with soil data, with mean density of 1 point per 50 square<br> kilometres, selecting data analysed for soil organic carbon content (SOC, dag kg-1), which were representative and well<br> distributed for the following environmental covariates: land use, geomorphology, and climate. The data were selected inbetween<br> 1990 al 2013. This was necessary in order to exclude the effect of the new soil protection policies of the Rural<br> Development Programme 2014-2020. For the RSS not included in the ISP, the data were selected from the national soil<br> database. 6748 point data were finally selected. SOC values obtained with the Springer and Klee and flash combustion<br> elemental analyser methods were retained for elaborations, because the 2 methods, were found to give statistically<br> equivalent results. SOC values obtained with Walkey and Black method were, instead, corrected with an empirical factor<br> of 1.3. 2292 of the 6748 point data had also measured bulk density (BD, Mg m–3). Pedotransfer functions were calibrated<br> to estimate BD were measured BD were missing, with the following as auxiliary variables: land use, soil regions, texture,<br> and SOC. The carbon stock (CS, ton ha–1) was calculated by multiplying: 0.3 (m) * SOC (dag kg-1) * fine earth fraction (1 -<br> skeletal content expressed as daL m–3) * BD (Mg m–3). CS of the first 30 cm depth was calculated as depth-weighted<br> average. A spatial statistics method was used for the CS interpolation. The following auxiliary variables were used: soil<br> regions, soil subregions, Corine land cover 2006, lithology, soils affected by natural constrains (gleyic, histic, vertic,<br> coarse, shallow, arenic, sodic, and acid), sand content, silt content, 30-m aster-DEM, distance from coast, distance from<br> relieves, soil aridity index, annual mean precipitations, mean annual air temperature, soil inorganic carbon, and soil<br> depth. For the soil region of Po valley, the land units at 1:250,000 scale were also used. The interpolation method was a<br> general linear regression for the soil regions of Po valley, and a radial basis function for the remaining Italian territory.<br> The 6748 point data were divided, by spatial random sampling, into 10 subsets. Ten interpolations were produced, each<br> time leaving out 1/10 of the dataset. Average (fig. 1), standard deviation and confidence intervals of these 10<br> interpolations were calculated. Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) were respectively<br> 25.5 and 36.4 Mg/ha.</p> <p>A.85 Italy Map source: Country submission Point data Number of samples: 6748 Sampling period: 1990-2013 SOC analysis method: SOC values obtained with the Springer and Klee and ’flash combustion elemental analyser’ methods were retained for elaborations. Uncorrected values obtained by the Walkey and Black method were corrected with an empirical linear equation, based on previous studies and as recommended by the Italian official methods. BD analysis method: Undisturbed sampling, core method and pit method Mapping method Mapping method details: Neural Networks and GLM, according to soil region Validation statistics: Mean Error (ME) of the prediction is 1.688 Mg/ha, MAE 25.57 Mg/ha, Root Mean Squared Error (RMSE) is 36.24 Mg/ha. Contact Data Holder: Research centre for agriculture and environment Contact: CREA Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria edoardo.costantini@crea.gov.it</p>
Datasets for Wohlfarth et al. (2023) An advanced thermal roughness model for airless planetary bodies - Implications for global variations of lunar hydration and mineralogical mapping of Mercury with the MERTIS spectrometer
<p>This document describes the datasets and modeling results presented and discussed in our full research article.<br> <br> Wohlfarth, K., Wöhler, C., Hiesinger, H., Helbert, J. 2023, An advanced thermal roughness model for airless planetary bodies - Implications for global variations of lunar hydration and mineralogical mapping of Mercury with the MERTIS spectrometer, Astronomy and Astrophysics, 672<br> <br> <a href="https://doi.org/10.1051/0004-6361/202245343">https://doi.org/10.1051/0004-6361/202245343</a><br> <br> We provide several visualization scripts that read and display the results for convenience. Access to the original MATLAB® code for the thermal model implementation is available upon request (<a href="mailto:kay.wohlfarth@tu-dortmund.de">kay.wohlfarth@tu-dortmund.de</a>).<br> <br> More info in Dataproducts.pdf</p>
Global Airborne Observatory: Mapped Infrastructure on Hawaii Island
<p>There are three mapping datasets showing infrastructure on Hawaii Island (Hawaii County) in the State of Hawaii. The datasets were created using a combination of a convolutional neural network (CNN) and gradient boosted trees (GBT) run on Global Airborne Observatory laser scanning and imaging spectroscopy data.. Full methodology is available in the following reference:</p> <p>Mason, R.E.; Vaughn, N.R.; Asner, G.P. Mapping Buildings across Heterogeneous Landscapes: Machine Learning and Deep Learning Applied to Multi-Modal Remote Sensing Data. <em>Remote Sensing </em><strong>2023</strong>, <em>15</em>, x. https://doi.org/10.3390/xxxxx</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.