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371 results for “base map”
Land-cover mapping of the central Arizona region based on 2015 National Agriculture Imagery Program (NAIP) imagery
Detailed land-cover mapping is essential for a range of research issues addressed by sustainability science, especially for questions posed of urban areas, such as those of the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) program. This project provides a 1-meter land-cover mapping of the CAP LTER study area (greater Phoenix metropolitan area and surrounding Sonoran desert). The mapping is generated primarily using 2015 National Agriculture Imagery Program (NAIP) four-band data, with auxiliary GIS data used to improve accuracy. Auxiliary data include the 2015 cadastral parcel data, the 2014 USGS LiDAR data (1-meter), the 2014 Microsoft/OpenStreetMap Building Footprint data, the 2015 Street TIGER/Line, and a previous (2010) NAIP-based land-cover map of the study area (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-cap&identifier=623). Among auxiliary data, building footprints and LiDAR data significantly improved the boundary detection of above-ground objects. Post-classification, manual editing was applied to minimize classification errors. As a result, the land-cover map achieves an overall accuracy of 94 per cent. The map contains eight land cover classes, including: (1) building, (2) asphalt, (3) bare soil and concrete, (4) tree and shrub, (5) grass, (6) water, (7) active cropland, and (8) fallow. When compared to the aforementioned, previous (2010) NAIP-based land-cover map for the study area, buildings and tree canopies are classified more accurately in this 2015 land-cover map.
A map selection of wigeon stopover sites (core areas) based on wetland expert knowledge
<p>Stopover areas (core areas only) along the migration route of wigeons tracked with GPS transmitters were selected when they exhibited forests on more than 50% of their total surface or had less than 50% cover by water and/or wetland on the ESA’s global land cover map. We created a sample of 5,630 regions of interest (3,403 for training and 2,227 for validation), delineated with polygons assigned to land classes listed in the Table 1. We used archives of Google Earth, ESRI, and BING satellites for the photointerpretation of the land classes as described in Table 1. The classification was performed with a Sentinel-2 MultiSpectral Instrument, Level-2A image collection in Google Earth Engine (GEE) through the R-package Rgee to create a batch process applying the GEE Random forest classifier to each selected core home range. The cloudless (maximum 3%) images were selected within the period from 01/06/2021 to 30/09/2021. The optimal number of trees was estimated at 100 for an out of bag error of 14%. The overall accuracy on the validation sample was 82 %. </p>
Global Ocean Heat Content Anomalies and Ocean Heat Uptake based on mapping Argo data using local Gaussian processes
<p>Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2004-2024, equatorward of 65 degree latitude) subtracting the mean over the period 2004-2024 from the monthly time series of OHC. Yearly OHCA time series are then calculated that include 1. one point per year, i.e., from averaging Jan to Dec (see files ending in “yearly.nc”), and 2. two points per year, i.e., from averaging Jan to Dec and Jul to Jun, respectively (see files ending in “yearly2.nc”). OHC fields are mapped using locally stationary Gaussian processes (defined over space and time) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA time series for 0-2000 dbar, 0-700 dbar, 700-2000 dbar (as indicated in the file names). The attribute "area" is included in the netcdf files and it tells the corresponding surface area for the estimates. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. Maps of the ocean masks used for the different vertical sections can be found in the .png files (blue shading indicates the area used for the horizontal integral); the bathymetry mask by Roemmich and Gilson (included in the file RG_ArgoClim_Temperature_2019.nc at https://sio-argo.ucsd.edu/RG_Climatology.html) is also used to define the ocean mask. Ocean Heat Uptake is calculated from the monthly OHCA and then averaged as described above to produce yearly time series included in the files for the different layers.</p> <p>For the uncertainty at each time point, the standard deviation of each OHCA/OHU value in the time series is included. When plotting a time series, the user may consider, e.g., shading plus/minus 1* or 1.96*standard deviation (corresponding to a confidence level of 68% or 95% respectively). These standard deviations in the files are estimated using spatially and temporally dependent conditional simulations of monthly gridded anomalies. When combining different layers, the standard deviation of the sum is conservatively estimated as the sum of the standard deviations. </p> <p>Finally, OHCA/OHU trends are estimated via a least-squares fit and reported in the variable metadata with uncertainties (confidence level of 68%). Trend uncertainties are estimated by repeating the fit for each member of the conditional simulation ensemble described above.</p> <p> </p>
Burned Area Maps based on MODIS Surface Reflectance
<p>Burned area (BA) was classified using in-house algorithms, described in detail by Woźniak and Aleksandrowicz (2019). This method utilizes images acquired before and after fire events. All MODIS surface reflectance products MOD09A1 (tiles 24_03 and 25_03) for the period 2002 – 2021 were investigated. Since the study area is obscured by clouds or covered with snow for most of the year, only images from the time window that maximized the number of available frames across most years were selected. Hence, only images acquired between the 145th and 241st day of each year (corresponding to the spring-summer period) were retained for further processing. </p>
Continental Europe surface lithology based on EGDI / OneGeology map at 1:1M scale
<p>Continental Europe surface lithology based on <strong><a href="http://www.europe-geology.eu/onshore-geology/geological-map/onegeologyeurope/">EGDI / OneGeology map</a></strong> at 1:1M scale produced by <a href="https://egdi.geology.cz/record/basic/5729ffdf-2558-48fc-a5d2-645a0a010855">GEOZS, Slovenia</a>. European datasets harvested from national WFS for geologic units or national geological units datasets, based on OneGeology and <strong><a href="https://inspire.ec.europa.eu/codelist/LithologyValue/">INSPIRE Lithology</a></strong> and Geochronologic Era URI codelists. Layers include:</p> <ul> <li>EGDI_GE_GeologicUnit_EN_1M_Surface_LithologyPolygon_v2_250m_epsg.3035.tif = original EGDI surface lithology map;</li> <li>dtm_surface.lithology_egdi.1m_c_250m_s_20000101_20221231_eu_epsg.3035_v20240530.tif = gap filled surface lithology map;</li> </ul> <p>Missing values in the original EGDI lithology map have been imputed by training a random forest classifier model based on parameters derived from DTM and soil regions map from Die Bundesanstalt für Geowissenschaften und Rohstoffe (BGR). By generating 1 million random points, geographically balanced over the whole pan-EU land area, each class in the map was covered properly. Classes whose number of samples is less than 10 were discarded from the model training. The hyperparameter tuning of the model was carried out via a Bayesian approach with a criteria to maximize accuracy of 5k-fold cross validation. The tuned random forest model achieved an accuracy of 47% (Kappa=0.43) for the testing data, 20% of the generated sample points. The lithology of Turkey, on the other hand, was digitised from the available geology map produced by the General Directorate of Mineral Research and Exploration (MTA). The available raster map was post-processed and classified as 20 lithology classes using the k-means algorithm. These classes were harmonized with the classes in the EGDI lithology map.</p> <p>Acknowledgment: GEOZS, Continental Shelf Department at the Ministry for Transport and Infrastructure.</p>
Global landform and lithology class at 250 m based on the USGS global ecosystem map
<p>Layers include: lithology (15) and landform (7) indicator maps (0-100%). Derived from the <a href="https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/">USGS Global Ecosystem Map</a>, i.e. the EcoTapestry map. Water bodies masked out. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>dtm = theme: digital terrain models / relief and soil,</li> <li>lithology = variable: lithological class,</li> <li>usgs.ecotapestry = determination method: USGS Global Ecosystem Map,</li> <li>p = probability 0-100%,</li> <li>250m = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2014 = time reference: year 2014,</li> <li>v1.0 = version number: 1.0,</li> </ul>
Global wildland-urban interface maps in 2000, 2010, and 2020, based on GlobeLand30
<p>This dataset provides global wildland-urban interface (WUI) maps at a spatial resolution of <strong>30 meters </strong>for the years <strong>2000, 2010, and 2020</strong>. The WUI is defined as areas where the 200-meter buffers of urban areas (characterized by artificial surfaces) intersect with the 400-meter buffers of wildland areas, including forests, shrublands, and grasslands. These maps are produced based on land cover classification results from the GlobeLand30 datasets.</p> <p><strong>Projection Information:</strong><br>The projection information aligns with GlobeLand30 standards:</p> <ul> <li><strong>Projection:</strong> UTM (Universal Transverse Mercator) for latitudes from S85 to N85, using a 6-degree zone system without zone numbers.</li> <li><strong>Polar Azimuthal Projection:</strong> Applicable for latitudes from S85 to N90 and N85 to N90, with the projection surface intersecting at the South and North Poles.</li> </ul> <p><strong>Naming Convention:</strong><br>The file naming convention is as follows:</p> <div> <div> <div> <div><strong>WUI_LHH_VV_YYYYlc030.tif</strong></div> <div> </div> </div> </div> </div> <p>Where:</p> <ul> <li><strong>L</strong> = Latitude code (N for the Northern Hemisphere, S for the Southern Hemisphere)</li> <li><strong>HH</strong> = Number of UTM zone</li> <li><strong>VV</strong> = Starting latitude of the tile (each tile crosses 5° latitude)</li> <li><strong>YYYY</strong> = Year mapped</li> <li><strong>lc</strong> = Land cover abbreviation</li> <li><strong>030</strong> = Spatial resolution of 30 meters</li> </ul> <p><strong>Example File Name:</strong><br>For instance, the file named <strong>WUI_n15_45_2020lc030.tif</strong> can be interpreted as follows:</p> <ul> <li><strong>WUI</strong>: Wildland-Urban Interface dataset</li> <li><strong>n</strong>: Northern latitude</li> <li><strong>15</strong>: UTM zone 15</li> <li><strong>45</strong>: Starting latitude of 45 degrees</li> <li><strong>2020</strong>: Product year of 2020</li> <li><strong>lc</strong>: Land cover classification</li> <li><strong>030</strong>: Spatial resolution of 30 meters</li> </ul>
Indicative distribution map for Ecosystem Functional Group M3.7 Chemosynthetic-based-ecosystems (CBE)
<p>This archive contains indicative distribution maps and profiles for <strong>M3.7 Chemosynthetic-based-ecosystems (CBE)</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Land Productivity Dynamics (LPD) maps based on approaches recommended by the United Nations Convention to Combat Desertification (UNCCD) for the Brazilian Semiarid Region (BSR).
<p>Maps of the land productivity dynamic (LPD) based on approaches recommended by the United Nations Convention to Combat Desertification (UNCCD) for the Brazilian Semiarid Region for the period 2001-2015. LPD from Trends.Earth (TE), LPD from the Joint Research Center (JRC-LPD), and LPD from the Food and Agriculture Organization – World Overview of Conservation Approaches and Technologies (FAO-WOCAT LPD). In addition, TE-based LPD with climate correction based on Rain Use Efficiency (RUE), Residual Trend Analysis (RESTREND), and Water Use Efficiency (WUE) calculated using the procedures described in the second version of the Good Practice Guidance for SDG Indicator 15.3.1. The annual LCLU maps from the MapBiomas project at 30 m spatial resolution for 2001 and 2015, and the 16-day MOD13Q1 NDVI dataset from 2001 to 2015 were used as inputs.</p> <p>**********************</p> <p>A total of seven GeoTIFF files in Geographic Tagged Image File Format (GeoTIFF) format are provided at 250 m spatial resolution.</p> <p>Coding for the LPDs.</p> <p>Value Meaning</p> <p>-32768 No data</p> <p>1 Declining</p> <p>2 Moderate decline</p> <p>3 Stressed</p> <p>4 Stable</p> <p>5 Increasing</p> <p>Funding: this study was undertaken as part of the Satellite Desertification Monitoring Program in the Brazilian Semiarid Region [Grant Number 403223/2021-0] supported by the CNPq. It also had the support of Capes, through Notice no. 28/2022 – PDPG Social Vulnerability & Human Rights [Grant Number 88881.705050/2022-01].</p>
Scrubbed data on Wikipedians in Residence in Libraries based on the Mapping GLAM-Wiki collaborations
<p><strong>Source</strong>: </p> <p><a href="https://docs.google.com/spreadsheets/d/1UVN-T19g5tE7cONFCkiBkquBJiecoU-w4Rb6F-qR6II/edit#gid=791098161">GLAM-Wiki Activities Mapping - Community Review and Feedback Sheet</a></p> <p><strong>Source's context: </strong></p> <p>Gill, Satdeep. ‘Mapping GLAM-Wiki Collaborations’. <em>This Month in GLAM</em>, March 2020. <a href="https://outreach.wikimedia.org/wiki/GLAM/Newsletter/March_2020/Contents/WMF_GLAM_report">https://outreach.wikimedia.org/wiki/GLAM/Newsletter/March_2020/Contents/WMF_GLAM_report</a>.</p> <p> </p> <p>Data was scrubbed using <a href="https://openrefine.org/download.html">OpenRefine 3.4.1</a></p> <p>The original spreadsheet had only partial information in many fields and it is a work in progress (for more see the "source's context" link above).</p> <p>I have only manually double checked those rows in which the “Primary partner institution” contains the stem “libr*” or “bibli*. The following eight rows where modified and “Library” was added in the “Type of institution” column: Municipal Library, Patiala, BRAU Library of the University of Naples Federico II, Library and Archives Canada, Eötvös Loránd University Library and Archives, National Health Library and Knowledge Service, National Doctors Training and Planning, Daniel Cosío Villegas Library, Cantonal and University Library, Nationaal Archief | Koninklijke Bibliotheek; and “Library association” was added to the Online Computer Library Center (OCLC) entry. All changes can be seen in the <a href="https://zenodo.org/api/files/ae475409-a2a8-4e51-98e2-68b3090d0fd0/WiRs-in-libraries_MGW_scrubbing-changes.json">WiRs-in-libraries_MGW_scrubbing-changes.json</a> file in this release.</p>
Landsat-based dataset for mapping annual center-pivot irrigated cropland in Brazil
<p>Center-pivot irrigated cropland (CPIC) is a critical component of irrigation and plays an essential role in improving water use efficiency and increasing food production. To automatically extract the spatial distribution of CPIC in Brazil based on the remote sensing technology, we constructed a training dataset that supports the semantic segmentation models.</p><p>The dataset were built with the <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/landsat-5">Landsat 5</a> , 7 and 8<a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/landsat-7"> </a> images as well as the CPIC maps from <a href="https://metadados.snirh.gov.br/geonetwork/srv/por/catalog.search#/metadata/e2d38e3f-5e62-41ad-87ab-990490841073">ANA reference</a> data. We used the Landsat images in 2005, 2010 and 2015 to build the dataset.</p><p>The samples in train_images and train_masks were used to train and valid the Convolutional Neural Network models; </p><p>The samples in valid_data were used to test the model's prediction accuracy.</p><p>Pixels with values 255 and 0 in the mask samples represent the CPIC and background categories.</p><p><strong>For technical details that used to create the dataset, please refer to </strong><i><strong>https://doi.org/</strong></i><strong>10.1016/j.isprsjprs.2023.10.007.</strong></p>
Integrated disturbance mapping over the Tibetan Plateau based on multiple detection algorithms
<p>This dataset contains the mapped representation of vegetation disturbance across the Tibetan Plateau, rendered at a 30-meter spatial resolution. The dataset comprehensively illustrates the extent of vegetation disturbance on the Tibetan Plateau during the period spanning 1986 to 2020. Notably, the map employs a categorization system, with values assigned to distinct classes: 0 denotes areas of disturbed vegetation, 1 denotes regions characterized by undisturbed vegetation, and 2 denotes zones devoid of vegetation.</p>
An updated map of GRCh38 linkage disequilibrium blocks based on European ancestry data
<p>A map of approximately independent linkage disequilibrium (LD) blocks has many uses in statistical genetics. Current publicly available LD block maps are based on sparse recombination maps and are only available for GRCh37 (hg19) and prior genome assemblies. We generated LD blocks in GRCh38 for European (EUR) ancestry populations using a recent recombination map based on more than 115,000 individuals. This new map consists of 1,361 independent LD blocks across the 22 autosomal chromosomes and can be accessed at https://github.com/jmacdon/LDblocks_GRCh38</p>
Kinematically collected reference fingerprint map (RFM) with the high precision tracking system for feature-based indoor positioning
<p>The offline referencing phase, one of the core phases of the fingerprinting-based indoor positioning system (FIPS), is the key stage for deploying the positioning system. The reference fingerprint map (RFM) is acquired for representing the relationship between location-relevant features and the corresponding locations and used for inferring the user’s location at the online stage. The kinematically collecting the RFM using the mobile device with the help of high precision tracking system is contributed to the community for benchmarking comparison of the indoor positioning performance. The detailed description of the data is cooming soon.<br> </p>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types probabilities (part 1)</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types probabilities (part 2)</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types classification and relative entropy</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
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>
OMAP-23: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of vermiform appendix (FFPE) with MICS on MACSima
<p><strong>Description</strong></p> <p> OMAP-23 was designed for MICS (MACSima imaging cyclic staining) imaging of FFPE human vermiform appendix sample. Tissue fixation and antigen retrieval is described in (<a href="https://www.biorxiv.org/content/biorxiv/early/2023/11/07/2023.10.27.564191.full.pdf">Spatial protein and RNA analysis on the same tissue section using MICS technology</a>). The MACSima technology is described in detail in the following publication (<a href="https://doi.org/10.1038/s41598-022-05841-4">MACSima imaging cyclic staining (MICS) technology reveals combinatorial target pairs for CAR T cell treatment of solid tumors</a>). All, antibodies in this panel are recombinant antibodies with a mutated human IgG1 constant region, removing Fc receptor binding capacity of human IgG1, eliminating the need for additional blocking steps and reducing non-specific binding. The use of human IgG1 recombinant antibodies allows for the addition of uncoupled monoclonal antibodies from other species followed by a fluorescence labelled secondary reagent specific for the species of the monoclonal antibody. The multiplex system has been described already for OMAP-10 and OMAP-21. A new dye VioB515 is used for one reagents. The fluorescence is removed by cleavage. The panel contains 28 antibodies and the nuclear marker DAPI for image alignment and nuclear segmentation. This OMAP provides a spatial context for six anatomical structures and most cell types present in the vermiform appendix (link to ASCT+B table added). OMAP-23 follows OMAP-21, with fewer antibodies but adding antibodies for non-immune cells.</p> <p>All reagents are from Miltenyi Biotec and have been rigorously tested through an internal quality control system to have minimal variation between lots. For this reason, lot information is not included in this table. Analysis was performed by an accompanied software package MACSIQ View Analysis also described in the MACSima publication (<a href="https://doi.org/10.1038/s41598-022-05841-4">https://doi.org/10.1038/s41598-022-05841-4</a>). The MACSima system is continuously evolving, this is the third OMAP for the MACSima system. A representative dataset created using OMAP-23 can be found here 10.5281/zenodo.14008816 .The AVRs for the dataset can be found here (to be added).</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>
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.