Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

datasets available to search

ShareScore release 0.9.0

Dataset results

13 results for “Digital Soil Mapping”

Learn how ShareScore rates datasets ↗

zenodo44/100

PEATGRIDS: Mapping global peat thickness and carbon stock via digital soil mapping approach, dataset

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.  Note: This version (2.0.1) clarifies the metric units for carbon stock per area in the previous version (2.0).

opencc-by-4.0Jul 2024View details →

zenodo44/100

[[Deprecated]] DIGITAL SOIL TEXTURE MAPS OF ARGENTINA

A new version has been uploaded by Guillermo Schulz.

opencc-by-4.0Jan 2022View details →

zenodo44/100

DIGITAL SOIL TEXTURE MAPS OF ARGENTINA

Soil fractions of Argentina in g/100g, Clay, Silt and Sand, for 4 standard depth intervals (0–15, 15-30, 30–60, 60–100) at 1000 m resolution. Including textural classes for the four standard layers and error estimation using random forest. Global accuracy based on cross-validation <table> <tbody> <tr> <td> sp </td> <td> RMSE </td> <td> Rsquared </td> <td> MAE </td> </tr> <tr> <td> Sand 0-15 cm </td> <td> 16.189 </td> <td> 0.640 </td> <td> 11.069 </td> </tr> <tr> <td> Sand 15-30 cm </td> <td> 16.320 </td> <td> 0.629 </td> <td> 11.213 </td> </tr> <tr> <td> Sand 30-60 cm </td> <td> 16.676 </td> <td> 0.618 </td> <td> 11.364 </td> </tr> <tr> <td> Sand 60-100 cm </td> <td> 16.762 </td> <td> 0.587 </td> <td> 11.472 </td> </tr> <tr> <td> silt 0-15 cm </td> <td> 12.011 </td> <td> 0.638 </td> <td> 8.352 </td> </tr> <tr> <td> silt 15-30 cm </td> <td> 11.807 </td> <td> 0.608 </td> <td> 8.388 </td> </tr> <tr> <td> silt 30-60 cm </td> <td> 11.504 </td> <td> 0.561 </td> <td> 8.168 </td> </tr> <tr> <td> silt 60-100 cm </td> <td> 11.728 </td> <td> 0.583 </td> <td> 8.263 </td> </tr> <tr> <td> clay 0-15 cm </td> <td> 8.766 </td> <td> 0.475 </td> <td> 5.721 </td> </tr> <tr> <td> clay 15-30 cm </td> <td> 10.723 </td> <td> 0.452 </td> <td> 7.432 </td> </tr> <tr> <td> clay 30-60 cm </td> <td> 11.211 </td> <td> 0.557 </td> <td> 7.842 </td> </tr> <tr> <td> clay 60-100 cm </td> <td> 11.005 </td> <td> 0.536 </td> <td> 7.734 </td> </tr> </tbody> </table>

opencc-by-4.0Feb 2022View details →

zenodo44/100

Redistribution of the shapefile of the digital soil map of the Flemish Region (status 2017-06-20)

This is a redistribution of the shapefile of the '<a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">Digitale bodemkaart van het Vlaams Gewest: bodemtypes, substraten, fasen en varianten van het moedermateriaal en de profielontwikkeling</a>' (digital soil map of the Flemish Region: soil types, substrates, phases, variants of the parent material, and profile development), originally published by 'Databank Ondergrond Vlaanderen’ (Subsurface Database of Flemish Region, DOV) under a CC-BY compatible license. Its shapefile has been redistributed as the <code>soilmap</code> data source, used in reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes. These workflows rely on a stable, clean (datafile-only) and uniform representation of each data source version, represented by a Zenodo DOI. The Belgian soil map was drawn up by intensive soil mapping from the 1950s to 1970s (Dudal et al., 2005). The map is based on the Belgian soil classification system. It is a national system that was set up exclusively for Belgian soils. The digital soil map of the Flemish Region is documented by Van Ranst & Sys (2000). Each spatial polygon, accurately digitized from the soil map sheets (published at map scale 1:20000) at scale 1:5000, includes information on soil types, substrates, phases, variants of the parent material and profile development. If available, the general characteristics and photos of a representative soil profile and environment can be obtained for each soil type. Finally, for each location it is possible to call in a scan of the analog soil map sheet, the corresponding explanation booklet and the basic maps on a 1: 5000 scale. The digital soil map was updated in 2017 with information from several military domains, a uniform soil type was generated for the polder area, and several mistakes were corrected. The data source is owned by ‘Vlaams Planbureau voor Omgeving’ (Flemish Planning Bureau for Environment, Department of Environment of the Flemish government).

opencc-by-4.0Sep 2019View details →

zenodo44/100

Global topsoil SOC stock from 1981 to 2018 estimated by combining process-based model and space-for-time digital soil mapping

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. The detail information on the products were given below: Name: DSMst-RothC 5-km global topsoil SOC stock products Period: 1981-2018 Spatial resolution: 0.041666667 degree Temporal resolution: 1 year CRS: geographic latitude/longitude (EPSG:4326 - WGS 84 – Geographic) Extent: -180°, -90°: 180°, 90° Data format: GeoTIFF Compression: LZW Data type: Float32 Unit: t C ha-1

opencc-by-4.0Jun 2021View details →

zenodo44/100

Spatial models of topsoil properties in Romania using digital soil mapping techniques

The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques, accepted for publication in: Cristian Valeriu Patriche, Bogdan Roșca, Radu Gabriel Pîrnău, Ionuț Vasiliniuc, Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning, PLOS ONE, 2023 The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques. The file names indicate the soil variable and the method used for interpolation (RK – regression - kriging, EML – ensemble machine learning, GWR_OK – Geographically Weighted Regression – Ordinary kriging). The raster data is classified and saved in tif format with a resolution of 100 x 100 m. The spatial reference is Stereographic projection 1970 (Pulkovo_1942_Adj_58_Stereo_70). The soil variables are classified as follows: <table> <tbody> <tr> <td> Variable </td> <td> Classes </td> </tr> <tr> <td> 1 </td> <td> 2 </td> <td> 3 </td> <td> 4 </td> <td> 5 </td> <td> 5 </td> <td> 7 </td> </tr> <tr> <td> pH </td> <td> ≤ 5 (strongly acid) </td> <td> 5.1 – 5.8 (moderately acid) </td> <td> 5.9 – 6.8 (weakly acid) </td> <td> 6.9 – 7.2 (neutral) </td> <td> 7.3 – 8.4 (weakly alkaline) </td> <td> 8.5 – 8.8 (moderately alkaline) </td> <td>   </td> </tr> <tr> <td> EC (mS m-1) </td> <td> ≤ 12.75 </td> <td> 12.76 – 16.49 </td> <td> 16.50 – 20.04 </td> <td> 20.05 – 24.18 </td> <td> 24.19 – 29.11 </td> <td> 29.12 – 35.23 </td> <td> ≤ 35.24 </td> </tr> <tr> <td> OC (g kg-1) </td> <td> < 7.5 (very low) </td> <td> 7.5 – 17.4 (low) </td> <td> 17.4 – 37.8 (moderate) </td> <td> 37.8 – 61.0 (high) </td> <td> > 61 (very high) </td> <td>   </td> <td>   </td> </tr> <tr> <td> CaCO3 (g kg-1) </td> <td> 0 (no carbonates) </td> <td> 1 – 10 (low) </td> <td> 11 – 40 (medium 1) </td> <td> 41 – 80 (medium 2) </td> <td> 81 – 107 (medium 3) </td> <td>   </td> <td>   </td> </tr> <tr> <td> P (mg kg-1) </td> <td> < 4 (extremely low) </td> <td> 4 – 8 (very low) </td> <td> 8 – 18 (low) </td> <td> 18 – 36 (medium) </td> <td> 36 – 72 (high) </td> <td> > 72 (very high) </td> <td>   </td> </tr> <tr> <td> N (g kg-1) </td> <td> ≤ 1 (very low) </td> <td> 1.1 – 1.4 (low) </td> <td> 1.5 – 2.0 (medium 1) </td> <td> 2.1 – 2.7 (medium 2) </td> <td> 2.8 – 6.0 (high) </td> <td> > 6 (very high) </td> <td>   </td> </tr> <tr> <td> K (mg kg-1) </td> <td> ≤ 40 * (extremely low) </td> <td> 41 – 65 * (very low) </td> <td> 66 – 130 (low) </td> <td> 131 – 200 (medium) </td> <td> 201 – 300 (high) </td> <td> > 300  (very high) </td> <td>   </td> </tr> <tr> <td> Clay (%) </td> <td> ≤ 25  (low 1) </td> <td> 26 – 32 (low 2) </td> <td> 33 – 40 (medium 1) </td> <td> 41 – 45 (medium 2) </td> <td> ≥ 46  (high) </td> <td>   </td> <td>   </td> </tr> <tr> <td> Silt (%) </td> <td> < 25 (medium 1) </td> <td> 25 – 32 (medium 2) </td> <td> 33 – 40 (high 1) </td> <td> 41 – 50 (high 2) </td> <td> > 50 (high 3) </td> <td>   </td> <td>   </td> </tr> <tr> <td> Sand (%) </td> <td> < 15 (low 1) </td> <td> 15 – 25 (low 2) </td> <td> 26 – 35 (low 3) </td> <td> 36 – 56 (medium) </td> <td> > 56 (high) </td> <td>   </td> <td>   </td> </tr> </tbody> </table>   * classes not present on the Romanian territory

opencc-by-4.0Jul 2023View details →

zenodo40/100

Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery"

Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery". This is the code for the revised version of the manuscript, after adressing comments from reviewers. The data and code for the preprint, before submission to peer review (Møller et al., 2020), is available at <a href="https://doi.org/10.5281/zenodo.3699130">https://doi.org/10.5281/zenodo.3699130</a>. The R code was written for R version 3.6.3. References Møller, A.B., Koganti, T., Beucher, A., Iversen, B.V. and Greve, M.H., 2020. Downscaling digital soil maps using electromagnetic induction and aerial imagery. EarthArXiv. <a href="http://dx.doi.org/10.31223/osf.io/a7xz6">http://dx.doi.org/10.31223/osf.io/a7xz6</a>. [preprint]

opencc-by-4.0Jul 2020View details →

zenodo36/100

soilmap_simple: a simplified and standardized derivative of the digital soil map of the Flemish Region

The data source <code>soilmap_simple</code> is a simplified and standardized derived form of the '<a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a>' (the shapefile of which we named <code>soilmap</code>, for analytical workflows in R) published by 'Databank Ondergrond Vlaanderen’ (DOV). It is a GeoPackage that contains a spatial polygon layer ‘<code>soilmap_simple</code>’ in the Belgian Lambert 72 coordinate reference system (EPSG-code <a href="https://epsg.io/31370">31370</a>), plus a non-spatial table ‘<code>explanations</code>’ with the meaning of category codes that occur in the spatial layer. Further documentation about the digital soil map of the Flemish Region is available in Van Ranst & Sys (2000) and Dudal et al. (2005). This version of <code>soilmap_simple</code> was derived from version '<code>soilmap_2017-06-20</code>' (<a href="https://doi.org/10.5281/zenodo.3387008">Zenodo DOI</a>) as follows: <ul> <li>all attribute variables received English names (purpose of standardization), starting with prefix <code>bsm_</code> (referring to the 'Belgian soil map');</li> <li>attribute variables were reordered;</li> <li>the values of the morphogenetic substrate, texture and drainage variables (<code>bsm_mo_substr</code>, <code>bsm_mo_tex</code> and <code>bsm_mo_drain</code> + their <code>_explan</code> counterparts) were filled for most features in the 'coastal plain' area. <ul> <li>To derive morphogenetic texture and drainage levels from the geomorphological soil types, a conversion table by Bruno De Vos & Carole Ampe was applied (for earlier work on this, see Ampe 2013).</li> <li>Substrate classes were copied over from <code>bsm_ge_substr</code> into <code>bsm_mo_substr</code> (<code>bsm_ge_substr</code> already followed the categories of <code>bsm_mo_substr</code>).</li> </ul> These steps coincide with the approach that had been taken to construct the <code>Unitype</code> variable in the <code>soilmap</code> data source;</li> <li>only a minimal number of variables were selected: those that are most useful for analytical work.</li> </ul> See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/b3c6696/src/generate_soilmap_simple">'n2khab-preprocessing' at commit b3c6696</a> for the creation from the <code>soilmap</code> data source. A reading function to return <code>soilmap_simple</code> (this data source) or <code>soilmap</code> in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>. The attributes of the spatial polygon layer <code>soilmap_simple</code> can have <code>mo_</code> in their name to refer to the Belgian Morphogenetic System: <ul> <li><code>bsm_poly_id</code>: unique polygon ID (numeric)</li> <li><code>bsm_region</code>: name of the region</li> <li><code>bsm_converted</code>: boolean. Were morphogenetic texture and drainage variables (<code>bsm_mo_tex</code> and <code>bsm_mo_drain</code>) derived from a conversion table (see above)? Value <code>TRUE</code> is largely confined to the 'coastal plain' areas.</li> <li><code>bsm_mo_soilunitype</code>: code of the soil type (applying morphogenetic codes within the coastal plain areas when possible, just as for the following three variables)</li> <li><code>bsm_mo_substr</code>: code of the soil substrate</li> <li><code>bsm_mo_tex</code>: code of the soil texture category</li> <li><code>bsm_mo_drain</code>: code of the soil drainage category</li> <li><code>bsm_mo_prof</code>: code of the soil profile category</li> <li><code>bsm_mo_parentmat</code>: code of a variant regarding the parent material</li> <li><code>bsm_mo_profvar</code>: code of a variant regarding the soil profile</li> </ul> The non-spatial table <code>explanations</code> has following variables: <ul> <li><code>subject</code>: attribute name of the spatial layer: either <code>bsm_mo_substr</code>, <code>bsm_mo_tex</code>, <code>bsm_mo_drain</code>, <code>bsm_mo_prof</code>, <code>bsm_mo_parentmat</code> or <code>bsm_mo_profvar</code></li> <li><code>code</code>: category code that occurs as value for the corresponding attribute in the spatial layer</li> <li><code>name</code>: explanation of the value of <code>code</code></li> </ul>

opencc-by-4.0Mar 2020View details →

zenodo36/100

Soil Survey of Scotland Staff (1970-1987). Soil maps of Scotland (partial coverage) at a scale of 1:25 000. Digital phase 8 release.

This is the digital dataset which was created by digitising the Soils of Scotland 1:25,000 Soil maps and the Soils of Scotland 1:25,000 Dyeline Masters. The Soils of Scotland 1:25,000 Soil maps were the source documents for the production of the Soils of Scotland 1:63,360 and 1:50,000 published map series. Where no 1:25,000 published maps exist 1:63,360 maps have been digitised for this data set, the field SOURCE_MAP describes the source of the data. The classification is based on Soil Associations, Soil Series and Phases which reflect parent material, major soil group, and soil sub-groups, drainage and (for phases), texture, stoniness, land use, rockiness, topography and organic matter. Phases are not always mapped. In general terms this dataset primarily covers the cultivated land of Scotland but also includes some upland areas.  This data set is undergoing a phased revision, the latest (phase 8) was released in August 2021. The digitising of the recently added data was funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. The data can also be downloaded from or viewed at <a href="https://www.hutton.ac.uk/soil-maps/">https://www.hutton.ac.uk/soil-maps/ </a>or viewed at <a href="https://map.environment.gov.scot/Soil_maps/?layer=2">Scotland's Soils - soil maps (environment.gov.scot). </a> This map should be cited as: 'Soil Survey of Scotland Staff (1970-1987). Soil maps of Scotland (partial coverage) at a scale of 1:25 000. Digital phase 8 release. James Hutton Institute, Aberdeen. DOI 10.5281/zenodo.5159133.

openother-openAug 2021View details →

zenodo32/100

Soil Survey of Scotland Staff (1970-1987). Soil maps of Scotland (partial coverage). Digital phase 10 release.

This is the digital dataset which was created by digitising the Soils of Scotland 1:25,000 Soil maps and the Soils of Scotland 1:25,000 Dyeline Masters. The Soils of Scotland 1:25,000 Soil maps were the source documents for the production of the Soils of Scotland 1:63,360 and 1:50,000 published map series. Where no 1:25,000 published maps exist 1:63,360 maps have been digitised for this data set, the field SOURCE_MAP describes the source of the data. The mapping is based on Soil Associations, Soil Series and Phases which reflect parent material, major soil group, and soil sub-groups, drainage and (for soil phases), texture, stoniness, land use, rockiness, topography and organic matter. Phases are not always mapped. In general terms this dataset primarily covers the cultivated land of Scotland but also includes some upland areas.  This data set is undergoing a phased revision, the latest (phase 8) was released in August 2021. The digitising of the recently added data was funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. The data can also be downloaded from or viewed at <a href="https://www.hutton.ac.uk/learning/natural-resource-datasets/soilshutton/soils-maps-scotland/download"> https://www.hutton.ac.uk/soil-maps/ </a>or viewed at <a href="https://map.environment.gov.scot/Soil_maps/?layer=2">Scotland's Soils - soil maps (environment.gov.scot). </a> This map should be cited as: 'Soil Survey of Scotland Staff (1970-1987). Soil maps of Scotland (partial coverage). Digital phase 10 release. James Hutton Institute, Aberdeen. DOI 10.5281/zenodo.6908156 .

openother-openJul 2022View details →

dryad32/100

Data from: Using digital soil maps to infer edaphic affinities of plant species in Amazonia: problems and prospects

Open the record for dataset details and reuse information.

publicOct 2017View details →

zenodo28/100

Soil Total Nitrogen Observations in Burkina Faso and Covariates for Digital Soil Mapping

Contains: <ol> <li>Dataset on Soil Total Nitrogen Observations (g/kg) in Burkina Faso obtained from the <a href="https://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/e50f84e1-aa5b-49cb-bd6b-cd581232a2ec">WoSIS snapshot - December 2023 </a>published by ISRIC-World Soil Information (License <a href="https://creativecommons.org/licenses/by-nc/4.0/legalcode">CC-BY-NC</a>)</li> <li>Environmental Variable Covariates extracted from Google Earth Engine including WorldClim, MODIS, SRTM (Licenses: Free).</li> </ol> Purpose: For the implementation of Digital Soil Mapping as a case study to explore the potential of Cartographic Visualizations in supporting the Readability, Interpretability and understanding of a Random Forest regression model outputs using Shapley Additive exPlanations (SHAP).

opencc-by-sa-4.0Jul 2024View details →

zenodo24/100

Data and R code for the preprint "Downscaling digital soil maps using electromagnetic induction and aerial imagery"

Data and R code used in the preprint "Downscaling digital soil maps using electromagnetic induction and aerial imagery" (Møller, 2020). This is the data and code for the preprint before submission for peer review. The data and code for the revised manuscript are available at <a href="https://doi.org/10.5281/zenodo.3959005">https://doi.org/10.5281/zenodo.3959005</a>. Code originally written for R version 3.6.2. References Møller, A.B., Koganti, T., Beucher, A., Iversen, B.V. and Greve, M.H., 2020. Downscaling digital soil maps using electromagnetic induction and aerial imagery. EarthArXiv. <a href="http://dx.doi.org/10.31223/osf.io/a7xz6">http://dx.doi.org/10.31223/osf.io/a7xz6</a>. [preprint]

openMar 2020View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record