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.
539
datasets available to search
ShareScore release 0.7.1
Dataset results
539 results for “organic carbon”
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD mean, 2012–2016</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 Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T 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 Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage (scaled 10x). </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://doi.org/10.5281/zenodo.13754343">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13771721">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13771841">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13771911">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13771967">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://doi.org/10.5281/zenodo.13779539">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13774064">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13774089">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13774114">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13774167">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://doi.org/10.5281/zenodo.13778472">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13773396">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13773765">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13773828">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13773953">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774003">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</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. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD mean, 2008–2012</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 Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T 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 Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage (scaled 10x). </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://doi.org/10.5281/zenodo.13754343">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13771721">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13771841">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13771911">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13771967">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://doi.org/10.5281/zenodo.13779539">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13774064">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13774089">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13774114">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13774167">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://doi.org/10.5281/zenodo.13778472">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13773396">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13773765">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13773828">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13773953">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774003">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</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. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD p975, 2000–2004</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 Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T 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 Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage (scaled 10x). </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://doi.org/10.5281/zenodo.13754343">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13771721">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13771841">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13771911">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13771967">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://doi.org/10.5281/zenodo.13779539">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13774064">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13774089">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13774114">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13774167">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://doi.org/10.5281/zenodo.13778472">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13773396">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13773765">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13773828">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13773953">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774003">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</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. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD p975, 2016–2020</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 Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T 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 Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage (scaled 10x). </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://doi.org/10.5281/zenodo.13754343">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13771721">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13771841">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13771911">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13771967">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://doi.org/10.5281/zenodo.13779539">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13774064">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13774089">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13774114">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13774167">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://doi.org/10.5281/zenodo.13778472">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13773396">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13773765">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13773828">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13773953">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774003">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</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. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD p975, 2012–2016</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 Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T 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 Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage (scaled 10x). </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://doi.org/10.5281/zenodo.13754343">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13771721">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13771841">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13771911">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13771967">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://doi.org/10.5281/zenodo.13779539">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13774064">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13774089">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13774114">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13774167">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://doi.org/10.5281/zenodo.13778472">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13773396">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13773765">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13773828">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13773953">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774003">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</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. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD mean, 2020–2022</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 Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T 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 Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage (scaled 10x). </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://doi.org/10.5281/zenodo.13754343">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13771721">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13771841">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13771911">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13771967">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://doi.org/10.5281/zenodo.13779539">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13774064">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13774089">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13774114">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13774167">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://doi.org/10.5281/zenodo.13778472">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13773396">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13773765">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13773828">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13773953">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774003">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</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. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Global patterns of soil organic carbon distribution in the 20–100 cm soil profile for different ecosystems: A global meta-analysis
<p><span><span> </span></span><span>The file named <span>“</span>Rawdata.xlsx<span>”</span> contains data sourced from the literature.<span> The file name is “GE_β.tif<span>”</span><span>,</span></span></span><span><span> GE represents</span></span><span> global ecosystems, which including cropland (CL), grassland (GL), and forestland (FL). “FL_β.tif” represents the spatial distribution of β for forestland at 20-100 cm depth. The file name is “GE_d_SOCD.tif”, where SOCD represents soil organic carbon density, d represents soil depth, for example, “FL_20-100_SOCD.tif” represents the spatial distribution of SOCD for forestland at 20-100 cm depth.</span></p>
Soil organic matter and plant carbon allocated to nitrogen acquisition simulated by the FUN-BioCROP model
<p>This data package contains the model input, results, and validation data from Juice et al (citation below). The FUN-BioCROP model (Fixation and Uptake of Nitrogen- Bioenergy Carbon, Rhizosphere, Organisms, and Protection) advances the field of bioenergy modeling by integrating new empirical paradigms of the role of belowground processes in shaping coupled carbon (C) and nitrogen (N) cycles. It was developed by modifying the FUN-CORPSE model (Fixation and Uptake of Nitrogen- Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment, Sulman et al. 2017 Ecology Letters) for use in bioenergy systems by including mechanistic tillage, organic matter addition, nitrogen fertilization, harvest, and feedstock-specific parameters, and to be driven by DayCent plant productivity and biomass data.</p>
Prediction stock of soil organic carbon in Argentina
<p>We standardized the Stocks soil organic carbon (SOC) at 0-30 cm depth for 5,073 soil samples. We spatially predicted SOC stock (kg/m2) using regression forest and associated prediction uncertainties using quantile regression forest at 1000 m resolution. Global accuracy based on cross-validation. We obtained a RMSE 2.624 and Rsquared 0.464.</p>
Globally-gridded data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon
<p>Supporting globally-gridded data products for manuscript: Georgiou K., Jackson R. B., Vindušková O., Abramoff R. Z., Ahlström A., Feng W., Harden J. W., Pellegrini A. F. A., Polley H. W., Soong J. L., Riley W. J., Torn M. S. Global stocks and capacity of mineral-associated soil organic carbon. <em>Nature Communications</em>, 2022.</p> <p>We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415) along with ancillary data on climate, vegetation, and soil characteristics to produce spatially-explicit global estimates of mineral-associated soil organic carbon stocks (MOC) and mineralogical carbon capacity (MOC<sub>max</sub>) in non-permafrost, non-desert mineral soils. Globally-gridded datasets are given in kgC/m<sup>2</sup> for topsoil (0-30cm) and subsoil (30-100cm) at 0.5 degree by 0.5 degree spatial resolution.</p>
Synthesis data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon
<p>Supporting synthesis data for manuscript: Georgiou K., Jackson R. B., Vindušková O., Abramoff R. Z., Ahlström A., Feng W., Harden J. W., Pellegrini A. F. A., Polley H. W., Soong J. L., Riley W. J., Torn M. S. Global stocks and capacity of mineral-associated soil organic carbon. <em>Nature Communications</em>, 2022.</p> <p>We performed an observational synthesis of soil fractionation data constituting 1,144 globally-distributed soil profiles from 78 studies that reported fractionation and bulk measurements of organic carbon across depths. This dataset includes measurements of mineral-associated, particulate, and bulk soil organic carbon, as well as ancillary data on edaphic, climate, and vegetation characteristics. We also performed a separate observational synthesis of soil carbon accrual from manipulation and chronosequence studies, which included changes in carbon stocks or concentrations, bulk density, experimental duration, and edaphic properties. This latter synthesis included 103 observations from 34 studies that spanned crop, pasture, grassland, and forest ecosystems across climates and soil types. Further details for both syntheses can be found in the methods and supplementary materials of the associated manuscript.</p>
Data to support the publication "Impact of agricultural management on soil aggregates and associated organic carbon fractions: Analysis of long-term experiments in Europe"
<p><strong>Raw data:</strong> Experimental plot ids and information, mass distribution of all aggregate fractions after wet sieving, Sand content of each fraction to conduct the sand correction, mass distribution of all fractions after isolating the micro-aggregates held within the macroaggregates, yields per treatment, carbon content per fraction (raw data)</p> <p><strong>All data per plot: </strong>SOC content, MAOM and POM content of each fraction presented in the fractionation scheme included in the manuscript, together with the mass of the relative fractions. </p> <p> </p>
Global derived datasets for use in k-NN machine learning prediction of global seafloor total organic carbon
<p>This dataset includes 663 predictor grids used for k-NN global prediction of seafloor total organic carbon.</p> <p>663 predictor grids available in netCDF4 HDF5 file format. Grids are cell-centered sized 4320 x 2160. File names adhere to the naming conventions discussed below. The naming structure is partioned by underscores and periods in the following order: interface to which the gridded values refer to, quantity of values contained within the grid, units and reference values/units (e.g. meters below sea level), data source, statistic calculated (if applicable), grid pitch, and file extension.</p> <p>Possible interfaces from the top – down:</p> <p>SS – Sea surface – atmosphere interface (may also be average of the entire water column)</p> <p>SF – Seafloor – water interface (may also be denoted by GL)</p> <p>GL – Ground level (e.g. bottom of pure liquid, top of dirt)</p> <p>SC – Sediment – crust interface (e.g. sediment above, igneous/metamorphic below)</p> <p>CM – Crust – mantle interface (e.g. Mohorovicic discontinuity)</p> <p>Appropriate reference naming marker (bold), original data source, and date of last access:</p> <p><strong>Becker</strong></p> <p>Becker, J. J., Wood, W. T., & Martin, K. M. (2014). <em>Global crustal heat flow using random decision forest prediction</em>, Abstract NG31A-3788 presented at 2014 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 06/23/2015.</p> <p><strong>CRUST1</strong> </p> <p>Pasyanos, M.E., Masters, G., Laske, G. & Ma, Z. (2012). <em>LITHO1.0 - An Updated Crust and Lithospheric Model of the Earth Developed Using Multiple Data Constraints</em>, Abstract T11D-09 presented at 2012 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 07/01/2014.</p> <p><strong>CRUST1_NOAA</strong></p> <p> As the NOAA sediment thickness database is globally not complete, data gaps in the NOAA grid with this have been supplemented by the CRUST1 sediment thickness (see above citation).</p> <p>Whittaker, J., Goncharov, A., Williams, S., Müller, R. D., & Leitchenkov, G. (2013) Global sediment thickness dataset updated for the Australian-Antarctic Southern Ocean, <em>Geochemistry, Geophysics, Geosystems. </em>https://doi.org/10.1002/ggge.2018.<em> </em>Last access: 09/02/2018.</p> <p><strong>GVP</strong></p> <p>Global Volcanism Program (2013) Volcanoes of the World. In E. Venzke (ed.). (Vol. 4.7.3). Smithsonian Institution. https://doi.org/10.5479/si.GVP.VOTW4-2013. Last access: 09/22/2014.</p> <p><strong>ETOPO2v2</strong></p> <p>National Geophysical Data Center (2006). 2-minute Gridded Global Relief Data (ETOPO2) v2. National Geophysical Data Center, NOAA. DOI: 10.7289/V5J1012Q. Last access: 02/06/2013.</p> <p><strong>PLATES</strong></p> <p>Coffin, M.F., Gahagan, L.M., & Lawver, L.A. (1998). Present-day Plate Boundary Digital Data Compilation. University of Texas Institute for Geophysics Technical Report (No. 174, pp. 5). Last access: 09/15/2014.</p> <p><strong>ONRL</strong></p> <p>Ludwig,W., Amiotte-Suchet, P., & Probst, J. L. (2011). ISLSCP II Global River Fluxes of Carbon and Sediments to the Oceans. In F. G. Hall, G. Collatz, B. Meeson, S. Los, E. Brown de Colstoun, and D. Landis (Eds.), <em>ISLSCP Initiative II Collection</em>. Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1028. Last Access: 02/15/2015.</p> <p><strong>Muller</strong></p> <p>Müller, R. D., Sdrolias, M., Gaina, C., & Roest, W. R. (2008). Age, spreading rates, and spreading asymmetry of the world’s ocean crust, <em>Geochemistry, Geophysics, Geosystems</em>, 9(4), Q04006. https://doi.org/10.1029/2007GC001743. Last accessed: 07/19/2011.</p> <p><strong>Woa13x</strong></p> <p>Boyer, T.P., Antonov, J. I., Baranova, O. K., Coleman, C., Garcia, H. E., Grodsky, A., et al. (2013) World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), <em>NOAA Atlas NESDIS 72, Technical Ed</em>. Silver Spring, MD. http://doi.org/10.7289/V5NZ85MT. Last Access: 09/18/2014.</p> <p><strong>KIM</strong></p> <p>Kim, S.S. & Wessel, P. (2011). New global seamount census from the altimetry-derived gravity data, <em>Geophysical Journal International</em>, 186, 615-631. https://doi.org/10.1111/j.1365-246X.2011.05076.x. Last access: 09/22/2014.</p> <p><strong>HYCOM</strong></p> <p>The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data.Last access: 03/19/2014.</p> <p><strong>NCEDC</strong></p> <p>NCEDC (2016). Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC. Last access: 09/21/2014.</p> <p><strong>Wei2010</strong></p> <p>Wei, C.-L., Rowe, G. T., Escobar-Briones, E., Boetius, A., Soltwedel, T., Caley, M. J., et al.(2010). Global patterns and predictions of seafloor biomass using random forests. <em>PLoS ONE</em>,5(12), e15323. https://doi.org/10.1371/journal.pone.0015323 Last access: 06/20/2016.</p> <p><strong>NGA_egm2008</strong></p> <p>Pavlis, N.K., Holmes, S. A., Kenyon, S. C., & Factor, J. K. (2008). <em>The</em> <em>EGM2008 Global Gravitational Model</em>, Abstract 2008AGUFM.G22A..01P presented at the 2008 General Assembly of the European Geosciences Union, Vienna, Austria. Last access: 07/10/2014.</p> <p><strong>WAVEWATCH3</strong></p> <p>The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data. Last access: 03/19/2014.</p> <p>Updated global seafloor porosity grid using our k-nearest neighbors algorithm using 5 nearest neighbors. Observed data used for prediction from Martin et al. (2015). </p> <p>Martin, K. M., Wood, W. T., & Becker, J. J. (2015). A global prediction of seafloor sediment porosity using machine learning. <em>Geophysical Research Letters</em>, 42(24), 10640. https://doi.org/10.1002/2015GL065279</p> <p>Other grids which have been generated by empirical means are latitude (and derivatives), longitude (and derivatives), Coriolis, coast_is_1.0, and the random noise grids. </p> <p>Units referenced are as follows:</p> <p>KGM3 - kilogram per cubic meter<br> MS - meters per second<br> KM - kilometer<br> M_ASL - meters above sea level (i.e. meters referenced to sea level)<br> MWM2 - milliwatt per square meter<br> TGCYR - terragram of carbon per year<br> TGYR - terragram per year<br> MA - megaannum<br> M - meters<br> MGCM2 - milligram of carbon per square meter<br> DEG - degree<br> S - seconds</p> <p>Statistics grids are calculated within a given radius (e.g. 10km, 50km, 125km, 250km, 500km, 1000km) of the respective cell-centered value. The statistics grids include mean (.men), average absolute deviation from the mean (.aad), and the common logarithm (.log) of the absolute value of the mean (.mlg). Additionally, some grids are a weighted count for given radii (e.g. seamounts) where weight is a cosine taper from the center of the grid cell. </p> <p>The grid pitch for this dataset is uniformly at 5-arc minute denoted by “.5m”. Additionally, the extension used (netCDF4) is denoted by “.nc”.</p>
Gridded spatial information on soil organic carbon content, density and stock in Hungary for 1992 and 2000
<p>Predictive soil organic carbon (SOC) content, density, and stock maps, along with the associated prediction uncertainty, are provided for the years 1992 and 2000, for the entire territory of Hungary. The maps refer to the topsoils (0–30 cm) with a spatial resolution of 100⨯100 m. The uncertainty associated with the SOC property maps is expressed by the lower and upper limits of the 90% prediction interval (PI), the range of values within which the true value is expected to occur 9 times out of 10. This means that there are two maps to each SOC property map, quantifying its prediction uncertainty. It should be added that all maps have been masked with open water bodies, as these areas are not relevant for soils.</p> <p><strong>For more details / to cite this dataset please use:</strong></p> <p><a href="https://doi.org/10.1038/s41597-024-04158-3">Szatmári, G., Laborczi, A., Mészáros, J., Takács, K., Benő, A., Koós, S., Bakacsi, Z., & Pásztor, L. (2024). Gridded, temporally referenced spatial information on soil organic carbon for Hungary. Scientific Data 11, 1312.</a></p> <p><strong>Custom code used for digital soil mapping and validation is available on GitHub:</strong></p> <p><a href="https://github.com/GaborSzatmari/HU-SOC-mapping" target="_blank" rel="noopener">https://github.com/GaborSzatmari/HU-SOC-mapping</a></p> <p><strong>Description of the files:</strong></p> <p>The resulting maps are shared as GeoTIFF files. The coordinate reference system is the Hungarian Unified National Projection System (HD72/EOV; EPSG: 23700) (<a href="https://epsg.io/23700" target="_blank" rel="noopener">https://epsg.io/23700</a>). The table below provides further information on the published maps. Note that the first file (00_Overview.jpg) gives an overview of the SOC property maps.</p> <table> <tbody> <tr> <td> <p><strong>SOC property maps</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Year</strong></p> </td> <td> <p><strong>Filename</strong></p> </td> </tr> <tr> <td> <p>SOC content map</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCc_0_30cm_1992_pred.tif</p> </td> </tr> <tr> <td> <p>SOC content, lower limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCc_0_30cm_1992_q05.tif</p> </td> </tr> <tr> <td> <p>SOC content, upper limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCc_0_30cm_1992_q95.tif</p> </td> </tr> <tr> <td> <p>SOC density map</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCd_0_30cm_1992_pred.tif</p> </td> </tr> <tr> <td> <p>SOC density, lower limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCd_0_30cm_1992_q05.tif</p> </td> </tr> <tr> <td> <p>SOC density, upper limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCd_0_30cm_1992_q95.tif</p> </td> </tr> <tr> <td> <p>SOC stock map</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCs_0_30cm_1992_pred.tif</p> </td> </tr> <tr> <td> <p>SOC stock, lower limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCs_0_30cm_1992_q05.tif</p> </td> </tr> <tr> <td> <p>SOC stock, upper limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCs_0_30cm_1992_q95.tif</p> </td> </tr> <tr> <td> <p>SOC content map</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCc_0_30cm_2000_pred.tif</p> </td> </tr> <tr> <td> <p>SOC content, lower limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCc_0_30cm_2000_q05.tif</p> </td> </tr> <tr> <td> <p>SOC content, upper limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCc_0_30cm_2000_q95.tif</p> </td> </tr> <tr> <td> <p>SOC density map</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCd_0_30cm_2000_pred.tif</p> </td> </tr> <tr> <td> <p>SOC density, lower limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCd_0_30cm_2000_q05.tif</p> </td> </tr> <tr> <td> <p>SOC density, upper limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCd_0_30cm_2000_q95.tif</p> </td> </tr> <tr> <td> <p>SOC stock map</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCs_0_30cm_2000_pred.tif</p> </td> </tr> <tr> <td> <p>SOC stock, lower limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCs_0_30cm_2000_q05.tif</p> </td> </tr> <tr> <td> <p>SOC stock, upper limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCs_0_30cm_2000_q95.tif</p> </td> </tr> </tbody> </table> <p> </p>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: WPCT p025, 2016–2020</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 Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T 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 Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage. </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://zenodo.org/records/13754343">2000-2004</a> <a href="https://zenodo.org/records/13771721">2004-2008</a> <a href="https://zenodo.org/records/13771841">2008-2012</a> <a href="https://zenodo.org/records/13771911">2012-2016</a> <a href="https://zenodo.org/records/13771967">2016-2020</a> <a href="https://zenodo.org/records/13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://zenodo.org/records/13779539">2000-2004</a> <a href="https://zenodo.org/records/13774064">2004-2008</a> <a href="https://zenodo.org/records/13774089">2008-2012</a> <a href="https://zenodo.org/records/13774114">2012-2016</a> <a href="https://zenodo.org/records/13774167">2016-2020</a> <a href="https://zenodo.org/records/13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://zenodo.org/records/13778472">2000-2004</a> <a href="https://zenodo.org/records/13773396">2004-2008</a> <a href="https://zenodo.org/records/13773765">2008-2012</a> <a href="https://zenodo.org/records/13773828">2012-2016</a> <a href="https://zenodo.org/records/13773953">2016-2020</a> <a href="https://zenodo.org/records/13774003">2020-2022</a> </li> <li><strong>WPCT mean:</strong><br> <a href="https://zenodo.org/records/13785010">2000-2004</a> <a href="https://zenodo.org/records/13785079">2004-2008</a> <a href="https://zenodo.org/records/13785170">2008-2012</a> <a href="https://zenodo.org/records/13785306">2012-2016</a> <a href="https://zenodo.org/records/13785419">2016-2020</a> <a href="https://zenodo.org/records/13785553">2020-2022</a> </li> <li><strong>WPCT p025:</strong><br> <a href="https://zenodo.org/records/13786250">2000-2004</a> <a href="https://zenodo.org/records/13786314">2004-2008</a> <a href="https://zenodo.org/records/13786449">2008-2012</a> <a href="https://zenodo.org/records/13786565">2012-2016</a> <a href="https://zenodo.org/records/13786594">2016-2020</a> <a href="https://zenodo.org/records/13786702">2020-2022</a> </li> <li><strong>WPCT p975:</strong><br> <a href="https://zenodo.org/records/13785722">2000-2004</a> <a href="https://zenodo.org/records/13785854">2004-2008</a> <a href="https://zenodo.org/records/13785917">2008-2012</a> <a href="https://zenodo.org/records/13786029">2012-2016</a> <a href="https://zenodo.org/records/13786119">2016-2020</a> <a href="https://zenodo.org/records/13786214">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</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. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: WPCT p025, 2020–2022</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 Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T 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 Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage. </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://zenodo.org/records/13754343">2000-2004</a> <a href="https://zenodo.org/records/13771721">2004-2008</a> <a href="https://zenodo.org/records/13771841">2008-2012</a> <a href="https://zenodo.org/records/13771911">2012-2016</a> <a href="https://zenodo.org/records/13771967">2016-2020</a> <a href="https://zenodo.org/records/13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://zenodo.org/records/13779539">2000-2004</a> <a href="https://zenodo.org/records/13774064">2004-2008</a> <a href="https://zenodo.org/records/13774089">2008-2012</a> <a href="https://zenodo.org/records/13774114">2012-2016</a> <a href="https://zenodo.org/records/13774167">2016-2020</a> <a href="https://zenodo.org/records/13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://zenodo.org/records/13778472">2000-2004</a> <a href="https://zenodo.org/records/13773396">2004-2008</a> <a href="https://zenodo.org/records/13773765">2008-2012</a> <a href="https://zenodo.org/records/13773828">2012-2016</a> <a href="https://zenodo.org/records/13773953">2016-2020</a> <a href="https://zenodo.org/records/13774003">2020-2022</a> </li> <li><strong>WPCT mean:</strong><br> <a href="https://zenodo.org/records/13785010">2000-2004</a> <a href="https://zenodo.org/records/13785079">2004-2008</a> <a href="https://zenodo.org/records/13785170">2008-2012</a> <a href="https://zenodo.org/records/13785306">2012-2016</a> <a href="https://zenodo.org/records/13785419">2016-2020</a> <a href="https://zenodo.org/records/13785553">2020-2022</a> </li> <li><strong>WPCT p025:</strong><br> <a href="https://zenodo.org/records/13786250">2000-2004</a> <a href="https://zenodo.org/records/13786314">2004-2008</a> <a href="https://zenodo.org/records/13786449">2008-2012</a> <a href="https://zenodo.org/records/13786565">2012-2016</a> <a href="https://zenodo.org/records/13786594">2016-2020</a> <a href="https://zenodo.org/records/13786702">2020-2022</a> </li> <li><strong>WPCT p975:</strong><br> <a href="https://zenodo.org/records/13785722">2000-2004</a> <a href="https://zenodo.org/records/13785854">2004-2008</a> <a href="https://zenodo.org/records/13785917">2008-2012</a> <a href="https://zenodo.org/records/13786029">2012-2016</a> <a href="https://zenodo.org/records/13786119">2016-2020</a> <a href="https://zenodo.org/records/13786214">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</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. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: WPCT p025, 2008–2012</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 Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T 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 Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage. </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://zenodo.org/records/13754343">2000-2004</a> <a href="https://zenodo.org/records/13771721">2004-2008</a> <a href="https://zenodo.org/records/13771841">2008-2012</a> <a href="https://zenodo.org/records/13771911">2012-2016</a> <a href="https://zenodo.org/records/13771967">2016-2020</a> <a href="https://zenodo.org/records/13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://zenodo.org/records/13779539">2000-2004</a> <a href="https://zenodo.org/records/13774064">2004-2008</a> <a href="https://zenodo.org/records/13774089">2008-2012</a> <a href="https://zenodo.org/records/13774114">2012-2016</a> <a href="https://zenodo.org/records/13774167">2016-2020</a> <a href="https://zenodo.org/records/13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://zenodo.org/records/13778472">2000-2004</a> <a href="https://zenodo.org/records/13773396">2004-2008</a> <a href="https://zenodo.org/records/13773765">2008-2012</a> <a href="https://zenodo.org/records/13773828">2012-2016</a> <a href="https://zenodo.org/records/13773953">2016-2020</a> <a href="https://zenodo.org/records/13774003">2020-2022</a> </li> <li><strong>WPCT mean:</strong><br> <a href="https://zenodo.org/records/13785010">2000-2004</a> <a href="https://zenodo.org/records/13785079">2004-2008</a> <a href="https://zenodo.org/records/13785170">2008-2012</a> <a href="https://zenodo.org/records/13785306">2012-2016</a> <a href="https://zenodo.org/records/13785419">2016-2020</a> <a href="https://zenodo.org/records/13785553">2020-2022</a> </li> <li><strong>WPCT p025:</strong><br> <a href="https://zenodo.org/records/13786250">2000-2004</a> <a href="https://zenodo.org/records/13786314">2004-2008</a> <a href="https://zenodo.org/records/13786449">2008-2012</a> <a href="https://zenodo.org/records/13786565">2012-2016</a> <a href="https://zenodo.org/records/13786594">2016-2020</a> <a href="https://zenodo.org/records/13786702">2020-2022</a> </li> <li><strong>WPCT p975:</strong><br> <a href="https://zenodo.org/records/13785722">2000-2004</a> <a href="https://zenodo.org/records/13785854">2004-2008</a> <a href="https://zenodo.org/records/13785917">2008-2012</a> <a href="https://zenodo.org/records/13786029">2012-2016</a> <a href="https://zenodo.org/records/13786119">2016-2020</a> <a href="https://zenodo.org/records/13786214">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</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. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: WPCT p025, 2012–2016</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 Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T 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 Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage. </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://zenodo.org/records/13754343">2000-2004</a> <a href="https://zenodo.org/records/13771721">2004-2008</a> <a href="https://zenodo.org/records/13771841">2008-2012</a> <a href="https://zenodo.org/records/13771911">2012-2016</a> <a href="https://zenodo.org/records/13771967">2016-2020</a> <a href="https://zenodo.org/records/13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://zenodo.org/records/13779539">2000-2004</a> <a href="https://zenodo.org/records/13774064">2004-2008</a> <a href="https://zenodo.org/records/13774089">2008-2012</a> <a href="https://zenodo.org/records/13774114">2012-2016</a> <a href="https://zenodo.org/records/13774167">2016-2020</a> <a href="https://zenodo.org/records/13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://zenodo.org/records/13778472">2000-2004</a> <a href="https://zenodo.org/records/13773396">2004-2008</a> <a href="https://zenodo.org/records/13773765">2008-2012</a> <a href="https://zenodo.org/records/13773828">2012-2016</a> <a href="https://zenodo.org/records/13773953">2016-2020</a> <a href="https://zenodo.org/records/13774003">2020-2022</a> </li> <li><strong>WPCT mean:</strong><br> <a href="https://zenodo.org/records/13785010">2000-2004</a> <a href="https://zenodo.org/records/13785079">2004-2008</a> <a href="https://zenodo.org/records/13785170">2008-2012</a> <a href="https://zenodo.org/records/13785306">2012-2016</a> <a href="https://zenodo.org/records/13785419">2016-2020</a> <a href="https://zenodo.org/records/13785553">2020-2022</a> </li> <li><strong>WPCT p025:</strong><br> <a href="https://zenodo.org/records/13786250">2000-2004</a> <a href="https://zenodo.org/records/13786314">2004-2008</a> <a href="https://zenodo.org/records/13786449">2008-2012</a> <a href="https://zenodo.org/records/13786565">2012-2016</a> <a href="https://zenodo.org/records/13786594">2016-2020</a> <a href="https://zenodo.org/records/13786702">2020-2022</a> </li> <li><strong>WPCT p975:</strong><br> <a href="https://zenodo.org/records/13785722">2000-2004</a> <a href="https://zenodo.org/records/13785854">2004-2008</a> <a href="https://zenodo.org/records/13785917">2008-2012</a> <a href="https://zenodo.org/records/13786029">2012-2016</a> <a href="https://zenodo.org/records/13786119">2016-2020</a> <a href="https://zenodo.org/records/13786214">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</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. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: WPCT p975, 2020–2022</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 Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T 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 Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage. </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://zenodo.org/records/13754343">2000-2004</a> <a href="https://zenodo.org/records/13771721">2004-2008</a> <a href="https://zenodo.org/records/13771841">2008-2012</a> <a href="https://zenodo.org/records/13771911">2012-2016</a> <a href="https://zenodo.org/records/13771967">2016-2020</a> <a href="https://zenodo.org/records/13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://zenodo.org/records/13779539">2000-2004</a> <a href="https://zenodo.org/records/13774064">2004-2008</a> <a href="https://zenodo.org/records/13774089">2008-2012</a> <a href="https://zenodo.org/records/13774114">2012-2016</a> <a href="https://zenodo.org/records/13774167">2016-2020</a> <a href="https://zenodo.org/records/13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://zenodo.org/records/13778472">2000-2004</a> <a href="https://zenodo.org/records/13773396">2004-2008</a> <a href="https://zenodo.org/records/13773765">2008-2012</a> <a href="https://zenodo.org/records/13773828">2012-2016</a> <a href="https://zenodo.org/records/13773953">2016-2020</a> <a href="https://zenodo.org/records/13774003">2020-2022</a> </li> <li><strong>WPCT mean:</strong><br> <a href="https://zenodo.org/records/13785010">2000-2004</a> <a href="https://zenodo.org/records/13785079">2004-2008</a> <a href="https://zenodo.org/records/13785170">2008-2012</a> <a href="https://zenodo.org/records/13785306">2012-2016</a> <a href="https://zenodo.org/records/13785419">2016-2020</a> <a href="https://zenodo.org/records/13785553">2020-2022</a> </li> <li><strong>WPCT p025:</strong><br> <a href="https://zenodo.org/records/13786250">2000-2004</a> <a href="https://zenodo.org/records/13786314">2004-2008</a> <a href="https://zenodo.org/records/13786449">2008-2012</a> <a href="https://zenodo.org/records/13786565">2012-2016</a> <a href="https://zenodo.org/records/13786594">2016-2020</a> <a href="https://zenodo.org/records/13786702">2020-2022</a> </li> <li><strong>WPCT p975:</strong><br> <a href="https://zenodo.org/records/13785722">2000-2004</a> <a href="https://zenodo.org/records/13785854">2004-2008</a> <a href="https://zenodo.org/records/13785917">2008-2012</a> <a href="https://zenodo.org/records/13786029">2012-2016</a> <a href="https://zenodo.org/records/13786119">2016-2020</a> <a href="https://zenodo.org/records/13786214">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</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. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: WPCT p975, 2016–2020</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 Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T 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 Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage. </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://zenodo.org/records/13754343">2000-2004</a> <a href="https://zenodo.org/records/13771721">2004-2008</a> <a href="https://zenodo.org/records/13771841">2008-2012</a> <a href="https://zenodo.org/records/13771911">2012-2016</a> <a href="https://zenodo.org/records/13771967">2016-2020</a> <a href="https://zenodo.org/records/13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://zenodo.org/records/13779539">2000-2004</a> <a href="https://zenodo.org/records/13774064">2004-2008</a> <a href="https://zenodo.org/records/13774089">2008-2012</a> <a href="https://zenodo.org/records/13774114">2012-2016</a> <a href="https://zenodo.org/records/13774167">2016-2020</a> <a href="https://zenodo.org/records/13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://zenodo.org/records/13778472">2000-2004</a> <a href="https://zenodo.org/records/13773396">2004-2008</a> <a href="https://zenodo.org/records/13773765">2008-2012</a> <a href="https://zenodo.org/records/13773828">2012-2016</a> <a href="https://zenodo.org/records/13773953">2016-2020</a> <a href="https://zenodo.org/records/13774003">2020-2022</a> </li> <li><strong>WPCT mean:</strong><br> <a href="https://zenodo.org/records/13785010">2000-2004</a> <a href="https://zenodo.org/records/13785079">2004-2008</a> <a href="https://zenodo.org/records/13785170">2008-2012</a> <a href="https://zenodo.org/records/13785306">2012-2016</a> <a href="https://zenodo.org/records/13785419">2016-2020</a> <a href="https://zenodo.org/records/13785553">2020-2022</a> </li> <li><strong>WPCT p025:</strong><br> <a href="https://zenodo.org/records/13786250">2000-2004</a> <a href="https://zenodo.org/records/13786314">2004-2008</a> <a href="https://zenodo.org/records/13786449">2008-2012</a> <a href="https://zenodo.org/records/13786565">2012-2016</a> <a href="https://zenodo.org/records/13786594">2016-2020</a> <a href="https://zenodo.org/records/13786702">2020-2022</a> </li> <li><strong>WPCT p975:</strong><br> <a href="https://zenodo.org/records/13785722">2000-2004</a> <a href="https://zenodo.org/records/13785854">2004-2008</a> <a href="https://zenodo.org/records/13785917">2008-2012</a> <a href="https://zenodo.org/records/13786029">2012-2016</a> <a href="https://zenodo.org/records/13786119">2016-2020</a> <a href="https://zenodo.org/records/13786214">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</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. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
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.