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Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: WPCT mean, 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 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. </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 mean, 2004–2008</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 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. </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 mean, 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. </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>
Data from: Microplastic and organic carbon storage in sediments of intertidal and subtidal seagrass meadows
<p>This datasets support the scientific article (submitted) " Microplastic and organic carbon storage in sediments of intertidal and subtidal seagrass meadows." It contains detailed data on sedimentary organic carbon content and the abundance of microplastics in both intertidal and subtidal seagrass meadows within the Ria Formosa lagoon (Southern Portugal). The datasets are accompanied by analysis code, available at GitHub repository, allowing for reproducibility and further exploration of the data.</p> <p>The data is composed by 4 datasets with the following variables:</p> <p><strong>data_cores.csv. </strong>Contains properties related to the sampling of the sediment cores.</p> <ul> <li>core_id [character] - unique core identification code used in the field.</li> <li>core_id_new [character] - unique core identification code used in the article.</li> <li>species [character] - species of the seagrass meadow.</li> <li>replicate [character] - replicate number of the core in each seagrass meadow.</li> <li>core_depth [numeric] - depth sampled with the core (in centimeters).</li> <li>sample_length [numeric] - length of the sampled core measured in the laboratory (in centimeters).</li> <li>compaction_factor [numeric] - fraction of the sample depth interval reduced due to compaction. It is calculated by dividing the core length by the core depth.</li> <li>compaction_perc [numeric] - core compaction in percentage (%). It is calculated as 100*(1 - compaction_factor).</li> </ul> <p><br><strong>data_samples.csv. </strong>Contains properties of the sediment samples.</p> <ul> <li>core_id [character] - unique core identification code used in the field.</li> <li>sample_id [character] - unique sample identification code (obtained by concatenating the core id and the minimum non-corrected depth of the sample).</li> <li>depth_middle [numeric] - middle depth of a sampling increment, calculating as the average of depth_min and depth_max (in centimeters).</li> <li>depth_min [numeric] - minimum depth of a sampling increment, corrected for compaction (in centimeters).</li> <li>depth_max [numeric] - maximum depth of a sampling increment, corrected for compaction (in centimeters).</li> <li>sample_volume [numeric] - volume of the sediment sample, corrected for compaction (in cubic centimeters).</li> <li>sample_dw [numeric] - dry mass of the sample (in grams of dry weight).</li> <li>percentage_organic_matter [numeric] - mass of organic matter relative to sample dry mass, obtained by loss-on-ignition (in percentage of dry weight).</li> <li>percentage_organic_carbon [numeric] - mass of organic carbon relative to sample dry mass, obtained by a local organic carbon to organic carbon ratio (as a percentage of dry weight).</li> <li>bag_id [character] - identification code for the aluminium envelope containing the sample.</li> <li>weight_sample_mp [numeric] - dry mass of the sample used for the microplastic extraction (in grams of dry weight).</li> <li>dry_bulk_density [numeric] - dry mass per unit volume of the sample. This is calculated as the sample_dw divided by the sample_dw (in grams of dry weight per cubic centimeter). </li> </ul> <p><br><strong>data_particles_visual.csv. </strong>Contains properties of the suspected microplastic particles found in the sediment samples and the negative controls, based on visual inspection.</p> <ul> <li>core_id [character] - unique core identification code used in the field.</li> <li>core_id_new [character] - unique core identification code used in the article.</li> <li>species [character] - species of the seagrass meadow.</li> <li>habitat_label [character] - text for labelling purposes regarding the habitat.</li> <li>type [factor] - whether the particle comes from a sediment sample ("sediment") or a control sample ("control"). </li> <li>cycle [character] - cycle in which samples were analysed.</li> <li>sample_id [character] - unique sample identification code (obtained by concatenating the core id and the minimum non-corrected depth of the sample).</li> <li>bag_id [character] - identification code for the aluminium envelope containing the sample.</li> <li>filter_id [character] - identification code of the filter used for the particle extraction.</li> <li>filter_area [numeric] - area of the filter that was screened for microplastics (in fraction of total).</li> <li>visual_id [character] - unique particle identification code based on visual identification.</li> <li>colour [factor] - particle colour category: black_grey, blue_green, brown_tan, opaque, orange_pink_red, transparent, white_cream, yellow.</li> <li>shape [factor] - particle shape category: film, foam, fragment, line, pellet.</li> <li>major [numeric] - longest dimension of the particle, analysed in ImageJ (in micrometers).</li> <li>minor [numeric] - Longest dimension perpendicular to major, analysed in ImageJ (in micrometers).</li> </ul> <p><br><strong>data_particles_ftir.csv. </strong>Contains properties of the suspected microplastic particles found in the sediment samples and the negative controls, based on the FTIR analysis.</p> <ul> <li>species [character] - species of the seagrass meadow.</li> <li>habitat_label [character] - text for labelling purposes regarding the habitat.</li> <li>core_id [character] - unique core identification code used in the field.</li> <li>core_id_new [character] - unique core identification code used in the article.</li> <li>filter_id [character] - identification code of the filter used for the particle extraction.</li> <li>filter_area [numeric] - area of the filter that was screened for microplastics (in fraction of total).</li> <li>cycle [character] - cycle in which samples were analysed.</li> <li>type [factor] - whether the particle comes from a sediment sample ("sediment") or a control sample ("control"). </li> <li>sample_id [character] - unique sample identification code (obtained by concatenating the core id and the minimum non-corrected depth of the sample).</li> <li>num_ftir [numeric] - numerical order in which particles were identified within a filter.</li> <li>colour [factor] - particle colour category: black_grey, blue_green, brown_tan, opaque, orange_pink_red, transparent, white_cream, yellow.</li> <li>shape [factor] - particle shape category: film, foam, fragment, line, pellet.</li> <li>ref_analysis [boolean] - whether the reflection analysis was preformed or not.</li> <li>atr_analysis [boolean] - whether the ATR analysis was preformed or not.</li> <li>ftir_match_ref [character] - name of the polymer with the highest match found using µFTIR for reflection analysis.</li> <li>match_ref [numeric] - percentage of match corresponding to highest match for reflection analysis.</li> <li>ftir_match_atr [character] - name of the polymer with the highest match found using µFTIR for ATR analysis.</li> <li>match_atr [numeric] - percentage of match corresponding to highest match for ATR analysis.</li> <li>plastic_ref [boolean] - whether the particle is classified as having a plastic composition or not, based on the reflection analysis.</li> <li>polymer_group_ref [factor] - polymer group based on the reflection analysis: Non-plastic, Nylon-polyamides, Poluacrylamides, Polyacrylates, Polyesters, Polyethylene, Polyglycols, Polyhaloolefins, Polymethylmethacrylate, Polypropylene, Polystyrene, Polyurethane, Polyvinylalcohol, Silicones, Others.</li> <li>plastic_atr [boolean] - whether the particle is classified as having a plastic composition or not, based on the ATR analysis.</li> <li>polymer_group_atr [factor] - polymer group based on the ATR analysis: Non-plastic, Nylon-polyamides, Poluacrylamides, Polyacrylates, Polyesters, Polyethylene, Polyglycols, Polyhaloolefins, Polymethylmethacrylate, Polypropylene, Polystyrene, Polyurethane, Polyvinylalcohol, Silicones, Others.</li> <li>ftir_match_final [character] - final decision on the polymer composition, including the option "unclear".</li> <li>plastic_final [factor] - whether the particle is classified as having a plastic composition or not, based on final decision "ftir_match_final", includes categories: yes, no, unclear.</li> <li>final_analysis [character] - the analysis performed and used for the final decision, includes categories: ref (reflection analysis), atr (ATR analysis), both-but-atr-more-conclusive, both-but-ref-more-conclusive, both-unclear.</li> <li>polymer_group_final [character] - polymer group based final decision: Non-plastic, Nylon-polyamides, Poluacrylamides, Polyacrylates, Polyesters, Polyethylene, Polyglycols, Polyhaloolefins, Polymethylmethacrylate, Polypropylene, Polystyrene, Polyurethane, Polyvinylalcohol, Silicones, Others.</li> </ul>
Data for Publication - Synergies and Trade-offs between Robusta Yield, Carbon Stocks and Biodiversity across Coffee Systems in the DR Congo
<p>Data used for the publication:</p> <p>"Synergies and Trade-offs between Robusta Yield, Carbon Stocks and Biodiversity across Coffee Systems in the DR Congo" - Ieben Broeckhoven, Jonas Depecker, Trésor Kasereka Muliwambene, Olivier Honnay, Roel Merckx and Bruno Verbist</p>
IODP Expedition 355 Carbonates composite report
This composite report includes data from two analyses (total carbon from Elemental analysis [CHNS], and inorganic carbon from [Coulometer]). Each row combines the CHNS and Coulometer data from measurements made on the same sample at the same time for a particular section and section offset (depth). If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate data type (Elemental analysis and Coulometer). If the same sample was measured multiple times by any of the methods, results in the report will be combined on one line where possible. Each additional replicate result will be shown in subsequent rows and will be combined where possible. Report includes results for carbon forms: total, inorganic, calcium carbonate, and organic by difference, along with total hydrogen, nitrogen, and sulfur.
IODP Expedition 355 Inorganic carbon (coulometer)
Inorganic carbon (carbonate) is determined by coulometry, which uses a photodetection cell to measure carbon dioxide evolved during sample acidification. Report includes percent inorganic carbon and calcium carbonate.
Dataset and code: One-tenth of EU's biomethane potential combined with carbon capture and storage can shift the region's ammonia production to net-zero
<h2>Overview</h2> <p>Repository to share the data and code associated with the scientific article <strong>Istrate et al. One-tenth of EU’s biomethane potential combined with carbon capture and storage can shift the region’s ammonia production to net-zero. One Earth (2024)</strong>. The repository contains data files and code to import the life cycle inventories (LCIs), reproduce the results, and generate the figures presented in the article.</p> <div> <h2>Repository structure</h2> </div> <p>The data folder includes:</p> <ul> <li><code>inventories.xlsx</code> contains the LCI datasets for biomethane and ammonia production formatted for use with <a href="https://github.com/brightway-lca">Brightway</a>.</li> <li><code>sustainable_biomethane_potential_Europe.xlsx</code> contains data on the sustainable biomethane potential in Europe disaggregated by feedstock and country.</li> <li><code>ammonia_production_europe.xlsx</code> contains ammonia production levels in the EU in 2021.</li> <li><code>SA_methane leakage_for presample.xlsx</code> contains data to perform sensitivity analysis on the methane leakage with <a href="https://github.com/PascalLesage/presamples">presamples</a></li> <li><code>SA_upgrading technology_presamples.xlsx</code> contains data to perform sensitivity analysis on upgrading technologies with <a href="https://github.com/PascalLesage/presamples">presamples</a></li> <li><code>results</code> folder within data contains csv files with the results, which are used in <code>05_visualization.ipynb</code> for analysis and visualization purposes.</li> </ul> <p>The notebooks folder includes:</p> <ul> <li><code>01_project_setup.ipynb</code> sets up a new Brightway project and imports the ecoinvent database.</li> <li><code>02_lci.ipynb</code> imports the LCIs and regionalize some datasets (e.g., biomethane supply based on the bimethane potential).</li> <li><code>03_lcia.ipynb</code> calculates life cycle impacts and all the additional results presented in the paper (e.g., calculation of blending ratios).</li> <li><code>04_sensitivity_analysis.ipynb</code> performs the sensitivity analysis.</li> <li><code>05_visualization.ipynb</code> imports all results and generates the figures presented in the scientific article.</li> </ul> <p>The src folder contains supporting functions required to regionalize LCIs and perform the calculations.</p> <div> <h2>How to get propertary data</h2> </div> <p>Some of the LCI datasets in the <code>inventories.xlsx</code> file are partially based on data from the ecoinvent LCI database. To comply with licensing requirements, the file shared in this repository does not include these data points. If you hold a valid ecoinvent license, please contact me directly to receive the full input files containing all ecoinvent data points.</p> <h2>Contact</h2> <p>Robert Istrate: i.r.istrate@cml.leidenuniv.nl</p>
GCAM input files for "Decarbonization pathways for Korea's industrial sector towards its 2050 carbon neutrality goal"
<p>GCAM input files for "Decarbonization pathways for Korea's industrial sector towards its 2050 carbon neutrality goal"</p>
IODP Expedition 356 Inorganic carbon (coulometer)
Inorganic carbon (carbonate) is determined by coulometry, which uses a photodetection cell to measure carbon dioxide evolved during sample acidification. Report includes percent inorganic carbon and calcium carbonate.
IODP Expedition 356 Carbonates composite report
This composite report includes data from two analyses (total carbon from Elemental analysis [CHNS], and inorganic carbon from [Coulometer]). Each row combines the CHNS and Coulometer data from measurements made on the same sample at the same time for a particular section and section offset (depth). If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate data type (Elemental analysis and Coulometer). If the same sample was measured multiple times by any of the methods, results in the report will be combined on one line where possible. Each additional replicate result will be shown in subsequent rows and will be combined where possible. Report includes results for carbon forms: total, inorganic, calcium carbonate, and organic by difference, along with total hydrogen, nitrogen, and sulfur.
IODP Expedition 359 Carbonates composite report
This composite report includes data from two analyses (total carbon from Elemental analysis [CHNS], and inorganic carbon from [Coulometer]). Each row combines the CHNS and Coulometer data from measurements made on the same sample at the same time for a particular section and section offset (depth). If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate data type (Elemental analysis and Coulometer). If the same sample was measured multiple times by any of the methods, results in the report will be combined on one line where possible. Each additional replicate result will be shown in subsequent rows and will be combined where possible. Report includes results for carbon forms: total, inorganic, calcium carbonate, and organic by difference, along with total hydrogen, nitrogen, and sulfur.
IODP Expedition 359 Inorganic carbon (coulometer)
Inorganic carbon (carbonate) is determined by coulometry, which uses a photodetection cell to measure carbon dioxide evolved during sample acidification. Report includes percent inorganic carbon and calcium carbonate.
Carbon Storage in Beaver Meadows of the Sierra Nevada, USA
<p><span>The purpose of this study was to examine the effects of active beaver dams on sequestered carbon in subalpine valleys of the Sierra Nevada across a temporal scale. Specifically, carbon stored in floodplain sediments and above and below ground vegetation was measured in 8 beaver meadows to quantify carbon storage.</span></p>
Paleoclimate signals and groundwater age distributions from 39 public water works in the Netherlands; insights from noble gases and carbon, hydrogen and oxygen isotope tracers [Data set].
<p>Data set covering the meta data of the 39 well fields, the macro chemistry data and the data of the noble gases and carbon, hydrogen and oxygen isotope tracers used for assessing the paleoclimate signals and age distributions in the publication in Water Resources Research (2021)</p> <p><strong>Paleoclimate signals and groundwater age distributions from 39 public water works in the Netherlands; insights from noble gases and carbon, hydrogen and oxygen isotope tracers</strong></p> <p>Hans Peter Broers, Jürgen Sültenfuß<sup> </sup>, Werner Aeschbach, Arne Kersting,, Armin Menkovich, Jasperien de Weert and Jeroen Castelijns</p>
Carbon emission and lifecycle costs supporting digital twins for managing railway maintenance and resilience
<p>The development of railway construction increases the system complexity, which results in difficulty in management with traditional methods. Building Information Modelling (BIM) as an interoperable concept is benefits via whole life-cycle assessment (LCA) of the project, and it has been widely adopted in architecture, construction, and engineering (ACE) fields. This dataset of lifecycle cost and carbon footprint supports the digital twins for managing railway maintenance and resilience.</p>
Areas of global importance for conserving terrestrial biodiversity, carbon, and water
<p><strong>Content:</strong><br> This data repository contains the results of the NatureMap ( naturemap.earth/) conservation prioritization effort. The maps were created by jointly optimizing biodiversity and NCPs such as carbon and/or water.</p> <p><strong>Usage notes:</strong><br> Maps are supplied at both 10km and 50km resolution unless specified differently in the manuscript.<br> All maps that aim to find priority areas for all species considered in the analysis, utilize a series of representative sets.<br> The ranks for each layer are area-specific and can be used to extract summary statistics by simple subsetting.<br> For example:<br> To obtain the top 30% of land area for biodiversity and carbon, one needs to create a mask of all areas lower than a value of 30 from the respective ranked layers.</p> <p>For convenience two files are supplied that contain the fraction of land area per grid cell times 1000. Multiplying those with the cell area (100km2, respectively 2500km2) gives the exact amount of land area in a given grid cell.<br> These are labelled " globalgrid_mollweide_**km.tif " can be used to create masks for the priority maps.</p> <p><strong>Spatial resolution:</strong></p> <p>10 and 50 km</p> <p><strong>Geographic projection:</strong><br> World Mollweide Equal Area projection<br> PROJ4 ( +proj=moll +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs )</p> <p><strong>Filename suffix description:</strong></p> <p><em>'minshort_speciestargets'</em><br> =- Problem formulation where targets were achieved by minimzing a shortfall</p> <p><em>'repruns10'</em><br> =- The number of representative that were used to create the ranked layer</p> <p><em>'biome.id'</em><br> =- Species distribution were split by biome, thus creating separate targets for subpopulation</p> <p><em>'withPA'</em><br> =- Fractions of current protected areas (Date: WDPA 2019) were locked in as baseline and starting budget. Approximately 15% of the globe. Note that not entire grid cells, but fractions were locked in and build opon!</p> <p><em>'carbon'</em><br> =- Carbon was included in the prioritization and jointly optimized together with the other assets by giving it equal weighting (see manuscript)</p> <p><em>'water'</em><br> =- Water was included in the prioritization and jointly optimized together with the other assets by giving it equal weighting (see manuscript)</p> <p><strong>License:</strong><br> CC-BY-SA 4.0</p> <p><strong>Citation:</strong><br> Jung, Martin, Andy Arnell, Xavier De Lamo, Shaenandhoa Garcia-Rangel, Matthew Lewis, Jennifer Mark, Cory Merow et al. (2021) "Areas of global importance for terrestrial biodiversity, carbon, and water." Nature Ecology & Evolution</p>
Structure and composition and carbon Stocks of woody plant community in assisted and unassisted ecological succession in a Tamaulipan thornscrub, Mexico
<p>In November of 2017, the structure and composition of woody plant communities were investigated through a floristic composition and diversity evaluation on three areas: a control area, an assisted ecological succession area and an unassisted ecological succession area.</p>
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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.