Find research datasets worth reusing

September 2003 surface water dissolved organic carbon concentrations at ten Georgia Coastal Ecosystems LTER sampling sites

Water samples were collected by Niskin bottle from just beneath the surface during tidal surveys at ten GCE-LTER sampling sites in September 2003. Water samples were immediately filtered through ashed Whatman GF/F filters and three replicate samples were acidified, refrigerated and transported to the laboratory for analysis. Concentrations of dissolved organic carbon were measured in each sample by high temperature combustion and chromatographic analysis using a Shimadzu TOC analyzer. This study was part of the GCE-LTER hydrographic monitoring program, and will be repeated quarterly.

June 2003 surface water dissolved organic carbon concentrations at ten Georgia Coastal Ecosystems LTER sampling sites

Water samples were collected by Niskin bottle from just beneath the surface during tidal surveys at ten GCE-LTER sampling sites in June 2003. Water samples were immediately filtered through ashed Whatman GF/F filters and three replicate samples were acidified, refrigerated and transported to the laboratory for analysis. Concentrations of dissolved organic carbon were measured in each sample by high temperature combustion and chromatographic analysis using a Shimadzu TOC analyzer. This study was part of the GCE-LTER hydrographic monitoring program, and will be repeated quarterly.

May 2004 surface water dissolved organic carbon concentrations at ten Georgia Coastal Ecosystems LTER sampling sites

Water samples were collected by Niskin bottle from just beneath the surface during tidal surveys at ten GCE-LTER sampling sites in May 2004. Water samples were immediately filtered through ashed Whatman GF/F filters and three replicate samples were acidified, refrigerated and transported to the laboratory for analysis. Concentrations of dissolved organic carbon were measured in each sample by high temperature combustion and chromatographic analysis using a Shimadzu TOC analyzer. This study was part of the GCE-LTER hydrographic monitoring program, and will be repeated quarterly.

December 2003 surface water dissolved organic carbon concentrations at ten Georgia Coastal Ecosystems LTER sampling sites

Water samples were collected by Niskin bottle from just beneath the surface during tidal surveys at ten GCE-LTER sampling sites in December 2003. Water samples were immediately filtered through ashed Whatman GF/F filters and three replicate samples were acidified, refrigerated and transported to the laboratory for analysis. Concentrations of dissolved organic carbon were measured in each sample by high temperature combustion and chromatographic analysis using a Shimadzu TOC analyzer. This study was part of the GCE-LTER hydrographic monitoring program, and will be repeated quarterly.

openCC (other)Sep 2025View details →

Particulate organic carbon (POC) and nitrogen (PON) concentrations in discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (1993-2024, ongoing)

An important part of the McMurdo Long Term Ecological Research (LTER) is evaluating carbon and nitrogen budgets in perennial ice-covered lakes. This dataset addresses this core area of research and quantifies the particulate organic carbon (POC) and nitrogen (PON) found at specific depths in lakes across the McMurdo Dry Valleys of Antarctica.

opencc-by-4.0Mar 2020View details →

Carbon emissions and economic assessment of farm operations under different tillage practices in organic rainfed almond orchards under semiarid Mediterranean conditions

This dataset corresponds to yield, price and fuel consumption from organic rainfed almond orchards in SE Spain  under different diversification and tillage practices. The objective is to carry out an integrated environmental (focused on the CO2 emissions) and economic assessment of farm operations under different diversification and tillage practices through a cradle-to-farm gate life cycle assessment (LCA) based on these data. These data correspond to the open-access article " Carbon emissions and economic assessment of farm operations under different tillage practices in organic rainfed almond orchards under semiarid Mediterranean conditions" published in Scientia Horticulturae. (https://doi.org/10.1016/j.scienta.2019.108978), funded by the European Commission Horizon 2020 project Diverfarming [grant agreement 728003].

opencc-by-4.0Nov 2020View details →

Data from: Dwarf shrubs impact tundra soils: drier, colder, and less organic carbon

In the tundra, woody plants are dispersing towards higher latitudes and altitudes due to increasingly favourable climatic conditions. The coverage and height of woody plants are increasing, which may influence the soils of the tundra ecosystem. Here, we use structural equation modelling to analyse 171 study plots and to examine if the coverage and height of woody plants affect the growing-season topsoil moisture and temperature (< 10 cm) as well as soil organic carbon stocks (< 80 cm). In our study setting, we consider the hierarchy of the ecosystem by controlling for other factors, such as topography, wintertime snow depth and the overall plant coverage that potentially influence woody plants and soil properties in this dwarf-shrub dominated landscape in northern Fennoscandia. We found strong links from topography to both vegetation and soil. Further, we found that woody plants influence multiple soil properties: the dominance of woody plants inversely correlated with soil moisture, soil temperature, and soil organic carbon stocks (standardised regression coefficients = -0.39; -0.22; -0.34, respectively), even when controlling for other landscape features. Our results indicate that the dominance of dwarf shrubs may lead to soils that are drier, colder, and contain less organic carbon. Thus, there are multiple mechanisms through which woody plants may influence tundra soils. Kemppinen, Niittynen, Virkkala, Happonen, Riihimäki, Aalto & Luoto (2021). Dwarf shrubs impact tundra soils: drier, colder, and less organic carbon. Ecosystems. These are the data from Kemppinen et al. (2021).

opencc-by-4.0Dec 2020View details →

Soil organic carbon stocks and trends (1984-2019) predicted at 30m spatial resolution for topsoil in natural areas of South Africa

Link to scientific publication: <a href="https://doi.org/10.1016/j.scitotenv.2021.145384">https://doi.org/10.1016/j.scitotenv.2021.145384</a> Soil organic carbon (SOC) stocks (kg C m-2) are predicted over natural areas (excluding water, urban, and cultivated) of South Africa using a machine learning workflow driven by optical satellite data and other ancillary climatic, morphometric and biological covariates. The temporal scope covers 1984-2019. The spatial scope covers 0-30cm topsoil in South Africa natural land area (84% of the country). See methodology in linked publication for details. Data are provided here at 30m spatial resolution in GeoTIFF files. There is a dataset for the long-term average SOC and trend in SOC. Each dataset is split into four files (suffix *_1, *_2 etc.) covering separate regions of South Africa for ease of download. The raster files are: <ul> <li>"SOC_mean_30m..." - average of annual SOC predictions between 1984 and 2019. Values are expressed in kg C m-2</li> <li>"SOC_trend_30m..." - long-term trend in SOC derived from the Sens slope (M) across annual SOC values between 1984 and 2019. Pixel values (Y) are expressed as a percentage change over the 35 years relative to the long-term mean (X). Y = M / X * 100 * 35 years</li> </ul> NB: All files are scaled by *100 and converted to floating data point to save space. To back-convert to original values, simply divide the raster values by 100.

opencc-by-4.0Mar 2024View details →

Database to: Cover crops affect pool specific soil organic carbon in cropland – A meta‐analysis

Database to a meta-analysis studying the effects of cover crops on the mineral-associated organic carbon pool (MAOC), the particulate organic carbon pool (POC) and the microbial biomass carbon pool (MBC). Consists of: 1. information on the database 2. legend 3. list of included studies, all extracted data necessary for response ratio calculation and moderator analysis, and additional information

opencc-by-4.0Nov 2024View details →

Soil grid data for 3 agricultural fields in Italy (Soil Moisture, soil organic carbon)

Soil data collected in an agricultural area with annual crops in Italy (west-central Lombardia Po Valley, province of Pavia). The data refers to soil properties of 320 soil samples for Soil Moisture and  120 for SOC, collected in the topsoil (around 5-10 cm), considering a regular sampling grid, within three agricultural field with different crops (spring-summer cycle) and soils type, Rice-Loamy, Sorghum-Sandy and, Maize-Clay, in a period (before the seeding of the crops),  when the soil was bare, in the framework of the EJP Steropes project. The aim of the collected dataset was to be able to analyse the influence of soil moisture in SOC (WP2 of the STEROPES project) prediction models from remote sensing. Data in the form of shape file (one shapefile for each agricultural field, for oth Soil Moisture and SOC), and pictures of the soil surface in .jpg format.

opencc-by-4.0Nov 2024View details →

Data supplement: Spatial autocorrelation in machine learning for modelling soil organic carbon

Spatial autocorrelation in machine learning for modelling soil organic carbon: Data supplement Alexander Kmoch, Clay Taylor Harrison, Jeonghwan Choi, Evelyn Uuemaa Spatial autocorrelation, the relationship between nearby samples of a spatial random variable, is often overlooked in machine learning models, leading to biased results. This study investigates various methods to account for spa- tial autocorrelation when predicting soil organic carbon (SOC) using random forest models. Five models incorporating spatial structure were compared against baseline models that did not have any added spatial components. Cross-validation showed slight improvements in accuracy for models consid- ering spatial autocorrelation, while Shapley Additive Explanations confirmed the importance of spatial variables. However, no decrease in spatial autocor- relation of residuals was observed. Raster-based models exhibited enhanced prediction detail, but high-resolution validation data availability limited thor- ough validation. The findings emphasize the value of incorporating spatial autocorrelation for improved SOC prediction in machine learning models. Considerations such as the distribution of predictions and computational complexity should help guide the selection of suitable approaches for specific spatial modelling tasks.

Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T

<h2>Sub-dataset: SOCD p025, 2016–2020</h2> <h2>Disclaimer</h2> 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. <h2>Description</h2> 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. 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. This data set includes: <ul> <li>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals): This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m3 (scaled 10x). </li> <li>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals): 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>SOCD mean: <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>SOCD p025: <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>SOCD p975: <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>Time period: 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li>Type of data: Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li>How the data was collected or derived: The data was derived using machine learning models.</li> <li>Statistical methods used: Quantile Random Forest</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> 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: <ol> <li>generic variable name: oc = organic carbon</li> <li>variable procedure combination: iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm3 for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li>Position in the probability distribution/variable type: m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li>Spatial support: 30m</li> <li>Depth reference: b0cm..20cm = depth range from 0 to 20cm</li> <li>Time reference begin time: 20000101 = 2000-01-01</li> <li>Time reference end time: 20041231 = 2004-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: 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>Sub-dataset: SOCD p025, 2008–2012</h2> <h2>Disclaimer</h2> 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. <h2>Description</h2> 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. 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. This data set includes: <ul> <li>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals): This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m3 (scaled 10x). </li> <li>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals): 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>SOCD mean: <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>SOCD p025: <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>SOCD p975: <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>Time period: 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li>Type of data: Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li>How the data was collected or derived: The data was derived using machine learning models.</li> <li>Statistical methods used: Quantile Random Forest</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> 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: <ol> <li>generic variable name: oc = organic carbon</li> <li>variable procedure combination: iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm3 for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li>Position in the probability distribution/variable type: m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li>Spatial support: 30m</li> <li>Depth reference: b0cm..20cm = depth range from 0 to 20cm</li> <li>Time reference begin time: 20000101 = 2000-01-01</li> <li>Time reference end time: 20041231 = 2004-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: 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>Sub-dataset: SOCD p025, 2004–2008</h2> <h2>Disclaimer</h2> 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. <h2>Description</h2> 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. 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. This data set includes: <ul> <li>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals): This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m3 (scaled 10x). </li> <li>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals): 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>SOCD mean: <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>SOCD p025: <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>SOCD p975: <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>Time period: 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li>Type of data: Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li>How the data was collected or derived: The data was derived using machine learning models.</li> <li>Statistical methods used: Quantile Random Forest</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> 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: <ol> <li>generic variable name: oc = organic carbon</li> <li>variable procedure combination: iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm3 for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li>Position in the probability distribution/variable type: m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li>Spatial support: 30m</li> <li>Depth reference: b0cm..20cm = depth range from 0 to 20cm</li> <li>Time reference begin time: 20000101 = 2000-01-01</li> <li>Time reference end time: 20041231 = 2004-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: 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>Sub-dataset: SOCD p025, 2000–2004</h2> <h2>Disclaimer</h2> 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. <h2>Description</h2> 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. 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. This data set includes: <ul> <li>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals): This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m3 (scaled 10x). </li> <li>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals): 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>SOCD mean: <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>SOCD p025: <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>SOCD p975: <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>Time period: 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li>Type of data: Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li>How the data was collected or derived: The data was derived using machine learning models.</li> <li>Statistical methods used: Quantile Random Forest</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> 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: <ol> <li>generic variable name: oc = organic carbon</li> <li>variable procedure combination: iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm3 for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li>Position in the probability distribution/variable type: m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li>Spatial support: 30m</li> <li>Depth reference: b0cm..20cm = depth range from 0 to 20cm</li> <li>Time reference begin time: 20000101 = 2000-01-01</li> <li>Time reference end time: 20041231 = 2004-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: 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>Sub-dataset: SOCD p975, 2008–2012</h2> <h2>Disclaimer</h2> 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. <h2>Description</h2> 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. 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. This data set includes: <ul> <li>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals): This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m3 (scaled 10x). </li> <li>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals): 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>SOCD mean: <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>SOCD p025: <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>SOCD p975: <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>Time period: 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li>Type of data: Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li>How the data was collected or derived: The data was derived using machine learning models.</li> <li>Statistical methods used: Quantile Random Forest</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> 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: <ol> <li>generic variable name: oc = organic carbon</li> <li>variable procedure combination: iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm3 for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li>Position in the probability distribution/variable type: m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li>Spatial support: 30m</li> <li>Depth reference: b0cm..20cm = depth range from 0 to 20cm</li> <li>Time reference begin time: 20000101 = 2000-01-01</li> <li>Time reference end time: 20041231 = 2004-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: 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>Sub-dataset: SOCD mean, 2004–2008</h2> <h2>Disclaimer</h2> 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. <h2>Description</h2> 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. 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. This data set includes: <ul> <li>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals): This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m3 (scaled 10x). </li> <li>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals): 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>SOCD mean: <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>SOCD p025: <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>SOCD p975: <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>Time period: 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li>Type of data: Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li>How the data was collected or derived: The data was derived using machine learning models.</li> <li>Statistical methods used: Quantile Random Forest</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> 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: <ol> <li>generic variable name: oc = organic carbon</li> <li>variable procedure combination: iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm3 for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li>Position in the probability distribution/variable type: m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li>Spatial support: 30m</li> <li>Depth reference: b0cm..20cm = depth range from 0 to 20cm</li> <li>Time reference begin time: 20000101 = 2000-01-01</li> <li>Time reference end time: 20041231 = 2004-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: 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>Sub-dataset: SOCD p975, 2020–2022</h2> <h2>Disclaimer</h2> 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. <h2>Description</h2> 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. 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. This data set includes: <ul> <li>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals): This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m3 (scaled 10x). </li> <li>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals): 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>SOCD mean: <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>SOCD p025: <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>SOCD p975: <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>Time period: 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li>Type of data: Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li>How the data was collected or derived: The data was derived using machine learning models.</li> <li>Statistical methods used: Quantile Random Forest</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> 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: <ol> <li>generic variable name: oc = organic carbon</li> <li>variable procedure combination: iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm3 for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li>Position in the probability distribution/variable type: m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li>Spatial support: 30m</li> <li>Depth reference: b0cm..20cm = depth range from 0 to 20cm</li> <li>Time reference begin time: 20000101 = 2000-01-01</li> <li>Time reference end time: 20041231 = 2004-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: 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>Sub-dataset: SOCD p975, 2004–2008</h2> <h2>Disclaimer</h2> 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. <h2>Description</h2> 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. 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. This data set includes: <ul> <li>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals): This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m3 (scaled 10x). </li> <li>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals): 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>SOCD mean: <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>SOCD p025: <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>SOCD p975: <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>Time period: 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li>Type of data: Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li>How the data was collected or derived: The data was derived using machine learning models.</li> <li>Statistical methods used: Quantile Random Forest</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> 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: <ol> <li>generic variable name: oc = organic carbon</li> <li>variable procedure combination: iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm3 for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li>Position in the probability distribution/variable type: m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li>Spatial support: 30m</li> <li>Depth reference: b0cm..20cm = depth range from 0 to 20cm</li> <li>Time reference begin time: 20000101 = 2000-01-01</li> <li>Time reference end time: 20041231 = 2004-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: 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>Sub-dataset: SOCD p025, 2012–2016</h2> <h2>Disclaimer</h2> 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. <h2>Description</h2> 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. 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. This data set includes: <ul> <li>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals): This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m3 (scaled 10x). </li> <li>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals): 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>SOCD mean: <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>SOCD p025: <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>SOCD p975: <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>Time period: 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li>Type of data: Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li>How the data was collected or derived: The data was derived using machine learning models.</li> <li>Statistical methods used: Quantile Random Forest</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> 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: <ol> <li>generic variable name: oc = organic carbon</li> <li>variable procedure combination: iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm3 for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li>Position in the probability distribution/variable type: m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li>Spatial support: 30m</li> <li>Depth reference: b0cm..20cm = depth range from 0 to 20cm</li> <li>Time reference begin time: 20000101 = 2000-01-01</li> <li>Time reference end time: 20041231 = 2004-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: v20240804 = version from 2024-08-04</li> </ol>