Find research datasets worth reusing

opencc-by-4.0Apr 2024View details →

A Case-Control Study to Measure Behavioral Risks of Malware Encounters in Organizations

The behavior of enterprise users (e.g. browsing at night or visiting gambling sites) is a potential factor that might increase the chances of malware encounters (e.g. coinminers vs ransomware) on the field. This dataset report the aggregated results of a case-control study on telemetry data collected by Trend Micro, a global cybersecurity vendor, to identify users’ behavioral characteristics that can be used to differentiate cybersecurity risks profiles.

opencc-by-4.0Apr 2024View details →

volatile organic compounds that were measred in various sites in Israel, available by the Israel Ministry of Environmental Protection

The dataset comprises measurements of volatile organic compounds sampled at multiple sites across Israel from 2010 to 2024, encompassing urban, rural, and suburban locations.

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.

NETTAG+ Data set on adsorption of inorganic (Cu and Pb) and organic (PAHs) pollutants in fishing nets

This data set includes the raw data associated with the article in Marine Pollution Bulletin "Potential of fishing nets for adsorption of inorganic (Cu and Pb) and organic (PAHs) pollutants"

opencc-by-4.0Jul 2024View details →

Supplementary materials for "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone"

This is a ReadMe for the supplementary material for "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone" written by Rina Noguchi and Wataru Nakagawa. ---------------------- [ReadMe.txt] ReadMe text file. [FigS1.png] This figure is a supplementary figure which appeared as "Figure S1" in the main text. Caption: Figure S1.  Examples of conduits (dashed green lines) and loser conduits (solid magenta lines) were observed in the experiments with original and contrast-enhanced images. [FigS2.png] This figure is a supplementary figure which appeared as "Figure S2" in the main text. Caption: Figure S2.  Relationships between the thickness of poured heated syrup and (A) mass losses caused by baking soda decomposition, (B) number of conduits, (C) total conduit area, (D) average conduit area, (E) number of failed conduits, and (F) sum number of conduits and failed conduits. Each plot and error bar represents the average and standard deviation in three repeated experiments, respectively. The red plots and error bars show the 350 g of heated syrup case, which performed ten repeated experiments to verify the reproducibility. Note that horizontal error bars are derived from the difficulty of strict heated syrup-pouring control. [Experimental_datasheet.xlsx] This EXCEL file includes two sheets: a mass loss change log and a summary of experimental results. [movie/SSS_X_x15.mp4] These MP4 files are fast-forward movies (x15) for each experiment. SSS = the amount of poured hearty syrup (g), and X = round in each condition. ---------------------- For more details, please refer to a research paper "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone". If you have any questions, please send an e-mail to: r-noguchi@env.sc.niigata-u.ac.jp or flugel555@gmail.com . (R. Noguchi)

Proportions of Phytoplankton Functional Groups (PFT) retrieved using a Self-Organizing Map in the North Atlantic

Concentrations of diagnostic pigments are retrieved from satellite data (Chl-a + Rrs at 4 wavelengths + SST) using a SOM trained on a global in-situ HPLC dataset (see El Hourany et al. 2019). Pigments are converted to PFTs using empirical coefficients. Three sets of coefficients are used and the results are averaged (see El Hourany et al. 2024). The PFT data is expressed as the proportion of each group in the total community abundance: <code>Proportion_i = (alpha_i * Pig_i) / sum_j(alpha_j * Pig_j)</code> There are seven PFT groups (diatoms, dinoflagellates, haptophytes, green algae, cryptophytes, pelagophytes, prokaryotes). The satellite input Chl-a data is included as well. It was retrieved from the Globcolour portal (<a href="https://hermes.acri.fr/">https://hermes.acri.fr/</a>) in 2022. The CHL-1 product for case 1 waters is used. It uses the AVW merging method: single-sensor level-2 Chl-a data is merged from multiple sensors (SeaWiFS, MERIS, MODIS-Aqua, VIIRS-NPP/JPSS1, OLCI-A/B). The data spans from 2002 to 2020, at a daily resolution. It is available on a regular latitude/longitude grid, at a resolution of 1/24° (approximately 4 km) in a window of bounds 15°N−55°N ; 82°W−40°W. <h2>Storage</h2> All variables are stored in the same daily file: <code>PFT_SOM-DAP_GLOB_4km_daily/[year]/[month]/PFT_[year][month][day]_GLOB_4.nc</code>. The PFT proportions are stored as percentages (ranging between 0 and 100). Files are NetCDF4, and the metadata follow CF conventions. The PFT variables are stored using linear packing (see <a href="https://nco.sourceforge.net/nco.html#Linear-Packing">https://nco.sourceforge.net/nco.html#Linear-Packing</a>) as 16-bits unsigned integers (<code>NC_USHORT</code>), with a scale factor of 1.54e-3. This means values are discretized between 0 and ~100.92 (<code>=(2**16 - 2) * 1.54e-3</code>), with a discretization step of 1.54e-3. As for the PFT variables, these values are in percent points. <h2>References</h2> <ul> <li>El Hourany, R., Abboud-Abi Saab, M., Faour, G., Aumont, O., Crépon, M., Thiria, S. “Estimation of secondary phytoplankton pigments from satellite observations using Self-Organizing Maps (SOMs)”, J. Geophys. Res. Oceans 124, 1357–1378, <a href="https://doi.org/10.1029/2018jc014450">https://doi.org/10.1029/2018jc014450</a>, 2019</li> <li> <div>El Hourany, R, Pierella Karlusich J., Zinger L., Loisel H., Levy M., and Bowler C. “Linking Satellites to Genes with Machine Learning to Estimate Phytoplankton Community Structure from Space”, Ocean Science 20 no. 1, 217−39. <a href="https://doi.org/10.5194/os-20-217-2024">https://doi.org/10.5194/os-20-217-2024</a>, 2024</div> </li> </ul> <h2>Changelog</h2> <h3>v1.2</h3> <ul> <li>[2024-01-24] Finished re-arranging data and checked validity. 553 missing days / source files (about 6% of total data).</li> <li>[2023-12-01] Fix SST projection at PFT generation. Data fully re-generated.</li> </ul> <h3>v1.1</h3> <ul> <li>[2023-08-30] Store PFT data using linear packing. </li> </ul> <h3>v1.0</h3> <ul> <li>[2023-08-03] those data are reorganised, and metadata is added, using the script <a href="https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads">https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads</a></li> <li>[2023-07] data generated with the SOM were stored on `spirit:/data/lollier/PFT`</li> </ul>

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.

opencc-by-4.0Oct 2024View details →

Bulk, isotopic, and organic geochemical proxies from core NYA17–154

This dataset containes all the data from core NYA17–154 showed in Ingrosso et al. (2024) Communications Earth & Environment

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>

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

<h2>Sub-dataset: SOCD mean, 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>

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

<h2>Sub-dataset: SOCD mean, 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 p975, 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>