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519 results for “organic soil”
Radiocarbon and stable carbon isotopes of carbon dioxide produced by respiration of dissolved organic carbon (DOC) leached from permafrost soils collected from the North Slope of Alaska in the summers of 2018 and 2022
Dissolved organic carbon (DOC) was leached from permafrost soils near the Toolik Field Station in the Alaskan Arctic, either kept in the dark or exposed to light treatments, and then incubated with native permafrost microbial communities. The radiocarbon (14C) and stable carbon (13C) isotopic compositions of the initial DOC present in the dark or light-exposed permafrost soil leachates and the carbon dioxide (CO2) produced by microbial respiration of dark or light-exposed permafrost DOC were quantified.
Methane concentrations in dissolved organic carbon (DOC) leachates from permafrost soils collected from the North Slope of Alaska in the summers of 2018 and 2019
Methane (CH4) concentrations were measured in dissolved organic carbon (DOC) leachates of permafrost soils collected from the frozen permafrost layer at five sites underlying tussock tundra or wet sedge vegetation on the North Slope of Alaska during the summers of 2018 and 2019.
Tree regeneration after fire: Yukon Lodgepole Pine Surveys, organic soil analysis
This dataset documents pre- and post-fire tree composition of stands along the current range edge of lodgepole pine (Pinus contorta ssp. latifolia) in the Yukon Territory. The objective of the study was to evaluate whether pine populations at the range edge appear to be expanding in association with fire disturbance. This dataset has been published as: Jill F. Johnstone and F. Stuart Chapin, 2003. Non-equilibrium succession dynamics indicate continued northern migration of lodgepole pine. Global Change Biology, 9(10): 1401-1409. Contains measurements of organic soil depth sampled along transects.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IV: Organic Soil Carbon and Nitrogen Content from Organic Soil Samples 2022
This dataset contains lab-quantified (and some field-measured) characteristics for post-fire residual organic soil samples collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Lab analyses were conducted in summer and fall of 2022 at UAF and NAU.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera V: Organic Soil Depth 2022
This dataset contains field-measured characteristics for post-fire residual organic soil samples and for additional organic soil depths collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): soil organic carbon stocks and radiocarbon measurements, 2009 & 2022
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes measurements of soil organic carbon stocks and radiocarbon (14C) values that are normalized to account for the effects of subsidence and ground collapse. SOC and 14C values were normalized using an equivalent ash approach described in Plaza et al. (2019) Nat Clim Change and Lathrop et al. (2025) Global Change Biology.
Organic and inorganic data for soil cores from Brazil and Florida Bay seagrasses to support Howard et al 2018, CO2 released by carbonate sediment production in some coastal areas may offset the benefits of seagrass “Blue Carbon” storage, Limnology and Oceanography, DOI: 10.1002/lno.10621
Using piston corers, soils from Florida Bay and Brazilian seagrass meadows were collected to complete organic and inorganic carbon inventories for the top 1 m of soil. Instrumental analyses and loss on ignition at 500C were used to measure C content of downcore slices.
Fall 2000 soil organic content survey -- ash-free dry weight analysis for soil samples from 10 GCE LTER sampling sites
Soil core samples were collected from the permanent plots at 10 GCE LTER sampling sites in October, 2000, to survey the fractional organic content in marsh sediments. Surveys will be conducted annually to assess changes in soil organic content in response to environmental factors documented by other GCE monitoring efforts.
Soil salinity and organic content at GCE-LTER vegetation monitoring plots in October 2009
Soil samples were collected in conjunction with Fall 2009 plant monitoring at half of the permanent vegetation monitoring plots in the creekbank and midmarsh zones at 10 GCE study sites. Pore-water salinity was determined by analysis of supernatant salinity in dried soil samples hydrated with a measured volume of deionized water. Organic content was measured gravimetrically by comparing ash-free dry weight and total weight of soil samples.
Soil salinity and organic content at GCE-LTER vegetation monitoring plots in October 2011
Soil samples were collected in conjunction with Fall 2011 plant monitoring at half of the permanent vegetation monitoring plots in the creekbank and midmarsh zones at 10 GCE study sites. Pore-water salinity was determined by analysis of supernatant salinity in dried soil samples hydrated with a measured volume of deionized water. Organic content was measured gravimetrically by comparing ash-free dry weight and total weight of soil samples.
WAT05 Soil organic matter response to thirty years of increased precipitation at Konza Prairie
This dataset contains carbon and nitrogen concentrations and stocks in total soil organic matter and its fractions from the Konza Prairie Irrigation Transect Experiment. The dataset also includes pyrogenic organic matter C and N, as well as microbial amino sugars and root quality measurements. Data are availble for irrigated and control plots. Total pyrogenic and unburned soil organic matter C and N are availble for both the upland and lowland positions at 0-5, 5-15, and 15-30cm depth increments. Fraction and root data are avaible at both landscape positions, but for only the 0-5cm and 5-15cm depths and 0-5 and 5-30cm depths, respectively. Amino sugar data are only available for the lowland plots for the 0-5 and 5-15cm depths.
Soil organic matter dynamics in the tabonuco forest, a plantation and a secondary forest in Guzman
In this project we try to find out the relationship between the primary production and the soil organic carbon fractions. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Long-term dynamics of soil organic matter and aboveground net primary production in a Chihuahuan Desert Grassland at the Sevilleta National Wildlife Refuge, New Mexico (1989-2014)
Drylands contain a third of the organic carbon stored in global soils; however, the long-term dynamics of soil organic carbon and soil organic matter (SOM) in drylands remain poorly understood relative to dynamics of the vegetation carbon pool. We examined long-term patterns in SOM against both climate and prescribed fire in a Chihuahuan Desert grassland in central New Mexico, USA. SOM was measured each spring and fall for 25 years (1989–2014) in unburned desert grassland and from 2003 to 2014 following a prescribed fire. SOM concentration from 0-20 cm depth did not show a clear long-term trend but fluctuated seasonally at both burned and unburned sites, ranging from a minimum of 0.9% to a maximum of 3.3%. SOM concentration declined nonlinearly in wet seasons and peaked in dry seasons. These results not only contrast with the positive relationships between aboveground net primary production and precipitation for this region, but also with previous reports of greater SOM in wetter sites across drylands globally, suggesting that space is not a good substitute for time in predicting the dynamics of dryland SOM. We suggest that declines in SOM in wet periods are caused by increased soil respiration, runoff, leaching, and soil erosion. In addition to tracking natural variability in climate, SOM concentration also decreased by 14% following prescribed fire, a response that magnified over time and has persisted for nearly a decade due to the slow recovery of primary production. Our results document the surprisingly dynamic nature of soil organic matter and its high sensitivity to climate and fire in this dryland ecosystem.
Data from: Dwarf shrubs impact tundra soils: drier, colder, and less organic carbon
<p>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.</p> <p>Kemppinen, Niittynen, Virkkala, Happonen, Riihimäki, Aalto & Luoto (2021). Dwarf shrubs impact tundra soils: drier, colder, and less organic carbon. Ecosystems.</p> <p>These are the data from Kemppinen et al. (2021).</p>
Soil organic carbon stocks and trends (1984-2019) predicted at 30m spatial resolution for topsoil in natural areas of South Africa
<p>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></p> <p>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:</p> <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> <p>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.</p>
Database to: Cover crops affect pool specific soil organic carbon in cropland – A meta‐analysis
<p>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:<br>1. information on the database<br>2. legend<br>3. list of included studies, all extracted data necessary for response ratio calculation and moderator analysis, and additional information</p>
Soil grid data for 3 agricultural fields in Italy (Soil Moisture, soil organic carbon)
<p><span>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.</span></p> <p><span>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.</span></p> <p><span>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. </span></p>
Data supplement: Spatial autocorrelation in machine learning for modelling soil organic carbon
<p>Spatial autocorrelation in machine learning for modelling soil organic carbon: Data supplement</p> <p><br>Alexander Kmoch, Clay Taylor Harrison, Jeonghwan Choi, Evelyn Uuemaa</p> <p>Spatial autocorrelation, the relationship between nearby samples of a spatial<br>random variable, is often overlooked in machine learning models, leading to<br>biased results. This study investigates various methods to account for spa-<br>tial autocorrelation when predicting soil organic carbon (SOC) using random<br>forest models. Five models incorporating spatial structure were compared<br>against baseline models that did not have any added spatial components.<br>Cross-validation showed slight improvements in accuracy for models consid-<br>ering spatial autocorrelation, while Shapley Additive Explanations confirmed<br>the importance of spatial variables. However, no decrease in spatial autocor-<br>relation of residuals was observed. Raster-based models exhibited enhanced<br>prediction detail, but high-resolution validation data availability limited thor-<br>ough validation. The findings emphasize the value of incorporating spatial<br>autocorrelation for improved SOC prediction in machine learning models.<br>Considerations such as the distribution of predictions and computational<br>complexity should help guide the selection of suitable approaches for specific<br>spatial modelling tasks.</p>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD p025, 2016–2020</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage (scaled 10x). </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://doi.org/10.5281/zenodo.13754343">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13771721">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13771841">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13771911">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13771967">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://doi.org/10.5281/zenodo.13779539">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13774064">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13774089">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13774114">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13774167">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://doi.org/10.5281/zenodo.13778472">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13773396">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13773765">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13773828">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13773953">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774003">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD p025, 2008–2012</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage (scaled 10x). </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://doi.org/10.5281/zenodo.13754343">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13771721">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13771841">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13771911">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13771967">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://doi.org/10.5281/zenodo.13779539">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13774064">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13774089">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13774114">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13774167">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://doi.org/10.5281/zenodo.13778472">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13773396">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13773765">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13773828">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13773953">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774003">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.