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539 results for “organic carbon”
Organic and inorganic carbon sinks reduce long-term deep carbon emissions in the continental collision margin of the southern Tibetan Plateau: Implications for Cenozoic climate cooling
<p>Hydrogeochemical data including aqueous chemistry, hydrogen and oxygen isotopes, gas components, gas helium, carbon isotopes from southern Tibet. Supporting the manuscript titled "Organic and inorganic carbon sinks reduce long-term deep carbon emissions in the continental collision margin of the southern Tibetan Plateau: Implications for Cenozoic climate cooling".</p>
Convergence in simulating global soil organic carbon by structurally different models after data assimilation
<p>This is the data for results shown in the article accepted by Global Change Biology: Convergence in simulating global soil organic carbon by structurally different models after data assimilation</p>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD p025, 2020–2022</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage (scaled 10x). </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://doi.org/10.5281/zenodo.13754343">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13771721">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13771841">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13771911">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13771967">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://doi.org/10.5281/zenodo.13779539">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13774064">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13774089">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13774114">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13774167">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://doi.org/10.5281/zenodo.13778472">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13773396">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13773765">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13773828">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13773953">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774003">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD mean, 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>
Dataset: The role of long-term hydrodynamic evolution in the accumulation and preservation of organic carbon-rich deposits in the shelf seas
<p>Output files from harmonic analysis of regional tidal model, glacial isostatic adjustment model input and output (relative sea level and ice sheet extent datasets), and scripts for figure generation.</p> <p>If using these data, please cite: </p> <p><strong>Ward, S.L., Bradley, S.L., Roseby, Z.A., Wilmes, S.B., Vosper, D.F., Roberts, C.M. and Scourse, J.D., 2025. The role of long‐term hydrodynamic evolution in the accumulation and preservation of organic carbon‐rich shelf sea deposits. <em>Journal of Geophysical Research: Oceans</em>, <em>130</em>(4), p.e2024JC022092. <a href="https://doi.org/10.1029/2024JC022092">https://doi.org/10.1029/2024JC022092</a></strong></p> <p> </p>
Suppression of Methanogenesis by Microbial Reduction of Iron-Organic Carbon Associations in Fully Thawed Permafrost Soil
<p>This data set contains data associated with the manuscript "Suppression of Methanogenesis by Microbial Reduction of Iron-Organic Carbon Associations in Fully Thawed Permafrost Soil". Currently under review.</p>
Reduction of iron-organic carbon associations shifts net greenhouse gas release after initial permafrost thaw
<p>This dataset contains data associated with the manuscript "Reduction of iron-organic carbon associations shifts net greenhouse gas release after initial permafrost thaw". doi: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.soilbio.2025.109735" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.soilbio.2025.109735</span></span></a></p>
Raw data for "Plot-scale variability of organic carbon in temperate agricultural soils - Implications for soil monitoring"
<p>This dataset is the raw data that belongs to a peer-reviewed study on the small-distance variability of soil organic carbon in agricultural soils in Germany. It consists of three different files. The first file gives the coordinates of the 16 soil cores that were taken at each of the 16 sites (eight cropland and eight grassland sites). The second file gives the soil properties measured at each individual core (n=16 per site) and the third file the soil properties measured at each indivdual soil profile (n=6 per site).</p>
Sinking efficiency of cyanobacteria-derived particulate organic carbon from one eutrophic lake and global perspectives on carbon burial flux in subtropical shallow lakes
<p>It is the data that support the findings entitled "Sinking efficiency of cyanobacteria-derived particulate organic carbon from one eutrophic lake and global perspectives on carbon burial flux in subtropical shallow lakes” </p>
Modeling the Organic Carbon Oxidation and Redox Sequence under the Partial- Equilibrium Approach: a Discussion by means of a Semi-Analytical Solution
<p>Filename: Analytical_solution.xls<br> File format: Excel<br> Description: It contains the calculation of the analytical model, programmed by means of excel. The sheet "advection" contains the calculation of the advection model and the sheet "diffusion" that of the diffusion model. The sheet "Phreeqc results" contains the results of the Phreeqc batch model (see Batch.pqi). Input data are highlighted in yellow.</p> <p>Filename: Batch.pqi<br> File format: Phreeqc<br> Description: It contains the input file for the calculation of the batch model by Phreeqc. It requires the Phreeqc database file Phreeqc.dat</p> <p>Filename: PEA_om_adv.pqi<br> File format: Phreeqc<br> Description: It contains the input file for the numerical calculation of the advection model by Phreeqc using the PEA (Partial Equilibrium Assumption). It requires the Phreeqc database file Phreeqc.dat</p> <p>Filename: PEA_om_dif.pqi<br> File format: Phreeqc<br> Description: It contains the input file for the numerical calculation of the diffusion model by Phreeqc using the PEA (Partial Equilibrium Assumption). It requires the Phreeqc database file Phreeqc.dat</p> <p>Filename: KIN_om_adv.pqi<br> File format: Phreeqc<br> Description: It contains the input file for the numerical calculation of the advection model by Phreeqc using the fully kinetic approach. It requires the Phreeqc database file PhreeqcKIN.dat</p> <p>Filename: KIN_om_dif.pqi<br> File format: Phreeqc<br> Description: It contains the input file for the numerical calculation of the diffusion model by Phreeqc using the fully kinetic approach. It requires the Phreeqc database file PhreeqcKIN.dat</p> <p>Filename: phreeqc.dat<br> File format: Phreeqc database<br> Description: It contains thermodynamic data for the batch model (see Batch.pqi) and the PEA models (see PEA_om_adv.pqi and PEA_om_dif.pqi). It is the default thermodynamic database of Phreeqc, dat), from which we removed ammonium to avoid the unrealistic reduction of NO3- and N2 to NH4+</p> <p>Filename: phreeqcKIN.dat<br> File format: Phreeqc database<br> Description: It contains thermodynamic data for the fully kinetic models (see KIN_om_adv.pqi and KIN_om_dif.pqi). It is the default thermodynamic database of Phreeqc, dat), from which we removed the relevant equilibrium redox reaction, so that they can be treated kinetically.</p> <p> </p> <p> </p>
Measurement Report: Investigation of pH- and particle size-dependent chemical and optical properties of water-soluble organic carbon: implications for its sources and aging processes
<p>The original dataset of the manuscript: "Measurement Report: Investigation of pH- and particle size-dependent chemical and optical properties of water-soluble organic carbon: implications for its sources and aging processes"</p>
Underlying data for Figures 1 - 6 in "Glacial troughs as centres of organic carbon accumulation on the Norwegian continental margin"
<p>This repository contains source data underlying Figures 1 - 6 in the publication titled "Glacial troughs as centres of organic carbon accumulation on the Norwegian continental margin".</p>
Predicted organic carbon content on the Norwegian continental margin
<p>Output data relating to R workflow for predicting organic carbon content on the Norwegian continental margin (https://github.com/diesing-ngu/TOC). The following files are included:</p> <p><strong>OC0-10cm_median_2024-02-16.tif</strong> - Georeferenced Tiff-file of the predicted organic carbon content in the upper 10 cm of the sediment column</p> <p><strong>OC0-10cm_PI90_2024-02-16.tif</strong> - Georeferenced Tiff-file of the 90% prediction interval. Can be used as a measure of uncertainty.</p> <p><strong>OC0-10cm_AOA_2024-02-16.tif </strong>- Georeferenced Tiff-file of the area of applicability of the model <a href="https://doi.org/10.1111/2041-210X.13650">(Meyer & Pebesma, 2021)</a></p> <p><strong>OC0-10cm_AOA_2024-02-16.shp</strong> - Same as above but as polygon shapefile</p>
Input data for predicted organic carbon content on the Norwegian continental margin
<p>Input data relating to R workflow for predicting organic carbon content on the Norwegian continental margin (https://github.com/diesing-ngu/TOC). The following files are included:</p><p><strong>mosaic_2023-04-21.csv</strong> - Data on organic carbon content in surface sediments from the <a href="https://doi.org/10.5194/essd-15-4105-2023">MOSAIC v2.0</a> database</p><p><strong>predictors_ngb.tif </strong>- Multi-band georeferenced TIFF-file of predictor variables</p><p><strong>predictors_description</strong>.txt - Information on variables stored in predictor_ngb.tif including units, statistics, time period and sources.</p><p><strong>GrainSizeReg_folk8_classes_2023-06-28.tif</strong> - Georeferenced TIFF-file of predicted substrate classes. Used to update the area of interest (exclude areas mapped as Rock and boulders).</p><p><strong>mud_2023-06-30.tif </strong>- Georeferenced TIFF-file of predicted mud content. Used as an additional predctor.</p>
Excess 210Pb, 137C, total organic carbon content, HBI biomarkers (IPSO25, HBI III) and biogenic silica of marine deposits from sediment core 2018_R2_2F from Sheldon Cove, Antarctic Peninsula
<p><strong>Description</strong>: Sediment core 2018_R2_2F was collected from Sheldon Cove, Antarctic Peninsula (67.55°S 68.27°W) from a water depth of 177 m, in December 2018 as part of expedition JR18003 by the British Antarctic Survey aboard RV James Clark Ross (Sands et al. 2019). Total core length was 25 cm. The dataset presented here consists of: gamma spectrometry measurements of excess 210Pb (calculated as a difference between the total 210Pb and the average of 214Pb and 214Bi) and 137Cs; total organic carbon (TOC) content; biogenic silica (BSi) content; and HBI biomarker (IPSO25, HBI III) concentrations. The excess 210Pb and 137Cs were measured at the Institute of Geology at Adam Mickiewicz University in Poznań, Poland, using a gamma detector Canberra BE3830, cooled with cryostat Cryo-Pulse®5 plus. The detector is placed in 10 cm thick lead shield walls and is equipped with a remote detector chamber option (RDC-6 inches) for low energy background reduction. The detector was commercially characterized by ISOCS (In-Situ Object Calibration Software) and LabSOCS (Laboratory Sourceless Object Calibration Software). Efficiencies for measured geometries were determined using LabSOCS code applying all corrections for sample geometry, matrix, and container type, and were verified with IAEA standards measurements. The results (spectra) were analyzed in Canberra GENIE-2000 v. 3.3 gamma spectrometry software and are presented with 2-sigma uncertainty ranges (Szczuciński, submitted). TOC concentrations (given in %) were measured at the Faculty of Earth Sciences, University of Silesia, Poland, using an Eltra CS-500 IRanalyzer with a Total Inorganic Carbon module according to the procedure described in Racka et al. (2010). TOC was calculated as the difference between TC (total carbon) and TIC (total inorganic carbon). Each TOC sample was analysed in duplicate. Analytical precision and accuracy were better than ±2% for TC and ±3% for TIC. HBI biomarker preparation and analysis followed slightly modified (Pieńkowski et al. 2021) standard protocols (Belt 2012). HBI concentrations are given per weight of sediment (ng/g sed), and organic carbon content (μg/g OC) (Belt et al. 2012). Biogenic (opaline) silica (BSi) analysis on dried, homogenised samples followed Heiri et al. (2001) and Bechtel et al. (2007). Each BSi and TOC sample was analysed in duplicate; values are given in %. BSi and TOC standard deviation calculations are based on data from the replication.</p> <p><strong>References</strong> <br><br>* Bechtel, A., Woszczyk, M., Reischenbacher, D., Sachsenhoffer, R., Gratzer, R., Püttmann, W. Spychalski, W., 2007: Biomarkers and geochemical indicators of Holocene environmental changes in Lake Sarbsko (Poland). Org. Geoch. 38, 1112–1131. <br>* Belt, S.T., Brown, T.A., Navarro Rodriguez, A., Cabedo Sanz, P., Tonkin, A., Ingle, R. 2012. A reproducible method for the extraction, identification and quantification of the Arctic sea ice proxy IP25 from marine sediments. Anal. Methods 4, 705-713. <br>* Heiri, O., Lotter, A. F., Lemcke, G., 2001. Loss on ignition as a method for estimating organic and carbonate content in sediments: reproducibility and comparability of results. J. Paleolimnol. 25, 101-110. <br>* Pieńkowski, A.J., Husum, K., Belt, S.T., Ninnemann, U., Köseoğlu, D., Divine, D.V., Smik, L., Knies, J., Hogan, K., Noormets, R. 2021. Seasonal sea ice persisted through the Holocene Thermal Maximum at 80°N. Commun. Earth Environ. 2, 124. <br>* Racka, M., Marynowski, L., Filipiak, P., Sobstel, M., Pisarzowska, A., Bond, D.P.G., 2010: Anoxic Annulata events in the Late Famennian of the Holy Cross Mountains (Southern Poland): geochemical and palaeontological record. Palaeogeography, Palaeoclimatology, Palaeoecology 297(3-4), 549-575. <br>* Sands, C.J., Annett, A., Apeland, B., Barnes, D.K.A, Bascur, M., Bruning, P., Costa, M., Dadd, G., De Lecea, A., Ensor, N., Featherstone, A., Flint, G., Goodger, D., Guzzi, A., Howard, F., Hunter, D., Jenkins, S., Kender, S., Lincoln, B., Munoz-Ramirez C., Pienkowski, A., Retallick, K., Roman-Gonzalez, A., Scourse, J., Sheen, K., Whitaker, T., Williams, J., Zhao, L., Zwerschke, N., 2019: JR18003 Cruise Report. British Antarctic Survey, 132 pp. <br>* Szczuciński, W. (submitted): Applications of gamma-emitting isotopes (210Pb and 137Cs) for assessment of sedimentary processes – insights from studies of lake, deltaic and continental shelf deposits. <br><br><strong>Projects</strong> <br><br>* CHARME: CHanging AntaRctic Marine Environments, <strong>Web</strong>: <a title="Follow link" href="https://charme.amu.edu.pl/" target="_blank" rel="nofollow noopener">https://charme.amu.edu.pl/</a>, <strong>Award</strong>: Norwegian Financial Mechanism 2014-2021, UMO-2020/37/K/ST10/04127 <br><br><strong>File descriptions</strong>: Excel file with all data, as well as core details (coordinates and water depth).</p> <p><strong>Comment</strong>: This dataset is related to the following article which has been accepted for publication:</p> <p>Pieńkowski, Anna J.; Szczuciński, Witold; Breszka, Agnieszka; Chyleński, Maciej; Juras, Anna; Romel, Paulina; Rozwalak, Piotr; Trzebny, Artur; Dabert, Mirosława; Belt, Simon; Jagodziński, Robert; Smik, Lukas; Włodarski, Wojciech. Sedimentary ancient DNA and HBI biomarkers as sea-ice indicators: a complementary approach in Antarctic fjord environments. Limnology Oceanography Letters. doi: 10.1002/lol2.10395</p>
Data: Impact of forest harvesting intensity and water table on biodegradability of dissolved organic carbon in boreal peat in an incubation experiment.
Open the record for dataset details and reuse information.
Temperature Controls the Relation between Soil Organic Carbon and Microbial Carbon Use Efficiency
<p>This is the dataset for the manuscript entitled "Temperature controls the relation between soil organic carbon and microbial carbon use efficiency".</p>
Data associated with "The importance of terrain and climate for predicting soil organic carbon is highly variable across local to continental scales"
<p>The zipped folder contains the processed soil datasets including covariates, soil depths, and SOC concentrations for training the deep learning models described in the paper "The importance of terrain and climate for predicting soil organic carbon is highly variable across local to continental scales". </p> <p>"soil_profile/" contains a table including the geolocations of all the soil profiles in this study. "patch_data/" and "point_data/" contain the covariates to feed the models with patch input and point input respectively. "depth/" contains the upper and lower depths of the soil samples. "y/" contains the target variable - SOC concentration of the soil samples. The data files with suffix "_1" is a small subset of their counterparts without "_1" (10 % in sample size) used for model hyperparameters tuning.</p>
Data from: Impacts of organic matter amendments on urban soil carbon and soil quality: A meta-analysis
<p>Organic matter amendment application is an important avenue of beneficial waste diversion and is used to improve soil quality in agricultural and urban settings. In urban regions, amendments are used to support local food production, maintain vegetation for landscaping and recreational use, and reclaim disturbed soils. Urban regions generate large quantities of wasted organic resources for potential application aiding in creating a circular nutrient economy. There is a growing interest in understanding the effects of amendments such as compost, biosolids, and biochar on soil properties in agricultural settings. Gaps remain, however, in assessing their effects in urban land uses. We conducted a literature review to assess the effects of compost, biochar, and biosolids on soil carbon and soil quality of urban soils managed for gardening, landscaping, recreation, and reclamation. Application of organic matter amendments led to an average increase of 3.6 units of soil organic matter% (SOM%). Compost and biochar improved SOM% the most, by 3.1 and 6.5 units of SOM%, respectively. Biosolids resulted in the smallest increase in SOM% but had greater nutrient benefits than other amendments. Parameters related to chemical and physical soil quality improved with the application of amendments. Gaps in the literature remain, such as assessing urban gardens, soil to depths greater than 30 cm, and the persistence of SOM in amended soils. This meta-analysis proposes that organic matter amendments are a powerful means to improve soil quality in urban regions, provide vital cobenefits to surrounding communities, and increase soil carbon storage.</p>
Multi-Scale Computational Design of Metal-Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization
<p>This repository contains CIF files for metal-organic frameworks and Grand canonical Monte Carlo (GCMC) simulation results for the article <em>Multi-Scale Computational Design of Metal-Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization</em> by Zijun Deng and Lev Sarkisov.</p>
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.