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Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD p975, 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 p975, 2012–2016</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage (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, 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>
Strawberry volatile organic compounds metabolomic data and QTL study
<p>This dataset contains the supplementary materials of the publication "Multivariate QTL approach reveals a major regulator of terpenoid production and other volatiles in strawberry" of the same authors. In this study we extracted volatile organic compounds from several strawberry samples and analysed their identity and abundance. We used this volatile data to perform an extensive multivariate QTL study, the results of which can be found in this dataset.</p> <p>All analysis, results and figures can be reproduced using the folder included in the <strong>supplementary data 1</strong>. If you want to reproduce part or all of our analysis, only download sup data 1. </p> <p>If you only need one of our results or data table you can download them individually:</p> <ul> <li>Sup data 2: abundances of volatile organic compounds from a biparental and diverse panel (GWAS) population.</li> <li>Sup data 3 and 4: p-value tables for all metabolites as well as multivariate traits (see publication for more information).</li> <li>Sup table 1: Summary of metabolite abundances and heritabilities across both populations.</li> <li>Sup tables 2 and 3: significant QTL signals for each trait individually and summarised per QTL locus.</li> <li>Sup table 4: previously reported VOC QTLs in strawberry, with positions imputed in the Royal Royce genome.</li> <li>Sup table 5: metadata about all the identified compounds on this and previous studies.</li> <li>Sup table 6: SNP array positions imputed in the "Royal Royce" genome assembly.</li> <li>Sup table 7: Number of markers per chromosome in this analysis.</li> </ul> <h3>Update 2025</h3> <p>We updated the underlying code and datasets to reflect several revisions made to this work. Most notably, the QTL results have been reworked. They now do not include Blink or FarmCPU results (only mixed model results, obtained through statgenGWAS). Additionally, heritability estimations, QQ-plots and other figures have been added to the reproducible results code.</p>
Global patterns of soil organic carbon distribution in the 20–100 cm soil profile for different ecosystems: A global meta-analysis
<p><span><span> </span></span><span>The file named <span>“</span>Rawdata.xlsx<span>”</span> contains data sourced from the literature.<span> The file name is “GE_β.tif<span>”</span><span>,</span></span></span><span><span> GE represents</span></span><span> global ecosystems, which including cropland (CL), grassland (GL), and forestland (FL). “FL_β.tif” represents the spatial distribution of β for forestland at 20-100 cm depth. The file name is “GE_d_SOCD.tif”, where SOCD represents soil organic carbon density, d represents soil depth, for example, “FL_20-100_SOCD.tif” represents the spatial distribution of SOCD for forestland at 20-100 cm depth.</span></p>
Dataset for publication: "Photosystem II supercomplexes lacking light-harvesting antenna protein LHCB5 and their organization in the thylakoid membrane"
<p>Data repository for "<strong>Photosystem II supercomplexes lacking light-harvesting antenna protein LHCB5 and their organization in the thylakoid membrane</strong>".</p> <p><strong>FIGURE </strong><strong>1</strong><strong><em> </em></strong><strong>Phenotype and photosynthetic characteristics of the <em>lhcb5</em> mutant. </strong>(A) Phenotype of <em>Arabidopsis thaliana</em> wild type (WT) and <em>lhcb5</em> mutant plants grown at controlled conditions for 6 weeks (8 h light/16 h dark cycle; 22/20°C; <br>110 µmol photons m<sup>-2</sup> s<sup>-1</sup>; 60% humidity). (B) Immunoblot analysis of thylakoid membranes of WT and <em>lhcb5</em> mutant plants with antibody directed against LHCB5. (C) Content of light-harvesting proteins LHCB1-6 evaluated relatively to the content of CP43 protein in the WT and the <em>lhcb5</em> mutant. The protein content was determined in isolated thylakoid membranes by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The columns represent means ± SD, individual points show technical replicates. All data passed the normality and equal variance tests and according to Student t-test the datasets of WT and <em>lhcb5</em> were not significantly different (α ≤ 0,05), except for the relative content of LHCB5/CP43. (D) Protein ratios of photosynthesis-related thylakoid membrane proteins of the WT and the <em>lhcb5</em> mutant. The protein content was determined by LC-MS/MS in isolated thylakoid membranes. PSII represents the sum of relative PG intensities of D1, D2, CP43, and CP47 proteins, LHCII - LHCB1–3 proteins, PSI - PSAA and PSAB proteins, LHCI - LHCA1–4 proteins, ATPS - α and β subunits of ATP synthase, and cyt f represents cytochrome f component of cytochrome b<sub>6</sub>f complex. The columns represent means ± SD, individual points show technical replicates. All data passed the normality and equal variance tests and according to Student t-test the datasets of WT and <em>lhcb5</em> are not significantly different (α ≤ 0,05).</p> <p><strong>FIGURE </strong><strong>2</strong><strong><em> </em></strong><strong>Separation and structural characterization of PSII supercomplexes from <em>lhcb5</em> mutant plants. </strong>(A) Separation of pigment–protein complexes from thylakoid membranes from <em>Arabidopsis thaliana</em> WT and <em>lhcb5</em> mutant plants by clear native polyacrylamide gel electrophoresis. Thylakoid membranes were solubilized by n-dodecyl α-D-maltopyranoside (detergent/chlorophyll mass ratio of 10). (B) Electron density maps of characteristic PSII supercomplexes from the separated green gel bands of the <em>lhcb5 </em>mutant designated as C<sub>2</sub>S<sub>2</sub>M<sub>2</sub>, C<sub>2</sub>S<sub>2</sub>M and C<sub>2</sub>SM. Projection maps are fitted by corresponding structural high-resolution models of PSII supercomplexes (Van Bezouwen et al., 2017) without LHCB5. Individual PSII subunits are color-coded according to (E). (C), (D) Comparison of structural models of the PSII C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> supercomplexes from <em>Arabidopsis thaliana</em> WT and the <em>lhcb5</em> mutant. (C) Projection map of the PSII C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> supercomplex from <em>Arabidopsis thaliana</em> wild type (Ilíková et al., 2021) fitted by the high-resolution structure from Van Bezouwen et al. (2017). (D) Overlay of structural models of the PSII C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> supercomplex from <em>Arabidopsis thaliana</em> wild type (surface representation, partially transparent) and the <em>lhcb5</em> mutant shows a specific shift of the S and M LHCII trimers as well as the monomeric antenna LHCB6 (see arrows in the corresponding colors) due to the absence of LHCB5. Individual PSII subunits are color-coded according to (E). (E) Legend of individual PSII subunits, which are color-coded as follows: PSII core complex in green, S and M LHCII trimers in red and blue, respectively, and the monomeric antenna proteins, LHCB4, LHCB5, LHCB6, in yellow, cyan, and dark orange, respectively.</p> <p><strong>FIGURE </strong><strong>3</strong><strong> </strong><strong>Organization of photosystem II in thylakoid membranes of the <em>lhcb5</em> mutant. </strong>(A, B) Examples of electron micrographs of negatively stained thylakoid membrane isolated from the <em>lhcb5</em> mutant with densities corresponding to the PSII core complex. Representative picture of PSII supercomplexes “randomly” organized (A) and organized into 2D semi-crystalline array (B). (C, D, E) Projection maps of PSII megacomplexes obtained using image analysis of PSII particles in thylakoid membranes. Three specific associations of PSII supercomplexes are shown and fitted by the model of PSII supercomplex C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> without LHCB5 (see Figure 2B). Megacomplexes are averaged projections of 1 925 (C), 2 241 (D), and 2 305 (E) particles. (F) Isolated PSII particle from thylakoid membranes with “randomly” organized PSII as an average projection of 3 741 particles fitted by the model of PSII supercomplex C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> without LHCB5 (see Figure 2B). (G) PSII supercomplexes organized into 2D semi-crystalline array as an average projection of 418 sub-areas together with the fitted model of PSII C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> supercomplexes (see Figure 2B). Projection maps of PSII supercomplexes show core complexes in green, S trimers in red, M trimers in blue, LHCB4 in yellow, and LHCB6 in dark orange color.</p> <p><strong>FIGURE </strong><strong>4</strong><strong><em> </em></strong><strong>Distribution of mutual distances between neighboring photosystem II particles in thylakoid membranes of <em>Arabidopsis thaliana</em> WT and the <em>lhcb5 </em>mutant. </strong>The distances between two closest neighboring PSII supercomplexes were analyzed using EM. Histograms are normalized to the maximum.</p> <p><strong>SUPPORTING FIGURE 1 Analysis of chosen photosystem I and II photosynthesis related parameters. </strong>(A) Quantum yield of photochemistry of PSI - Y(I). (B) Quantum yield of photochemistry of PSII - Y(II). (C) Non-photochemical quenching – NPQ. Parameters were measured during actinic light exposure (800 µmol photons m<sup>-2</sup> s<sup>-1</sup>) and dark relaxation using saturating light pulses (300 ms, 10 000 µmol photons m<sup>-2</sup> s<sup>-1</sup>) in WT and <em>lhcb5</em> mutant plants. Results represent mean values ± SD from 4 measurements. Plants were dark-adapted for 30 min before the measurement.</p> <p><strong>SUPPORTING FIGURE 2 Single-particle image analysis and classification of protein complexes from CN−PAGE C<sub>2</sub>S<sub>2</sub>M<sub>2 </sub>band from the Arabidopsis <em>lhcb5</em> mutant (Figure 2A). </strong>Number of averaged projections in given classes are indicated.</p> <p><strong>SUPPORTING FIGURE 3 Single-particle image analysis and classification of protein complexes from CN−PAGE C<sub>2</sub>S<sub>2</sub>M<sub> </sub>band from the Arabidopsis <em>lhcb5</em> mutant (Figure 2A). </strong>Number of averaged projections in given classes are indicated.</p> <p><strong>SUPPORTING FIGURE 4 Single-particle image analysis and classification of protein complexes from CN−PAGE C<sub>2</sub>SM band from the Arabidopsis <em>lhcb5</em> mutant (Figure 2A). </strong>Number of averaged projections in given classes are indicated.</p> <p><strong>SUPPORTING FIGURE 5<em> </em>A histogram of the relative abundance of PSII semi-crystalline arrays </strong><strong>in thylakoid membranes of Arabidopsis </strong><strong><em>lhcb5</em></strong><strong> mutant. </strong>The bars represent the number of electron micrographs where the 2D arrays cover the indicated percentage of the membrane. The histogram was obtained by evaluation of 50 randomly selected images.</p> <p><strong>SUPPORTING TABLE 1</strong> Physiological parameters of Arabidopsis WT and lhcb5 mutant plants.</p> <p><strong>SUPPORTING TABLE 2 </strong>Density of bands corresponding to LHCB5-less PSII supercomplexes evaluated relatively to WT.</p> <p><strong>Figure 1 C-D</strong> - source data for Figure 1. (panels C-D) Documentation of similar physiology of Arabidopsis thaliana wild type (WT), and its mutant with loss of LHCB5 protein subunit (lhcb5): (C) relative content of photosysthesis related proteins in thylakoid membranes of Arabidopsis thaliana lhcb5 genotype normalised to WT determined by LC-MS/MS; (D) relative protein ratios normalised to WT of photosynthesis related thylakoid membrane proteins of Arabidopsis thaliana lhcb5 genotype determined in isolated thylakoid membranes by LC-MS/MS.</p> <p><strong>Figure 4</strong> - source data for Figure 4. Relative distribution of photosystem II (PSII) distances in thylakoid grana membranes of Arabidopsis thaliana wild type (WT) and mutant with missing LHCB5 protein (lhcb5).</p> <p><strong>Supporting figure 1</strong> Source data for supporting figure 1 Photosynthesis related parametres describing PSI and PSII function. (A) quantum yield of photochemistry of PSI (Y(I)) in Arabidopsis thaliana WT and lhcb5 genotype leaves during red acitinic light exposure and dark relaxation; (B) quantum yield of photochemistry of PSII (Y(II)) in Arabidopsis thaliana WT and lhcb5 genotype leaves during red acitinic light exposure and dark relaxation; (C) non-photochemical quenching of Arabidopsis thaliana genotypes: The level of NPQ estimated during red acitinic light exposure and dark relaxation of WT and lhcb5 leaves.</p> <p><strong>Supporting figure 5</strong> Source data for Supplement figure 4. Relative abundance of 2D PSII arrays in the thylakoid membranes of Arabidopsis thaliana lhcb5 mutant from 50 randomly selected images.</p> <p><strong>Supporting table 1 - source data</strong> Source data for supporting table 1. Physiological parameters of witl type (WT) Arabidopsis thaliana and its mutant lacking LHCB5 protein (lhcb5): Repetitions of data measured for each genotypes.</p> <p><strong>Supporting table 2 - source data</strong> Source data for supporting table 2. Density of bands corresponding to LHCB5-less PSII supercomplexes evaluated relatively to WT: Repetitions of data measured for each genotypes.</p> <p><strong>Figure 1B source WB </strong>Source WB picture for FIGURE 1B.</p> <p><strong>Figure 1B source WB, marker </strong>Source WB picture with molecular marker for FIGURE 1B.</p>
Improvement of abalone hatchery and nursery production processes using organic preparation techniques
<p>France Haliotis tested the effects of different settlement cues on settlement and survival of European abalone (Haliotis tuberculata)</p>
Soil organic matter and plant carbon allocated to nitrogen acquisition simulated by the FUN-BioCROP model
<p>This data package contains the model input, results, and validation data from Juice et al (citation below). The FUN-BioCROP model (Fixation and Uptake of Nitrogen- Bioenergy Carbon, Rhizosphere, Organisms, and Protection) advances the field of bioenergy modeling by integrating new empirical paradigms of the role of belowground processes in shaping coupled carbon (C) and nitrogen (N) cycles. It was developed by modifying the FUN-CORPSE model (Fixation and Uptake of Nitrogen- Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment, Sulman et al. 2017 Ecology Letters) for use in bioenergy systems by including mechanistic tillage, organic matter addition, nitrogen fertilization, harvest, and feedstock-specific parameters, and to be driven by DayCent plant productivity and biomass data.</p>
Modeling Early Life Histories of Marine Organisms
<p>This is a recorded presentation to introduce students to ecosystem modeling. The presentation was developed for students of an early life histories class so discusses lagrangian individual-based modeling but the supporting material for understanding eulerian physical and lower trophic level models is also introduced.</p> <p>If you use part or all of this educational material as part of your lesson content it would be appreciated if you could inform the author (gagibson@alaska.edu) for tracking purposes.</p>
A Thermogelling Organic-Inorganic Hybrid Hydrogel with Excellent Printability, Shape Fidelity and Cytocompatibility for 3D Bioprintingg
<p>Dataset for manuscript submitted for peer review</p>
Output data of the models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 1: moderately surface active organics" by Vepsäläinen et al. (2022)
<p>Output data of the different models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 1: moderately surface active organics" by Vepsäläinen et al. (2022).</p> <p>Output data is included for 50 nm particles containing malonic acid (mna), succinic acid (sca) and glutaric acid (glutarica), mixed with ammonium sulphate (AS) in different organic mass fractions. </p> <p>A plotter that allows the user to plot the Köhler curves, surface tensions and organic<br> partitioning factors during droplet growth from the model output data provided is included. </p>
Nicotiana benthamiana as a model organism for plant biology study
<p><em>Nicotiana benthamiana</em> is an amenable model organism for plant biology study. Several functional genomics tools, including viral vectors, RNAi, ethylmethanesulfonate (EMS) mutagenesis, CRISPR-mediated genome editing, and agroinfiltration, are available in the <em>N. benthamiana</em> experimental system. These tools can be applied to research in genomics, biochemistry, metabolomics, cell biology and pathology as well as other topics in plant biology.</p> <p>*This is an updated graphical abstract for commnetary article "Dude, where is my mutant? <em>Nicotiana benthamiana</em> meets forward genetics" (Derevnina et al., 2019, New Phytologist 221(2):607-610).</p>
Supporting data for review article: The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective
<p>Supporting data and code for review article: Sokol N.W., Whalen E.D., Kallenbach C., Pett-Ridge J., Georgiou K. The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective. <em>Functional Ecology, </em>2022.</p> <p>We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415). For this review article, we specifically focused on measurements of bulk and mineral-associated soil organic carbon concentrations (reported in units of gC/kg soil) and the proportion of bulk soil organic carbon that is mineral-associated (reported as a %). This subset also includes auxiliary data regarding climate and biome characteristics extracted from the synthesized papers; for more variables, see the original full dataset. Köppen-Geiger climate zones were extracted from a georeferenced global database (using R package 'kgc' v1.0.0.2) with site coordinates, where available. Three files are provided in this repository: (1) data file, (2) metadata file, and (3) code for manuscript figures and summary statistics.</p>
Prediction stock of soil organic carbon in Argentina
<p>We standardized the Stocks soil organic carbon (SOC) at 0-30 cm depth for 5,073 soil samples. We spatially predicted SOC stock (kg/m2) using regression forest and associated prediction uncertainties using quantile regression forest at 1000 m resolution. Global accuracy based on cross-validation. We obtained a RMSE 2.624 and Rsquared 0.464.</p>
Single-cell analyses of axolotl forebrain organization, neurogenesis, and regeneration
<p>Preprint: https://doi.org/10.1101/2022.03.21.485045</p> <p>Abstract:</p> <p>Salamanders are important tetrapod models to study brain organization and regeneration, however the identity and evolutionary conservation of brain cell types is largely unknown. Here, we delineate cell populations in the axolotl telencephalon during homeostasis and regeneration, representing the first single-cell genomic and spatial profiling of an anamniote tetrapod brain. We identify glutamatergic neurons with similarities to amniote neurons of hippocampus, dorsal and lateral cortex, and conserved GABAergic neuron classes. We infer transcriptional dynamics and gene regulatory relationships of postembryonic, region-specific direct and indirect neurogenesis, and unravel conserved signatures. Following brain injury, ependymoglia activate an injury-specific state before reestablishing lost neuron populations and axonal connections. Together, our analyses yield key insights into the organization, evolution, and regeneration of a tetrapod nervous system.</p> <p> </p> <p>File description:</p> <p>all_nuclei_clustered_highlevel_anno.rds - Seurat object including all snRNA-seq data from uninjured pallium, both from microdissections and whole pallium multiome.</p> <p>pallium_metadata_simp.csv - csv file containing a simplified version of the metadata for the uninjured pallium</p> <p>Edu_1_2_4_6_8_12_fil_highvarfeat.rds - Seurat object containing all Div-seq data for the pallium injury time course</p> <p>divseq_predicted_metadata.csv - csv file containing a simplified version of the metadata for the pallium injury time course</p> <p>ep_wpi_srat.rds - Seurat object containing an integrated version of ependymoglia cells from uninjured and injured pallium (see Fig 6 in the preprint).</p> <p>D1_113_sub_b.rds - Seurat object containing a Visium data for the axolotl pallium</p> <p>multiome_integATAC_SCT.rds - Signac object containing the data used for multiome analysis of the uninjured whole pallium</p> <p>predictions_cell2loc.csv - csv file containing cell2location scores for the uninjured pallium cell types in the Visium dataset</p>
Globally-gridded data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon
<p>Supporting globally-gridded data products for manuscript: Georgiou K., Jackson R. B., Vindušková O., Abramoff R. Z., Ahlström A., Feng W., Harden J. W., Pellegrini A. F. A., Polley H. W., Soong J. L., Riley W. J., Torn M. S. Global stocks and capacity of mineral-associated soil organic carbon. <em>Nature Communications</em>, 2022.</p> <p>We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415) along with ancillary data on climate, vegetation, and soil characteristics to produce spatially-explicit global estimates of mineral-associated soil organic carbon stocks (MOC) and mineralogical carbon capacity (MOC<sub>max</sub>) in non-permafrost, non-desert mineral soils. Globally-gridded datasets are given in kgC/m<sup>2</sup> for topsoil (0-30cm) and subsoil (30-100cm) at 0.5 degree by 0.5 degree spatial resolution.</p>
Synthesis data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon
<p>Supporting synthesis data for manuscript: Georgiou K., Jackson R. B., Vindušková O., Abramoff R. Z., Ahlström A., Feng W., Harden J. W., Pellegrini A. F. A., Polley H. W., Soong J. L., Riley W. J., Torn M. S. Global stocks and capacity of mineral-associated soil organic carbon. <em>Nature Communications</em>, 2022.</p> <p>We performed an observational synthesis of soil fractionation data constituting 1,144 globally-distributed soil profiles from 78 studies that reported fractionation and bulk measurements of organic carbon across depths. This dataset includes measurements of mineral-associated, particulate, and bulk soil organic carbon, as well as ancillary data on edaphic, climate, and vegetation characteristics. We also performed a separate observational synthesis of soil carbon accrual from manipulation and chronosequence studies, which included changes in carbon stocks or concentrations, bulk density, experimental duration, and edaphic properties. This latter synthesis included 103 observations from 34 studies that spanned crop, pasture, grassland, and forest ecosystems across climates and soil types. Further details for both syntheses can be found in the methods and supplementary materials of the associated manuscript.</p>
European Aerosol Phenomenology - 8: Harmonised Source Apportionment of Organic Aerosol using 22 Year-long ACSM/AMS Datasets
<p>Organic aerosol (OA) is a key component of total submicron particulate matter (PM<sub>1</sub>), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM<sub>1</sub> levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013–2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables quantification of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average = 71.1%, range from 43.7 to 100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution (30–240 min) and long-term data coverage (9–36 months), providing essential information to improve/validate air quality, health impact, and climate models.</p>
Crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3), GIXD analysis results: diffraction features and crystal structure
<p>Analysis result of an <em>in-situ</em> measurement of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) on a glass substrate.</p> <p>This dataset contains the positions, sizes, and integrated intensities of extracted diffraction peaks with 0.1s time resolution.</p> <p>For crystal structure matching, the provided CIF file (CCDC 1446529) was used.</p>
Dimethylsulfoniopropionate-derived compound concentrations, volatile organic compound concentrations, and microorganism abundances around two corals and a seaweed in the reefs of Moorea (French Polynesia)
<p>These data belong in the paper: </p> <p>M. Masdeu-Navarro, J-F. Mangot, L. Xue, M. Cabrera-Brufau, S.G. Gardner, D.J. Kieber, J.M. González, R. Simó (2022). Spatial and diel patterns of volatile organic compounds, DMSP-derived compopunds and planktonic microorganisms around a tropical scleractinian coral colony. <em>Frontiers in Marine Science</em>.</p> <p>Concentrations of DMSP, acrylate, DMSO, DMS, DMDS, COS, CS2, isoprene, CH3I, CH2ClI, CH2Br2 and CHBr3 in seawater samples around colonies of the corals Acropora pulchra and Pocillopora sp., and the brown seaweed Turbinaria ornata. Abundances of high-DNA and low-DNA bacteria, Prochlorococcus, Synechococcus, picoeukaryotes and nanoeukaryotes in the same samples, as determined by flow cytometry. All samples were collected in April 2018 in the coral reefs of Mo'orea, French Polynesia. </p> <p>The upper set of data contains concentrations at the distance of 0.5 cm from the coral polyps on the branch tips or verrucae, as well as from the seaweed thalli (samples IN), and 2 m away, downcurrent (samples OUT). The second set of data corresponds to A. pulchra only, and contains seawater samples IN, OUT and AL, the latter being sampled at 0.5 cm from the base of the dead branches colonized by a turf alga. IN, OUT and AL samples were collected over an entire diel cycle, every 6 hours for a period of 30 hours.</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.