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245 results for “soil datasets”
Dataset: Effect of soil organic matter content and nutrient loading on productivity of Spartina patens (v.0.10)
<p class="MsoNormal">River sediment diversions in the Mississippi River Delta have been planned as a keystone strategy for wetland restoration in coastal Louisiana. The introduction of mineral sediment and dissolved nutrients present in Mississippi River water could drastically alter the abiotic environment of the surrounding wetlands, and the effects on wetland vegetation are unclear. In this study, 50 sods were transplanted from a <em>Spartina patens-</em>dominated brackish marsh into a greenhouse and grown in either mineral or organic soil in combination with one of five levels of nutrient enrichment. The primary component of the mineral soil was silt collected from the bank of the Mississippi River, while that of the organic soil was peat collected from the aforementioned <em>S. patens</em> marsh. Nutrient treatments were based on a range of Mississippi River diversion discharge rates of nitrate, phosphate, sulfate, potassium, and iron as well as a control with no nutrient addition. After one summer, we found that total porewater conductivity increased with nutrient loading under both soil types. Under higher nutrient treatments the concentrations of porewater sulfide were elevated to phytotoxic levels, but this effect was only observed in the organic soil treatment. Nitrate and ammonium porewater concentrations were affected by both nutrient loading and soil type; however, nitrate was higher in the mineral soil while ammonium was higher in the organic soil. Porewater phosphorous and potassium concentrations were elevated in organic soil conditions and higher nutrient loading, while porewater iron was higher under mineral soil and lower nutrient loading. Aboveground standing crop, belowground biomass accumulation (i.e., ingrowth), and soil shear strength were also sampled at the end of the growing season with the goal of examining their relationships with nutrient and sediment enrichment and porewater chemistry.</p>
Dataset and Analyses for manuscript: "Counteracting effects of soil biota on emergence and growth of herbaceous plants"
<p>Dataset and analyses for manuscript: "Counteracting effects of soil biota on emergence and growth of herbaceous plants"</p>
Soil_Rock_SantaCruz_RPaque2023_Dataset
<p>Information about rock and soil samples from 10 sites along a climosequence in Santa Cruz Island: location, elevation, lithology, rock age, rock density, soil bulk density, soil pH, elemental chemistry composition.</p>
Dataset for: Calcium promotes persistent soil organic matter by altering microbial transformation of plant litter
<p>Dataset and code for the publication:</p> <p>Shabtai, I.A., Wilhelm, R.C., Schweizer, S.A. <em>et al.</em> Calcium promotes persistent soil organic matter by altering microbial transformation of plant litter. <em>Nat Commun</em> <strong>14</strong>, 6609 (2023). https://doi.org/10.1038/s41467-023-42291-6<br><br></p>
Carbon sequestration and nitrogen loss drive the evolution of French forest soils: dataset
<p>This dataset contains forest floor and mineral soil chemical properties down to 40 cm depth for two soil sampling campaigns carried out at around 15-year time interval (campaign 1 from 1993 to 1995, campaign 2 from 2007 to 2012) within the 102 permanent plots of the French Network for the Monitoring of Forest Ecosystems (RENECOFOR).</p> <p>Data are reported for soil organic carbon (Corg), total nitrogen (Ntot), C/N ratio (CNratio), exchangeable calcium (Caexch), exchangeable magnesium (Mgexch), exchangeable potassium (Kexch), exchangeable aluminium (Alexch), effective cation exchange capacity (ECEC), extractable phosphorus (P), pH CaCl<sub>2</sub> and pH H<sub>2</sub>O.</p>
Dataset for "Soil fluxes of carbonyl sulfide (COS), carbon monoxide, and carbon dioxide in a boreal forest in southern Finland"
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Dataset: Effect of soil organic matter content and nutrient loading on productivity of Spartina patens (v.0.10)
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Dataset for "A Numerical Investigation of Bedrock Groundwater Recharge and Exfiltration on Soil Mantled Hillslopes."
<p>Model input and output data used to produce the results in the publication.</p>
STEROPES - Dataset 19: Collected soil data, ILVO
<p>Dataset 19: Collected soil data, ILVO (Belgium)</p> <p><strong>Origin: </strong>First field trial on BOPACT LTE (S15) and on field trial S2 in Gavere, 16 samples from topsoil 0-10 cm; in 5m-radius around center of each plot): soil carbon and soil moisture). Second field trial, 20 samples from top soil 0-10 cm during 2021 and 2022 collected and soil organic carbon measured. </p> <p><strong>Purpose and objectives:</strong> to analyse the input of plant residue in SOC prediction from remote sensing data under WP4. </p> <p><strong>Format</strong>: Data in the format of CSV . </p> <p><strong>Data utility: </strong>Data is collected based on the experiment where each plot has different amount of plant residual from without plant residual till completely covered with plant residual. </p>
Dataset for "EA-ERT: a new ensemble approach to convert ERT data to soil moisture"
<p>This dataset supports the research study "EA-ERT: a new ensemble approach to convert ERT data into soil moisture" by B. Loiseau, S. D. Carrière, N. K. Martin-StPaul, R. Clément, C. Champollion, V. Mercier, J. Thiesson, S. Pasquet, C. Doussan, T. Hermans and D. Jougnot.</p> <p>We provide electrical resistivity tomograph (ERT) time-lapse data for the two sites studied (Avignon and Larzac), with associated volumetric water contents measured by the probes in the field. Meanwhile, we also offer the R code for the calculations of each step of the EA-ERT method. </p>
Dataset 5 - Reinforced concrete and soil monitoring system in operational environment
<p>The data demonstrate reinforced concrete and soil prototype sensing devices used in an operational environment. The data will be kept confidential for Intellectual Property and exploitation purposes until further notice. </p>
A 100 m annual soil salinization dataset from 1985 to 2024 in the Western Songnen Plain, China
<p><span lang="EN-US">In our study, we identified and classified soil salinization degrees in the Western Songnen Plain at 100 m spatial resolution using ground surveys data and remote sensing imagery, combined with machine learning algorithms over the period 1985 to 2024.</span></p> <p><span lang="EN-US"> </span></p> <p><strong><span lang="EN-US">The publicly available data used in this paper, as well as the modeling code, are shown below: </span></strong></p> <p><span lang="EN-US">1) </span><span lang="EN-US">The remote sensing satellite data used in this study are freely available on GEE platform (available at </span><span lang="EN-US"><a href="https://code.earthengine.google.com/">https://code.earthengine.google.com/</a></span><span lang="EN-US">). </span></p> <p><span lang="EN-US">2) </span><span lang="EN-US">Land cover data can be accessed at </span><span lang="EN-US"><a href="https://doi.org/10.5281/zenodo.4417810">https://doi.org/10.5281/zenodo.4417810</a></span><span lang="EN-US">. </span></p> <p><span lang="EN-US">3) </span><span lang="EN-US">SSSG dataset is available at </span><span lang="EN-US"><a href="https://files.isric.org/public/global_soil_salinity/salmap1986/" target="_blank" rel="noopener">https://files.isric.org/public/global_soil_salinity</a></span><span lang="EN-US">. </span></p> <p><span lang="EN-US">4) </span><span lang="EN-US">LDSS data are available at </span><span lang="EN-US"><a href="https://doi.org/10.6084/m9.figshare.13295918.v1">https://doi.org/10.6084/m9.figshare.13295918.v1</a></span><span lang="EN-US">. </span></p> <p><span lang="EN-US">5) </span><span lang="EN-US">Yearly identification results of saline soils are available at </span><span lang="EN-US"><a href="https://code.earthengine.google.com/3dd47875d7455825297b9a6a8766a8ee">https://code.earthengine.google.com/3dd47875d7455825297b9a6a8766a8ee</a></span><span lang="EN-US">. </span></p> <p><span lang="EN-US">6) </span><span lang="EN-US">The data required for model inputs (TIR</span>、<span lang="EN-US">SIT</span>、<span lang="EN-US">PDI) are available at</span> <span lang="EN-US"><a href="https://code.earthengine.google.com/7fc0e7ee2cf4cb6b744aaf4706a140c9">https://code.earthengine.google.com/7fc0e7ee2cf4cb6b744aaf4706a140c9</a>.</span></p> <p><span lang="EN-US"> </span></p> <p><strong><span lang="EN-US">The presented data file contains:</span></strong></p> <p><span lang="EN-US">1) </span><span lang="EN-US">Soil EC sampling points</span></p> <p><span lang="EN-US">2) </span><span lang="EN-US">TIRSITPDI_predicted.mat</span></p> <p><span lang="EN-US">3) </span><span lang="EN-US">Soil_EC_prediction_model.m</span></p> <p><span lang="EN-US">4) </span><span lang="EN-US">Statistical results of soil salinization degree by area (1985-2024)</span></p> <p><span lang="EN-US">5) </span><span lang="EN-US">Yearly degree of soil salinization (1985-2024)</span></p> <p><span lang="EN-US">6) </span><span lang="EN-US">Yearly identification results of saline soils (1985-2024)</span></p> <p><span lang="EN-US">7) </span><span lang="EN-US">Yearly soil EC results (1985-2024)</span></p> <p><span lang="EN-US"> </span></p> <p><strong><span lang="EN-US">Samples:</span></strong></p> <p><span lang="EN-US">The file, Soil_EC_sampling_points.csv, provides essential information on 942 sample points.</span> <span lang="EN-US">Below are the descriptions for each column in the metadata:</span></p> <ul> <li><span lang="EN-US"> </span><span lang="EN-US">Municipal: Location where the point is located </span></li> <li><span lang="EN-US"> </span><span lang="EN-US">LnEC: The observed LnEC</span> <span lang="EN-US">using a conductivity meter (Light Magnetic, model: DDS-307A).</span></li> <li><span lang="EN-US"> </span><span lang="EN-US">TIR</span>、<span lang="EN-US">SIT</span>、<span lang="EN-US">PDI:<strong> </strong>Characteristic parameters in the models.</span></li> </ul> <p><span lang="EN-US"> </span></p> <p><strong><span lang="EN-US">Models:</span></strong></p> <p><span lang="EN-US">We trained on this data using eight models (NNF, GPR, LSBoost, KPLS, Tree, SVM, LR, and SVM Kernel) from the Matlab (R2024a) Toolbox.</span> <span lang="EN-US">To optimize the NNF for accurate soil salinity estimation (R² = 0.467; RMSE = 0.729 dS m⁻¹), we trained the model using nested loops and fine-tuned parameters such as the training algorithm, the number of hidden layer nodes, and the learning rate. After 14,000 rounds of cycling, the optimal model </span><span lang="EN-US">TIRSITPDI_predicted.mat</span><span lang="EN-US"> is finally obtained. Finally, the </span><span lang="EN-US">characteristic parameters</span><span lang="EN-US"> with the model are put into </span><span lang="EN-US">Soil_EC_prediction_model.m</span><span lang="EN-US"> to obtain the soil salinity values.</span></p> <p><span lang="EN-US"> </span></p> <p><strong><span lang="EN-US">Results:</span></strong></p> <ul> <li><span lang="EN-US">Statistical results of soil salinization degree by area (1985-2024) shows</span> <span lang="EN-US">statistical results for each class (Slightly, Moderately, Highly, Extremely) as well as total saline acreage in the Western Sonnen Plain for the years 1985-2024.</span></li> <li><span lang="EN-US">Yearly degree of soil salinization (1985-2024) presents the results of saline soil classification based on the U.S. Salinity Laboratory’s classification system for the </span><span lang="EN-US">Western Sonnen Plain</span><span lang="EN-US"> from 1985-2024. </span></li> <li><span lang="EN-US">Yearly identification results of saline soils (1985-2024) displays the results of saline soil identification in the </span><span lang="EN-US">Western Sonnen Plain</span><span lang="EN-US"> from 1985-2024.</span></li> <li><span lang="EN-US">Yearly soil EC results (1985-2024) exhibits the results of model-predicted soil EC content in the </span><span lang="EN-US">Western Sonnen Plain</span><span lang="EN-US"> for the years 1985-2024.</span></li> </ul>
Dataset for paper "Soil-Rock Interface Modulating Basal Shearing within Landslide Shear Zones"
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A 100 m annual soil salinization dataset from 1985 to 2021 in the Western Songnen Plain, China
<p>Using machine learning algorithms, we developed a novel, high-resolution soil salinization dataset by integrating ground survey data, and remote sensing imagery to classify soil salinity over the period from 1985 to 2021. Also, we created a regional soil salinity prediction model which was further refined to account for soil moisture effects, enabling the generation of annual, 100 m resolution soil salinization maps.</p>
Dataset for Journal Paper - Liquefaction assessment in unsaturated soils
<p>This is a dataset for the journal paper "Liquefaction assessment in unsaturated soils". The related article has been published in ASCE Journal of Geotechnical and Geoenvironmental Engineering. DOI: <a href="https://doi.org/10.1061/(ASCE)GT.1943-5606.0002851">10.1061/(ASCE)GT.1943-5606.0002851</a></p>
A semantic segmentation dataset consisting of soil-root images with multiple growth stages.
<p> A semantic segmentation dataset consisting of soil-root images with multiple growth stages.</p> <blockquote> <p><strong>Last Update: 2022.08.10</strong></p> </blockquote>
Dataset for paper "Soil-Rock Interface Modulating Basal Shearing within Landslide Shear Zones"
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A 100 m annual soil salinization dataset from 1985 to 2021 in the Western Songnen Plain, China
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Dataset for McClelland et al. 2022. Infrequent compost applications increased plant productivity and soil organic carbon in irrigated pasture but not degraded rangeland. Agriculture, Ecosystems, and Environment.
<p>Raw data files accompanying the published article "Infrequent compost applications increased plant productivity and soil organic carbon in irrigated pasture but not degraded rangeland" in <em>Agriculture, Ecosystems, and Environment</em>. <a href="https://authors.elsevier.com/a/1etJPcA-Ik6yb">https://authors.elsevier.com/a/1etJPcA-Ik6yb</a></p> <p>Units for response variables in .csv files are as follows. Please reach out to scm229@cornell.edu with any questions about using the files or the data within.</p> <p>--</p> <p>Aboveground biomass: total (Mg ha-1), carbon (Mg C ha-1), nitrogen (kg N ha-1)</p> <p>Bulk density: g cm-3</p> <p>Respiration (Rs): micro mol m-2 s-1</p> <p>Roots: Mg C ha-1</p> <p>Soil C and N: organic and inorganic carbon (Mg C ha-1), nitrogen (Mg N ha-1)</p> <p> </p>
Dataset for "Unveiling the potential of Zn-Al-NO3 LDH as nanofertilizer: Effects on the maize growth and soil biological and chemical properties"
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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.