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245 results for “soil datasets”
Soil texture dataset from the publication: "Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula'
<p>Clay, silt and sand distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The coefficient of variation and quantile data represent the spatial uncertainty of the predictions. For more information about the methodology used, users are referred to the article: </p> <p>Siqueira, R.G., Moquedace, C.M., Francelino, M.R., Schaefer, C.E.G.R., Fernandes-Filho, E.I., 2023. Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula. Geoderma 432, 116405. https://doi.org/10.1016/j.geoderma.2023.116405</p> <p>The .zip file has the following folders:</p> <p>1) soil_texture_antarctica: soil texture information containing clay, silt and sand contents</p> <p>2) soil_texture_coefficient_variation: uncertainty from the coefficient of variation of the soil texture prediction</p> <p>3) soil_texture_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil texture prediction</p> <p>4) soil_texture_quantile05: quantile 5% of the soil texture prediction</p> <p>5) soil_texture_quantile95: quantile 95% of the soil texture prediction</p>
Soil chemistry dataset from the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica"
<p>Bases sum, H+Al (potential acidity), pH, phosphorous, remaining P (P-rem), sodium and total organic carbon distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The quantile and prediction interval data represent the spatial uncertainty of the predictions.</p> <p>As soon as the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica" is published, the paper will be cited here. </p> <p>The .zip file contains the following folders:</p> <p>1) soil_chemistry_antarctica: data containing the soil chemical attributes distribution</p> <p>2) soil_chemistry_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil attributes prediction</p> <p>4) soil_texture_quantile05: quantile 5% of the soil attributes prediction</p> <p>5) soil_texture_quantile95: quantile 95% of the soil attributes prediction</p>
Dataset for the article "Beyond PLFA: Concurrent extraction of neutral and glycolipid fatty acids provides new insights into soil microbial communities"
<p>The following are data and code used for statistical analysis and figure plotting in the manuscript</p> <p>Gorka et al. (2023) "Beyond PLFA: Concurrent extraction of neutral and glycolipid fatty acids provides new insights into soil microbial communities", Soil Biology and Biochemistry</p> <p>It contains the following files:</p> <p>1. Pure lipid standard data</p> <ul> <li>Total ion chromatogram (TIC) area data (<strong>area.csv</strong>)</li> <li>Assignment of lipids that the measured fatty acids originate from (<strong>LipidClass.csv</strong>)</li> <li>An R script reproducing the calculations and plotting for Fig. 2 and Fig. S1 (<strong>pure_lipids.R</strong>)</li> </ul> <p>2. Microbial pure culture fatty acid data data</p> <ul> <li>TIC area data of the PLFA, NLFA, and GLFA data from pure culture extracts (<strong>area.csv</strong>)</li> <li>Files needed for calculating the data and assigning taxonomic groups in the R code (<strong>weights.csv</strong>, <strong>C_atoms.csv</strong>, <strong>species_list.csv</strong>)</li> <li>An R script reproducing the calculations and plotting for Fig. 3, Fig. 4, Fig. S2, and Fig. S3 (<strong>pure_cultures.R</strong>)</li> </ul> <p>3. Soil fatty acid data</p> <ul> <li>Absolute abundance data in nmol C g<sup>-1</sup> dry weight of the PLFA, NLFA, and GLFA data from soil extracts (<strong>nmolC.csv</strong>)</li> <li>Taxonomic group assignments of fatty acids needed to run the R code (<strong>phylum.csv</strong>)</li> <li>An R script reproducing the calculations and plotting for Fig. 5, and Fig. S4 (<strong>soil.R</strong>)</li> </ul>
Dataset to Schiedung et al (2023): Soil carbon losses due to priming moderated by adaptation and legacy effects
<p>Data set to: Schiedung et al. (2023) Soil carbon losses due to priming moderated by adaptation and legacy effects, Nature Geoscience</p> <p>All file informations are presented in 0_Read_me_description.csv</p> <p>All .csv files are separated by ",". All .xlsx files contain the isotopic excess calculations condcuted in Microsoft Excel (Version 2301 Build 16.0.16026.20002).</p> <p>This repository contains all data of the soils and sites, incubation and fractionation presented in the above mentioned publication.</p> <p>For further questions and requests contact Marcus Schiedung (marcusschiedung@gmail.com)</p>
Dataset for Phosphite as an Engineered Niche for Pseudomonas veronii in a Synthetic Soil Bacterial Community
<p>Files containing the source data and analyses used in the manuscript "Phosphite as an Engineered Niche for <em>Pseudomonas veronii </em>in a Synthetic Soil Bacterial Community".</p> <p> </p> <p>Clara Bailey (1), Philip Gwyther (2), Senka Čaušević (2), Brandon L. Greene (1), and Jan Roelof van der Meer (2)</p> <p>1) Department of Chemistry and Biochemistry, University of California, Santa Barbara, Santa Barbara, California, United States</p> <p>2) Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland</p> <p> </p> <p>This dataset contains 16S rRNA gene amplicon sequencing data (in the form of an abundance table, "abund.csv" and combined with CFU counts in "abund_cfu.csv"), toluene quantification data, and CFU counts. All data analysis, statistical tests, and generated figures are contained in the relevant .R script. Refer to README files for data tables as well as the README section at the header of the R script. The dataset has been updated from version 1 to include manuscript revisions, and updated calculations and figures. </p> <p> </p>
A global dataset of soil particulate organic carbon
Open the record for dataset details and reuse information.
Laboratory-based hyperspectral visible near-infrared reflectance spectral dataset of soil samples across a range of surface orientations
Open the record for dataset details and reuse information.
Dataset: Ideas and perspectives: patterns of soil CO2, CH4, and N2O fluxes along an altitudinal gradient - a pilot study from an Ecuadorian neotropical montane forest
<p>These datasets contain data from (i) soil CO<sub>2</sub>, CH<sub>4,</sub> and N<sub>2</sub>O flux measurements (ii) physicochemical soil properties, and (iii) soil temperature and moisture of four tropical forests located on the western flanks of the Andes in northern Ecuador.</p> <p>A manuscript using these datasets has been submitted to Biogeosciences under the title: <strong><em>"Ideas and perspectives: patterns of soil CO2, CH4, and N2O fluxes along an altitudinal gradient - a pilot study from an Ecuadorian neotropical montane forest"</em></strong>.</p>
Soil microbial dataset, crop diversity experiment
<p>This is the dataset of the corresponding study : https://www.biorxiv.org/content/10.1101/2020.11.27.401224v1</p> <p> </p>
Dataset of soil, climatic and stand variables in Pinus sylvestris and Pinus halepensis plantations in Spain
<p>Soil, climatic and stand variables measured in 35 <em>Pinus sylvestris</em> and 32 <em>Pinus halepensis </em>plantations in Castilla y León region (Spain)</p>
Dataset of the analysis described in the article: EM signal penetration in a planetary soil simulant: Estimated attenuation rates using GPR and TDR in volcanic deposits on Mount Etna
<p>GPR and TDR Data collected at two sites on Mount Etna.</p> <p>See for more details the article</p>
STEMMUS-SCOPE for PLUMBER2: A Physically Consistent Dataset Across the Soil-Plant-Atmosphere Continuum
<p>High-quality and long-term measurements of water, energy, and carbon fluxes between the land and atmosphere are critical for climate monitoring and land surface model (LSM) benchmarking. This dataset presents high-quality and long-term fluxes, and the corresponding above and below-ground hydrological, physiological, photosynthetic and radiative data derived from the STEMMUS-SCOPE model simulations for the PLUMBER2 project at 170 FLUXNET sites. The generated datasets by STEMMUS-SCOPE are in good agreement with quality in-situ measurements. Therefore, we recommend future applications of the datasets, for such as the detection and attribution of historical changes of fluxes and SM and associated extreme events, providing the initial and boundary conditions for atmospheric models, benchmarking various types of models, and monitoring drought risks. The published dataset can contribute to the development of earth system models in better representing land surface processes and land-atmosphere exchanges in forms of energy, water, and carbon.</p>
Dataset of global plant-soil feedback
<p>Plant-soil feedback (PSF) is an important mechanism determining plant community dynamics and structure. Understanding the geographic patterns and drivers of PSF is essential for understanding the mechanisms underlying geographic plant diversity patterns. We compiled a large dataset containing 5969 observations of PSF from 202 studies to demonstrate the global patterns and drivers of PSF for woody and non-woody species. Overall, PSF was negative on average and was influenced by plant attributes and environmental settings. Woody species PSFs did not vary with latitude, but non-woody PSFs were more negative at higher latitudes. PSF was consistently more positive with increasing aridity for both woody and non-woody species, likely due to increased mutualistic microbes relative to soil-borne pathogens. These findings were consistent between field and greenhouse experiments, suggesting that PSF variation can be driven by soil legacies from climates. Our findings call for caution to use PSF as an explanation of the latitudinal diversity gradient and highlight that aridity can influence plant community dynamics and structure across broad scales through mediating plant soil-microbe interactions.</p>
Dataset and R-scripts to the manuscript: Beyond salinity: plants show divergent responses to soil ion composition
<p>Dataset and R-scripts to the article: Pätsch R., Midolo G., Dítě Z., Dítě, D., Wagner, V., Pavonič, M., Danihelka, J., Preislerová Z., Ćuk M., Stroh H.G., Tóth T., Chytrá H., Chytrý M. (2024) Beyond salinity: plants show divergent responses to soil ion composition. <em>Global Evology and Biogeography, </em>00, e13821. <a href="https://doi.org/10.1111/geb.13821%E2%80%8B" target="_blank" rel="noopener">https://doi.org/10.1111/geb.13821</a></p>
Dataset and R-codes for Publication: "Best performances of visible-near infrared models in soils with little carbonate - a field study in Switzerland" (accepted version)
<p><span>In this upload you can find the R-codes and dataset for the Publication: </span></p> <p><span>"Best performances of visible-near infrared models in soils with little carbonate - a field study in Switzerland" by Simon Oberholzer Laura Summerauer, Markus Steffens and Chinwe Ifejika Speranza accepted in SOIL (https://doi.org/10.5194/egusphere-2023-1087)</span></p> <p><span>To reproduce the results of the manuscript, start with the R-file “ResampleandPreprocess.R” to prepare spectral data and then continue with the R-file “PLSRmodelling_GroupedCV.R” for the modelling.</span></p> <p><span>The R-files “control_train.R” and “rep_grouped_kfold_CV.R” are helper-functions for the grouped cross-validation. The file metadata.csv explains the column names in the spectral data (spcdata.RDS).</span></p>
Soil microbiome dataset from the University of Wisconsin Arlington and Lancaster agricultural research stations and cheese maker and vegetable processor wastewater land application sites
<p>Cheese making and vegetable processing are trillion-dollar industries globally. However, they generate immense volumes of high nitrogen wastewater that must be processed safely and cost effectively. Land application systems are frequently used by rural medium and smaller processing facilities that lack ready access to wastewater resource recovery facilities. This study utilized soil microbial data to determine system differences leading to high denitrification rates observed in incubation studies in agricultural soil collected from University of Wisconsin Agricultural Research Stations (ARS), Arlington and Lancaster stations, compared to industry cheese making and vegetable processing land application water treatment facilities. It was hypothesized that decade long frequent treatment with facility wastewater would alter the microbial communities in the system soils, but this is not the case. No clear correlations were found between soil denitrification rates and biotic or abiotic system factors and the microbial communities observed in the industry systems are similar to the ARS soils under agricultural production and to literature reported denitrifying systems such as wetlands and wastewater resource recovery facilities. Knowing that land application system management does not alter the microbial biome will allow any management advances that increase denitrification efficiency in other denitrifying systems to be readily applied to industry wastewater land application facilities. </p>
Datasets from hydrus simulations for three soils
<p>The data were obtained from hydrus simulation results and consisted of three parts for different texture soils.</p>
Dataset of bank soil parameters and stochastic modelling of bank erosion processes in the Middle Yangtze River
<p><span>A probabilistic process-based model of bank erosion has proposed, </span><span>embedding the probability distributions of</span><span> different bank soil parameters. <span><span> The dataset includes the spatial distribution characteristics of critical shear stress, friction angle, and cohesion. The prediction results analyzed the effects of soil erosion resistance capacity and the variability of shear strength parameters in the simulation of bank erosion processes, obtaining the probability of mass failure and the distributions of bank erosion width. Additionally, the study investigated the effect of varying water content on bank erosion modeling and further analyzed how considering more influencing factors in the model affects prediction uncertainty and accuracy. Moreover, the relationship between the variability of these factors and river morphology was discussed. The dataset provides the aforementioned prediction results, and relevant plots were generated using MATLAB or Python, with the associated plotting code also uploaded.</span></span></span></p>
A Unified Ensemble Soil Moisture Dataset Across the Continental United States
<p>A unified ensemble soil moisture (SM) package has been developed over the Continental United States (CONUS). The data package includes 19 products from land surface models, remote sensing, reanalysis, and machine learning models. All datasets are unified to a 0.25-degree and monthly spatiotemporal resolution, covering various temporal spans and providing a comprehensive view of surface SM dynamics. The statistical analysis of the datasets leverages the Koppen-Geiger Climate Classification to explore surface SM’s spatiotemporal variabilities. The extracted SM characteristics highlight distinct patterns, with the western CONUS showing larger coefficient of variation values and the eastern CONUS exhibiting higher SM values. Remote sensing datasets tend to be drier, while reanalysis products present wetter conditions. In-situ SM observations serve as the basis for wavelet power spectrum analyses to explain discrepancies with respect to temporal scales across the 16 datasets facilitating daily SM records. This study provides a comprehensive soil moisture data package and an analysis framework that can be used for Earth system model evaluations and uncertainty quantification, quantifying drought impacts and land–atmosphere interactions, and making recommendations for drought response planning.</p> <p>Data details: 1. scripts: 1) process data from original spatial resolution to 0.25 degree; 2) process data from original temporal resolution to monthly; 3) process the monthly data to seasonal mean analysis; 4) wavelet analysis. 2. data: 1) monthly 0.25deg data processed from raw datasets; 2) monthly and seasonal climatology data for comparison; 3) site data for wavelet analysis.</p> <p>Reference: The data manuscript is under review now and will add it here later.</p> <p>Contact: Mingjie Shi <mingjie.shi@pnnl.gov>; Lingcheng Li <lingcheng.li@pnnl.gov></p>
GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change
<p>We complied the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change (GSOCS-LULCC) from 632 papers documented in Web of Science till the June 2024. This database comprises 1,187 sites with 5,805 records at multiple sample depths.<br>This dataset (in csv formats) is associated to the "GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change" by Chen et al. (2025). The README file includes the full explanation of all the columns.<br>Manuscript citation: Chen, S., Shuai, Q., Arrouays, D., Chen, Z., Dai, L., Hong, Y., Hu, B., Huang, Y., Ji, W., Li, S., Liang, Z., Ma, Y., Richer-de-Forges, A.C., Schillaci, C., Su, Y., Teng, H., Wang, N., Wang, X., Wang, Y., Wang, Z., Wang, Z., Xu, D., Xue, J., Ye, S., Zhang, X., Zhou, Y., Zhu, P., Shi, Z. , 2025. GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change. In preparation.<br>When using the data, please cite repositories as well as the original manuscript.<br>For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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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.