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2,078 results for “soil data”
data sets for the article Short‑term impact of crop diversifcation on soil carbon fuxes and balance in rainfed and irrigated woody cropping systems under semiarid Mediterranean conditions
<p>Diversifcation practices such as intercropping in woody cropping systems have recently been proposed as a promising management strategy for addressing problems related to soil degradation, climate change mitigation and food security. In this study, we assess the impact of several diversifcation practices in diferent management regimes on the main carbon fuxes regulating the soil carbon balance under semiarid Mediterranean conditions.</p>
Data and scripts associated with "Rock weathering controls soil carbon storage potential at a continental scale"
<p>The zipped folder contains the following files:</p> <p>CONUS_mineral_grid.csv [gridded soil mineralogy maps, with coordinates representing cell centers]</p> <p>CONUS_grid.csv [blank grid, used for reproducing the analysis]</p> <p>NCSS_datamerge_071321.R [core script used for running the analyses presented in the associated publication]</p> <p>apply_depthwtavg.R [wrapper function for depth weighted averaging]</p> <p>attach_ENV.R [function to attach climate data]</p> <p>bootstrap_functions.R [functions for spatial bootstrap statistics]</p> <p>depthweightavg.R [function for calculating depth weighted averages]</p> <p>get_MWBM.R [function for compiling climate data into averages]</p> <p>get_SLP.R [function for reading and pre-processing the NASGLP dataset</p> <p>SLP_IDW.R [inverse distance weighting function interpolating the NASGLP data at query points]</p> <p>weather_calcs.R [secondary calculations partitioning Al and Fe; weathering rate estimation]</p>
Incubation data, CO2 and CH4 flux data and soil properties of thaw slump soils on Kurungnakh, Lena Delta in July 2016 and July 2019
<p>CO2 and CH4 rates from incubations and potential fluxes: This dataset contains rates of CO2 and CH4 production and the potential CO2 and CH4 emission rates calculated from these incubation fluxes</p> <p>in situ CO2 and CH4 chamber fluxes: This dataset contains CO2 and CH4 fluxes measured with closed chambers from different sites on Kurungnakh in July 2016 and July 2019</p> <p>simulated soil temperature and modelled CO2 fluxes: This dataset contains daily mean soil temperature data simulated with JSBACH for 2016 and the annual CO2 fluxes simulated with a Q10 model and the Introductory Carbon Balance Model (ICBM)</p> <p>thaw depth, TOC in active layer, soil temperature 2016: This dataset contains the thaw depth, TOC pools in the active layer and the soil temperature during the measurement period in July 2016</p> <p>thaw depth, TOC in active layer, soil temperature 2019: This dataset contains the thaw depth, TOC pools in the active layer and the soil temperature during the measurement period in July 2019</p> <p> </p> <p> </p> <p> </p> <p> </p>
Data from: Invasive plants have greater growth than co-occurring natives in live soil subjected to a drought-rewetting treatment
<p>Although several studies indicate that invasive plant species respond more negatively to drought than native plant species, little remains understood of how and whether drought-rewetting events may affect growth of invasive and co-occurring native plant species both directly and indirectly through soil microorganisms. In a fully crossed factorial design, we grew individuals of four congeneric pairs of invasive and native plant species in 2.5 L pots that contained live or sterilized field soil under one of three drought treatments: no-drought, drought, drought-rewetting. Results show that drought caused a significantly greater decline in total biomass of invasive plants than that of native plants regardless of the presence of live soil microorganisms. However, total biomass of the invasive plants exhibited a greater recovery from drought following rewetting than did that of the native plant species. Moreover, the recovery from drought in invasive species tended to be stronger in live soil than in sterilized soil, while for the native plants, recovery from drought was stronger in sterilized soil than in live soil. Overall, these results suggest that soil biota may enable invasive plants to grow larger than co-occurring native plant species in ecosystems that experience cycles of drought and rewetting.</p>
Data from: Specific leaf area is lower on ultramafic than on neighbouring non-ultramafic soils
<p>These are datasets in csv format for ultramafic and non-ultramafic sampling sites where specific leaf area (SLA) was collected in five climatically diverse regions: Puerto Rico (tropical wet), Costa Rica (tropical dry), South Africa (subtropical), California and Lesbos (both with Mediterranean climate). The datasets include plant species level mean SLA values, regional coordinates, regional climate data, bioclimatic variables for individual sites, and a joined dataset that includes site level coordinates and climate variables. The R script to reproduce all analyses and figures is also included.</p>
High-resolution soil moisture data (1km)
<p>High-resolution soil moisture data based on ESA CCI surface soil moisture data in southwestern Europe (Iberia Peninsula).</p> <p>Refs:</p> <p>He, K., Zhao, W., Brocca, L., and Quintana-Seguí, P.: SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation, Hydrol. Earth Syst. Sci., 27, 169–190, https://doi.org/10.5194/hess-27-169-2023, 2023.</p> <p> </p>
Data for: The interactive effects of soil fertility and tree mycorrhizal association explain spatial variation of diversity-biomass relationships in a subtropical forest
<p><span>Observed biodiversity-ecosystem function (BEF) relationships are highly variable, particularly in natural forests. However, our understanding of the factors that generate these often contradictory patterns, especially the role of different mycorrhizal associations, is still limited. By relating tree species richness and aboveground biomass (AGB) in a fully-mapped 24-ha subtropical forest dynamics plot, we evaluated the impacts of soil fertility and tree mycorrhizal type in mediating BEF relationships at multiple spatial scales. Our results demonstrate a highly positive total richness effect on AGB for arbuscular mycorrhizal (AM) trees but a negative effect on AGB for ectomycorrhizal (EcM) trees, and their relationships were highly spatial scale dependent. However, the observed BEF relationships turned into positive at small spatial scales (i.e., 10 m and 20 m) after controlling for other confounding factors (i.e., topography, soil fertility, and AM proportion). In addition, we found significant interactions between soil fertility and species richness on AGB. Specifically, the positive effect of total species richness on AGB for major mycorrhizal types gradually weakened with increasing soil fertility, while the positive effect of EcM species richness on AM AGB gradually enhanced at small spatial scales, suggesting the observed diversity effects can be largely attributed to resource niche complementarity and the role of EcM fungi.</span></p> <p><span>Synthesis. We conclude that the variable BEF relationships among forest communities could be explained by spatial variation in abiotic environments and community mycorrhizal composition because different types of symbionts perform different nutrient uptake strategies and ability in protection from antagonists. Our findings provide novel insights into the understanding of the variation in the shape of BEF relationships in natural forests, which is critical for forest management, conservation, and restoration in a changing world.</span></p>
Data from: Microgeographic variation in early fitness traits of Pinus sylvestris from contrasting soils
<p>Seedling phenotypic data from two Iberian <em>Pinus sylvestris</em><em> </em>provenances from contrasting soils (calcareous vs. siliceous) grown in a 3-year greenhouse experiment on both types of natural local substrate. Each line in the file corresponds to a different seedling. Phenotypic variables as described in the original paper published in American Journal of Botany, doi <a href="https://doi.org/10.1002/ajb2.16159">10.1002/ajb2.16159</a></p>
Data for: The effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis
<p><strong><em>Data description</em></strong></p> <p>This dataset includes detailed information about five different types of land use change reported in “The effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis (Agriculture, Ecosystems and Environment; <a href="https://doi.org/10.1016/j.agee.2023.108402)">https://doi.org/10.1016/j.agee.2023.108402)</a>”. </p> <p> </p> <p>Lists of five different types of land use change</p> <p>1) conversion of primary forest to cropland</p> <p>2) conversion of primary forest to grassland</p> <p>3) conversion of cropland to forest</p> <p>4) conversion of grassland to forest</p> <p>5) conversion of grassland to cropland</p> <p> </p> <p>Lists of detailed information</p> <ul> <li>Land use change (pre-LUC, post-LUC)</li> <li>Country, Location, Geographic position (Longitude, Latitude) </li> <li>Altitude (m)</li> <li>Climate zone</li> <li>Weather [rainfall (mm yr<sup>-1</sup>) and temperature (°C)]</li> <li>Reported time of change (years)</li> <li>Vegetation type (pre-LUC, post-LUC)</li> <li>Fertilizer (pre-LUC, post-LUC: type, application; change)</li> <li>Soil sampling depth (cm)</li> <li>Soil type [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil pH, bulk density, CEC [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil organic carbon [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil total nitrogen [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil total phosphorus [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil C:N [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil C:P [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil N:P [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Reference</li> </ul> <p> </p> <p><em><strong>Data collection method</strong></em></p> <p>We analyzed five different types of LUC: 1) conversion of primary forest to cropland, 2) conversion of primary forest to grassland, 3) conversion of cropland to forest, 4) conversion of grassland to forest, and 5) conversion of grassland to cropland.</p> <p>We classified primary forest as forest that had not previously been cleared and used for other land uses. The conversion of cropland or grassland to forest includes naturally generated and intentionally planted forest. Cropland is land used for growing agricultural crops and may include short pasture phases, and grassland is land used continuously for grazing purposes, but may include occasional and repeated pasture-renewal phases.</p> <p>While we tried to make categorical distinctions between these land-use types, land uses are often more fluid in practice, which may not always have been stated in the publications underlying our data compilation.</p> <p>When a paper reported both contents and stocks, we used the stock-based measure. We used reported stocks if the original work had already been corrected to equivalent soil mass (Ellert and Bettany, 1995) or if corrected stocks had been reported in previous reviews or meta-analyses (Don et al., 2011; Poeplau et al., 2011; Guo and Gifford, 2002). Where bulk-density correction had not been applied, we tried to make those corrections to estimate changes to equivalent soil mass if studies provided sufficient information on soil bulk density and depth, using the method of Zhang et al. (2004). If that was not possible, we used the reported SOC, TN, or TP contents.</p> <p> </p> <p><em><strong>Acknowledgements</strong></em></p> <p>We thank scientists who measured, analyzed, and published the data compiled for this study. We are especially grateful to Drs. Axel Don, Christopher Poeplau, Lex Bouwman, and Gaihe Yang, who provided their global meta-data through personal communication. D.-G.K. acknowledges support from the IAEA CRP D15020. M.U.F.K and L.L.L. were supported by the Strategic Science Investment Fund (SSIF) of New Zealand’s Ministry of Business, Innovation and Employment.</p>
Data for: Heavy metal pollution impacts soil bacterial community structure and antimicrobial resistance at the Birmingham 35th Avenue Superfund Site
<p>The data in this archive are the results of a study on the impact of heavy metals (HMs) on the soil microbiota of an urban Superfund site in Alabama. HMs are known to modify bacterial communities both in the laboratory and in situ. Consequently, soils in HM-contaminated sites such as the U.S. Environmental Protection Agency (EPA) Superfund sites are predicted to have altered ecosystem functioning, with potential ramifications for the health of organisms, including humans, that live nearby. Further, several studies have shown that heavy metal-resistant (HMR) bacteria often also display antimicrobial resistance (AMR), and therefore HM-contaminated soils could potentially act as reservoirs that could disseminate AMR genes into human-associated pathogenic bacteria. To explore this possibility, topsoil samples were collected from six public locations in the zip code 35207 (the home of the North Birmingham 35th Avenue Superfund Site) and in six public areas in the neighboring zip code, 35214. 35027 soils had significantly elevated levels of the HMs As, Mn, Pb, and Zn, and sequencing of the V4 region of the bacterial 16S rRNA gene revealed that elevated HM concentrations correlated with reduced microbial diversity and altered community structure. While there was no difference between zip codes in the proportion of total culturable HMR bacteria, bacterial isolates with HMR almost always also exhibited AMR. Metagenomes inferred using PICRUSt2 also predicted significantly higher mean relative frequencies in 35207 for several AMR genes related to both specific and broad-spectrum AMR phenotypes. Together, these results support the hypothesis that chronic HM pollution alters the soil bacterial community structure in ecologically meaningful ways and may also select for bacteria with increased potential to contribute to AMR in human disease.</p>
NAIADES Soil moisture sensors raw data
<p>Raw data from initial soil moisture sensors (LSE-01) tests performed during the initial phases of the NAIADES project. Sensors were installed in flower boxes and flowerbeds across the city of Carouge, Switzerland, data was transmitted each 20 minutes via LoRaWAN.</p> <p>This data was exported from influxdb, unrelevant fields were omitted, device addresses were partially obfuscated.</p> <p>Units: (field:unit):</p> <p>water_SOIL: V/V%</p> <p>temp_SOIL:°C</p> <p>conduct_SOIL:uS/cm</p> <p> </p>
Data from: Subtle responses of soil bacterial communities to corn-soybean-wheat rotation
<p>Crop rotational diversity can improve crop productivity and soil health and boost soil microbial diversity. This research hypothesized that a three-year rotation of corn-soybean-wheat (CSW), compared to a two-year corn-soybean (CS) rotation, would result in a more diverse and more complex soil bacterial community, together with a greater abundance of beneficial bacteria. This was evaluated in a replicated experiment established in 2013 at two locations in Ohio (USA). The soil bacterial communities under soybean were compared between CS and CSW, at both studied sites, in 2018 and 2019, through 16S rDNA amplicon metabarcoding. </p>
Dataset used in "Comment on "Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam" by Kim-Anh Nguyen, Yuei-An Liou, Ha-Phuong Tran, Phi-Phung Hoang and Thanh-Hung Nguyen"
<p>The Excel file provides all the data included in Tab.4 of Nguyen et al. 2020 plus reflectances extracted from the Landsat 8 OLI image acquired on 14 February 2017 and downloaded from the USGS Earth Explorer website. Observations on the number of pixels falling of water, land and mixed water/land surfaces are provided as well as water percentage cover estimated using regular spaced points. </p> <p>The dataset includes vector files (kml format) of the grids corresponding to the selected L8 pixels as well as the regularly spaced points generated within the selected pixels. These files can be imported in QGIS, Google Earth Pro and other free GIS software.</p>
Global Soil Temperature code and data
<p>Version 2 - updated and corrected data.</p>
Data of soil mineralization rates, carbon and nitrogen pools in a rainfed almond crop and an irrigated mandarin crop derived from Diverfarming project
Data of soil carbon and nitrogen dynamics, auxiliary data and methods metadata from a rainfed almond crop and an irrigated mandarin crop studied in Diverfarming project
After the sun: A nanoscale comparison of the surface chemical composition of UV and soil weathered plastics data
<p>The present folder contains the raw data as well as the R script used in the following publication : <em>After the sun: A nanoscale comparison of the surface chemical composition of UV and soil weathered plastics. </em>To extract the data:</p> <p> </p> <ol> <li>Download the .zip file and extract all the files it contains in a folder, without changing the hierarchy</li> <li>Open the Rproject by double-clicking on it</li> <li>Run the script and explore the outputs in the <em>Script_01_output </em>folder</li> </ol> <p>The data extracted from the HDF5 files before any processing are saved as .csv files in the <em>CSV </em>folder.</p>
Supplementary data for multidimensional drivers of mercury distribution in global surface soils
<p>Global distribution of Hg in surface soils predicted by a machine learning method. </p> <p>Random forest modeling.</p> <p>Latitude 1 degree by longitude 1 degree.</p>
Data and code for the publication "Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil"
<p><strong>Background</strong></p> <p>The dataset contains data on soil properties and microplastic abundance in soil samples taken in Langel-Merkenich (Cologne, Germany). It was analysed in the paper by M. Rolf, H. Laermanns, L. Kienzler, C. Pohl, J.N. Möller, C. Laforsch, M.G.J. Löder and C. Bogner, “Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil” <a href="https://doi.org/10.1016/j.scitotenv.2022.155141">https://doi.org/10.1016/j.scitotenv.2022.155141</a>) published in Science of the Total Environment.</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>The zipped folder</p> <ul> <li><strong>data.zip</strong> contains subfolders <strong>imagelab_data</strong> (contains microplastics data by depth and the blanks), <strong>additional_data</strong> (weights of aliquots and polymer densities) and <strong>soil_data</strong> (data on soil properties and images of soil profiles).</li> <li><strong>images.zip</strong> contains the workflow diagram, which is plotted in the R notebook Data_read_in.Rmd.</li> <li><strong>results.zip</strong> contains the results of reading and wrangling of microplastics data.</li> </ul> <p> </p> <p><strong>Description of the code</strong></p> <p>The R notebooks *.Rmd contain the code to read and wrangle the microplastics data (Data_read_in_corrected.Rmd), analyse and plot it (Analysis_plots_MP.Rmd) and plot data on soil properties (Plots_soil_data.Rmd).</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>The data and code are provided as is without any warranty.</p> <p> </p> <p><strong>Funding</strong></p> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357.</p> <p> </p> <p><strong>References</strong></p> <p>M. Rolf, H. Laermanns, L. Kienzler, C. Pohl, J.N. Möller, C. Laforsch, M.G.J. Löder and C. Bogner, “Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil” <a href="https://doi.org/10.1016/j.scitotenv.2022.155141">https://doi.org/10.1016/j.scitotenv.2022.155141</a></p> <p>R Core Team, 2021, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, https://www.R-project.org/</p>
Data from : Successful recycling of construction wastes into multifunctional soil for green infrastructures, Pruvost & al 2022
<p>Dataset of the Paper "Successful recycling of construction wastes into multifunctional soil for green infrastructures" by Pruvost et al. 2023.</p> <p>macrofauna.csv</p> <p>soil_properties.csv</p> <p>vegetation.csv</p> <p>vegetation_biomass.csv</p>
Data from: Long-term changes in soil carbon and nitrogen fractions in switchgrass, native grasses, and no-till corn bioenergy production systems
<p>Cellulosic bioenergy is a primary land-based climate mitigation strategy, with soil carbon (C) storage and nitrogen (N) conservation as important mitigation elements. Here, we present 13 years of soil C and N change under three cellulosic cropping systems: monoculture switchgrass (<em>Panicum virgatum</em> L.), a five native grasses polyculture, and no-till corn (<em>Zea mays</em> L.). Soil C and N fractions were measured four times over 12 years. Bulk soil C in the 0–25 cm depth at the end of the study period ranged from 28.4 (± 1.4 se) Mg C ha<sup>−1</sup> in no-till corn, to 30.8 (± 1.4) Mg C ha<sup>−1</sup> in switchgrass, and to 34.8 (± 1.4) Mg C ha<sup>−1</sup> in native grasses. Mineral-associated organic matter (MAOM) ranged from 60% to 90% and particulate organic matter (POM) from 10% to 40% of total soil C. Over 12 years, total C as well as both C fractions persisted under no-till corn and switchgrass and increased under native grasses. In contrast, POM N stocks decreased 33% to 45% across systems, whereas MAOM N decreased by less than 13% and only in no-till corn. Declining POM N stocks likely reflect pre-establishment land use, which included alfalfa and manure in earlier rotations. Root production and large soil aggregate formation explained 69% (p < 0.001) and 36% (p = 0.024) of total soil C change, respectively, and 60% (p = 0.020) and 41% (p = 0.023) of soil N change, demonstrating the importance of belowground productivity and soil aggregates for producing and protecting soil C and conserving soil N. Differences between switchgrass and native grasses also indicate a dependence on plant diversity. Soil C and N benefits of bioenergy crops depend strongly on root productivity and pre-establishment land use.</p>
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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.
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DANDI Archive for NWB datasets
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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.