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709 results for “soil carbon”
Soil Moisture Active Passive (SMAP) L4 Carbon Ancillary FPAR Climatology V001
This ancillary SMAP product contains a static climatology data set. The climatology data is derived from MODIS Fractional Photosynthetically Active Radiation (FPAR) models and represents a global 8-day average.
Soil Moisture Active Passive (SMAP) L4 Carbon Ancillary Meteorology Preprocessor Output Log Files V001
This ancillary SMAP product contains daily meteorological model log files, including model outputs. The meteorological model is derived from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data set and used as an input in the SMAP L4 Carbon algorithm.
Soil Moisture Active Passive (SMAP) L4 Carbon Ancillary MODIS FPAR Preprocessor Output Log Files V001
This ancillary SMAP product contains MODIS Fractional Photosynthetically Active Radiation (FPAR) model log files, including model outputs.
50-year fire legacy regulates soil microbial carbon and nutrient cycling responses to new fire
GEO Series GSE274211. soil metagenome. 39 samples. Type: Expression profiling by high throughput sequencing.
Metagenomic analysis revealed the microbial-mediated soil organic carbon loss under the degeneration succession in alpine meadow
GEO Series GSE93158. uncultured soil microorganism. 20 samples. Type: Other.
Nutrient addition enhances temperature sensitivity of soil carbon decomposition across forest ecosystems
<p>Data of the Q10 value and soil properties for the study entitled "Nutrient addition enhances temperature sensitivity of soil carbon decomposition across forest ecosystems".</p>
A global distribution of dissolved organic carbon in soil and in leaching - (database)
<p>Current global carbon (C) models are not representing the fraction of C which is displaced along the terrestrial aquatic continuum thus overestimating the land sink capacity. In order to obtain more reliable C budgets, we need to integrate the lateral transfers of C from terrestrial ecosystems through the inland water network down to the oceans, including biogeochemical transformation during transport and C exchange with the atmosphere.Representing the production and cycling of dissolved organic C (DOC) in the soil column and the leaching of DOC into the inland water network is a first major step in this development.</p> <p>In this study we used newly developed model JULES-DOCM to obtain the first global estimate of global soil DOC stock and DOC concentration, DOC concentration in runoff and DOC leaching flux.</p> <p>In this dataset model produced files are stored as netcdf files including soil DOC stocks (at Top (0-35 cm) and Total soil (0-300cm), soil DOC concentration (at Top (0-35 cm) and Bottom ( 35-300cm)), DOC leaching flux (averaged over 1980-2010) and DOC concentration in runoff (averaged over 1980-2010).</p> <p>The measured DOC collected database is enclosed as the Excel file.</p>
Data for "Loss of grazing by large mammalian herbivores can destabilize the soil carbon pool"
<p>Data on soil-carbon and soil-nitrogen. </p> <p>D. G. T. Naidu, S. Roy, S. Bagchi, Loss of grazing by large mammalian herbivores can destabilize the soil carbon pool. <em>Proceedings of the National Academy of Sciences</em> <strong>in press</strong> (2022).</p> <p> </p>
Mapping Soil Organic Carbon in the World's Largest Arid Mangrove Forest (Indus Delta, Pakistan): A Multi-Sensor Remote Sensing and Machine Learning Approach
<p><span>Mangrove forests play a crucial role in carbon sequestration, especially in arid regions where their ability to store carbon in soil is vital for mitigating climate change. The Indus Delta in Pakistan, the world’s largest arid mangrove forest system, lacks spatially explicit data on Soil Organic Carbon (SOC) despite its importance for conservation and carbon budgeting. This study aims to establish a baseline SOC map 2020 at 10 m spatial resolution using Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (MultiSpectral Instrument) satellite imagery, integrated with in-situ soil sampling. SOC predictions were made using a Classification and Regression Tree (CART) machine learning model within the Google Earth Engine platform, leveraging 40 predictor variables, including spectral bands and derived indices. A total of 53 topsoil (0-10 cm) samples were collected in February 2020 across the Indus Delta, and SOC was analyzed using the Walkley-Black method. The results showed an average SOC value of 65.88 Mg C ha</span><span>⁻</span><span>¹ with substantial spatial variability, ranging from 15.06 Mg C ha</span><span>⁻</span><span>¹ to 138.03 Mg C ha</span><span>⁻</span><span>¹ with a total of 0.91 Pg C. The CART model demonstrated high accuracy, with an R² of 0.95 and an RMSE of 9.18 Mg C ha</span><span>⁻</span><span>¹. However, the region faces challenges such as seawater intrusion and salinity, which threaten its ability to sequester carbon. With the first high-resolution SOC map for the Indus Delta, this study provides valuable insights for ecosystem management, conservation planning, and carbon budgeting. These findings of this study have the potential to significantly influence initiatives like REDD+ and Blue Carbon projects, which aim to enhance carbon sequestration while addressing the ecological challenges facing Pakistan’s mangroves</span></p>
Drought exacerbates dryland soil carbon loss from inorganic carbon under warming
<p>Soil and microbial data for the study entitled "Drought exacerbates dryland soil carbon loss from inorganic carbon under warming".</p>
Soil Moisture Active Passive (SMAP) L4 Carbon Ancillary Model Output Log Files V001
This ancillary SMAP product contains SMAP L4 Carbon model log files, including model outputs.
Soil Moisture Active Passive (SMAP) L4 Carbon Ancillary Model Run Time Input Parameters V001
This ancillary SMAP product contains SMAP L4 Carbon model configurations, including model inputs.
Differential effects of nitrogen addition on soil organic carbon decomposition correlate with changes in microbial C-degradation functional potentials in a Pinus tabulaeformis forest
GEO Series GSE147041. uncultured soil microorganism; Bacteria; Eukaryota; Viruses; Archaea. 16 samples. Type: Other.
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>
Particulate organic carbon controlled the upper limit of soil organic carbon in natural alpine ecosystems of northeast Qinghai-Tibet Plateau
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Carbonate (caco3) soil maps of the Upper Colorado River Basin
<p>The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:</p> <p>Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release,<a href="http://https//doi.org/10.5066/P9SK0DO2"> https://doi.org/10.5066/P9SK0DO2</a>.</p> <p>Associated publication:</p> <p>Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. <a href="https://doi.org/10.1002/saj2.20080">https://doi.org/10.1002/saj2.20080</a>.</p> <p>Repository includes maps of carbonate content (caco3) as defined by United States soil survey program. Content is calculated on the fine earth fraction (<2mm).</p> <p>These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p> <p>The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.</p> <p>Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal.</p> <p>File Name Details:</p> <p>ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (NRCS field pedons plus NRCS laboratory pedons; file ending _CV_plots.tif) and for just the CV results at laboratory pedons (file ending _CV_SCD_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).</p> <p>Elements are separated by underscore (_) in the following sequence:</p> <p>property_r_depth_cm_geometry_model_additional_elements.extension</p> <p>Example: caco3_r_0_cm_2D_QRF_bt.tif</p> <p>Indicates carbonate (caco3) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest that is has gone through transfomation and backtransformation (_bt) in the modeling process. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions.</p> <p>The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).</p> <p>_95PI_h: Indicates the layer is the upper 95% prediction interval value.</p> <p>_95PI_l: Indicates the layer is the lower 95% prediction interval value.</p> <p>_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.</p> <p>References</p> <p> Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma.</p>
Soil Carbon pulse Legume trial Fredericton New Brunswick Canada
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Gabetto et al_2024_Biochar from crop residues mitigates N2O emissions and raises carbon content in tropical soils
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Data for "Functional substitutability of native herbivores by livestock for soil carbon stock is mediated by microbial decomposers"
<p>Data for "Functional substitutability of native herbivores by livestock for soil carbon stock is mediated by microbial decomposers". </p> <p>Contains one csv file with data on multiple variables. </p>
Carbon and nitrogen stocks and distributions associated with different vegetation covers and soil profile types in Abisko, northern Sweden.
<p>Dataset used to compute carbon and nitrogen stocks in the vegetation and the soil of various arctic habitats near Abisko Research Station, northern Sweden. The dataset contains vegetation inventories and soil measurements on 45 quadrats, a birch tree inventory and C and N contents of soils and dominant species.</p>
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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.