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245 results for “soil datasets”
Dataset from: Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest Biogeosciences 2019
<p>Dataset used for the manuscript <strong>Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest</strong> in Biogesciences, 2019</p>
HSK531E_Soil_Moisture_Dataset
<p>These soil moisture datasets were used in the hydrologic modeling course within the scope of the project.</p>
Dataset from: Transport and water age dynamics in soils: a comparative study of spatially-integrated and spatially-explicit models
<p>This dataset contains high-resolution vegetated lysimeter experimental dataset carried out in EPFL, Lausanne, Switzerland in March-August 2016. The lysimeter is 100 cm long with a diameter of 120 cm. During this experiment, a simultaneous spike injection of five different solutes (2,5-DFBA, 2-TFMBA, 3,4-DFBA, 2,6-DFBA, 3-TFMBA) took place on the 3rd of March 2016 at 14:00 in an hour. The solutes' mass recovery at the bottom of lysimeter was observed for these solutes.</p> <p>This dataset contains three files which are described in the following:</p> <ul> <li>"hydrologic_data.dat" contains the hourly fluxes (precipitation, irrigation, evapotranspiration measured from load cells, and the water draining at the bottom of lysimeter ) in mm/hr between the 19th of Feb-the 1st of Sep 2016.</li> <li>"tracer_data.dat" contains the tracer concentration observed at the bottom of lysimeter in mg/L. These samples are collected at variable frequencies with an approximate average of 1.5 samples per day.</li> <li>"additional_data.dat" informs you on dry mass and soil volume in the lysimeter, vegetation type, and volume of injection per solute.</li> </ul> <p> </p>
Dataset from two meteorological stations with water and soil temperature measurements in the Alqueva reservoir (Portugal)
<p>In the multidisciplinary <strong>AL</strong>entejo <strong>O</strong>bservation and <strong>P</strong>rediction systems<strong> </strong>project (ALT20-03-0145-FEDER-000004), which aims to strengthen research and innovation in the Alentejo region (southern Portugal), one of the main objectives was to study and model the meteorological conditions in the Alqueva reservoir, in particular their spatial variations within a few hundred meters.</p> <p>The shared hourly dataset, using Coordinated Universal Time (UTC), covers the period from 2018 to 2023 and includes measurements from two meteorological stations located in the Alqueva reservoir, the largest artificial lake in Europe. One station, Montante, is located on a floating platform with a water depth of approximately 70 meters (38.2235 N, 7.4595 W), to the west of the second station, CidAlmeida (38.21539 N, 7.45454 W), which is about 1 km away on land, very close to the water.</p> <p>According to the World Meteorological Organisation (WMO) standards, the data were sampled every second, and hourly data were calculated in post-processing. The dataset includes hourly accumulated precipitation (<em>mm</em>) and hourly average measurements of surface water temperature (at a depth of 0.25 m), soil temperature (at a depth of 0.15 m) and various meteorological parameters: wind speed (<em>m/s</em>) and direction (<em>degrees</em>), relative humidity (%), upward/downward solar radiation (<em>W/m</em><sup><em>2 </em></sup>) and air temperature (<em>°C </em>). All parameters are measured at both stations, except the hourly average water temperature at a depth of 0.25 m, which is only available at the Montante station, and the hourly accumulated precipitation, hourly average soil temperature and wind direction, which are only available at the CidAlmeida station.</p> <p>Hourly data were not subjected to rejection criteria based on the percentage of errors; instead, a column with this percentage is provided, allowing potential data users to apply their own rejection criteria. Daily extremes (daily maximums and minimums for air temperature, relative humidity, and daily maximum gust) are only provided for days where the percentage of errors does not exceed 25% of the 1440 minutes of each day. The daily error percentage for each of the measured parameters at the two stations for the period 2018-2023 is available.</p> <p>Finally, the repository also includes two codes (one for each weather station) written in Visual Basic, which enable data transmission and real-time statistical processing.</p> <p><strong>Fundings:</strong></p> <p>Gonçalo Rodrigues was supported by the Portuguese Foundation for Science and Technology, I.P (Grant 2020.05752.BD). The work is co-funded by national funds through FCT – Fundação para a Ciência e Tecnologia, I.P., in the framework of the ICT project (references UIDB/04683/2020 and UIDP/04683/2020) and by the ALOP project (ALT20-03-0145-FEDER-000004). </p>
Modelling alternative harvest effects on soil CO2 and CH4 fluxes from peatland forests [dataset]
<p>This contains the forest floor soil respiration data and the water level data used in the model simulations in the paper titled "Modelling alternative harvest effects on soil CO2 and CH4 fluxes from peatland forests " (Li et al, 2024; <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.scitotenv.2024.175257" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.scitotenv.2024.175257</a>)</p>
INSA soil image dataset
<h2>Camera description</h2> <h3>1 RGB Camera</h3> <p><br>Images of each mixture are captured at a distance of ∼ 40cm from a Luxonis OAK-D Pro RGB camera under artificial (ambient) lighting. The camera hasthe following specifications:<br>• Resolution: 12MP (4056x3040)<br>• Frame rate: 60 FPS<br>• Lens Size: 1/2.3 inch<br>• 3 color channels Red+Green+Blue (RGB)</p> <h3>2 InfraRed Camera</h3> <p>To generate the image data under artificial (ambiant) lighting, an Aerial Map-ping Camera (MAPIR) is used which is a low-cost, high-resolution Near-InfraRed (NIR) camera capable of capturing multiple non-distorted images. Some specifications of the camera include:<br>• Resolution: 4000x3000, JPG (24bit)<br>• Shutter Speed: automatic<br>• Lens: 19mm 87° HFOV, F2.8 Fixed Aperture (Survey3W)<br>• Color channels NIR+Green+Blue (NGB)<br>Distance of cameras from soil: ∼40cm</p> <h2>Soil description</h2> <p>The dataset contains:<br>• 3 coarse soils: gravel, Hostun sand and Synthetic coarse sand. Different mixtures are realised with gravel/Hostun sand and gravel/Synthetic sand. Images are taken with the RGB camera.<br>• 2 fine soils: Kaolin (white clay) and silt of Dieppe. The soils were mixed with water at different water content values, and images are taken with the RGB and the InfraRed camera.</p> <p> </p> <h2>Acknowledgment</h2> <p><br>The authors thank the Department of Civil Engineering and Urban Planning of INSA Lyon (GCU INSA Lyon) for the donation and preparation of soils.</p> <p>This work was supported by ANR within the joint French-German project RE- MATCH ANR-21-FAI2-0009. We would like to express our sincere appreciation to them for their support.</p> <p> </p>
Unlocking higher methane yields and digestate nitrogen availability in soil through thermal treatment of feedstocks in a two-step anaerobic digestion- Dataset
<p>This is a data set for the article "<span>Unlocking higher methane yields and digestate nitrogen availability in soil through thermal treatment of feedstocks in a two-step anaerobic digestion", published in Chemical and Biological Technologies in Agriculture journal.</span></p>
Dataset of: "Surface-Wave Dispersion in Partially Saturated Soils: the Role of Capillary Forces"
<p>This package contains the dataset of the paper entitled: Surface-Wave Dispersion in Partially Saturated Soils: the Role of Capillary Forces. Further information is given in the README files located within each folder.</p>
Interpretable Geotechnical Artificial Intelligence (XGeoT-AI) Application to Demystify Image Recognition of Soil Cracks [Datasets]
<p>Here is the test data for the paper "Interpretable Geoscience Artificial Intelligence (XGeoS-AI): Application to Demystify Image Recognition".</p>
Dataset for: Exploring the legacy effect of biochar application on soil nitrous oxide emissions
<p><span>This dataset contains data for exploring the legacy effects of biochar addition on soil nitrous oxide emissions and their effects on functional gene abundance associated with soil nitrogen cycling. The dataset contains two data files, one is a data table on the impact of biochar application on soil nitrous oxide emissions, which contains the coordinates of the study site, climate, basic physical and chemical properties of the soil, biochar characteristics and study duration, crop type and management. The other is the data table on the effect of biochar application on the abundance of functional genes related to soil nitrogen cycling, which contains information such as test site coordinates, climate, biochar characteristics, crop type and management. This data can be referenced and reused without any legal or ethical considerations</span>.</p>
Dataset associated with the manuscript: Soil management legacy interacts with wheat genotype to determine access to organic N in a dryland system
<p>Organic nutrient management through the application of compost and/or cover crops provides mineralizable sources of nutrients for plants while often building soil organic matter (SOM) and various aspects of soil health. Variability in nutrient acquisition strategies between crop genotypes may confer advantages under different soil health contexts and could be important for crop selection and breeding, but crop response under field conditions remains unexplored. We investigated the ability of different genotypes of winter wheat (<em>Triticum aestivum</em> L.) to access nitrogen (N) from newly added cover crop residues in two soils with contrasting levels of SOM and biological activity. We planted three previously characterized wheat genotypes in a long-term dryland compost amendment field trial: 1) Byrd (modern, deep roots, low exudation), 2) Cheyenne (historic, drought susceptible, intermediate exudation), and 3) Snowmass (modern, drought-susceptible, high exudation). <sup>15</sup>N-labelled cover crop residue was added to each plot and traced into wheat tissue. In the low SOM soil, the high exudate genotype Snowmass and historic genotype Cheyenne took up the most residue-derived N (6.4-8.1 kg N ha<sup>-1</sup>) compared to the low-exudate genotype Byrd (4.4 kg N ha<sup>-1</sup>), suggesting a strong exudate effect in the more carbon-limited soil. However, the low-exudate, deep rooted genotype, Byrd, took up the most residue N in the high SOM soils (4.6 kg N ha<sup>-1 </sup>vs. 2.8 and 3.3 hg N ha<sup>-1</sup> for Cheyenne and Snowmass, respectively), which indicated higher native N cycling activities and great importance of drought resistance. Enzyme activity, inorganic N, and microbial communities were not influenced by genotype, though did show strong effects of compost application legacy. Our results show that belowground allocation strategies that favor microbial stimulation may be less successful under water limitation, especially when high SOM can support mineralization of residue N without added investment in root inputs. Increased soil health through SOM-building management likely enhances nutrient cycling, and may better support root strategies that invest less in microbial stimulation in favor of other limiting resources.</p>
Dataset of "Stabilized explicit 𝑢−𝑝𝑤 solution in soil dynamic problems near the undrained-incompressible limit"
<p>Dataset of "Stabilized explicit 𝑢−𝑝𝑤 solution in soil dynamic problems near the undrained-incompressible limit"</p>
ChinaCropSM 1km: a fine 1 km daily Soil Moisture dataset for dryland wheat and maize across China during 1993–2018
<p>ChinaCropSM1km provides 1km gridded daily Soil Moisture for Crop drylands across China over 1993–2018, which contains four daily datasets separately by crop type and soil depth (crop-type <sub>soil-depth</sub>). This dataset is wheat<sub>0-10.</sub></p> <p>*** The data file is in “.tif" format</p> <p>*** Spatial extent: wheat drylands across China</p> <p>*** Soil depth: 0-10cm</p> <p>*** Temporal Resolution: Daily</p> <p>*** Pixel size: 1000 m</p> <p>*** Projection information: GCS_WGS_1984</p> <p>*** Filename convention: Year_DOY.tif</p> <p>*** Year: Values from 1993 to 2018</p>
Dataset for: Long-term soil warming decreases microbial phosphorus utilization by increasing abiotic phosphorus sorption and losses
<p><span>Phosphorus (P) is an essential and often limiting element that could play a crucial role in terrestrial ecosystem responses to climate warming. However, it has yet remained unclear how different P cycling processes are affected by warming. Here we investigated the response of soil P pools and P cycling processes in a mountain forest after 14 years of soil warming (+4°C). Long-term warming decreased soil total P pools, likely due to higher outputs of P from soils by increasing net plant P uptake and downward transportation of colloidal and particulate P. Warming increased the sorption strength to more recalcitrant soil P fractions (absorbed to iron oxyhydroxides and clays), thereby further reducing bioavailable P in soil solution. As a response, soil microbes enhanced the production of acid phosphatase, though this was not sufficient to avoid decreases of soil bioavailable P and microbial biomass P (and biotic phosphate immobilization). This study therefore highlights how long-term soil warming triggers changes in biotic and abiotic soil P pools and processes, which can potentially aggravate the P constraints of the trees and soil microbes and thereby negatively affect the C sequestration potential of these forests.</span></p>
A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data
<p> </p> <p>A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data.</p> <p>Introduction</p> <p>This dataset is the Planet Labs PBC (VanderSat B.V.) contribution to the ESA 4DMED hydrology project (<a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org/</a>). It includes Soil Moisture, Land Surface Temperature and Vegetation Optical Depth for the 4DMED spatial domain and time period (2015-2021) at 1km pixel size. If you use the data please include the following reference:</p> <blockquote> <p>Jaap Schellekens, Tessa Kramer, Michel van Klink, Robin van der Schalie, Yoann Malbeteau, Arjan Geers, Richard de Jeu. (2022) <em>A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data</em>. DOI: 10.5281/zenodo.7684993. Planet Labs PBC/VanderSat B.V., ESA Contract No. 4000136272/21/I-EF</p> </blockquote> <p> </p> <p><em>Figure 1: Average L-Band Soil moisture for 2020 over the 4dmed spatial domain</em></p> <p>Variables and files</p> <p>The dataset consists of the following files and products for the 4DMED domain. Detailed information about the products can also be found at <a href="https://docs.vandersat.com/data_products/soil_water_content/specification.html">docs.vandersat.com</a>:</p> <ul> <li><strong><code>planet-teff-4dmed-V4.0.zip</code></strong> - LST (TEFF) ascending (daytime) and descending (nighttime) <ul> <li><code>TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime (13:30 solar time) at 1 km</li> </ul> </li> <li><code>TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature nighttime (01:30 solar time) at 1 km</li> </ul> </li> </ul> </li> <li><strong><code>planet-teff-qf-4dmed-V4.0.zip</code></strong> - LST (TEFF) quality flags <ul> <li><code>QF-TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> <li><code>QF-TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> </ul> </li> <li><strong><code>planet-vod-4dmed-V4.1.zip</code></strong> - vegetation optical depth C and X band (interpolated from C3S passive soil moisture) <ul> <li><code>VOD_AMSR2_C1_DESC_V41_1000</code> <ul> <li>C1 band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> <li><code>VOD_AMSR2_X_DESC_V41_1000</code> <ul> <li>X band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-4dmed-V4.0.zip</code></strong> - All soil moisture products (C1, X and L-band) <ul> <li><code>SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-qf-4dmed-V4.0.zip</code></strong> - Soil moisture quality maps see <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python</a> <ul> <li><code>QF-SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture quality flags (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-cor-4dmed-V4.0.zip</code></strong> - Yearly correlation maps of soil moisture derived from the difference microwave bands. To be used as an extra quality indicator (for example undetected RFI) or for uncertainty estimation <ul> <li><code>SM-CORR-C1-X-DESC_V4.0_1000</code> - yearly C1 vs X band pearson's correlation maps</li> <li><code>SM-CORR-L-C1-DESC_V4.0_1000</code> - yearly L vs C1 band pearson's correlation maps</li> <li><code>SM-CORR-L-X-DESC_V4.0_1000</code> - yearly L vs X band pearson's correlation maps</li> </ul> </li> <li><strong><code>planet-aux-flags-4dmed-V4.0</code></strong> - Extra flags for frozen soil and bare soil. Determined at 0.25 degree and interpolated to the 4dmed grid <ul> <li><code>QF-SNOWFROZEN-AMSR2-ASC_1000::RD</code> - Frozen soil determined from dayttime data</li> <li><code>QF-SNOWFROZEN-AMSR2-DESC_1000::RD</code> - Frozen soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-DESC_1000::RD</code> - Bare soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-ASC_1000::RD</code> - Bare soil determined from daytime data</li> </ul> </li> </ul> <p>All files are archived into one zip file per product group. Each individual netcdf file in the zip file consists of one observation for the whole domain. If you need you can combine the files into one file using the cdo software <a href="https://code.mpimet.mpg.de/projects/cdo">https://code.mpimet.mpg.de/projects/cdo</a> (e.g. <code>cdo -f nc4c mergetime *.nc outfile.nc</code>).</p> <p>License</p> <p>The data for 4DMED is released under the Creative Commons license: CC BY-NC-SA 4.0 (<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>)</p> <ul> <li>Contains modified Copernicus Sentinel data 2015-2021</li> <li>Contains modified JAXA GCOM-W1/AMSR2 data 2015-2021</li> <li>Contains modified SMAP L1B Radiometer data: Piepmeier, J. R., P. Mohammed, J. Peng, E. J. Kim, G. De Amici, J. Chaubell, and C. Ruf. 2020. SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures, Version 4,5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: <a href="https://doi.org/10.5067/ZHHBN1KQLI20">https://doi.org/10.5067/ZHHBN1KQLI20</a></li> </ul> <p>Contact</p> <p>Jaap Schellekens: <a href="mailto:jaap@planet.com">jaap@planet.com</a></p> <p>Versions</p> <ul> <li>1.0 Initial creation</li> <li>1.1 Adjusted 4DMED Mask. Data itself unchanged but more LST (TEFF) measurements added</li> <li>1.2 Removed VOD and replaced by 25km C3S VOD interpolated to 1km (V4.1)</li> </ul> <p>Further information</p> <p>More information on the data and the flags can be found at <a href="https://docs.vandersat.com/">https://docs.vandersat.com</a> and <a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org</a></p> <p>Background publications</p> <p>R.A.M. De Jeu, A.H.A. De Nijs, M.H.W. Van Klink (2016) <em>Method and system for improving the resolution of sensor data</em>, US10643098B2,EP3469516B1, WO2017216186A1</p> <p>De Jeu, R. A., Holmes, T. R., Parinussa, R. M., & Owe, M. (2014). <em>A spatially coherent global soil moisture product with improved temporal resolution</em>. Journal of hydrology, 516, 284-296.</p> <p>Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I. and Forkel, M., 2020. <em>The global long-term microwave vegetation optical depth climate archive (VODCA)</em>. Earth System Science Data, 12(1), pp.177-196.</p> <p>Schmidt, L., Forkel, M., Zotta, R.-M., Scherrer, S., Dorigo, W. A., Kuhn-Régnier, A., van der Schalie, R., and Yebra, M.: <em>Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties, Biogeosciences Discuss.</em> [preprint], <a href="https://doi.org/10.5194/bg-2022-85">https://doi.org/10.5194/bg-2022-85</a>, in review, 2022</p> <p>Van der Schalie, R., de Jeu, R.A.M., Kerr, Y.H., Wigneron, J.P., Rodríguez-Fernández, N.J., Al- Yaari, A., Parinussa, R.M., Mecklenburg, S. and Drusch, M. (2017), <em>The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E</em>, Remote Sensing of Environment, 189, pp.180-193.</p> <p>van der Vliet, M., van der Schalie, R., Rodriguez-Fernandez, N., Colliander, A., de Jeu, R., Preimesberger, W., Scanlon, T., Dorigo, W., 2020. Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records. Remote Sensing 12, 3439. <a href="https://doi.org/10.3390/rs12203439">https://doi.org/10.3390/rs12203439</a></p>
Dataset associated with "Assessment of the effects of the 2021 Caldor megafire on soil physical properties, eastern Sierra Nevadas, USA"
<p>This dataset includes both raw and processed data associated with the publication entitled "Assessment of the effects of the 2021 Caldor megafire on soil physical properties, eastern Sierra Nevadas, USA", published in MDPI Fire (doi: <a href="https://doi.org/10.3390/fire6020066">10.3390/fire6020066</a>). Raw files include exported .xlsx files from Meter Group HYPROP analyses, .csv files from 10 replicate measurements of saturated hydraulic conductivity for each analyzed sample using the Meter Group KSAT device, and raw .dat files from measurement of bulk thermal properties. A single additional file also documents the laboratory results from particle size and loss on ignition analyses. Processed data includes curve fitting parameters associated with fitting the soil water retention curves (SWRC) and thermal conductivity functions (TCFs) for each sample, as described in Sion et al. (2023). Additional requests associated with data from Sion et al. (2023) should be directed to the lead author.</p>
Dataset of manuscript "No detectable upper limit of mineral associated carbon in temperate agricultural soils"
<p>Dataset of carbon fractions (mineral associated organic carbon and particulate organic carbon) detected in a subset of topsoil samples of the first German Agricultural Soil Inventory.</p>
Indian Soil Erodibility Dataset (ISED)
<p>Soil erosion has been spotted as one of the major global problems, and soil erodibility is the property of soil which refers to the erosive nature of the soil. India is also struggling with the erosion problem in most parts of the country. A national-scale soil erosion study is needed to assess such types of problems. ISED (Indian Soil Erodibility Dataset) is a step toward building datasets in mapping soil erosion at a national scale after IRED (Indian Rainfall Erosivity Dataset). This dataset consists of K-factor (t-ha-h/ha/MJ/mm), soil erodibility indices like CR (Clay Ratio), Modified Clay Ratio (MCR), and CLOM (Critical Level of Organic Matter) map over India at high resolution (250 m). It also includes a Susceptibility to Erosion map of India due to Organic Matter availability. Distribution maps of soil erodibility corresponding to the districts and soil types of India are also added to this dataset. This dataset is an additional dataset at a national scale for soil erosion modeling which will be handy data for experts and researchers.</p>
The Long-term, High-accuracy and Seamless Soil Moisture (LHS-SM) dataset over the Qinghai-Tibet Plateau: part 2 (2011-2020)
<p>Soil moisture (SM) is a vital variable in the water-energy cycle and characterizing its spatiotemporal dynamics is crucial for understanding the impacts of climate change. Although substantial efforts have been devoted to derive SM data at fine scale, there is still a research gap in obtaining the long-term, high-accuracy and high-resolution SM data over the Qinghai-Tibet Plateau (QTP) due to its complex topography. Therefore, this study generated the long-term, high-accuracy and seamless soil moisture (LHS-SM) dataset over the QTP during 2001-2020 using a two-step downscaling method. First the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1km utilizing five machine learning approaches. Then a dynamic data merging method that considers the spatiotemporal nonstationary error was applied to derive the final LHS-SM data. Results indicated that LHS-SM data exhibited satisfying accuracy (mean R = 0.55, ubRMSE = 0.049 m³/m³) and certain improvement to the ESA CCI SM data both at station and network scales. The dataset can be used for various regional hydrology, meteorology, ecological analysis and modeling.</p>
Reproducibility of the wet part of the soil water retention curve: a European interlaboratory comparison (dataset)
<p>The soil water retention curve (SWRC) is a key soil property required for predicting basic hydrological processes. The SWRC is often obtained in the laboratory with non-harmonized methods. Moreover, procedures associated with each method are not standardized. This can induce a lack of reproducibility between laboratories using different methods and procedures or using the same methods with different procedures. The goal here was to estimate the inter- and intralaboratory variability of the measurement of the wet part (from 10 to 300 hPa) of the SWRC. An interlaboratory comparison was carried out between 14 laboratories, using artificially constructed, porous reference samples that were transferred between laboratories according to a statistical design. The retention measurements were modelled by a series of linear mixed models using a Bayesian approach. This allowed the detection of sample-to-sample variability, interlaboratory variability, intralaboratory variability and the effects of sample changes between measurements. Here we present the dataset containing measured data from the interlaboratory comparison, R code to implement models, and final model results.</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.