Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
245
datasets available to search
ShareScore release 0.9.0
Dataset results
245 results for “soil datasets”
Dataset of the soil CO2 collected at Vulcano in 2021
<p>Dataset of the soil gas survey performed at Vulcano in 2021.</p> <p>The CO2 flux was measured in agreement with the "Dynamic Concentration" Method (Gurrieri and Valenza, 1988: Camarda et al., 2006).</p> <p>The δ<sup>13</sup>C-CO<sub>2</sub> values were against V-PDB international standard (<sup>13</sup>C/<sup>12</sup>C = 0.0111802).</p> <p>Figure shows the Island of Vulcano (Aeolian Island, Italy) and its location in the Mediterranean.</p>
Dataset - Spatially explicit linkages between redox potential cycles and soil moisture fluctuations
<p>This repository holds data collected during three lysimeter experiments where laboratory column scale lysimeters have been subjected to different wetting/drainage cycles. The lysimeters have been filled with forest soil in the Lausanne forest characterised by the SwissMEX project </p> <p>The following dataset contains soil redox potential and soil moisture measurements that have been continuously monitored in time for different depths. Pore water samples of specific dissolved chemicals have been collected at the end of each cycle. </p> <p> </p> <p>Specifically, this dataset is composed by the following files:</p> <ul> <li> "Lysimeter_configuration.png" illustrates the lysimeter used and the sensors scheme adopted.</li> <li>"METADATA.txt" contains specific information about each recorded variable and data point collected throughout the three experiments SM-B, SM-I1 and 2.</li> <li>Pore water analysis data</li> <li>Soil moisture and tension data</li> <li>Soil redox potential data</li> </ul> <p>We thank Pascal Froidevaux for providing the lysimeter used for SM-B experiment. The authors acknowledge key funding provided by the Swiss National Science Foundation through its grant number CRSII5 186422.</p> <p> </p> <p> </p>
Dataset used in "Deep learning with multisite data reveals the lasting effects of soil type, tillage and vegetation history on biopore genesis"
<p>Please see the paper "Deep learning with multisite data reveals the lasting effects of soil type, tillage and vegetation history on biopore genesis" how the images were captured, manual counting was performed, training datasets were prepared and models were trained.</p>
WRF dataset for soil moisture initialization experiments
<p>Modeling results of "Role of Land–Atmosphere Interaction in the 2016 Northeast Asia Heat Wave: Impact of Soil Moisture Initialization", by Yoon et al. (submitted). Data contains the modeling outputs (500GPH, SAT and soil moisture) from CTL and LIS experiments. Detailed description can be founded in the research paper.</p>
CASM: A long-term Consistent Artificial-intelligence based Soil Moisture dataset based on machine learning and remote sensing
<p>Paper to cite: Skulovich, O., Gentine, P. A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset. <em>Sci Data</em> 10, 154 (2023). https://doi.org/10.1038/s41597-023-02053-x</p> <p> </p> <p>The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data as a target and is aimed at extrapolating SMAP-like quality SM data back in time with previous satellite microwave platforms. Machine learning approach, such as neural network (NN) has the advantage of being both nonlinear, and state-dependent, and naturally imposing a global distribution matching between the source and the target data. Utilizing this, the new CASM dataset was created using high-quality SMAP SM as a target and Soil Moisture and Ocean Salinity (SMOS) or Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E/2) brightness temperature as a source, which allowed extrapolating SM data 13 years back from before SMAP mission launch. CASM represents SM in the top soil layer, defined on a global 25 km EASE-2 grid and covers 2002-2020 with a 3-day temporal resolution. The resulting dataset exhibits excellent spatial and temporal homogeneity, without compromising the interannual variability, and is in excellent agreement with the SMAP data (with a mean correlation of 0.97 between the SMAP and CASM SM for the period when the two overlap). Moreover, the input and target datasets were divided into seasonal cycle and residuals, with the NN trained on the residuals. This approach ensures that the high performance does not mask a simple seasonal cycle matching but rather exemplifies the skill targeted at predicting extremes; with the NN achieving a correlation of 0.75 on the test data for the residuals. Comparison to 367 global in-situ SM monitoring sites shows a SMAP-like median correlation of 0.66 between station SM and CASM SM from the corresponding grid cell. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. Mean epistemic uncertainty, related to the NN model structure, ranges from 0.007 m<sup>3</sup>/m<sup>3</sup> to 0.014 m<sup>3</sup>/m<sup>3</sup> and on average is close to a desired SM product stability threshold of 0.01 m<sup>3</sup>/m<sup>3</sup> per year. Aleatoric uncertainty, defined as input noise propagated through the system, depends on the introduced level of noise. With 10% noise applied to the residuals, the resulting mean standard deviation of the model outputs rises from 0.005 to 0.007 m<sup>3</sup>/m<sup>3</sup>. </p>
Dataset of measurements of the soil CO2 flux and soil brightness temperature at Le Biancane (geothermal field of Larderello-Travale, Tuscany, Italy) in the May-June 2021 period.
<p>Dataset of measurements of the soil CO<sub>2</sub> flux and soil brightness temperature at Le Biancane (geothermal field of Larderello-Travale, Tuscany, Italy) in the period May-June 2021. The dataset is structured as follows:</p> <p>Column A is the progressive number of the point (#);</p> <p>Column B is the Longitude of the point, datum WGS 1984;</p> <p>Column C is the Latitude of the point, datum WGS 1984;</p> <p>Column D is the Universal Transverse Mercator (UTM) Longitude coordinate, datum WGS 1984, zone 32N;</p> <p>Column E is the Universal Transverse Mercator (UTM) Latitude coordinate, datum WGS 1984, zone 32N;</p> <p>Column F is the soil brightness temperature, in °C;</p> <p>Column G is the soil CO<sub>2</sub> flux in grams of CO<sub>2</sub> per square meter, per day (g m<sup>-2</sup> day<sup>-1</sup>)</p>
Dataset to: Deforestation for agriculture leads to soil warming and enhanced litter decomposition in subarctic soils
<p>Deforestation for agriculture leads to soil warming and enhanced litter decomposition in subarctic soils<br> T. Peplau, C. Poeplau, E. Gregorich, J. Schroeder</p> <p>This repository contains a dataset of soil temperature, soil parameters, farm management and additional site informations.</p> <p>Soil_temperature_data_Yukon.zip: Temperature data from different farms across the Yukon.<br> Each .xlsx file contains data from one temperature logger that logged soil temperature every 2 hours. The individual sheets are named in the following scheme:<br> Farm_landuse_depth.xlsx<br> Farm contains two letters corresponding to the identifier in the soil data set<br> landuse contains either F ("Forest"), CM ("Cropland / Market Garden") or G ("Grassland")<br> Depth is either 10 cm or 50 cm</p> <p>teabags.csv contains raw data about the initial weight of the teabags buried, their location and their weight after two years in the soil</p> <p>tea_decomposition contains the mean decomposition (n=3) of the tabags from each plot and corresponding temperature statistics, based on the logger data</p> <p>Soil_I_IV.csv contains soil parameters from soil samples at 0-10 cm and 40-60 cm</p> <p>site_data_R.csv contains geographical information and soil data that has only been measured once per site</p>
Vigilantes del Suelo (Soil Watchers) - Dataset
<p>This dataset encompasses the data collected and processed during the citizen science project “Soil Watchers” (“Vigilantes del Suelo”), conducted by Fundación Ibercivis and CITA in Spain during 2023-2024.</p> <p><br><strong>Project Overview</strong></p> <p><br>“Soil Watchers” engages participants through citizen science campaigns to collect soil samples from their surroundings and analyse them to assess their health. The results from each area contribute to creating a national map of soil health.</p> <p><br><strong>Target Audience</strong></p> <p><br>Throughout the course of the project, we conducted two soil collection and analysis campaigns:</p> <ol> <li> A campaign aimed at secondary school students from educational centres across Spain.</li> <li>An open campaign targeted at collecting data from rural soils, encouraging participation from a broader, more adult audience, including senior citizens.</li> </ol> <p><br><strong>Tools and Methods</strong></p> <p><br>Participants in “Soil Watchers” are provided with a kit for collecting and analysing soil samples. The results can be hosted and geo-referenced through a mobile application, contributing to the creation of a national soil quality map.</p> <p><br>Individual participation in “Soil Watchers” is facilitated by purchasing the project’s kit, detailed in the protocol guide. Analysis data can be stored and shared via the Geonity App, available from December 2023. This mobile application includes the “Soil Watchers” project and contributions from all participants.</p> <p><br><strong>Cities of Engagement</strong></p> <p><br>Eight Spanish cities – Málaga, Madrid, Zaragoza, Castellón, Burgos, Villanueva de la Serena, A Coruña, and Ciudad Ciencia (CSIC for national deliveries) – serve as ambassadors for “Soil Watchers,” aiding in the distribution of measurement kits to participants.</p>
AI4AGRI Soil roughness estimation dataset
<p>The dataset contains two sets of images, representing digital images of soil samples used for training ML models to estimate the soil roughness. For more details on the experiments please refer to (and cite): Ivanovici, M., Popa, S., Marandskiy, K., & Florea, C. (2024). Deep automatic soil roughness estimation from digital images. European Journal of Remote Sensing. DOI: https://doi.org/10.1080/22797254.2024.2342955 </p> <p>The zip archives containts a readme text file with the detailed description of the dataset.</p> <p>Funded by the European Union. The AI4AGRI project entitled “Romanian Excellence Center on Artificial Intelligence on Earth Observation Data for Agriculture” received funding from the European Union’s Horizon Europe research and innovation programme under the grant agreement no. 101079136. </p> <p>Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. </p>
Dataset for the article Maintaining favourable carbon balance in boreal clay soil is challenging even under no-till and crop diversification
Open the record for dataset details and reuse information.
SOIL-WATERGRIDS v1, mapping dynamic changes in soil moisture and depth of water table from 1970 to 2014, dataset and modelling
<p>SOIL-WATERGRIDS is a comprehensive data product of the monthly estimates of volumetric soil water content at three depths within the root zone and the depth of the water table globally gridded at a resolution of 0.25x025 degree per grid cell from 1970 to 2014. The SOIL-WATERGRIDS data product also provides the full-scale global model (BRTSim, https://sites.google.com/site/thebrtsimproject/home) that allows third party users to assess the entire volumetric soil water content and water table dynamics from land surface to 50 m depth. </p> <p>This package includes a Technical Documentation with the details about the use of the data product.</p>
A comparative study of red brick powder and lime as soft soil stabilizer (Dataset)
<p>This data is the result of laboratory CBR testing in soaked and unsoaked conditions with or without additional stabilization</p>
Dataset: Remotely sensed soil moisture can capture dynamics relevant to plant water uptake
<p><strong>Dataset Description</strong><br> Stable isotope water uptake profiles were consulted across 45 datasets to determine the primary zone of root water uptake ("Uptake Range Top" to "Uptake Range Bottom"), whether the uptake increases in proportion nearer to the surface ("Decay of Water Uptake With Depth"), and whether uptake temporarily switches to shallow soils ("Temporary Uptake of Upper Layers"). More details on the data collection are shared in our Water Resources Research publication (in revision).</p> <p>Correlation length scales, or the effective depth of representation of L-band satellite soil moisture, are estimates in Short Gianotti et al. 2019 using SMAP surface soil moisture and GPM precipitation retrievals.</p> <p><strong>Citations</strong><br> Those that use the stable isotope table are asked to cite our Water Resources Research publication (in revision) as well as the 45 references contributing to the table.<br> Those that use the correlation length scale dataset are asked to cite:<br> Short Gianotti, D.J., Salvucci, G.D., Akbar, R., McColl, K.A., Cuenca, R., Entekhabi, D., 2019. Landscape water storage and subsurface correlation from satellite surface soil moisture and precipitation observations. Water Resour. Res. 9111–9132. https://doi.org/10.1029/2019wr025332</p>
A 1 km daily soil moisture dataset over the Qinghai-Tibet Plateau (2001-2010)
<p>Soil moisture is the key variable of water and energy cycle, but the long-term, high-resolution soil moisture data with high accuracy is still relatively lacking on the Qinghai-Tibet Plateau. Therefore, we provide the 1 km seamless daily soil moisture data over the Qinghai-Tibet Plateau during 2001-2010 (named as BTCH). Firstly, several predictors including the vegetation index (NDVI and EVI), land surface temperature (LST), evapotranspiration, precipitation, topography (DEM, aspect, slope, TWI), soil properties and three soil moisture related indices (SWCI SIWSI VSDI) were utilized. Five machine/deep learning methods including the artificial neural network (ANN), convolutional neural network (CNN), residual neural networks (ResNet), the long short-term memory network (LSTM) and XGBoost were trained for each year taking the ESA CCI soil moisture data as target. Then the Bayesian three-cornered hat method was adopted for model integration and the final dataset was generated. Evaluaion against four in-situ measurement networks shows that the BTCH dataset has relativey high accuracy both as station and network scales with mean unbiased RMSE values of 0.048 m3/m3 and 0.034 m3/m3. The dataset can be used for various regional hydrological, meteorological, ecological analysis and modeling.</p>
Dataset: Strauss et al. 2023 Sustainable soil management measures: a synthesis of stakeholder recommendations
<p>The provided dataset contains the used information for the scientific publication</p> <p>Strauss, V., Paul, C., Dönmez, C., Löbmann, M., & Helming, K. (2023). Sustainable soil management measures: a synthesis of stakeholder recommendations. <em>Agronomy for Sustainable Development</em>, <em>43</em>(1), 17</p> <p> </p> <p>Due to the assessment language, parts of the dataset are in German.</p> <p>Specifically, it contains:</p> <p>- Stakeholder documents (welche ausgewertet wurden) & Einteilung in Stakeholder Groups<br> - Erfassung der Aussagen & Kategorisierung<br> - Auswertung zur Longlist<br> - Ergebnisse der Farmer Survey</p>
ReCon Soil Project: Dataset for Identification of Soil Health Indicators in Construction
<p>This dataset represents experimental results from Work Package 4 of the ReCon Soil Project: https://www.plymouth.ac.uk/research/institutes/sustainable-earth/the-recon-soil-project </p> <p>Soil health indicators for maintenance in a construction context, are less about soil fertility and more about maintaining those indicators identified, to minimise loss of carbon (C) and nitrogen (N), maintain soil microbial activity and to ensure stockpiled soils are maintained to a point where they can be reused effectively in the future. To this end, an experiment was designed in order to identify and monitor key soil health indicators for construction, and to investigate methods to mitigate against carbon losses in stockpiled soils.</p> <p>The main questions that were addressed were:</p> <p>1. Will sowing grass and other plant species in stockpiled soils reduce losses of C and N from the soil? </p> <p>2. What effect does soil stockpiling have on microbial communities? </p> <p>3. What effect does wood chip application on stockpiled soils have on C and N stocks and microbial communities? </p> <p>4. How do rapid in-field methods for carbon and microbial analysis compare with established lab methods?</p> <p>The experiment was established at the end of August 2022. Any vegetation was cleared from the study site by using a digger to scrape away the top 5cm of soil. Diggers were used to take soil from an area adjacent to the study site which had been undisturbed for 3-4 years, vegetation was cleared from this area and the topsoil was formed into 12 stockpiles (6m length x 4m width x 2m height). The stockpiles were then randomly assigned one of three treatments; amenity grass seed mix, herbal ley seed mix or control. For the seeded treatments, each stockpile was sown by hand to an application rate of 70g/m<sup>2</sup>. After seeds were sown, wood chip was applied by hand to half of each stockpile (randomly assigned), to a depth of covering of approximately 2cm. This resulted in 4 replicates of each combination producing 24 sample plots.</p> <p>Soil samples were collected using a soil sampling auger to collect samples at 0-30 cm and 90-100cm. One sample was taken from each replicate at both depths, these were taken at baseline (T<sub>0</sub>), and intervals of 1 week (T<sub>1</sub>), 2 weeks (T<sub>2</sub>), 4 weeks (T<sub>4</sub>) and every 4 weeks after that. The soil cores were sealed in sterile plastic bags and transported to the laboratory for processing.</p> <p>At each time point, soil was analysed in the laboratory at Eden Project Learning for the following parameters: non-purgeable organic carbon (NPOC), total nitrogen (TN), soil organic matter (SOM), gravimetric water content, pH, and microbial activity using a fluorescein diacetate assay. All remaining bagged soil samples were then transferred to a freezer and stored at -18°C. These were later defrosted and analysed for total organic carbon (TOC), available iron, ammonia, nitrate and nitrite at a subset of time points. Using in-field equipment, soils were analysed for CO<sub>2</sub> emissions at every time point, fungi:bacteria ratio and for soil carbon every 4 weeks. Samples for soil eDNA were taken from each replicate at T<sub>0</sub>, T<sub>8</sub> and T<sub>20</sub>.</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>
The Long-term, High-accuracy and Seamless Soil Moisture (LHS-SM) dataset over the Qinghai-Tibet Plateau: part 1 (2001-2010)
<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>
Munsell soil color chart: A hyperspectral dataset
<p>This dataset contains hyperspectral images obtained using SPECIM IQ for the Munsell soil color chart (MSC). </p> <p>The hyperspectral images are stored in ENVI format. For those who are only interested in the endmember spectra for the MSC, we also provided the spectral library .sli and .hdr inside the <strong>endmembers </strong>folder. </p> <p>The acquisition details for each image can be found in the .hdr file and metadata folder inside the <strong>whole</strong> folder. For the whole image, the acquisition details are:</p> <p>Table 1. Acquisition details</p> <table> <tbody> <tr> <td>samples</td> <td>512</td> </tr> <tr> <td>lines</td> <td>512</td> </tr> <tr> <td>bands</td> <td>204</td> </tr> <tr> <td>default bands</td> <td>70, 53,19</td> </tr> <tr> <td>binning</td> <td>1,1</td> </tr> <tr> <td>tint (integration time)</td> <td>10 (ms)</td> </tr> <tr> <td>fps</td> <td>100</td> </tr> <tr> <td>wavelength range</td> <td>397.32 - 1003.58 nm</td> </tr> </tbody> </table> <p>The dataset is organized into several folders, each containing different types of datasets. </p> <ul> <li><strong>whole</strong> folder contains the entire scene hyperspectral image. This folder contains <strong>capture</strong>, <strong>metadata, </strong>and<strong> results </strong>subfolder. <ul> <li>.png inside the folder is natural color plotting (RGB from default bands in Table. 1) from captured hyperspectral image. </li> <li><strong>capture </strong>folder contains dark reference, white reference and radiance data </li> <li><strong>metadata </strong>folder<strong> </strong>contains the metadata of the acquisition and device settings. </li> <li><strong>results </strong>contains the reflectance calculated by the device (in .dat, .hdr and rendered natural plotting in .png ) from the hyperspectral camera and .png images of the scene, background, and viewfinder from the device's RGB camera. </li> </ul> </li> <li> <p><strong>chips</strong> folder contains only the cropped 20*20 voxels for each color chip reflectances. Each page has its own folder and each folder contains .hdr and .img for each color chip. </p> </li> <li> <p><strong>endmembers</strong> folder contains the spectral library (.sli and .hdr). Each page in MSC have their own .sli and .hdr.</p> </li> </ul> <p>Some of the code snippets that might help to read the dataset</p> <p>using python spectral library to load the dataset</p> <pre><code class="language-python">from spectral import * import matplotlib.pyplot as plt # load the hyperspectral image .hdr and store it to a variable hsi = open_image(PATH) # get the natural RGB plotting of the hyperspectral image using the SPECIM main band hsi_rgb = hsi[:,:,[70,53,19]] # read the spectral library .sli and store it to a variable sli = open_image(PATH) # plot the first endmember plt.plot(sli.spectra[0]) # get the endmembers name sli.names</code></pre> <p>if you have any question kindly reach me on riestiyf@stud.ntnu.no</p>
Munsell soil color chart: a spectral reflectance dataset
<p>This dataset contains the calculated spectral reflectance of the 2009 edition of Munsell soil colour chart measured using Konika Minolta 2000 spectroradiometer with a tungsten lamp as the light source. The spectroradiometer has a 380-780nm spectral range with a 1nm spectral sampling.</p> <p>Reflectance is obtained by using the following equation with the assumption of a uniformly-illuminated surface.<br> <span class="math-tex">\(R(\lambda) = \frac{\Phi(\lambda)_{Reflected}}{\Phi(\lambda)_{Incident}} \)</span></p> <p><span class="math-tex">\( \Phi(\lambda)_{Incident} = \frac{\Phi(\lambda)_{sample}}{R(\lambda)_{sample}} \)</span><br> using a known <span class="math-tex">\(R(\lambda)\)</span> of white reference tile used to obtain <span class="math-tex">\(\Phi(\lambda)_{Incident}\)</span> of the tungsten lamp.</p> <p> </p> <p>If you have any questions regarding this dataset, kindly contact me on riestiya.z.fadillah@ntnu.no</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.