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29 results for “Groundwater dataset”
Water stable isotope, temperature and electrical conductivity dataset (snow, ice, rain, surface water, groundwater) from a high alpine catchment (2019-2021).
<p>Data collected in the Otemma forefield in Switzerland (45°56’03”N,7°24’42”) from July 2019 to October 2021.<br> Data were collected by the research teams of Bettina Schaefli<sup>2</sup> and Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>Description of the dataset</strong></p> <p>This dataset contains water stable isotope (δ<sup>2</sup>H, δ<sup>17</sup>O, δ<sup>18</sup>O), water temperature and water electrical conductivity (EC) measurements collected from the Otemma glacier catchment.</p> <p>All water isotope samples were collected directly from the source and stored in 12 mL amber glass vials with an air-tight caps. River samples were first collected with an automatic ISCO 6712 portable water sampler with 1L open plastic bottles and transferred in 12 mL vials every one to two weeks. All isotope analysis were performed using a Wavelength-Scanned Cavity Ring Down Spectrometer (Picarro 2140-I, Santa Clara, California, USA) and expressed relative to the international Vienna Standard Mean Ocean Water (VSMOW) standards.</p> <p>All EC and water temperature measurements were performed with a WTW Multi 3510 IDS logger with a IDS TetraCon® 925 probe.</p> <p>The dataset contains measurements performed at various locations within the catchment. A total of approximately 1500 measurements are provided. In the dataset each point correspond to a measurement station (column "<strong>Station</strong>") which we classified in specific class of water (column "<strong>Type</strong>") as follows :</p> <ul> <li><strong>Stream </strong>: samples collected at three locations, from the glacier snout, after a small outwash plain and 2km downstream.</li> <li><strong>Tributary </strong>: 5 hillslopes tributaries originating from small seasonal overland flow or small springs at the base of the morainic hillslope. Those tributaries were monitored weekly. In addition, a few other seasonal lateral streams were sampled in various locations (Type: Other tributaries).</li> <li><strong>Bedrock </strong>: A few exfiltrations directly leaking out of the bedrock outcrop were sampled.</li> <li><strong>Ice </strong>: Ice was sampled either as surface ice (small cores 5 cm deep), as deeper cores (5 to 8m deep) or as meltwater from supraglacial gullies. All solid ice samples were melted at ambiant air temperature in air-tight plastic bags before being transferred into 12 mL vials.</li> <li><strong>Snow </strong>: The snowpack was sampled either at the surface (0 to 5cm) or at about 20 cm depth. Where possible, meltwater leaking from the snowpack was sampled. At 3 locations in 2021, we dug snowpits from which we sampled snow at different layers with depth. All solid snow samples were melted at ambiant air temperature in air-tight plastic bags before being transferred into 12 mL vials</li> <li><strong>Rain </strong>: Rainwater was mostly sampled at our camp site at 2450 m. asl. Rainwater samples represent single rain events which are identified by dry periods of at least one day long.</li> <li><strong>Groundwater </strong>: shallow (2 to 3 meters) fully-screened groundwater wells were installed in the outwash plain and water sampled monthly in the snow-free season.</li> </ul> <p>- GPS coordinates are provided with each point (Swiss coordinate system CH1903+ / LV95<strong> (EPSG: 2056)).</strong></p> <p>- Dates are provided in local timezone (GMT+1 with daylight saving time) and in UTC date format.</p> <p>- Analyitcal error from the Picarro spectrometer is reported as 1 standard deviation.</p> <p>More information can be accessed in the corresponding publication by Müller et al. (to be published in 2023).</p> <p><strong>Data files</strong></p> <ul> <li><em>Otemma_isotope_EC_T_2019_2021.csv</em> : file containing all data with GPS coordinates</li> <li> <p><em>isotope_locations_Otemma.jpg</em> : an overview of the locations of each measurement point</p> </li> <li> <p><em>Otemma_Isotopes_2019-2020.html </em>: interactive plots of all datasets (δ<sup>2</sup>H, EC, temperature), classified by Type.</p> </li> </ul>
Large Ensemble Dataset for Discovering Global Peak Water Limit of Future Groundwater Withdrawals Using 900 GCAM Runs
<h2><strong>Global Groundwater Withdrawals Peak Over the 21st Century </strong></h2> <p>The large ensemble dataset contains groundwater related model outputs from 900 scenarios modeled using <a href="http://jgcri.github.io/gcam-doc/toc.html">Global Change Analysis Model (GCAM)</a>. The scenario ensemble members include five Shared Socioeconomic Pathways (SSPs), four Representative Concentration Pathways (RCPs), five global climate model outputs, three groundwater depletion limits, two surface water storage expansion regimes, and two historical groundwater depletion trends.</p> <h3><strong>Journal Article</strong></h3> <p>Niazi, H., Wild, T.B., Turner, S.W.D., Graham, N.T., Hejazi, M., Msangi, S., Kim, S., Lamontagne, J.R., & Zhao, M. (2024). <a href="https://rdcu.be/dFpb5">Global peak water limit of future groundwater withdrawals</a>. <em>Nature Sustainability, 7</em>(4), 413–422. <a href="https://doi.org/10.1038/s41893-024-01306-w" rel="nofollow">https://doi.org/10.1038/s41893-024-01306-w</a></p> <p>Read full-text here: <a href="https://rdcu.be/dFpb5">https://rdcu.be/dFpb5</a> </p> <h3><strong>Data Repository </strong></h3> <p>This <em><strong>data</strong></em> repository is to be used in combination with the <em><strong>main</strong></em> <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">meta-repository</a> containing all scripts and files for reproducing the experiment as well as the analysis and post-processing of the model outputs.</p> <p>Scripts and smaller files are provided in the <a href="https://github.com/JGCRI/niazi-etal_202X_xyz">GitHub meta-repository</a> whereas larger files are provided in this data repository. Please complete the repository by placing the files as described hereunder. Please find the GitHub meta-repository here: <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">https://github.com/JGCRI/niazi-etal_2024_nature-sustainability</a></p> <p>Descriptions of files:</p> <ol> <li><em><strong>gcam-5.7z</strong></em> contains the GCAM version used to simulate 900 scenarios of plausible futures. The model folder contains all necessary input files to reproduce the simulations. <ul> <li>The model is to be used in combination with the <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">meta-repository</a> to setup batch runs on cluster.</li> <li>Please navigate to <a href="https://github.com/JGCRI/niazi-etal_202X_xyz/tree/main/model">model/</a> folder for other scenario-specific and model setup folders and files. <em><strong>gcam-5</strong></em> is to be extracted in the same directory (./<em>model/gcam-5/</em>). </li> <li>For the first-time users of GCAM, please follow guidance on <a href="http://jgcri.github.io/gcam-doc/toc.html">GCAM wiki</a> to setup GCAM or for background knowledge. </li> </ul> </li> <li><em><strong>crop_yeild.7z</strong></em>: This file contains inputs related to climate impacts on crop yields. This is to be downloaded and extracted in the <a href="https://github.com/JGCRI/niazi-etal_202X_xyz/tree/main/model/combined_impacts">model/combined_impacts/</a> folder. </li> <li><em><strong>outputs-all.7z: </strong></em>Key model outputs queried and collated from 900 GCAM runs are explained hereunder. The files could be downloaded individually (.csv files) or all at once in .7z format (<a href="../api/files/80b237d3-b22f-499f-8b8e-76c3846720a0/outputs-all.7z">outputs-all.7z</a>). These files are to be placed in the <a href="https://github.com/JGCRI/niazi-etal_202X_xyz/tree/main/model/outputs">model/outputs</a> folder of the <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">meta-repository</a>. <ul> <li><em><strong>ag_prod_all_GW_scenarios.csv</strong></em> - Agricultural production across all scenario for 2050 and 2100 (tonnes)</li> <li><em><strong>prices_water_withdrawal_all.csv</strong> - </em>Water prices across all scenarios and years ($/km<sup>3</sup>)</li> <li><em><strong>global_irrigated_prod_by_crop.csv</strong></em> - All irrigated agricultural production for each crop across and scenarios all years (tonnes)</li> <li><em><strong>surface_water_production_all.csv</strong></em> - Runoff across all scenarios and years (km<sup>3</sup>)</li> <li><em><strong>groundwater_production_FINAL.csv</strong></em> - Groundwater withdrawals across all scenarios and years (km<sup>3</sup>)</li> <li><em><strong>water_withdrawals_desal_all.csv</strong></em> - Water withdrawals from desalination plants across all scenarios and years (km<sup>3</sup>)</li> </ul> </li> </ol> <h3><strong>Short introduction to the study</strong></h3> <p>Using 900 GCAM runs, this study finds that global groundwater withdrawals are expected to peak around mid-century, followed by a decline through 21st century, exposing about half of the population living in one-third of basins to groundwater stress, with cost and availability of surface water storage being the most significant driver of future groundwater withdrawals. This first-ever robust, quantitative confirmation of the peak-and-decline pattern for groundwater, previously only known for fossil fuels and minerals, raises concerns for basins heavily dependent on groundwater.</p> <p>Niazi, H., Wild, T.B., Turner, S.W.D., Graham, N.T., Hejazi, M., Msangi, S., Kim, S., Lamontagne, J.R., & Zhao, M. (2024). <a href="https://rdcu.be/dFpb5">Global peak water limit of future groundwater withdrawals</a>. <em>Nature Sustainability, 7</em>(4), 413–422. <a href="https://doi.org/10.1038/s41893-024-01306-w" rel="nofollow">https://doi.org/10.1038/s41893-024-01306-w</a></p> <p>Read full-text here: <a href="https://rdcu.be/dFpb5">https://rdcu.be/dFpb5</a></p> <h3><strong>Contact </strong></h3> <p>Please reach out to Hassan Niazi at <a href="mailto:hassan.niazi@pnnl.gov">hassan.niazi@pnnl.gov</a> for any questions. </p>
Groundwater monitoring with an underwater DAS1 cable, processed dataset
<p>This repository contains data to reproduce figures from the article "Groundwater monitoring with an underwater DAS cable " by Destin Nziengui Bâ, Aurélien Mordret, Olivier Coutant, Camille Jestin and Ludovic Bodet </p>
Multi-year dataset for groundwater level, temperature, and chemical and isotopic compositions of different water bodies in an alpine catchment on the northeastern Qinghai-Tibet Plateau, China
<p>Here we provide the multi-year dataset for groundwater level, temperature, and chemical and isotopic compositions of different water bodies in an alpine catchment on the northeastern Qinghai-Tibet Plateau, China. The first file contains monitoring data, including groundwater levels and ground temperatures. The second file includes the results of the sample analyses as well as the numbers and locations of the sampling sites.</p>
River-groundwater Interaction and Recharge Effects on Microplastics Contamination of Groundwater in Confined Alluvial Aquifers: Original dataset
<p>The dataset includes all the microplastics analysed for the manuscript "River–Groundwater Interaction and Recharge Effects on Microplastics Contamination of Groundwater in Confined Alluvial Aquifers" by Edoardo Severini, Laura Ducci, Alessandra Sutti, Stuart Robottom, Sandro Sutti and Fulvio Celico (https://doi.org/10.3390/w14121913). The dataset "MP (ctrl and sample).txt" contains the raw data of environmental and ctrl samples. The values of the ctrl samples were used to analyse the statistical differences between ctrl and environmental samples and subsequently used to define the identification parameters (threshold) for the microplastics identification in surface water and groundwater, contained in the file "MP (after ctrl sample).txt".</p>
An in-situ daily dataset for benchmarking temporal variability of groundwater recharge
<p>A newly developed benchmark dataset of groundwater recharge per unit specific yield (RpSy, n meters) at daily temporal resolution is presented. The data has been obtained through the application of the Water table Fluctuation (WTF) method at groundwater wells within the continental US. To ensure high-fidelity estimates, only wells that meet a set of stringent criteria have been considered. The RpSy dataset may serve as a benchmark for validating the temporal consistency of recharge products and daily simulation results from land surface and integrated hydrologic models.</p> <p>The resulting product is a continuous daily RpSy (n meters) time series data for 485 groundwater wells. The data files are provided in the .csv format and consist of three columns for each observation well. The first column lists the local time, while the second and third columns provide the RpSy and RpSyu (considering a groundwater depth-dependent specific yield) time series in meters per day. Additionally, a file containing site information for all the selected wells is included. It contains four columns that detail the USGS ID of the groundwater well, its latitude (Lat), longitude (Long), and screen depth (depth, in meters). The data file can be accessed in most text editors and spreadsheets.</p>
Datasets of groundwater level and surface water budget in a central Mediterranean site (June 21, 2017 - October 1, 2022)
<p>The datasets contain data of groundwater levels and surface water budget measured in the Salento University Campus ‘Ecotekne’, in Lecce province, Italy (40°20’ N, 18°06’ E) between June 2017 and October 2022. The groundwater data are obtained from two historic wells (FW and BW) hand dug in the Miocene karst Aquifer of Central-Eastern Salento. They are close (few hundreds of meters) to the micrometeorological station of the CNR-ISAC (National Research Council Institute for Atmospheric Science and Climate), that provided the surface water budget data. </p> <p> </p>
Datasets used in 'Streambed hydraulic conductivity estimated by spectral induced polarization imaging can help to improve groundwater modeling'
<p>These datasets pertain to the manuscript entitled 'Streambed hydraulic conductivity estimated by spectral induced polarization imaging can help to improve groundwater modeling', which is currently submitted for revision in Water Resources Research. They comprise raw data from measurements taken in the field at 2 sites in terms of (1) pressure time series during the performed slug tests, (2) impedance from spectral induced polarization, (3) submersion levels of the used electrodes below the top of the water column, and (4) three-dimensional Cartesian coordinates relating the measurements spatially. The coordinates have been projected to a local coordinate system for each site, to comply with a non-disclosure agreement of the measurement locations. The projection of the coordinates still allows to fully reproduce the presented results, if using the methods described in the manuscript. The naming convention throughout the datasets is consistent with site labels used in the manuscript. All data is given as comma-separated values with intuitive file names and self-explanatory headers containing a list of field names. The slug test data includes multiple repetitions of the same measurement, and the impedance measurements contain normal as well as reciprocal readings – as described in the manuscript.</p> <p>The data is separated into two compressed file archives, named according to the site names given in the manuscript. Each file pertaining to slug tests at a certain location, in the subfolder “slugTestRecordings” has the following naming convention: “<locationTag>_<finalDepth>.csv”, where <locationTag> corresponds to the local coordinates given in “coordinates.txt”, and where <finalDepth> is an integer describing the largest depth in cm at which a slug test was performed according to the protocol described in the manuscript. Each file pertaining to impedance measurements at a certain profile, in the subfolder “SIPRecordings”, has the following naming convention: “<locationTag>_<frequency>.dat”, where <locationTag> corresponds to the local coordinates given in “coordinates.txt”, and where <frequency> is a zero-padded integer describing the measurement frequency in Hz at which the measurement was performed according to the protocol described in the manuscript. Submersion levels of the electrodes below the top of the stream’s water column are given in m in the file “submersionLevels.txt”, corresponding to the local coordinates given in “coordinates.txt”. The local coordinates given in m in “coordinates.txt” have the following convention for the column “locationTag”: “<profileTag>-<electrodeNumber>, where <profileTag> pertains to a name of the electrical array and <electrodeNumber”> is a continuous number for the electrode. Slug tests were exclusively perfomed at the location of electrodes and files are, thus, as described above, named accordingly.</p>
Dataset used in "Catchment landforms predict groundwater-dependent wetland sensitivity to recharge changes".
<p>Dataset in the form of text files, all 60 presented catchments are identified by the HydroATLAS database ID in each file. Catchment topography figures are available following their HydroATLAS ID. For the seepage distribution, one text file is presented for each catchment named with the corresponding catchment ID. Code to generate desaturation parameters (lambda and n) and ML models are saved in a common directory (catchment_desaturation_estimator), code is commented to facilitate reproducibility. </p>
Dataset of relative seismic velocity variations (dv/v) of R66E3 Raspberry Shake and groundwater level variations of BSS002NNZL borehole from 2022/04/28 to 2025/02/04
<p>Dataset of relative seismic velocity variations (dv/v) of R66E3 Raspberry Shake and groundwater level variations of BSS002NNZL borehole from 2022/04/28 to 2025/02/04.</p> <p>A Raspberry Shake RS3D, a three-components geophone (station R66E3 and network code AM) with a natural frequency of 4.5 Hz (electronically extended to 0.5 Hz), is installed in the technical room located 8 m from the BSS002NNZL borehole (a groundwater monitoring borehole), in the town of La Trinité, Martinique. </p> <p>Seismic recordings for station R66E3 (network AM: <a href="https://urldefense.com/v3/__https:/doi.org/10.7914/SN/AM__;!!KbSiYrE!iv8kuFqjRdPI_Ko8AW4XCYuOXVn1VnpNP__Nya_VHhUPUgY40Hpa99szk_9JYls8gw61SbHCjstW1a3C0w$">https://doi.org/10.7914/SN/AM</a>) are collected from the Raspberry Shake data center (<a href="https://urldefense.com/v3/__https:/data.raspberryshake.org/fdsnws/__;!!KbSiYrE!iv8kuFqjRdPI_Ko8AW4XCYuOXVn1VnpNP__Nya_VHhUPUgY40Hpa99szk_9JYls8gw61SbHCjsumhGvdYA$">https://data.raspberryshake.org/fdsnws/</a>). The raw continuous seismic recordings and relative velocity variations are processed by using MATLAB (<a href="https://urldefense.com/v3/__https:/mathworks.com__;!!KbSiYrE!iv8kuFqjRdPI_Ko8AW4XCYuOXVn1VnpNP__Nya_VHhUPUgY40Hpa99szk_9JYls8gw61SbHCjss0LIo2Pw$">https://mathworks.com</a>).</p> <p>This dataset contains the resulting 2-3 Hz frequency range relative velocity variations data. </p> <p>The dataset also contains daily mean groundwater level data (in altitude and pressure) of BSS002NNZL borehole. The borehole is equipped with a PARATRONIC SNP pressure sensor with data recording every minutes. The probe resolution is ±1 mm.</p> <p>Rainfall and air temperature are available close to the site thanks to the French climatic network operated by Météo-France (<a href="https://meteo.data.gouv.fr/">meteo.data.gouv.fr</a>). Daily rainfall (from Morne des Esses station) and daily air temperature data (from Spoutourne station) are also provided in the dataset.</p>
Localizing Hydrological Drought Early Warning using In-Situ Groundwater Sensors - Veness et al. (2022) - Dataset
<p>This upload contains full input data and modelling scripts to support AGU WRR's 'Localizing Hydrological Drought Early Warning using In-situ Groundwater Sensors' (Veness et al., 2022).</p> <p>'WRR_Data_Extraction' contains input data and processing of these for model input.</p> <p>'WRR_Modelling_Methods' contains two folders. 'Groundwater Model & Calibration' provides the code for the modified AquiMod, formatted for input in to an automated calibration procedure (run time approx. 5 minutes with 10,000 runs). The 'Plotting Files' folder contains the code converting the groundwater levels from the automated calibration procedure to plots seen in the paper.</p> <p>These scripts require use of the data and functions within the 'data_and_functions' folder, which may require careful directory management and minor edits to the code to ensure the scripts can communicate.</p>
Central_Valley_Groundwater_Datasets
<p>The datasets were used to calculate groundwater storage variation in Central Valley, CA, using GRACE and terrestrial water storage components.</p>
Data from: A comprehensive occurrence dataset for European Ostracoda inhabiting groundwater and groundwater-dependent ecosystems
Open the record for dataset details and reuse information.
Supporting Dataset for the study "Influence of Floodplains and Groundwater Dynamics on the Present-Day Climate simulated by the CNRM Model"
<p>This archive contains the data used in the study "Influence of Floodplains and Groundwater Dynamics on the Present-Day Climate simulated by the CNRM Model" submitted to <a href="https://www.earth-system-dynamics.net/">Earth System Dynamics</a>.</p> <p> </p>
Dataset to the article 'Redox-zoning in high-energy subterranean estuaries as a function of storm floods, temperatures, seasonal groundwater recharge and morphodynamics'
<p>This repository contains model input files, and pre- and post-processing scripts for the research article:</p> <p>Janek Greskowiak, Stephan L. Seibert, Vincent E.A. Post, Gudrun Massmann (2023), Redox-zoning in high-energy subterranean estuaries as a function of storm floods, temperatures, seasonal groundwater recharge and morphodynamics, Estuarine, Coastal and Shelf Science, https://doi.org/10.1016/j.ecss.2023.108418</p> <p><br> Data:</p> <p>The folder Laserascannerdata_top_and_hydr_heads contains five cross-shore topography profiles of a high energy beach on Spiekeroog Island, Germany, including a python-script that assembles them to a daily times-series over one year and that calculates the tide-averaged hydraulic heads in the intertidal zone as detailed in Greskowiak and Massmann (2021), The impact of morphodynamics and storm floods on pore water flow and transport in the subterranean estuary, Hydrological Processes, 35:e14050, https://doi.org/10.1002/hyp.14050</p> <p><br> Model input:</p> <p>The file Dynamod_1_redox_basecase.zip contains a folder with the model input textfiles (SEAWAT and PHT3D) for the case that considers all dynamic influence factors investigated in the paper, i.e., seasonal meteoric groundwater recharge, stormfloods, temperature-dependence of reaction rates and morphodynamics.</p> <p>The files pht3d_ph.dat and pht3d_datab.dat are PHT3D model specific files defining the reacants and reactions, and have to be copied into the model folder before PHT3D is started.</p> <p><br> Run models:</p> <p>First the SEAWAT model needs to be run with the name file 'Dynamod_1_redox_basecase.nam'. This creates the mt3d link file 'mt3d_link.ftl'. After that, PHT3D has to be started with the name file 'pht3d.nam'</p> <p>Note that running the models will generate 22 Gybte output.</p> <p><br> Pre-processing:</p> <p>All model input files were generated with the python-script Dynamod_1_redox_basebase.py using Flopy, a python-based groundwater modelling user-interface:<br> https://www.usgs.gov/software/flopy-python-package-creating-running-and-post-processing-modflow-based-models</p> <p><br> Post-processing:</p> <p>For the figures 3,4 and S1, S2 in the paper, the corresponding python scripts are provided in this database. Note that with respect to Figures 5 and S3, only the results for the case with all dynamic influence factors (Figure S3_h) are being plotted.</p> <p> </p> <p>Other model output:<br> Animation_A1_Dynamod_redox.mp4 is a video animation showing the dynamic modelled salinity and temperature distribution, and redox zoning in the subterranean estuary all dynamic influence factors investigated in the paper, i.e., seasonal meteoric groundwater recharge, stormfloods, temperature-dependence of reaction rates and morphodynamics.</p>
Self-Potential dataset for mapping groundwater flow patterns in the Chaîne des Puys (Auvergne, France)
<table> <tbody> <tr> <td>This dataset was created from self potential data acquired over a period of time between 1989 and 2022. The data acquired on the Volvic watershed were first used in a DIREN report (Labazuy, 2000) and in the thesis of Joux (2002). This database has been largely completed in the framework of the CAPRICE project (Aumar, 2022) on the Come and Veyre watersheds.</td> </tr> </tbody> </table>
Disentangling coastal groundwater level dynamics in a global dataset - data
<p>Data to reproduce the research study "Disentangling coastal groundwater level dynamics in a global dataset" by Nolte et al. containing:</p> <ol> <li>cluster_indices.csv: well identifier; cluster from k-means; country code (ISO 3166-1 alpha-2); 45 index values</li> <li>cluster_attributes.csv: well identifier; cluster from k-means; country code (ISO 3166-1 alpha-2); 28 attribute values</li> </ol> <p>These data enable cluster analysis and random forest modeling. However, please note that the repository does not include raw groundwater level time series data.</p> <p>The code to reproduce the cluster analysis and random forest modeling as conducted in the study can be made available upon request from the first author.</p>
Stable Strontium (δ88/86Sr) and Calcium (δ44/40Ca) isotope fractionation in coastal groundwater and implications for transport of dissolved cations to the oceans: Dataset
Open the record for dataset details and reuse information.
Grand Bahama Groundwater and Soil Salinity Dataset
<p>This file contains groundwater salinity and water levels and soil salinity data collected post hurricane Dorian during the period of February 2020 - November 2020. </p>
Dataset for contaminant risk assesment in groundwater
<p><span>This data that helps in mapping groundwater contaminant risk (nitrate and phosphate) using the DRASTIC model for a case study in Ethiopia. The data contains a total of </span>9<span> parameters, each having data points ranging from 10 to 33 (196 records in total).</span></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.