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.
48
datasets available to search
ShareScore release 0.9.0
Dataset results
48 results for “electrical resistivity”
Electrical Resistivity Tomography (ERT) datasets from the Otemma glacier forefield and outwash plain
<p><strong>Electrical Resistivity Tomography (ERT) datasets collected in the Otemma forefield (Switzerland) from 2019 to 2021.</strong><br> Data were collected by the research teams of Bettina Schaefli<sup>1,2</sup>, Stuart N. Lane<sup>1</sup> and James Irving<sup>3</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><sup>3</sup> Institute of Earth Sciences (ISTE), University of Lausanne, 1015 Lausanne, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>This dataset is first referenced and discussed in the research paper by Müller et al., 2022.</strong></p> <p>------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Data Description</strong></p> <p>Electrical Resistivity Tomography (ERT) profiles were collected around the outwash plain of the Otemma glacier forefield (WGS84 : 45.93434 / 7.41209). All data were collected with a <a href="http://www.iris-instruments.com/syscal-pro.html">Syscal Pro</a> Switch 48 from Iris Instruments, using an array of maximum 48 electrodes with a spacing between 1 and 10 meters. For each line, measurements were performed using a Dipole-Dipole (dd) and a Wenner-Schlumberger (ws) electrode configuration.</p> <p>All ERT lines locations can be visualized in <em><strong>ERT_map_lines_2019-2021.jpg</strong>.</em></p> <p>A result overview can be vizualized in <em><strong>ERT_allResults_3Doverview.png</strong>.</em></p> <p><strong>Data Structure</strong></p> <p>Two <a href="https://jupyter.org/">Juypter Notebook</a> files are provided and can be used to reproduce all inversion analyses.</p> <ul> <li><em><strong>1_createInput_prosys_to_pygimli.ipynb</strong></em> : Transforms the raw data from Syscal Pro (exported with <a href="http://www.iris-instruments.com/download.html">ProsysII</a> software as .csv) to a processed .dat file formated for inversion using the <a href="https://www.pygimli.org/">pyGIMLi</a> library.</li> <li><em><strong>2_ERT_inversion.ipynb</strong></em> : Reads the processed .dat file and performs a 2D robust inversion for a set of regularization parameters for the selected line.</li> </ul> <p>In <strong>ERT_data.zip</strong>, 3 folders with similar structure contain all data for year 2019, 2020 and 2021. Each folder contains :</p> <ol> <li><strong>GPS </strong>: folder with electrodes coordinates for each ERT line</li> <li><strong>inputGiMLi</strong> <ul> <li><strong>prosys_csv</strong>: contains the raw field measurements (downloaded from the Syscal device using ProsysII)</li> <li><strong>input_ERT </strong>: stores the processed .dat file. (created from notebook 1)</li> <li><strong>results_lambda</strong> : contains a .png image with the inversion results using different values of the regularization parameter lambda used to assess the sensitivity of the inversion results (over/underfitting). Analysis is performed for each line and each electrode configuration (dd or ws). (created from notebook 2)</li> <li><strong>results_final</strong> : contains a .png image with the final inversion results for each line and electrode configuration (dd or ws) using the optimal lambda parameter only (all arrays are shown from East to West). (created from notebook 2)</li> <li><strong>vtk</strong> : contains a .vtk file for each final results for 3D vizualization in the <a href="https://www.paraview.org/">Paraview</a> software.</li> </ul> </li> <li><strong><em>ERT_line_description_yyyy.csv</em> </strong>: a file describing the ERT arrays characteristics (read in notebook 1 and 2)</li> </ol> <p>The <strong>results </strong>folder contains :</p> <ul> <li><strong>ERT_3Dview_paraview</strong> folder : contains Paraview state files (.pvsm) for 3D vizualization of all results, as well as image files.</li> <li><em><strong>ERT_results_all.pdf</strong></em> : A summary of all final results for all years (similar content as <em>ERT/inputGiMLi</em><strong>/</strong><em>results_final</em> folders)</li> <li><em><strong>ERT_results_bedrock.pdf</strong></em> : Contains the vizualization of specific ERT profiles in the outwash plain and their field location . The separation between a surface layer of water-saturated sediments (resistivity <2500 Ωm) and the underlying bedrock is delimited. The likely presence of buried ice blocks (isolated blocks with resistivity >5000-10000 Ωm) is also highlighted.</li> <li><em><strong>ERT_timelapse_salt_tracer.gif</strong></em> : results of a time-lapse ERT measurement performed on 9 August 2019 to track the movement of a salt plume injected at 06 am, 9.38 meters upslope (see paper by<em> Müller et al., 2022</em> for detailed analysis). The tracer starts to appear at 12:45 at a distance of 30m on the array. Minimum resistivity is reached at between 16:45 and 17:45.</li> </ul>
Supplementary materials for: Imaging the Devene fault system beneath the Iskar floodplain in Bulgaria through shallow electrical resistivity profiling
<p>Supplementary materials for the paper Imaging the Devene fault system beneath the Iskar floodplain in Bulgaria, submitted to Review of the Bulgarian Geological Society </p> <p>We used shallow electrical resistivity profiling to image the Nivyanin fault zone from the Devene fault system in NW Bulgaria. We aimed to verify whether a portion of<br>the Devene fault system has affected Quaternary fluvial deposits. The Supplementary materials contain the coordinates (WGS84) of measuring sensors and resistivity data in Boundless Electrical Resistivity Tomography (BERT) file format. The file bert.cfg.txt is the configuration file for running BERT software to obtain the resistivity model in figure 1c in paper.</p>
Soil moisture determinations by Electrical Resistivity (ERT) Experiment at the Kellogg Biological Station, Hickory Corners, MI (2009)
Dataset AbstractLarge-scale conversion of croplands to perennial biofuel crops could substantially impact regional water, nutrient, and C cycles due to the longer growing seasons and differences in rooting systems compared with most annual crops. However, these differences in crop water use are not well known due to the limited tools available to nondestructively study the spatiotemporal patterns of root water uptake in situ at field scales. Geophysical imaging tools such as electrical resistivity (ER) reveal changes in water content in the soil profile. Data used in: https://doi.org/10.1002/vzj2.20124original data source http://lter.kbs.msu.edu/datasets/222
A new repository of electrical resistivity tomography and ground penetrating radar data from summer 2022 near Ny-Ålesund, Svalbard.
<p>We present the geophysical data set acquired in summer 2022 close to Ny-Ålesund (Western Svalbard, Brøggerhalvøya peninsula, Norway) as part of the project ICEtoFLUX (MUR/PRA2021 project-0027). The data set is composed of Electrical Resistivity Tomography (ERT) and GroundPenetrating Radar (GPR) surveys, which are well-known geophysical techniques for the characterization of glacial and hydrological processes and features. 18 ERT profiles and 10 GPR lines were acquired, for a total surveyed length of 9.3 km. The data have been organized in a consistent repository that includes both raw and processed (filtered) data. Some representative examples of 2D models of the subsurface are provided, that is, 2D sections of electrical resistivity (from ERT) and 2D radargrams (from GPR). These examples can support the identification of the active layer and the occurrence of spatial variation of soil conditions at depth. The aim of the investigation is to characterize the role of groundwater flow in correspondence of the active layer as well as through and/or below the permafrost. The data set is of major relevance because scant attention has been paid to the publication of geophysical data from the Ny-Ålesund area so far. Moreover, these geophysical data can foster multidisciplinary scientific collaborations in the fields of hydrology, glaciology, climate, geology, geomorphology, etc. To a large extent, the data set can provide new insight into the hydrological dynamics and polar and climate changes studies on the Ny-Ålesund area. </p>
Time-lapse electrical resistivity tomography and seismic reflection imaging of a shallow ground-water aquifer (0-50 m): Mississippi River levee seepage across the Duncan Point bar, Baton Rouge, Louisiana, U.S.A.
<p>The electrical resisitivity raw data files are slightly processed to remove bad data points but can be inverted using tomographic inversion code. </p> <p>The seismic data were assembled in Seismic Unix format, a shortened version of the SEG-Y format (Society of Exploration Geophysicists Exchange Format-Y https: //seg. org/Publications/SEG-Technical-Standards), that has the 3200-byte EBCDIC and 400-byte tape header removed. The data uploaded online (<a href="https://zenodo.org/records/14776025">https://zenodo.org/records/14776025</a>) is a CMP brute-stacked seismic section. </p> <p>During data collection, shotpoint location changed proceeding along a 136-degree azimuth (south-easterly direction), and spaced every 1 m.</p> <p>A total of 48, horizontal-component 28-Hz nominal geophones were placed every one meter and shotpoints were located half-way between geophones. Geophones remained fixed at their locations throughout the survey and so the CMP spacing is nominally 0.5-m but fold varies linearly from a value of 1 from either side of the survey to a central maximum of 24. The seismic source consisted of a partially buried 20-lb steel I-beam struck repeatedly on either side three times by an 8-lb sledge hammer. Data of the same striking polarity were added in-phase in the field. Data with opposing polarity at each shotpoint location were subtracted later to enhance SH-wave data and suppress converted SH-to-P waves.</p> <p>Seismic processing is minimal and consists of standard surface-wave muting, elimination of bad seismic traces, normal moveout, bandpass filtering (between 12 Hz and 50 Hz) and preliminary stacking with trace mixing every 3 CMPs. The data were stacked with a single velocity throughout that ranged from 80 m/s (Vs) at 0.2 s, to 100 m/s at 0.35 s and reached 180 m/s at 0.5 s of two-way traveltime.</p> <p> </p>
Electrical tomography resistivity (ERT) monitoring time series
<p>Multi-temporal electrical tomography resistivity (ERT) measurements for monitoring the performance of the bio-degradable bentonite mat in OAL-Austria. The first measurement was conducted on 30 July 2020 before the implementation of the mat, afterwards seasonal measurement (except for winter due to snow cover) were obtained: 19 Oct 2020, 27 Arpil 2021, 10 August 2021, 4 October 2021, 13 April 2022. A time-lapse inversion algorithm was used to prepare the final results. See OPERANDUM deliverable 4.6 for more details.</p> <p>Device: Lippmann 4point light 10 W</p>
Datasets for Electrical Resistivity Tomography HotBENT-Lab experiment
<p>The dataset comprises Electrical Resistivity Tomography (ERT) data from the HotBENT-Lab experiments, which are aimed at understanding moisture dynamics and related swelling effects in bentonite under thermal-hydrological-mechanical-chemical (THMC) conditions. These experiments simulate environmental conditions similar to those in geological repositories for high-level radioactive waste.</p> <ol> <li> <p><strong>calibration.csv</strong>: This file contains ERT data collected from HotBENT-Lab 2, which was designed to closely mimic field conditions by using a higher dry density (1.45 g/cm³) and reduced initial water content (5.3%). The dataset includes detailed electrical conductivity measurements over time and across the experimental columns, which were used to estimate water content and track changes due to swelling effects.</p> </li> <li> <p><strong>c1c2_midtc_data.csv</strong>: This file contains ERT data from HotBENT-Lab 1, which serves as a foundational experiment with a lower dry density (1.2 g/cm³) and higher initial water content (18%). This dataset captures early-stage moisture distribution and temperature effects, allowing for comparative analysis of swelling and conductivity changes.</p> </li> </ol> <p>Both datasets are integral for validating the multi-scale ERT-based framework used to estimate moisture dynamics and assess the scalability of petrophysical models in controlled laboratory conditions.</p>
Open data for the article "Low-resistivity, high-resolution W-C electrical contacts fabricated by direct-write focused electron beam induced deposition"
<p>Open data for the article "Low-resistivity, high-resolution W-C electrical contacts fabricated by direct-write focused electron beam induced deposition", which will be published in Open Research Europe</p>
Composition and electrical resistance results of a Ir-Pd-Pt-Rh-Ru composition spread thin film materials library
<p>The dataset contains the results of electrical resistance measurement and composition analysis of a thin film composition spread materials library. </p> <p>342 measurement areas were evaluated for chemical composition using energy dispersive X-ray spectroscopy and electrical resistance using a 4-point probe.</p> <p>CSV columns:</p> <p>x: x-coordinate of materials library in µm</p> <p>y: y-coordinate of materials library in µm</p> <p>Ir: relative chemical composition in at.%</p> <p>Pd: relative chemical composition in at.%</p> <p>Pt: relative chemical composition in at.%</p> <p>Rh: relative chemical composition in at.%</p> <p>Ru: relative chemical composition in at.%</p> <p>Resistance: electrical resistance in Ohm</p> <p> </p> <p>This dataset is supplementary information for an associated publication. A link to the publication will be provided after publishing.</p>
Data and results for manuscript "Small scale characterization of vine plant root water uptake via 3D electrical resistivity tomography and Mise-à-la-Masse method"
<p>This package contains measured raw ERT and MALM data used to generate the plots in the manuscript.</p> <p> </p>
Arcing Fault Electrical Signatures Data Base - Sinusoidal power supply (230 V - 400 Hz) - Resistive loads - part 1
<p>The dataset contains series arc faults voltage and current signatures in a AC low power network.</p> <p>Sinusoidal power supply (230 V – 400Hz, 600Hz and 800Hz) - Resistive Loads</p> <p>The data provided can be used for the development of methods for the detection of arcing faults.</p> <p>The data files are current and voltage signatures experimentally measured.</p> <p>Two technique are used to produce an arcing fault : Open contact electrodes and Carbonized path wires</p> <p>The ReadMe file describes :</p> <p>- the test set up and the the procedure followed to make the measurements</p> <p>- the name of the data files</p> <p>- the type of arcing faults</p>
Physical link between effective viscosity and electrical resistivity for dislocation creep in upper mantle and its application in Northwest Xinjiang, China
<p>Cross-section of electrical resistivity extracted from the preferred 3-D resistivity model from Liu (2022)</p> <p>Format: X (Km), Z (Km), rho (ohm-m), T (K)</p> <p>Notes: Temperature(T) extracted from Sun et al., 2022, available at https://doi.org/10.5281/zenodo.6459746</p> <p> (lat, lon) of the ends of the profile: (,40.71,79.8300), -->, (,46.84,86.0700)</p>
Dataset - Speeding up high-throughput characterization of materials libraries by active learning: autonomous electrical resistance measurements
<p>With the trend towards multinary materials and the associated increase in measurement time, there is a clear need for increasing the efficiency of measurement procedures. In systems requiring long materials characterization times, the implementation of active learning can help decreasing the measurement duration significantly. This dataset is part of the publication in Digital Discovery under the same title and holds the algorithm as well as the data used to test its performance. The algorithm leverages an active learning approach with a Gaussian process model capable of selecting the next measurement area of a library of materials based on the highest uncertainty. Ten materials libraries were manufactured by magnetron sputtering, the composition was measured with EDX and the electrical resistance was measured using the described test stand. The code can also be found on <a href="https://gitlab.ruhr-uni-bochum.de/fthelen/auto-resist-meas">Gitlab</a>.</p>
Electrical Resistivity Tomography measurements of a limestone wall during fires
<p>The dataset comprises median reisistivity from resistivities acquiered during fires taking place in an underground limestone quarry. It comprises 24 lines, corresponding to each of the time an ERT image was acquired (the time = 0 is the time the fire was ignited); and 9 columns corresponding to each depth (median calculated on a 2-cm thick interval).</p>
Data Sets and Code for Simple formulas for pseudoposition for electrical resistivity and IP in vertical boreholes based on mean positions of the sensistivity
<p>Data and Matlab code in figures.</p>
Dataset for "Mapping Water Flow Pathways in the Fengjiaping Landslide Using Self-Potential and Electrical Resistivity Tomography"
<p>This dataset includes soil temperature, moisture, and electrical conductivity measurements taken at a depth of approximately 50 cm, as well as the digital elevation model, electrical resistivity tomography, and self-potential data used in the manuscript "Mapping Water Flow Pathways in the Fengjiaping Landslide Using Self-Potential and Electrical Resistivity Tomography" submitted to <em>Comptes Rendus Geoscience</em>.</p>
Data and software for "Metal Pad Sensing: exploiting the electrical double layer to improve resistance-based microfluidic cell tracking, with applications to label-free mechanophenotyping"
<p>Data and software for "Metal Pad Sensing: exploiting the electrical double layer to improve resistance-based microfluidic cell tracking, with applications to label-free mechanophenotyping"</p>
Spatiotemporal variation of the 2010 Yushu Mw 6.9 earthquake sequence: Insight from the 3-D electrical resistivity structure
<p>This dada set will be available in GRL, it was support by the Chinese Earthquake Administration, and it was also supported by the National Natural Science Foundation of China (Grant No. 41674081).</p>
Supplemental data files for: Evidence for the superposition of tectonic systems in the northern Songliao Block, NE China, revealed by a 3-D electrical resistivity model
<p>Data files for a 3-D electrical resistivity model in the northern Songliao Block, NE China, including the MT data observed there (note the data format is for 3-D inversion using ModEM), and the preferred resistivity model.</p> <p>The software EMdesk from Jilin Kingti Geoexploration Tech, Ltd (Changchun, China) can be used for data analysis and modeling (http://www.kingti.net).</p>
Data for manuscript sumitted to Open Research Europe, entitled "Low-resistivity, high-resolution W-C electrical contacts fabricated by direct-write focused electron beam induced deposition"
<p>These are the data obtained experimentally and used to draw the figures in the article submitted to Open Research Europe</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.