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PsPM-VIS: SCR, ECG, respiration and eyetracker measurements in a delay fear conditioning task with visual CS and electrical US
<p>This dataset consists of a three-block experiment conducted with 29 healthy unmedicated participants (17 females and 12 males aged 25.3 +/- 3.7). The experiment contains a classical (Pavlovian) discriminant delay fear conditioning test. CSs are 2 full-screen fractals of approximate brightness, contrast, and spatial frequency. US is a train of electric square pulses delivered with a constant current stimulator on participants' dominant forearm through a pin-cathod/ring-anode configuration. SOA between the CS and US is 3.5 s. The first 2 blocks are fear acquisition, with 15 CS+US+, 15 CS+US-, and 30 CS- in each block, and the last block is an extinction phase with 20 CS- and 20 CS+ trials without US delivery. The order of trials in each block was randomized. No fixation cross was presented during CS. ITI is randomly determined on each trial to be an integer between 7 - 11 s. During ITI, a black fixation cross was presented in the center of a grey background (RGB 0.7, 0.7, 0.7). The blocks were recorded on the same day with a self-paced break. For all three blocks this dataset contains skin conductance responses (SCR), electrocardyogram (ECG), respiration, pupil size (PSR), and gaze coordinates measurements.</p>
Testing 3D modelling software. Modelling charging pads for WPT of electric vehicles for EM emissions simulation.
<p>Even for the experienced 3D FEM modelers it may not be obvious which geometry discretization is the most appropriate and suitable for this type of problem. It may be a conservative approach to test the computation tool on a simplified geometry, on which the magnetic field distribution is known. As part of the “Metrology for inductive charging of electric vehicles” (MICEV) project (www.micev.eu), an axisymmetric geometry was used, with the results reported.</p>
plan4res public dataset for case study 3 "Cost of RES integration and impact of climate change for the European Electricity System in a future world with high shares of renewable energy sources"
<p>The objective of the plan4res project is to provide a well-structured and highly modular modelling framework to enable consistent insights into the different needs of future energy system. Three case studies will highlight the potentials of this framework by dealing with different aspects of a future energy systems.<br> Case study 3 will focus on cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources. Ist overall objectives are to identify the Cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources will be the main focus of case study 3.<br> The present dataset contains all the public data built for this case study.</p> <p>The related documentation is included in plan4res deliverable D4.5 </p> <pre>https://doi.org/10.5281/zenodo.3785010</pre>
PUDL Raw NREL Annual Technology Baseline (ATB) for Electricity and Transportation
<p>The NREL Annual Technology Baseline (ATB) for Electricity publishes annual projections of operational and capital expenditures (by technology and vintage), as well as operating characteristics (by technology). Archived from <a href="https://atb.nrel.gov/">https://atb.nrel.gov/</a></p> <p>This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by <a href="https://catalyst.coop">Catalyst Cooperative</a>. It is organized into <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Packages</a>. For additional information about this data and PUDL, see the following resources: </p><ul> <li><a href="https://github.com/catalyst-cooperative/pudl">The PUDL Repository on GitHub</a></li> <li><a href="https://catalystcoop-pudl.readthedocs.io">PUDL Documentation</a></li> <li><a href="https://zenodo.org/communities/catalyst-cooperative/">Other Catalyst Cooperative data archives</a></li> </ul> <p></p>
California Electric Vehicle Loads by Feeder Circuit
Open the record for dataset details and reuse information.
Towards an open pipeline for the detection of Critical Infrastructure from satellite imagery – A case study on electrical substations in The Netherlands
<p><strong>Abstract.</strong> Critical infrastructure (CI) are at risk of failure due to the increased frequency and magnitude of climate extremes related to climate change. It is thus essential to include them in a risk management framework to identify risk hotspots, develop risk management policies and support adaptation strategies to enhance their resilience. However, the lack of information on the exposure of CI prevents their incorporation in large-scale risk assessment studies. This study sets out to improve the representation of CI for risk assessment studies by building a neural network model to detect CI assets from optical remote sensing imagery. We present a pipeline that extracts CI from OpenStreetMaps, processes the imagery and assets' masks, and trains a Mask R-CNN model that allows for instance segmentation of CI at the asset level. This study provides an overview of the pipeline and tests it with the detection of electrical substations assets in the Netherlands. Several experiments are presented for different under-sampling percentages of the majority class (25%, 50% and 100%) and hyperparameters settings (batch size and learning rate). The best metrics achieved are an Average Precision at an Intersection over Union of 50% of 30.93 and a tile F-score of 89.88%. This allows us to confirm the feasibility of the method and invite disaster risk researchers to use this pipeline for other infrastructure types. We conclude by exploring the different avenues to improve the pipeline by addressing the class imbalance, Transfer Learning and Explainable AI.</p>
Dataset and supplementary files - Behavioral response of chub (Squalius cephalus), barbel (Barbus barbus) and brown trout (Salmo trutta) to pulsed direct current electric fields and resulting optimal waveform for use at electrified bar racks
<p><strong>Behavior Library.zip: </strong>For each species and behavior observed during the experiments an exemplary video is provided. </p><p><strong>Behavior_all.pdf: </strong>Additional plots showing the thresholds for the first time each individual behavior was observed for all fish species and tested waveforms</p><p><strong>Species.pdf: </strong>Additional plot allowing direct comparison of observed thresholds for the tested species when subjected to different waveforms. </p><p><strong>data.csv:</strong> All data necessary to reevaluate the conducted experiments. The dataset consists of</p><ul><li>Experiment ID</li><li>waveform - indicating the set of electrical parameters used</li><li>fish species and fish id </li><li>behavior - observed behavior</li><li>time from and time to - time in s after the start of the experiment that a behavior was started and ended respectively</li><li>type - point or interval referring to whether a behavior is considered instantaneous or continuous</li><li>voltage - applied voltage at the start of the given behavior</li><li>experiment_timestamp - date and time of the start of the experiment</li><li>breathing rate start - breathing rate at the start of the experiment</li><li>water conductivity - water conductivity at a reference temperature of 25°C [muS/cm]</li><li>water temperature [°C]</li><li>breathing rate end - breathing rate at the end of the experiment</li><li>meta behavior - assigned category of meta behavior based on the observe behavior category</li><li>standard length, total length and height - standard length, total length and height of the tested fish in [mm]</li><li>volume - calculated fish volume based on the measured length and height and an assumed elliptical form of the fish</li><li>Fangdatum - Date of catch</li><li>t.Pulse - pulse length of the tested waveform [ms]</li><li>Frequency - Frequency of the tested waveform</li><li>N.Pulses.Group - Number of pulses per group of pulses for the waveform pattern</li><li>t.Gap - time between two pulses within a group of pulses [ms]</li><li>DutyCycle - Percentage of time current is flowing for a given waveform. Calculated based on the waveform parameters</li><li>usage - first, second or third time a fish was used in the experiments. </li><li>field strength - field strength at the time of this behavior calculated based on the applied voltage</li><li>c_w ambient water conductivity [muS/cm]</li><li>p_d - power density calculated based on the field strength and the ambient water conductivity</li><li>p_t - power transferred to the fish calculated based on the field strength, the ambient water conductivity and an assumed conductivity of the fish of 115 muS/cm</li></ul><p> </p><p> </p>
Simulated Local Electrical Impedance in Atrial Tissue With Varying Contact Force
<p>In this dataset we can find geometrical setups that served as an input to carry forward electrical impedance simulations with EIDORS. <br>A 3D geometrical models of one ablation catheters combining measurements of local impedance (LI) and contact force (CF) commercially available is included. The objective of these in silico experiments laid on understanding how CF and tissue deformation affect LI measurements.<br>To achieve it, using the catheter against the tissue, several grams of force are applying.<br>The dataset consists of the original geometrical models before deformation and a couple of examples of the deformed one.</p> <h2>Data structure</h2> <ul> <li>geos: original geometries of the catheter and the tissue in stl <ul> <li>catheter.stl</li> <li>tissue.stl</li> </ul> </li> <li>geos_deformed: deformed geometries at 5 and 10 grams, respectively. Includes the catheter, the mesh, and the tissue <ul> <li>5 g <ul> <li>catheter.stl</li> <li>tissue_5g.stl</li> <li>mesh_5g.stl</li> </ul> </li> <li>10 g <ul> <li>catheter.stl</li> <li>tissue_10g.stl</li> <li>mesh_10g.stl</li> </ul> </li> </ul> </li> </ul>
In Silico Local Electrical Impedance Measurements in the Atria
<div>This document describes a dataset provided in the context of the manuscript “In Silico Study of Local Electrical Impedance Measurements in the Atria - Towards Understanding and Quantifying Dependencies in Human” [1].</div> <div> </div> <div>Authors: Unger LA, Anton CM, Stritt M, Wakili R, Haas A, Kircher M, Dössel O, Luik A</div> <div> </div> <div>The dataset contains in silico simulation setups and results from forward electrical impedance simulations with EIDORS. Geometrical models include the commercially available ablation catheters IntellaNav MiFi and IntellaNav StPt catheter measuring local impedance (LI). </div> <div>Catheter geometries were embedded in different surrounding conditions of clinical importance. Catheter tissue interaction with and without scar, the insertion of the catheter into a pulmonary vein (PV), the withdrawal into a transeptal sheath, and catheter irrigation were modeled to quantify the respective effect on LI measurements. In vitro and clinical data used for validation purposes are included in the dataset as well.</div> <div> </div> <div>Abbreviations: </div> <div>LI: local impedance, all numbers are given in Ohms</div> <div>MiFi: IntellaNav MiFi catheter</div> <div>PV: pulmonary vein</div> <div>StPt: IntellaNav StPt catheter</div> <div> </div> <div> </div> <div>Simulation results, in vitro measurements and clinically measured traces are provided in the following MATLAB files in the subdirectory „results_LI“:</div> <div> </div> <div>• impConductivities.mat</div> <div>In vitro measurements and simulation results for MiFi and StPt in NaCl solutions of different concentrations as described in section III A of the related publication [1]. The struct "impedance" includes the following fields:</div> <div>⁃ conc: concentration of NaCl solutions from in vitro measurements in weight percentages</div> <div>⁃ cond: conductivities of the NaCl solutions from in vitro measurements in S/m</div> <div>⁃ temp: interpolated temperature curves from in vitro measurements in °C</div> <div>⁃ condSim: different conductivities of the NaCl solutions from in silicon experiments in S/m</div> <div>⁃ LI_MiFi_iV: 41x9 matrix with interpolated in vitro LI measurements with the MiFi catheter in 9 different NaCl solutions and at 41 interpolated temperature values</div> <div>⁃ LI_StPt_iV: 41x9 matrix with interpolated in vitro LI measurements with the StPt catheter in 9 different NaCl solutions and at 41 interpolated temperatures values</div> <div>⁃ LI_MiFi_iV_RT: LI values for different NaCl solutions at room temperature interpolated from in vitro MiFi measurements</div> <div>⁃ LI_MiFi_iV_BT: LI values for different NaCl solutions at body temperature interpolated from in vitro MiFi measurements</div> <div>⁃ LI_StPt_iV_RT: LI values for different NaCl solutions at room temperature interpolated from in vitro StPt measurements</div> <div>⁃ LI_StPt_iV_BT: LI values for different NaCl solutions at body temperature interpolated from in vitro StPt measurements</div> <div>⁃ LI_MiFi_sim: LI extracted from simulations with the MiFi catheter for different NaCl solutions</div> <div>⁃ LI_StPt_sim: LI extracted from simulations with the StPt catheter for different NaCl solutions</div> <div> </div> <div>• impSheath.mat</div> <div>Simulation results and clinical measurements of LI with MiFi and StPt for different overlaps with a transeptal sheath as described in section III B of the related publication [1]. The struct „impSheath“ contains the following fields:</div> <div>⁃ distance: vertical distance between catheter tip and distal edge of the sheath in mm. Negative distances describe an insertion of the catheter into the sheath</div> <div>⁃ LI_MiFi_sim: LI extracted from simulations with the MiFi catheter for different vertical distances between catheter tip and distal edge of the sheath corresponding to the field distance</div> <div>⁃ LI_StPt_sim: LI extracted from simulations with the StPt catheter for different vertical distances between catheter tip and distal edge of the sheath corresponding to the field distance</div> <div>⁃ LI_MiFi_cd: 281x2 matrix containing clinical LI measurements with the MiFi catheter in the second column and corresponding time steps in the first column</div> <div>⁃ LI_StPt_cd: 301x2 matrix containing clinical LI measurements with the StPt catheter in the second column and corresponding time steps in the first column</div> <div> </div> <div>• impTissue.mat</div> <div>Simulation results for MiFi and StPt with variable distance and angle between catheter and tissue as described in section III C of the related publication [1]. The struct „impTissue“ contains the following fields:</div> <div>⁃ distance: 25 different distances between catheter tip and endocardial surface in mm</div> <div>⁃ distanceSel: 5 selected distances between catheter tip and endocardial surface in mm</div> <div>⁃ angle: 13 different angles between catheter and endocardial tissue surface in degrees</div> <div>⁃ LI_MiFi_d_alpha: 5x13 matrix with simulated LI values for the MiFi catheter at 5 selected distances (distanceSel) and 13 angles between catheter and tissue.</div> <div>⁃ LI_MiFi_d_90: 25 simulated LI values for the MiFi catheter for different distances between catheter tip and endocardial surface corresponding to the field “distance” for orthogonal catheter placement</div> <div>⁃ LI_StPt_d_alpha: 5x13 matrix with simulated LI values for the StPt catheter at 5 selected distances (distanceSel) and 13 angles between catheter and tissue.</div> <div>⁃ LI_StPt_d_90: 25 simulated LI values for the StPt catheter different distances between catheter tip and endocardial surface corresponding to the field “distance” for orthogonal catheter placement</div> <div> </div> <div>• impTissueScar.mat</div> <div>Simulation results for MiFi and StPt interacting with tissue in the presence of scar as described in section III C of the related publication [1]. The struct „impTissueScar“ contains the following fields:</div> <div>⁃ distance: vertical distance between catheter tip and endocardial surface for all simulation setups in mm</div> <div>⁃ centerX: horizontal distance between the catheter tip and the center of the line of scar for all simulation setups in mm</div> <div>⁃ LI_MiFi3mm: simulated LI for the MiFi catheter for all combinations of horizontal and vertical distances with a central line of scar of 3mm width</div> <div>⁃ LI_StPt3mm: simulated LI for the StPt catheter for all combinations of horizontal and vertical distances with a central line of scar of 3mm width</div> <div>⁃ LI_MiFi6mm: simulated LI for the MiFi catheter for all combinations of horizontal and vertical distances with a central line of scar of 6mm width</div> <div>⁃ LI_StPt6mm: simulated LI for the StPt catheter for all combinations of horizontal and vertical distances with a central line of scar of 6mm width</div> <div> </div> <div>• impPV.mat</div> <div>Simulation results for MiFi and StPt insertion into a pulmonary vein (PV) as described in section III D of the related publication [1]. The struct „impPV“ includes the following fields:</div> <div>⁃ distance: vertical distance between catheter tip and tissue surface in mm. Negative distances describe an insertion of the catheter into the vein.</div> <div>⁃ radius: inner radius of the PV in mm</div> <div>⁃ thickness: thickness of the PV tissue in mm</div> <div>⁃ LI_MiFi_d_r_th: 31x4x4 matrix containing the LI simulation results for the MiFi catheter for all combinations of 31 distances, 4 radii, and 4 thicknesses.</div> <div>⁃ LI_StPt_d_r_th: 31x4x4 matrix containing the LI simulation results for the StPt catheter for all combinations of 31 distances, 4 radii, and 4 thicknesses.</div> <div> </div> <div>• impFlush.mat</div> <div>Simulation results for MiFi and StPt flush with NaCl at different flow rates as described in section III E of the related publication [1]. The struct „impFlush“ includes the following fields:</div> <div>⁃ radius: radius of the NaCl spheres at the irrigation holes in mm</div> <div>⁃ LI_MiFi_NaCl: LI extracted from simulations with MiFi catheter for NaCl irrigation spheres of different sizes corresponding to the respective radius</div> <div>⁃ LI_StPt_NaCl: LI extracted from simulations with StPt catheter for NaCl irrigation spheres of different sizes corresponding to the respective radius</div> <div> </div> <div>Additionally, exemplary geometrical setups and results are provided as VTK files in the subdirectory „selectedGeometriesAndSimResults“:</div> <div> </div> <div>Each VTK file contains the following data fields:</div> <div>⁃ Ids (point data): integer specifying the Id of the respective vertex</div> <div>⁃ Voltage (point data): electric potential of the respective vertex with respect to a reference potential in mV</div> <div>⁃ Conductivity (cell data): conductivity of the material of the respective cell in S/mm</div> <div>⁃ Current (cell data): current density of the respective cell in nA/mm^2</div> <div>⁃ Ids (cell data): integer specifying the Id of the respective cell</div> <div>⁃ Material (cell data): integer specifying the material of the respective cell (for MiFi setups: 1: distal ring electrode, 2: middle ring electrode, 3: proximal ring electrode, 4: tip electrode, 5: outer insulator, 6: inner insulator, 7: mini electrode 1, 8: insulator mini electrode 1, 9: mini electrode 2, 10: insulator mini electrode 2, 11: mini electrode 3, 12: insulator mini electrode 3, 13: tissue, 14: blood, 15: sheath, 16:NaCl, 17: scar tissue; for StPt setups: 1: distal ring electrode, 2: middle ring electrode, 3: proximal ring electrode, 4: tip electrode, 5: outer insulator, 6: inner insulator, 7: tissue, 8: blood, 9: NaCl, 10: scar tissue)</div> <div> </div> <div>• mifi.vtk: MiFi catheter in blood </div> <div>• stpt.vtk: StPt catheter in blood</div> <div>• mifiTissue_dist000_angle0000.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 0°</div> <div>• mifiTissue_dist000_angle0450.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 45°</div> <div>• mifiTissue_dist000_angle0900.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 90°</div> <div>• mifiTissue_dist000_angle1350.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 135°</div> <div>• mifiTissue_dist000_angle1800.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 180°</div> <div>• mifiTissueScar_dist0000_angle0900_centerX0000_line3mm.vtk: MiFi catheter positioned centrally and orthogonally at a line of scar tissue of 3mm width</div> <div>• mifiTissueScar_dist0000_angle0900_centerX0000_line6mm.vtk: MiFi catheter positioned centrally and orthogonally at a line of scar tissue of 6mm width</div> <div>• mifi_PV_d0060_r030_th20.vtk: MiFi catheter 6mm above the endocardial surface with a PV of 3 mm radius and 2mm PV tissue thickness</div> <div>• mifi_PV_d-070_r030_th20.vtk: MiFi catheter inserted into a PV of 3 mm radius and 2mm PV tissue thickness; insertion depth = 7mm</div> <div>• mifi_flush_050-0.50.vtk: MiFi catheter within blood with NaCl spheres of 0.5mm radius at irrigation holes</div> <div>• mifi_sheath_0100.vtk: MiFi catheter within transeptal sheath extracted by 10mm</div> <div> </div> <div>[1] Unger LA, Anton CM, Stritt M, Wakili R, Haas A, Kircher M, Dossel O, Luik A. In Silico Study of Local Electrical Impedance Measurements in the Atria - Towards Understanding and Quantifying Dependencies in Human. IEEE Trans Biomed Eng. 2023 Feb;70(2):533-543. doi: 10.1109/TBME.2022.3196545. Epub 2023 Jan 19. PMID: 35925848.</div> <p> </p>
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>
Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland
<p>The probabilistic projections are part of the work: <br><em>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, </em><a href="https://doi.org/10.1093/pnasnexus/pgad321"><em>https://doi.org/10.1093/pnasnexus/pgad321</em></a></p> <p>Please cite the article together with the Zenodo link when you use the data.</p> <p>The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:</p> <p>Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.<br>Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.<br>Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.</p> <p>The computations were performed at University of Geneva using Baobab HPC service.</p> <p>This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.</p>
Prebuilt Electricity Network for PyPSA-Eur based on OpenStreetMap Data
<p>This dataset contains a<strong> topologically connected representation of the European high-voltage grid (220 kV to 750 kV)</strong> <strong>constructed using OpenStreetMap data</strong>. Input data was retrieved using the Overpass turbo API (<a title="Overpass turbo" href="https://overpass-turbo.eu" target="_blank" rel="noopener">https://overpass-turbo.eu</a>). A heurisitic cleaning process was used to for lines and links where electrical parameters are incomplete, missing, or ambiguous. Close substations within a radius of <strong>500 m</strong> are aggregated to single buses, exact locations of underlying substations is preserved. Unique identifiers for lines and links are preserved, e.g. an AC line/cable with the ID <em>way/83742802-1</em> can be viewed on OpenStreetMap using the query <a title="OpenStreetMap example (AC)" href="https://www.openstreetmap.org/way/83742802" target="_blank" rel="noopener">https://www.openstreetmap.org/way/83742802</a>. A DC line/cable with the ID <em>relation/15781671</em> can be accessed using the query <a title="OpenStreetMap example (DC)" href="https://www.openstreetmap.org/relation/15781671" target="_blank" rel="noopener">https://www.openstreetmap.org/relation/15781671</a></p> <p>A detailed explanation on the <strong>background, methodology, and validation </strong>can be found in the article published in <a href="https://www.nature.com/articles/s41597-025-04550-7"><strong>Nature Scientific Data</strong></a>:</p> <blockquote> <p><em>Xiong, B., Fioriti, D., Neumann, F., Riepin, I., Brown, T.</em> Modelling the high-voltage grid using open data for Europe and beyond. <em>Sci Data</em> <strong>12</strong>, 277 (2025). <a href="https://doi.org/10.1038/s41597-025-04550-7" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-025-04550-7</a></p> </blockquote> <p><strong>Countries</strong> included in the dataset:</p> <blockquote> <p>Albania (AL), Austria (AT), Belgium (BE), Bosnia and Herzegovina (BA), Bulgaria (BG), Croatia (HR), Czech Republic (CZ), Denmark (DK), Estonia (EE), Finland (FI), France (FR), Germany (DE), Greece (GR), Hungary (HU), Ireland (IE), Italy (IT), Kosovo (XK), Latvia (LV), Lithuania (LT), Luxembourg (LU), Moldova (MD), Montenegro (ME), Netherlands (NL), North Macedonia (MK), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Slovakia (SK), Slovenia (SI), Spain (ES), Sweden (SE), Switzerland (CH), Ukraine (UA), United Kingdom (GB)</p> </blockquote> <p>The dataset was constructed as part of the workflow within the open-source, sector-coupling model PyPSA-Eur and will be updated continuously as data and/or the cleaning process improves. </p> <p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a>.</p> <p><strong>Not all data dependencies</strong> are shipped with the <a href="https://github.com/PyPSA/PyPSA-eur">code repository</a>, since git is not suited for handling large changing files. Instead we provide separate <strong>data bundles</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-eur.readthedocs.io/en/latest/installation.html">documentation</a>.</p> <p>While the <a href="https://github.com/PyPSA/PyPSA-eur">code</a> and provided dataset in PyPSA-Eur is released as free software under the MIT, <strong>different licenses and terms of use</strong> apply to the underlying input data.</p> <p><strong>Extract from OpenStreetMap Terms of Use</strong></p> <blockquote> <p>OpenStreetMap<sup><a href="https://www.openstreetmap.org/copyright#trademarks">®</a></sup> is <em>open data</em>, licensed under the <a href="https://opendatacommons.org/licenses/odbl/">Open Data Commons Open Database License</a> (ODbL) by the <a href="https://osmfoundation.org/">OpenStreetMap Foundation</a> (OSMF).</p> <p>You are free to copy, distribute, transmit and adapt our data, as long as you credit OpenStreetMap and its contributors. If you alter or build upon our data, you may distribute the result only under the same licence. The full <a href="https://opendatacommons.org/licenses/odbl/1.0/">legal code</a> explains your rights and responsibilities.</p> <p>Our documentation is licensed under the <a href="https://creativecommons.org/licenses/by-sa/2.0/">Creative Commons Attribution-ShareAlike 2.0</a> license (CC BY-SA 2.0).</p> </blockquote> <p>This processed dataset is provided under the Open Data Commons Open Database License (ODbL 1.0) license.</p> <p><strong>Changelog from version 0.5 to 0.6:<br></strong></p> <ul> <li>Added electric parameters to lines (e.g. nominal current, resistance r, reactance x, susceptance b). This allows the dataset to be used outside of PyPSA/PyPSA-Eur.</li> <li>Interactive map.html now bundled with the dataset.</li> <li>Tags columns include what the element contains (e.g. merged lines contain lines that were aggregated together).</li> </ul> <p><strong>Changelog from version 0.4 to 0.5:<br></strong></p> <ul> <li>Exact locations of original substations and converter stations (interior point/Pole of Inaccessibility) are preserved.</li> <li>Clustering resolution improved from 5000 to 500 meters.</li> <li>Lines of same electric parameters are merged, if they cross a virtual bus (that is not a real substation).</li> <li>Information from OSM relations are used, wherever applicable. To avoid doubling, members (ways) of the relation are dropped in the set of lines, accordingly.</li> <li>There are now unique transformers for each voltage level in each station. Transformers now have a nominal capacity, representing the maximum of line capacities connected to either side/bus of the transformer (n-0, nominal capacity).</li> <li>Wherever applicable, OSM IDs are preserved and used in the index of the network components.</li> </ul>
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>
Supporting information of paper "Direct Laser Writing of Liquid Crystal Elastomers Oriented by a Horizontal Electric Field"
<p>This dataset contains the <em>underlying data </em>and the <em>extended data </em>for the paper “Direct Laser Writing of Liquid Crystal Elastomers Oriented by a Horizontal Electric Field”, submitted for consideration and open review in Open Research Europe.</p> <p>In the follow the description of the files is reported:</p> <p><strong>Underlying_data_Surce_Images.zip</strong> It contains the source original microscopy image files used for assembling final paper Figures (1-5), enclosed in the main text. List and pictures' information are reported in the file "Source Picture Description.txt" included in the zip.</p> <p><strong>SIVideo_LCAlignment.avi </strong>- [Supporting VideoZ1] showing the response of the unpolymerized mesogens to an electric field of 1.7 V/µm</p> <p><strong>SIVideo_NailActuation.wmv</strong> - [Supporting VideoZ2] showing the thermally activated actuation of a LCE nail microstructure upon several rt-75 °C cycles.</p> <p><strong>SIVideo_SimulationCube.gif</strong> - [Supporting VideoZ3] Video showing a simulation of the actuation of a LCE microcube upon heating.</p> <p><strong>SIVideo_SimuulationNail.gif</strong> - [Supporting VideoZ4] - Video showing a simulation of the actuation of a LCE micronail upon heating.</p> <p><strong>ImageZ1.png - </strong>[Supporting Figure Z1] Simulation of the distribution of the electric field generated by flat electrode, view along the plane (cross section) with magnification of the central part of the working field.</p> <p><strong>ImageZ2.png</strong> - [Supporting Figure Z2] - Photographs and thermal images of a LCE film obtained using a resin formulation identical to that employed in DLW on identical substrates that show the thermal response. Top: photograph of LCE films obtained by UV polymerization of a 7:3 LC-MA:LC-DA mixture over interdigitated flat ITO electrodes with an applied DC voltage. Upon heating with an infrared lamp (right column) the film bends and shortens. Middle: images acquired with a thermocamera highlighting the actuation temperature. Bottom: control LCE film polymerized as casted (without electric field).</p> <p><strong>ImageZ3.png</strong> - [Supporting Figure Z3] - a) Optical microscopy images of a double-cantilever acquired through perpendicularly oriented polarizes placed at 45° with respect to the applied electric field. b) Example of thermally responsive actuation via a comparison of images recorded at room temperature (upper) and at 70 °C (bottom).</p> <p><strong>Simulations.zip - </strong>Comsol (Version 5.6) source simulation files for LCE microcube and LCE micronail upon heating, and for the electric field electric generated by flat electrode in experimental conditions. Complete Report file in PDF is also available, containing all the parameters and equations used for simulation. Finally, the simulation results are provided as text .csv files.</p> <p><strong>Files_Blender_LCE.zip</strong> - Blender (Version 2.93) source file (“.blend”) for microstructure fabrication and generated surface “.stl” files, including cube, nail, cantilever and pyramid geometries.</p> <p><strong>Cube Describe.zip </strong>- Example of DeScribe software output files containing all needed parameters used for printing cubes, generated starting from “cube.stl” file. Other geometries have been realised using the same writing parameters starting from appropriate “.stl” file.</p>
Supporting data to the paper "Modelling charge profiles of electric vehicles based on charges data"
<p>This dataset contains the<em> underling data</em> and the <em>extended data</em> for the paper Modelling charge profiles of electric vehicles based on charges data”, submitted for consideration and open review in Open Research Europe.</p> <p>In the follow the description of the files is reported:</p> <p>HISTORIC DATA 2019 ELECTROLINERES AMB.csv: contains information on the charge events at the public charging points managed by the municipality in the metropolitan area of Barcelona in 2019. Fields are: charging point name; connector typology and number; charge start time; charge stop time; charge duration in minutes, energy delivered in kWh; vehicle manufacturer (optional); vehicle model (optional).</p> <p>STATIC INFORMATION CHARGING POINTS AMB 29042020.csv: contains the information about the public charging points of the metropolitan area of Barcelona. Fields are: charger typology (Quick/Normal); Charging point name and address; OCCP version; charger location; longitude; latitude; 7 flag fields for the connector type; observations; charging point maker.</p> <p>Lataustapahtumat, julkiset latauslaitteet 2019.csv: contains the information about the Turku Energia charge events for the city of Turku in 2019. Fields are: date of record creation, Station ID, Station name, charge start time, charge stop time, charge duration in minutes, energy delivered in Wh, Plug type (AC 22 kW/DC 50 kW), Cumulative energy delivered in the year (Wh), Average charge power (W)</p> <p>EV.csv: containes data on battery size retrived from vehicle datasheet or manufacturer website. Fields are: record ID, vehicle manufacturer ; vehicle model; battery size in kWh.</p> <p>Charge2019_EV_AMB.csv: contains the data on charge requests ( HISTORIC DATA 2019 ELECTROLINERES AMB.csv ) combined with the information on vehicle battery (EV.csv).</p> <p> </p>
Spain's marginal electricity mix and its relevance for assessing the environmental performance of installations with variable load or power
<p>This upload contains the Supplementary Information file and the underlying data as Excel-file for the Journal article with the same name. More specifically, it provides time series of the Spanish electricity generation mix for the years 2015-2020 for energy system analysis and the life cycle inventory data for import into openLCA and re-use in combination with the ecoinvent databse (Version 3.7.1). Further details are available on request.</p>
Electric Motor Vibrations Dataset
<p><strong>Scope:</strong></p> <p>This repository contains data provided by vibrations sensor that can be used in designing and testing ML algorithms for general classification problems or more specific one such as predictive maintenance.</p> <p><strong>Source of the data:</strong></p> <p>Data were obtained in the framework of CHIST-ERA SOON project with the aim of testing machine learning predictive maintenance algorithms.</p> <p><strong>Special remarks:</strong></p> <p>Each file name codes how the data was obtained and and implicitly the data label.</p> <p>In experiment were used two electrical motors named m1 and m2, where <em>m1</em> is the tested motor and <em>m2</em> is a second motor for obtaining a more complex testing environment (eg. supplemental noise source).</p>
Great Britain (GB) Domestic Electricity Usage by Low Carbon Technology by Season
<p><strong>Important</strong>: As an research not-for-profit organisation, if you found this dataset useful we would appreciate your time in filling out <a href="https://docs.google.com/forms/d/e/1FAIpQLSfqCAoQt4AzuGH8Th5tJjnkGP956Fgc6O8T6wJaM7Nhd_nRdg/viewform?usp=pp_url&entry.1276408097=10.5281/zenodo.6576108">this short survey</a>.</p> <p> </p> <p>This dataset contains 3 aggregate datasets from the electricity smart meter data of over 25,000 customers in Great Britain (GB) from March 2021 - March 2022.</p> <p>For each consumer, we know (via a survey) what low carbon technologies (LCTs) they own. The potential LCT options are: Solar PV, Heat Pump (Air Source, or Ground Source), Electric Vehicle, Battery, Electric Storage Heaters.</p> <p>For simplicity, this dataset contains only customers with one type of LCT (with the exception of Solar PV, where we include Solar PV + Battery customers as is common in GB). We do not include customers with multiple LCTs (for example home battery + EV)</p> <p>We include quantiles of usage for each half hour (the "profile") for each type of LCT ownership "archetype", both overall (when season=None) and by season. As is common in the literature, we normalise by the square meterage of the house using open EPC data in GB (https://epc.opendatacommunities.org/) to get the watt hours per square meter. You can also find the raw, unnormalised, kwh values by quantile in this release. These two datasets have the quantiles for each half hour period. In addition, we release the daily quantiles of electricity consumption, in kwh per square meterage, by LCT type.</p> <p>In summary the data we are releasing, aggregated over 25,000 customers over 1 year of usage from March 2021 - March 2020 is:</p> <ul> <li>daily_elec_consumption_quantiles_by_lct_ownership.csv - The daily quantiles of usage [kWh/m2] by LCT</li> <li>lct_elec_consumption_profiles.csv - The half hourly quantiles of usage [Wh/m2] by LCT by season</li> <li>lct_elec_consumption_profiles_kwh.csv - The half hourly quantiles of usage [kWh] by LCT by season</li> </ul> <p>We believe this data will be useful for modelling efforts, as customers with different types of LCTs use energy at different times of the day, and by different amounts daily. By releasing this data openly, we hope forecasting scenarios for the future energy system are more accurate. We have a supporting blog post on our website at https://www.centrefornetzero.org/res/lessons-from-early-adopters-electricity-consumption-profiles/.</p>
Data file for: Three-Dimensional Electrical Imaging Across the Cona Woka Rift and Yalaxiangbo Dome in Southern Tibetan Plateau
<p>The magnetotellurics data were used to study the lithospheric electrical structure across the Cona Woka rift and Yalaxiangbo dome in the southern Tibetan plateau, conducted by Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences. The data file of CN6.dat was generated by the Matlab code EM3DVP.</p> <p>You are recommended to refer to the Kelbert et al., 2014 paper: https://doi.org/10.1016/j.cageo.2014.01.010 for a brief understanding of the data file formats.</p> <p> </p>
References and Metadata for Electric vehicles' consumer behaviours: Mapping the field and providing a research agenda (https://doi.org/10.1016/j.jbusres.2022.06.011)
<p>The bibliography and metadata used for the analysis published in the Journal of Business Research - Electric vehicles' consumer behaviors: Mapping the field and providing a research agenda (https://doi.org/10.1016/j.jbusres.2022.06.011).</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.