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3,427 results for “Electricity”
Raw Data - 3D Printing Temperature Tailors Electrical and Electrochemical Properties through Changing Inner Distribution of Graphite/Polymer
<p>This Data set contains the raw data of the article:</p> <p>3D Printing Temperature Tailors Electrical and Electrochemical Properties through Changing Inner Distribution of Graphite/Polymer, Small, 2021, 17, 2101233.</p> <p>C. Iffelsberger, C. W. Jellett, and M. Pumera*,</p> <p>https://doi.org/10.1002/smll.202101233</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
EGFxSet: Electric guitar tones processed through real effects of distortion, modulation, delay and reverb
<p>EGFxSet (Electric Guitar Effects dataset) features recordings for all clean tones in a 22-fret Stratocaster, recorded with 5 different pickup configurations, also processed through 12 popular guitar effects. Our dataset was recorded in real hardware, making it relevant for music information retrieval tasks on real music. We also include annotations for parameter settings of the effects we used.</p> <p>More details can be found in <a href="http://egfxset.github.io">egfxset.github.io</a></p> <p>The dataset can also be accessed with <a href="https://mirdata.readthedocs.io/en/stable/source/mirdata.html#module-mirdata.datasets.egfxset">mirdata</a></p> <p>Effects and parameters included:</p> <table> <tbody> <tr> <td>Effect</td> <td>Model</td> <td>Effect Type</td> <td>Knob Names</td> <td>Knob Type</td> <td>Setting</td> </tr> <tr> <td>blues driver</td> <td>Boss BD-2 Blues Driver</td> <td>distortion</td> <td>['level', 'tone', 'gain']</td> <td>['volume','eq','effect amount']</td> <td>[0.5,0.5,1.0]</td> </tr> <tr> <td>tube screamer</td> <td>Ibanez Mini Tube Screamer</td> <td>distortion</td> <td>['tone', 'overdrive', 'level']</td> <td>['eq','effect amount','volume']</td> <td>[0.5,1.0,0.5]</td> </tr> <tr> <td>distortion</td> <td>Pro Co Sound RAT2 Distortion</td> <td>distortion</td> <td>['distortion', 'filter', 'volume']</td> <td>['effect amount','eq','volume']</td> <td>[1.0, 0.5,1.0]</td> </tr> <tr> <td>chorus</td> <td>Boss CE-3 Chorus</td> <td>modulation</td> <td>['rate', 'depth', 'stereo mode']</td> <td>['rate','effect amount','selector']</td> <td>['120 bpm', 1.0, False]</td> </tr> <tr> <td>flanger</td> <td>Mooer E-Lady</td> <td>modulation</td> <td>['color', 'type', 'range', 'rate']</td> <td>['eq','selector','effect amount','rate']</td> <td>[0.5, 'normal', 1.0, '120 bpm']</td> </tr> <tr> <td>phaser</td> <td>MXR Phase 45</td> <td>modulation</td> <td>['speed']</td> <td>['rate']</td> <td>['120 bpm']</td> </tr> <tr> <td>tape echo</td> <td>Line 6 DL4 Delay</td> <td>delay</td> <td>['effect selector', 'delay time', 'repeats', 'tweak (bass)', 'tweez (treble)', 'mix']</td> <td>['selector', 'rate', 'effect decay', 'eq', 'eq', 'effect amount']</td> <td>['tape echo', '120 bpm', 0.6, 0.5, 0.5, 0.5]</td> </tr> <tr> <td>digital delay</td> <td>Line 6 DL4 Delay</td> <td>delay</td> <td>['effect selector', 'delay time', 'repeats', 'tweak (bass)', 'tweez (treble)', 'mix']</td> <td>['selector', 'rate', 'effect decay', 'eq', 'eq', 'effect amount']</td> <td>['digital delay', '120 bpm', 0.6, 0.5, 0.5, 0.5]</td> </tr> <tr> <td>sweep echo</td> <td>Line 6 DL4 Delay</td> <td>delay</td> <td>['effect selector', 'delay time', 'repeats', 'tweak (sweep speed)', 'tweez (sweep depth)', 'mix']</td> <td>['selector', 'rate', 'effect decay', 'rate', 'effect amount', 'effect amount']</td> <td>['sweep echo', '120 bpm', 0.6, '120 bpm',1.0,0.5]</td> </tr> <tr> <td>plate reverb</td> <td>Orange CR-60 Combo Amplifier</td> <td>reverb</td> <td>['volume', 'bass', 'treble', 'type', 'reverb', 'master volume', 'clean']</td> <td>['volume','eq','eq','selector','effect amount', 'volume', 'selector']</td> <td>[0.5, 0.5, 0.5, 'plate', 1.0, 0.2, True]</td> </tr> <tr> <td>hall reverb</td> <td>Orange CR-60 Combo Amplifier</td> <td>reverb</td> <td>['volume', 'bass', 'treble', 'type', 'reverb', 'master volume', 'clean']</td> <td>['volume','eq','eq','selector','effect amount', 'volume', 'selector']</td> <td>[0.5, 0.5, 0.5, 'hall', 1.0, 0.2, True]</td> </tr> <tr> <td>spring reverb</td> <td>Orange CR-60 Combo Amplifier</td> <td>reverb</td> <td>['volume', 'bass', 'treble', 'type', 'reverb', 'master volume', 'clean']</td> <td>['volume','eq','eq','selector','effect amount', 'volume', 'selector']</td> <td>[0.5, 0.5, 0.5, 'spring', 1.0, 0.2, True]</td> </tr> </tbody> </table> <p><br>Please cite these papers if using EGFxSet:</p> <p>Pedroza HE, Abreu W, Corey R, Roman IR. "Leveraging real electric guitar tones and effects to improve robustness in guitar tablature transcription modeling." <em>In 27th International Conference on Digital Audio Effects (DAFx),</em> 2024.</p> <p>Pedroza, Hegel, Gerardo Meza, and Iran R. Roman. "EGFxSet: Electric guitar tones processed through real effects of distortion, modulation, delay and reverb." <em>ISMIR Late Breaking Demo, </em>2022.</p>
Electrical conductivity of the world ocean and marine sediments
<p>Copy of dataset (as was on 2022-01-12) of electrical conductivity and conductance grids for the ocean and marine sediments at 0.1 degree lateral resolution, from https://github.com/agrayver/seasigma. These models are presented in the work</p> <p>Grayver, A. V. (2021). Global 3-D electrical conductivity model of the world ocean and marine sediments. Geochemistry, Geophysics, Geosystems, 22, e2021GC009950. <a href="https://doi.org/10.1029/2021GC009950">doi: 10.1029/2021GC009950</a></p> <p>Please cite this publication if you use the provided models in your work.</p>
Historical Annual Revenue of Energy Storage on European Electricity Markets
<p>This dataset provides the optimized annual revenue for 96 generic storage technologies (12 efficiency x 8 discharge duration). It covers 17 European electricity markets for up to 16 years (Austria, AT; Belgium, BE; Switzerland, CH; Czech Republic, CZ; German, DE; Spain, ES; France, FR; Italy, IT; Ireland, IR; Netherlands, NL; Nordpool which includes Denmark, Estonia, Finland, Latvia, Lithuania, Norway, Sweden, NP; Poland, PL; Portugal, PT; Romania, RO; Slovakia, SK; United Kingdom, UK). The optimization is an adaptation of the model presented in Gaudard et al. [2013]. It assumes perfect foresight assumption and a stochastic algorithm. Therefore, the results are an approximation of the maximum rather than the absolute optimum. Further information is provided in "Gaudard L. and Madani K., Energy storage race: Has the monopoly of pumped-storage in Europe come to an end?, forthcoming". </p> <p>The following information is provided:</p> <p>Country: Code of the specific market (also the filename)</p> <p>Currency: The currency in which the results are expressed</p> <p>Discharge duration [hours]: The time required to empty at full nominal power a device that is fully charged. </p> <p>Efficiency: Ratio between the amount of discharged and charged energy during a full cycle.</p> <p>Year: From January 1st to December 31st.</p> <p>The given numbers are in euros or GBP per year and normalized to 1kWh of energy storage. This means that for a specific device, the given figures must be multiplied by the volume of energy storage (in terms of kWh). As an example, for an energy device with the efficiency of 0.95, discharge duration of 6h and volume of energy storage of 2000kWh, the revenues in 2003 in Austria would be 9.26 x 2000=18520 euros. </p> <p>For any questions or further requirements, please feel free to get in touch with the authors.</p>
Reference Data Set: Electricity, Heat, and Gas Sector Data for Modeling the German System
<p>This reference data set representing the status quo of the German electricity, heat, and natural gas sectors was compiled within the research project ‘LKD-EU’ (Long-term planning and short-term optimization of the German electricity system within the European framework: Further development of methods and models to analyze the electricity system including the heat and gas sector).</p> <p>While the focus is on the electricity sector, the heat and natural gas sectors are covered as well. With this reference data set, we aim to increase the transparency of energy infrastructure data in Germany. Where not otherwise stated, the data included in this report is given with reference to the year 2015 for Germany. The data set is documented in DIW Data Documentation 92 (see references).</p> <p>The project is a joined effort by the German Institute for Economic Research (DIW Berlin), the Workgroup for Infrastructure Policy (WIP) at Technische Universität Berlin (TUB), the Chair of Energy Economics (EE2) at Technische Universität Dresden (TUD), and the House of Energy Markets & Finance at University of Duisburg-Essen. The project was funded by the German Federal Ministry for Economic Affairs and Energy through the grant ‘LKD-EU’, FKZ 03ET4028A-D.</p>
Accuracy and Reliability of Noninvasive Stroke Volume Monitoring via ECG-Gated 3D Electrical Impedance Tomography in Healthy Volunteers
<p>3D EIT dataset of ten healthy human volunteers, as described in the corresponding <a href="http://dx.doi.org/10.1371/journal.pone.0191870">journal publication at PLOS ONE</a> or the first author's <a href="http://dx.doi.org/10.5075/epfl-thesis-8343">PhD thesis at EPFL</a>. Please also read the attached ReadMe file.</p> <p>When using this data please cite the corresponding journal publication:</p> <blockquote> <p>Accuracy and Reliability of Noninvasive Stroke Volume Monitoring via ECG-Gated 3D Electrical Impedance Tomography in Healthy Volunteers, PLOS ONE, 2018, <a href="http://dx.doi.org/10.1371/journal.pone.0191870">https://dx.doi.org/10.1371/journal.pone.0191870</a></p> </blockquote>
Experimental study dataset: "Enhancing Touch Sensibility with Sensory Electrical Stimulation and Sensory Retraining"
<p>Experimental study dataset: "Enhancing Touch Sensibility with Sensory Electrical Stimulation and Sensory Retraining".</p> <p>This research was supported by the Swiss National Science Foundation through the grant PP00P2 163800. This work was also supported by SENACYT and IFARHU, the Panamanian Government.</p> <p>Files and data to upload:</p> <ol> <li>Description of variables </li> <li>Continue robot data (pickles)</li> <li>Discontinue robot data (task performance)</li> <li>EEG data</li> </ol>
Data for Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty
<p>Data from the paper "Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty"</p>
Task 3 Dataset for Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion Models
Open the record for dataset details and reuse information.
Electric power outages from 900k simulated hurricanes in a changing climate, for the United States and Puerto Rico
<p>This dataset is described and explored in Rice et al. 2025, "<a href="https://doi.org/10.1088/1748-9326/adad85">Projected Increases in Tropical Cyclone-induced U.S. Electric Power Outage Risk</a>", published in Environmental Research Letters.</p> <p>This dataset collects peak outage levels modeled for 900,000 synthetic tropical cyclones (TCs; also commonly known as hurricanes) representative of a modeled historical (1980-2015) and future (2066-2100) period under SSP5-8.5 warming. Synthetic TCs are generated with the Risk Analysis Framework for Tropical Cyclones (RAFT; see Xu et al. 2024 and Balaguru et al. 2023), forced by climate simulation data from the Coupled Model Intercomparison Project phase 6 (CMIP6; see Eyring et al. 2016). Outages are modeled with the newly introduced Electric Power Outages from Cyclone Hazards (EPOCH) model, which was trained on county-level outage data from 23 historical TC events in the EAGLE-I dataset (Brelsford et al. 2024). </p> <p>The EPOCH model predicts outages based on county population and the maximum wind speed and rainfall rate experienced during the TC. Predicted outage levels are provided in the form of peak outage fraction: the maximum fraction of electricity customers expected to experience an outage at any one time during the storm's lifetime. Although we do not model outage duration, other research suggests peak outage level is strongly correlated with duration (Jamal and Hasan, 2023).</p> <p><strong>Data Format</strong></p> <p>The data is provided in NetCDF4 files, one for each CMIP6 model and time period. Each NetCDF4 files has the following:</p> <p>Dimensions:</p> <ul> <li>ncounties = 2715. The counties in the study domain</li> <li>ntracks = 50000. The number of storms</li> </ul> <p>Variables:</p> <ul> <li>int pseudofips(ncounties). The FIPS code for each county. Puerto Rico data is not available at county level, but instead for six utility-defined regions. We assign "pseudo-FIPS" codes to these region starting at 100000</li> <li>double centroid_lons(ncounties). Longitude of approximate center of county, in the range [-180, 0].</li> <li>double centroid_lats(ncounties). Latitude of approximate center of county, in the range [0, 90].</li> <li>float outage_prediction(ntracks, ncounties). The predicted peak outage fraction for each county, for each storm. Due to the particularities of ensemble models, some predictions may be slightly below zero or above one; we clip these values to the range [0,1] before any analysis in our study.</li> <li>ubyte prediction_complete_flag(ntracks). A verification flag used during dataset generation. This flag should equal 1 everywhere for complete data.</li> </ul> <p>Each file also contains the raw predictors at a county level for every storm, inside the 'predictors' group, for feature analysis.</p> <p>Also provided for convenience is 'counties_pseudofips.csv', which maps the pseudo-FIPS codes to the the name and spatial extent (WKT format) of each county. It can be read easily by Python GeoPandas, or other software.</p>
Synthetic Dataset of charging processes by electric vehicles at workplace in Germany
<p>The dataset shows eight cluster groups that depict the mobility behavior of electric vehicle users in the employee context. For this purpose, 23.9 million data entries were analyzed, corresponding to 37,238 charging sessions. These data were collected over the year 2023. The 220 charging points were exclusively accessible to employees (private use case). From the data, cluster groups were derived using the Gaussian Mixture Model, and a synthetic dataset was generated through Monte Carlo sampling.</p> <p><span>The dataset consists of 8000 synthetic profiles, offering a robust scientific basis. By retaining the same statistical attributes as the empirical data, the synthetic profiles represent eight different mobility clusters, each containing 1000 entries, including full-time and part-time employees, shift workers, pool vehicle users, and opportunists.</span> Each cluster is represented by the mean parking start hours (arrival time - in decimal hours), mean parking duration (in decimal hours), the average energy recharged, and the average charging duration, each including the cluster-specific standard deviation and median.</p> <p>Further information can be obtained from the upcoming publication: "Synthetic Dataset of charging processes by electric vehicles at workplace in Germany."</p>
Electricity consumption and real vaue-added data for the Swiss and Genevan secondary and tertiary sectors.
<p>This file contains datasets of electricity consumption and real value added (base year 2000) in the secondary and tertiary sector for Switzerland and Geneva for the years between 2000 and 2015. The structure of the Swiss and Genevan datasets has been matched to ensure comparability of results from index decomposition analyses conducted on each region. It also contains heating and cooling degrees for both Switzerland and Geneva.</p>
Dataset for the paper: "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU."
<p>Dataset used for the development of scenarios in the publication "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU. Sustainability 2018, 10, 3685." and used for a case study in "Di Felice L., Dunlop T., Giampietro M., Kovacic Z., Renner A., Ripa M., Velasco-Fernández R. – Report on the Quality Check of the Robustness of the Narrative behind Energy Directives. MAGIC (H2020–GA 689669) Project Deliverable 5.4, 30 November 2018". (link: https://magic-nexus.eu/documents/d54-report-narratives-behind-energy-directives).</p> <p>Sources of other secondary data (from papers, reports) specified in the dataset (under tab "input codes")</p>
Effects of a Delayed Expansion of Interconnector Capacities in a High RES-E European Electricity System - Dataset
<p>Input and output data of the modelling work for the paper Effects of a Delayed Expansion of Interconnector Capacities in a High RES-E European Electricity System</p> <ul> <li>Considered scenario years: 2030, 2040 and 2050</li> <li>The profiles are based on the historical year 2016.</li> <li>Two scenarios are included: lower connectivity and high connectivity</li> <li>The data cover the ENTSO-E member countries except Iceland and Cyprus and is given in country-specific resolution.</li> </ul> <p><strong>Input:</strong></p> <ul> <li>Demand as hourly profile in MWh</li> <li>Variable RES-E as hourly profile in MWh</li> <li>Power plant fleet as capacities in MW</li> <li>NTCs as capacities in MW</li> </ul> <p><strong>Output:</strong></p> <ul> <li>CO2 emissions as annual data in Mt</li> <li>Variable electricity generation costs as annual data in MEur</li> <li>Variable electricity generation costs per generation as annual data in Euro/MWh</li> <li>Electricity generation as annual data in TWh</li> <li>Electricity export as annual data in TWh</li> <li>Electricity import as annual data in TWh</li> <li>Transit flows as annual data in TWh</li> </ul> <p>The sources are described in the corresponding paper under the following link: <a href="https://www.mdpi.com/1996-1073/12/16/3098">https://www.mdpi.com/1996-1073/12/16/3098</a></p>
The Time Variable Ionospheric Electric Field (TiVIE) Model Outputs v 1.0
<p>These are the outputs for the TiVIE model v 1.0 produced by Maria-Theresia Walach, Lancaster University for the publication Walach, M.-T., and Grocott, A. (submitted 2024). </p>
Weekly plots of Great Britain's half-hourly electrical system weather dependent generation, net imports and overall demand from 2008-11-10
<p>Plots that show the electrical system transition of Great Britain, they were created to form the individual frames for a video of the transition.</p>
Number of BEV (a battery electric vehicle) and PHEV (a plug-in hybrid electric vehicle) vehicles for each Country (2019)
<p>According to the Global E.V. Outlook 2020, China ranks first in vehicles in operation with electric or hybrid engines. In second place in the U.S. and third place in Norway.</p>
Result data related to "Tröndle et al (2019) -- Home-made or imported: on the possibility for renewable electricity autarky on all scales in Europe"
<p>The files include results to out study investigating the possibility for renewable electricity autarky in Europe. For each administrative unit on the continental, national, regional, and municipal levels these files include:</p> <ol> <li>Name, country, population, current electricity demand, land cover statistics, shared coast with exclusive economic zone</li> <li>Potential in terms of area [km2], installable capacity [MW], annual electricity yield [TWh]</li> </ol> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Pfenninger, S., & Lilliestam, J. (2019). Home-made or imported: on the possibility for renewable electricity autarky on all scales in Europe. <em>Energy Strategy Reviews</em>, <em>26</em>.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 3 (2021-07-19)</p> <p>* Fix ID of EEZ in shared-coast.csv files.</p> <p>Version 2 (2019-11-08)</p> <p>* Add land cover statistics for each unit.</p>
Loon Stratospheric Electrical Measurements above Thunderstorms
<p>Loon LLC telemetry data containing measurements from a corona current sensor and also aligned with lightning indicators from BCI (CDO, CloudHeight) and the Geostationary Lightning Mapper (GLM). See README for more details.</p>
MD simulations of SARS-CoV-2 Spike Protein under static electric fields
<p>This dataset contains trajectories corresponding to all-atom MD simulations of segments of the SARS-CoV-2 Spike Protein, and in-silico mutations, under the influence of moderate external electric fields. The final structures of some of the simulations were used to perform in-silico docking with ACE2 receptor to evaluate the effect of comformational changes (docking was perform with PyDOCK).</p> <p>The file trajectories_6vsb_dt1ns.zip contains trajectories of simulations that were performed on a segment of the Protein Data Bank ID 6VSB comprising RBD, SD1 and SD2. The file trajectories_6m0j_dt1ns.zip correspond to the RBD in Protein Data Bank ID 6M0J. The file trajectories_in-silico_mutations_dt1ns.zip correspond to simulations performed on in-silico generated mutations following the mutations corresponding to WHO Variants of Concern UK, South Africa and Brazil. In all cases, simulations were performed at different electric field intensities ranging between 10<sup>4</sup> V/m and 10<sup>7</sup> V/m, with an extra short simulation under very high intensity (10<sup>9</sup> V/m). The file docked_structures_6m0j.zip contains the 100 best scored docked structures for each case as the output of PyDOCK.</p> <p>Trajectories are stored in GROMACS compressed trajectory file format (.xtc), downsampled to a 1ns timestep. Individual trajectories length are between 300 nanoseconds and 1 microsecond. In-silico docked structures are in PDB format. See linked preprint for more details.</p>
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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)
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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.