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300 results for “Voltage”
Low-voltage Secondary Electron Emission Spectromicroscopy using a Scanning Auger Microscope
<p>Secondary electron emission is considered a well-established nano-scale probe for mapping the surface morphology of materials. It has also been demonstrated that secondary electrons (SE) emitted from materials can provide additional information on the local work function, bulk density of state (DOS), surface potential, charging/discharging characteristics, and elemental/chemical properties of bulk materials. The nano-scale lateral resolution and surface sensitivity of low-voltage scanning microscopes give them a unique advantage for the investigation of surfaces. However, the surface contamination caused by exposure to electron beams has always been a limiting factor for this purpose. Since the yield of SE emission is higher than that of Auger emission, the secondary electron emission spectromicroscopy (SEES) performed in an ultra-high vacuum chamber using a scanning Auger microscope (SAM) can be a very powerful tool for surface characterization, especially in the case of ultra-thin materials.</p> <p>We adapt our scanning auger microscope (SAM), equipped with a cylindrical mirror analyzer (CMA) and operated in an ultra-high vacuum, to SEES by tilting the sample holder and applying a negative bias to the sample. We also presented SEES signals of Chromium thin film at low voltages of 500 and 1000 V.</p>
High-Voltage Disconnector State Identification: synthetic and real images of substation disconnectors
<p>This dataset contains the training and test images used in the work detailed in the article: Barpp Gomes, V., Marchesi, B., Gruber, Y.A. <em>et al.</em> Exploring Synthetic Data for Training Deep Learning Models for High-Voltage Disconnector State Identification. <em>J Control Autom Electr Syst</em> (2025). <a href="https://doi.org/10.1007/s40313-025-01204-2">https://doi.org/10.1007/s40313-025-01204-2</a></p> <p>Contais about 940,000 synthetic (CGI-rendered) and 60,000 real (camera-captured) samples of four types of substation disconnectors, on both open and closed states:</p> <ul> <li>230 kV center break (type 1, as indicated in the article);</li> <li>230 kV center break (type 2);</li> <li>230 kV double side break</li> <li>525 kV horizontal semi-pantograph</li> </ul> <p>Each zip file contains images of one type of substation disconnector. Images are sized 320x128 and are organized in folders, as follows:</p> <ul> <li>00_train_synth: Synthetic training images.</li> <li>01_train_real: A small set of real training images, as indicated in the article.</li> <li>02_test_real_normal1: One set of real test images.</li> <li>03_test_real_normal2: Another set of real test images, from a different time period.</li> <li>04_test_real_maneuvers: A special set of real test images in which the switches have been operated (are in different states).</li> </ul>
Voltage and current data for IEC 62600-30 power quality monitoring from the Mutriku Wave Power Plant and Lir National Ocean Test Facility electrical laboratory
<p>This Technical Note describes the electrical data collected from the Mutriku Wave Power Plant (MWPP) and the Lir National Ocean Test Facility (NOTF) electrical laboratory at the MaREI Centre in the Environmental Research Institute, at University College Cork.</p> <p>In summary, the electrical data collect is for the purpose of analysing the power quality output of a Wave Energy Converter (WEC). The data includes voltage and current signals from the output of a WEC sampled at 15 kHz from the MWPP and a WEC emulator sampled at 20 kHz from the Lir NOTF electrical laboratory. There are 24 datasets from the MWPP taken at various sea state conditions, and there are 56 datasets from the Lir NOTF which are taken with at various sea state conditions, with different control laws, and grid connections.</p> <p>This data is published for purpose of power quality analysis and comparison for future tests. For OPERA, power quality analysis was performed as part of WP5 T5.2 and T5.5, and presented in depth in Deliverables D5.2 and D5.4.</p> <p>See accompanying technical note for more Information.</p>
Lithium-ion battery charge and discharge testing data - current, voltage, soc, ta - at constant levels of power
<p>This dataset helped in the composition of a battery testing and modelling validation, of a lithium-ion battery. The data has the charge and discharge testing acquisition data - current, voltage, soc, ta - at constant levels of power.</p>
Dataset of lightning flashovers on medium voltage distribution lines
<p>This <strong>synthetic dataset</strong> was generated from <strong>Monte Carlo</strong> simulations of <strong>lightning flashovers</strong> on medium voltage (MV) <strong>distribution lines</strong>. It is suitable for training <strong>machine learning</strong> models for classifying lightning flashovers on distribution lines. The dataset is <strong>hierarchical</strong> in nature (see below for more information) and <strong>class imbalanced</strong>.</p> <p>Following five different types of lightning interaction with the MV distribution line have been simulated: (1) direct strike to phase conductor (when there is no shield wire present on the line), (2) direct strike to phase conductor with shield wire(s) present on the line (i.e. shielding failure), (3) direct strike to shield wire with backflashover event, (4) indirect near-by lightning strike to ground where shield wire is not present, and (5) indirect near-by lightning strike to ground where shield wire is present on the line. Last two types of lightning interactions induce overvoltage on the phase conductors by radiating EM fields from the strike channel that are coupled to the line conductors. Three different methods of indirect strike analysis have been implemented, as follows: Rusck's model, Chowdhuri-Gross model and Liew-Mar model. Shield wire(s) provide shielding effects to direct, as well as screening effects to indirect, lightning strikes.</p> <p><strong>Dataset</strong> consists of two independent distribution lines, with heights of 12 m and 15 m, each with a flat configuration of phase conductors. Twin shield wires, if present, are 1.5 m above the phase conductors and 3 m apart [2]. CFO level of the 12 m distribution line is 150 kV and that of the 15 m distribution line is 160 kV. Dataset consists of <strong>10,000 simulations</strong> for each of the distribution lines.</p> <p>Dataset contains following variables (features):</p> <ul> <li>'<em>dist</em>': perpendicular distance of the lightning strike location from the distribution line axis (m), generated from the Uniform distribution [0, 500] m,</li> <li>'<em>ampl</em>': lightning current amplitude of the strike (kA), generated from the Log-Normal distribution (see IEC 60071 for additional information),</li> <li>'<em>front</em>': lightning current wave-front time (us), generated from the Log-Normal distribution; it needs to be emphasized that amplitudes (ampl) and wave-front times (front), as random variables, have been generated from the appropriate bivariate probability distribution which includes statistical correlation between these variates,</li> <li>'<em>veloc</em>': velocity of the lightning return-stroke current defined indirectly through the parameter "w" that is generated from the Uniform distribution [50, 500] m/us, which is then used for computing the velocity from the following relation: v = c/sqrt(1+w/I), where "c" is the speed of light in free space (300 m/us) and "I" is the lightning-current amplitude,</li> <li>'<em>shield</em>': binary indicator that signals presence or absence of the shield wire(s) on the line (0/1), generated from the Bernoulli distribution with a 50% probability,</li> <li>'<em>Ri</em>': average value of the impulse impedance of the tower's grounding (Ohm), generated from the Normal distribution (clipped at zero on the left side) with median value of 50 Ohm and standard deviation of 12.5 Ohm; it should be mentioned that the impulse impedance is often much larger than the associated grounding resistance value, which is why a rather high value of 50 Ohm have been used here,</li> <li>'<em>EGM</em>': electrogeometric model used for analyzing striking distances of the distribution line's tower; following options are available: 'Wagner', 'Young', 'AW', 'BW', 'Love', and 'Anderson', where 'AW' stands for Armstrong & Whitehead, while 'BW' means Brown & Whitehead model; statistical distribution of EGM models follows a user-defined discrete categorical distribution with respective probabilities: p = [0.1, 0.2, 0.1, 0.1, 0.3, 0.2],</li> <li>'<em>ind</em>': indirect stroke model used for analyzing near-by indirect lightning strikes; following options were implemented: 'rusk' for the Rusck's model, 'chow' for the Chowdhuri-Gross model (with Jakubowski modification) and 'liew' for the Liew-Mar model; statistical distribution of these three models follows a user-defined discrete categorical distribution with respective probabilities: p = [0.6, 0.2, 0.2],</li> <li>'<em>CFO</em>': critical flashover voltage level of the distribution line's insulation (kV),</li> <li>'<em>height</em>': height of the phase conductors of the distribution line (m),</li> <li>'<em>flash</em>': binary indicator that signals if the flashover has been recorded (1) or not (0). This variable is the outcome/label (i.e. binary class).</li> </ul> <p>Mathematical background used for the analysis of lightning interaction with the MV distribution line can be found in the references cited below.</p> <p><strong>References</strong>:</p> <ol> <li>A. R. Hileman, "Insulation Coordination for Power Systems", CRC Press, Boca Raton, FL, 1999.</li> <li>J. A. Martinez and F. Gonzalez-Molina, "Statistical evaluation of lightning overvoltages on overhead distribution lines using neural networks," in IEEE Transactions on Power Delivery, vol. 20, no. 3, pp. 2219-2226, July 2005.</li> <li>A. Borghetti, C. A. Nucci and M. Paolone, An Improved Procedure for the Assessment of Overhead Line Indirect Lightning Performance and Its Comparison with the IEEE Std. 1410 Method, IEEE Transactions on Power Delivery, Vol. 22, No. 1, 2007, pp. 684-692.</li> </ol>
Dataset used in the publication entitled "Decomposition by Approximation with Pulse Waves Allowing Further Research on Sources of Voltage Fluctuations"
<p>Dataset obtained from experimental research carried out in the prepared laboratory setup. Based on the dataset, the proposed new decomposition method by approximation with pulse waves has been validated in the publication: Kuwałek P., Decomposition by Approximation with Pulse Waves Allowing Further Research on Sources of Voltage Fluctuations. The description of the prepared laboratory setup is presented in this publication. The research results are part of the work under the project entitled "Voltage fluctuation diagnostic focused on identification and localization disturbing loads in power grids" funded by the National Science Centre, Poland - 2021/41/N/ST7/00397.</p>
H2020 Platone German Demonstrator - Baseline Active Power Exchange at Grid Connection Point (Medium Voltage/Low Voltage)
<p>The given data are computed values for the active power exchange at the medium (MV)/low voltage grid connecting feeder (active power). The data are provided as 15-minutes mean values in kilowatt. The computed indicate the power exchange that would have been measured, in case no use case would have been applied in the field (control of batteries).</p> <p><strong>Data Description:</strong></p> <ul> <li>p_tei_c_mean = arithmetic mean of p_tei computed in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</li> <li>p_tei_c_min = the minimum value (1-minute mean) computed within the period of p_tei_mean (15-minutes)</li> <li>p_tei_c_max = the maximum value (1-minute mean) computed within the period of p_tei_mean period (15-minutes)</li> </ul> <p><strong>Field Test Setup</strong></p> <p>The field test setup of the demonstrator consists of a MV/LV substation, 89 households, 450kW of installed PV generation capacity, a large scale battery with 300 kW and 850 kWh capacity. </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300</p>
Low-Voltage Icing Protection Film for Automotive and Aeronautical Industries
<p>dataset on </p> <p>Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Dynamic Light Scattering</p> <p>FTIR spectroscopy, Thermogravimetric analysis, Differential Scanning Calorimetry,</p> <p>Electro-Temperature Measurement, Thermal Image Camera, Water sorption measurement,</p> <p>Transmission Electron Microscopy and Stress Strain</p>
Dynamics of activation in the voltage-sensing domain of Ci-VSP
<p>This dataset contains unbiased molecular dynamics trajectories, stripped of water and lipid coordinates due to size constraints. It also contains full initial and final structures for all trajectories.</p><h3>Description of the data and file structure</h3><p>The data are deposited as a .tar.gz file: it can be unpacked by running tar -xzvf traj-data.tar.gz.</p><p>The trajectories are organized into two groups (group1 and group2), each containing a folder with stripped_trajs as .xtc files, as well as the full starting and ending coordinates (with waters and lipids) as intial_structs and final_structs. The models for the down--, down, up, and up+ states are in models.</p><p>The parameter file is civsd-amber-top.parm7 and the topology files with and without<br>the waters/lipids are civsd.psf and civsd-pro.psf</p><h3>Sharing/Access information</h3><p>Derived data (collective variables computed from the raw trajectories) are available as supplementary information to the accompanying publication.</p><h3>Code/Software</h3><p>Analysis of the trajectories can be performed using the MDAnalysis, MDTraj, PyEMMA, scikit-learn, and VMD, softwares along with custom codes written in Python/Jupyter notebooks and tcl. Custom codes are available at <a href="https://github.com/dinner-group/ci-vsd">https://github.com/dinner-group/ci-vsd</a>.</p>
Data for "Electroferrofluids with Non-Equilibrium Voltage-Controlled Magnetism, Diffuse Interfaces, and Patterns"
<p>This dataset contains the raw data used for the publication "Electroferrofluids with Non-Equilibrium Voltage-Controlled Magnetism, Diffuse Interfaces, and Patterns".</p>
Datasets for the paper "High-frequency voltage-driven vibrations in dielectric elastomer membranes" by G. Moretti et al.
<p>This upload contains the numerical datasets used for the numerical plots reported in the paper "High-frequency voltage-driven vibrations in dielectric elastomer membranes" by G. Moretti et al., Mechanical Systems and Signal Processing, Elsevier, 2022 (https://doi.org/10.1016/j.ymssp.2021.108677).</p> <p>Please refer to the readme file for information on the files structures and content. </p>
Lightning flashover simulations on medium voltage distribution lines
<p>[Version 1.2] This version of the dataset fixes a bug found in the previous versions (see below for more information).</p> <p>Dataset has been generated from the <strong>Monte Carlo</strong> simulations of <strong>lightning flashovers</strong> on medium voltage (MV) <strong>distribution lines</strong>. It is suitable for training <strong>machine learning</strong> models for classifying lightning flashovers on distribution lines, as well as for line insulation coordination studies. The dataset is hierarchical in nature (see below for more information) and class imbalanced.</p> <p>Following five different types of lightning interaction with the MV distribution line have been simulated: (1) direct strike to phase conductor (when there is no shield wire present on the line), (2) direct strike to phase conductor with shield wire(s) present on the line (i.e. shielding failure), (3) direct strike to shield wire with backflashover event, (4) indirect near-by lightning strike to ground where shield wire is not present, and (5) indirect near-by lightning strike to ground where shield wire is present on the line. Last two types of lightning interactions induce overvoltage on the phase conductors by radiating EM fields from the strike channel that are coupled to the line conductors. Shield wire(s) provide shielding effects to direct, as well as screening effects to indirect, lightning strikes.</p> <p><strong>Dataset</strong> consists of the following variables:</p> <ul> <li>'dist': perpendicular distance of the lightning strike location from the distribution line axis (m), generated from the Uniform distribution [0, 500] m,</li> <li>'ampl': lightning current amplitude of the strike (kA), generated from the Log-Normal distribution (see IEC 60071 for additional information),</li> <li>'veloc': velocity of the lightning return stroke current (m/us), generated from the Uniform distribution [50, 500] m/us,</li> <li>'shield': binary indicator that signals presence or absence of the shield wire(s) on the line (0/1), generated from the Bernoulli distribution with a 50% probability,</li> <li>'Ri': average value of the impulse impedance of the tower's grounding (Ohm), generated from the Normal distribution (clipped at zero on the left side) with median value of 50 Ohm and standard deviation of 12.5 Ohm; it should be mentioned that the impulse impedance is often much larger than the associated grounding resistance value, which is why a rather high value of 50 Ohm have been used here,</li> <li>'EGM': electrogeometric model used for analyzing striking distances of the distribution line's tower; following options are available: 'Wagner', 'Young', 'AW', 'BW', 'Love', and 'Anderson', where 'AW' stands for Armstrong & Whitehead, while 'BW' means Brown & Whitehead model; statistical distribution of EGM models follows a user-defined discrete categorical distribution with respective probabilities: p = [0.1, 0.2, 0.1, 0.1, 0.3, 0.2],</li> <li>'CFO': critical flashover voltage level of the distribution line's insulation (kV); following three levels have been used: 150, 150, and 160 kV, respectively, for three different distribution lines of height 10, 12, and 14 m,</li> <li>'height': height of the phase conductors of the distribution line (m); distribution line has flat configuration of phase conductors with following heights: 10, 12, and 14 m; twin shield wires, if present, are 1.5 m above the phase conductors and 3 m apart; data set consists of 10000 simulations for each line height,</li> <li>'flash': binary indicator that signals if the flashover has been recorded (1) or not (0). This variable is the outcome (binary class).</li> </ul> <p><strong>Note</strong>: It should be mentioned that the critical flashover voltage (CFO) level of the line is taken at 150 kV for the first two lines (10 m and 12 m) and 160 kV for the third line (14 m), and that the diameters of the phase conductors and shield wires for all treated lines are, respectively, 10 mm and 5 mm. Also, average grounding resistance of the shield wire is assumed at 10 Ohm for all treated cases (it has no discernible influence on the flashover rate). Dataset is class imbalanced and consists in total of 30000 simulations, with 10000 simulations for each of the three different MV distribution line heights (geometry) and CFO levels.</p> <p><strong>Important</strong>: Version 1.2 of the dataset fixes an important bug found in the previous data sets, where the column 'Ri' contained duplicate data from the column 'veloc'. This issue is now resolved.</p> <p>Mathematical background used for the analysis of lightning interaction with the MV distribution line can be found in the references below.</p> <p><strong>References</strong>:</p> <p>J. A. Martinez and F. Gonzalez-Molina, "Statistical evaluation of lightning overvoltages on overhead distribution lines using neural networks," in IEEE Transactions on Power Delivery, vol. 20, no. 3, pp. 2219-2226, July 2005, doi: 10.1109/TPWRD.2005.848734.</p> <p>A. R. Hileman, "Insulation Coordination for Power Systems", CRC Press, Boca Raton, FL, 1999.</p>
Code and measurement data - State of charge and state of health diagnosis of batteries with voltage-controlled models
<p><strong>This dataset contains the research data (code and measurement data) of the journal article: <a href="https://doi.org/10.1016/j.jpowsour.2022.231828">J. A. Braun, R. Behmann, D. Schmider, W. G. Bessler, "State of charge and state of health diagnosis of batteries with voltage-controlled models", Journal of Power Sources 544 (2022), 231828</a>.</strong></p> <p> </p> <p><strong>Abstract:</strong><br> The accurate diagnosis of state of charge (SOC) and state of health (SOH) is of utmost importance for battery users and for battery manufacturers. State diagnosis is commonly based on measuring battery current and using it in Coulomb counters or as input for a current-controlled model. Here we introduce a new algorithm based on measuring battery voltage and using it as input for a voltage-controlled model. We demonstrate the algorithm using fresh and pre-aged lithium-ion battery single cells operated under well-defined laboratory conditions on full cycles, shallow cycles, and a dynamic battery electric vehicle load profile. We show that both SOC and SOH are accurately estimated using a simple equivalent circuit model. The new algorithm is self-calibrating, is robust with respect to cell aging, allows to estimate SOH from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.</p> <p> </p> <p><strong>Intellectual property information:</strong><br> The Matlab codes and the research data provided here are under <strong><a href="https://creativecommons.org/licenses/by-nc/4.0/legalcode">CC-BY-NC-4.0</a></strong> license. Please note that the algorithms themselves are subject to industrial property rights, including, but not necessarily limited to, German patent <strong><a href="https://patents.google.com/patent/DE102019127828B4/en">DE102019127828B4</a></strong> and international patent application <strong><a href="https://patents.google.com/patent/WO2021073690A2/en">WO2021073690A2</a></strong>. Any use of the codes and algorithms presented here is subject to these property rights.</p> <p> </p> <p><strong>Overview of files:</strong><br> <strong>SOC_SOH_simple_model.m:</strong> Matlab script performing SOC and SOH diagnosis with the voltage-controlled "simple" equivalent circuit model. The script also reproduces the figures shown in the manuscript.</p> <p><strong>SOC_SOH_simple_extended.m:</strong> Matlab script performing SOC and SOH diagnosis with the voltage-controlled "extended" equivalent circuit model. The script also creates figures of additional data not shown in the manuscript.</p> <p><strong>Experimental_data_fresh_cell.csv:</strong> Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (99 h total with 1 s resolution) of a fresh lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</p> <p><strong>Experimental_data_aged_cell.csv:</strong> Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (85 h total with 1 s resolution) of a pre-aged lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</p> <p><strong>OCV_vs_SOC_curve.csv:</strong> Tabulated experimentally-derived open-circuit voltage (OCV) as function of state of charge (SOC). 1001 data points between SOC = 0 and SOC = 1 in increments of 0.001.</p> <p><strong>readme.txt:</strong> Overview of files with a short description.</p>
Supplement to "Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks"
<p>This deposit contains the scripts and data used in the research article "Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks", which proposed a novel method for electricity demand forecasting in low voltage networks.</p> <p>The scripts are written in the form of R markdown and include additional commentary on the methodology. Both input data and the resulting forecast data and evaluation results are provided, though the latter two may be regenerated by running the scripts.</p>
2746-node Polish Energy System Data of Transmission and Voltage Constraints Contribution to the Formation of LMP
<p>This is the dataset that is used for the original article: "Contribution of Transmission and Voltage Constraints in the Formation of Locational Marginal Prices"</p> <p> </p> <p>MATPOWER is required. The dataset obtained by MATPOWER ver6.0 tool [1].</p> <p>Run run_matpower_acopf.m in MATLAB to start AC OPF. Offers should be saved in the MATLAB search path. Results of AC OPF are saved in bus, branch and gen files. Column names correspond to MATPOWER case file.</p> <p>Price-bonding factors are saved in lambda_P_PBF and lambda_Q_PBF. Ones in MP, MQ columns correspond to marginal nodes for real and reactive power respectively. Ones in CV, CD columns correspond to controlled voltage magnitude and phase respectively. </p> <p>Acronyms in column names:</p> <ul> <li>C(N) - bidding price of a marginal generator at node N </li> <li>Fmax(N) - maximum allowable real power throught line N, where N - index number of a line in branch.csv with binding constraint</li> <li>LAMP_P - LMP for real power</li> <li>LAM_Q - LMP for reactive power</li> <li>PBF - price-bonding factor</li> <li>TC - transmission constraint</li> <li>VC - voltage constraint</li> <li>V(N) - maximum (minimum) allowed voltages at node N</li> </ul>
Sparsification of AP firing in adult-born hippocampal granule cells via voltage-dependent alpha5-GABAA receptors
<p>The ZIP file consists of folders, containing RAW data used for analysis and used to prepare the Figures 1-6 of the paper published in Cell Reports 2021, with the title mentioned above.</p> <p>All recordings were made from acute hippocampal brain slices, obtained from adult C57BL6 mice. Whole-cell voltage-clamp and current-clamp recordings of mature and adult-born young hippocampal granule cells were performed as outlined in methods and recordings were digitized using a power1401 interface from CED (Cambridge Electronic Design, UK), and saved to file using the CFS library support from CED. The contents of the different files is described in File_Description.pdf.</p>
DATASET: Electric Potential Distribution Inside the Electrolyte During High Voltage Electrolysis
<p>This project contains all the data shown in the figures of the manuscript (and the supporting information) entitled:<br> 'Electric Potential Distribution Inside the Electrolyte During High Voltage Electrolysis'<br> (doi:10.26434/chemrxiv-2022-nw4sp).</p> <p>The data to each figure is provided in a subfolder with further information.</p>
Dataset used in the publication entitled "Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids" presented at 2022 20th International Conference on Harmonics and Quality of Power (ICHQP)
<p>Dataset obtained from experimental research carried out in a real power grid. Based on the dataset, the problem of decomposition in identification of sources of voltage fluctuations has been presented in the publication: Kuwałek P., Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids, <em>Proceedings of the 20th International Conference on Harmonics and Quality of Power</em>, IEEE , art. no. 43, 2022, Italy, Naples. The description of the power grid model is presented in this publication. The research results are part of the work under the project entitled "Voltage fluctuation diagnostic focused on identification and localization disturbing loads in power grids" funded by the National Science Centre, Poland - 2021/41/N/ST7/00397.</p>
Reference data set for a Norwegian medium voltage power distribution system
<p>This reference data set describes a representative Norwegian radial, medium voltage (MV) electric power distribution system operated at 22 kV. The data set is developed in the Norwegian research centre CINELDI and will in brief be referred to as the CINELDI MV reference system.</p> <p>Data for a real Norwegian distribution system were provided by a distribution grid company. The data have been anonymized and processed to obtain a simplified but still realistic grid model with 124 nodes. The data set consists of the following three parts:<br> 1. Grid data files: describe the base version of the reference system that represents the present-day state of the grid, including information about topology, electrical parameters, and existing load points.<br> 2. Load data files: comprise load demand time series for a year with hourly resolution and scenarios for the possible long-term development of peak load. These data describe an extended version of the reference system with information about possible new load points being added to the system in the future.<br> 3. Reliability data files: contain data necessary for carrying out reliability of supply analyses for the system.</p> <p>The data set is described in detail in the following data article:<br> I. B. Sperstad, O. B. Fosso, S. H. Jakobsen, A. O. Eggen, J. H. Evenstuen, and G. Kjølle, “Reference data set for a Norwegian medium voltage power distribution system,” Data in Brief, 109025, 2023, doi: 10.1016/j.dib.2023.109025.</p>
Voltage-based strategies for preventing battery degradation under diverse fast-charging conditions
<p>Here are the simulated datasets for the work 'Voltage-based strategies for preventing battery degradation under diverse fast-charging conditions', published in ACS Energy Letters, September 2023. The utilization of these datasets is demonstrated in the associated GitHub project folder https://github.com/zachkonz/Voltage-based-plating-prevention.</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.