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326 results for “electricity data”
Phase field modelling combined with data-driven approach to unravel the orientation influenced growth of interfacial Cu6Sn5 intermetallics under electric current stressing
<p><strong>Description:</strong></p> <p>The datasets are constituted by two folders, namely, (A) data_features_and_metric.zip and (B) grain_area_prediction.zip. </p> <p><strong>(A) data_features_and_metric.zip:</strong></p> <p>The following are the contents of this folder</p> <p>(i) <em>grainTheta.csv file</em> : The "grainTheta.csv" file consists the datasets generated from multiple phase field simulations. Name of the columns in the csv file are:</p> <p> <strong>gnid </strong>= grain id number "n", <strong>ntheta = </strong>orientation angle of n<sup>th</sup> grain (<sup>o</sup>); <strong>nltheta </strong>= orientation angle of grain to the left of n<sup>th</sup> grain (<sup>o</sup>); <strong>nrtheta </strong>= orientation angle of grain to the right of n<sup>th</sup> grain (<sup>o</sup>); <strong>j </strong>= current density (A/m<sup>2</sup>);<strong> t =</strong> time (s); <strong>area</strong> = area of n<sup>th</sup> grain (m<sup>2</sup>);<strong> tl</strong> = horizontal length of the top edge of grain "n" (m) ; <strong>bl </strong>= horizontal length of the bottom edge of grain "n" (m) </p> <p>The features gnid, ntheta, nltheta and nrtheta for a given observation are determined during the design of initial conditions of the corresponding phase field simulation. The value of "j" for the observation is determined via the boundary condition in the same numerical simulation. The result from the finite element method based phase field simulation has provided the numerical quantities for t, area, tl and bl attributes. The multiple observations in the data file have been obtained from multiple phase field simulations. </p> <p>(ii) <em>imc_theta.ipynb, imc_theta.py and imc_theta.html files</em>: These files contain the code to build the Pearson's Correlation Coefficient (PCC) heatmap analysis of the data contained in grainTheta.csv file. </p> <p>(iii) <em>comparison_mse.csv</em>: This data file includes the information about mean square error for training data (tmse) and mean square error for validation data (vmse) at Epoch = 199 resulting from 10 different artificial neural network (ANN) models distinguished by 10 different values of learning rates (lr) . Thus, the name of the columns in this csv file are <strong>modelno</strong>, <strong>lr</strong>, <strong>tmse</strong> and <strong>vmse</strong>. </p> <p>(iv) <em>mse_comparison.gnu</em>: This file consists the codes required to output a png image from the data provided in comparison_mse.csv<em>. </em></p> <p>(v) train_loss.csv and val_loss.csv: These files consist of the data of tmse and vmse at all points of Epochs for the ANN model with lr = 2.5E-4 . Thus, the first column in train_loss.csv file corresponds to tmse whereas the second column is Epochs number. Similarly, vmse and Epochs represent the two columns in val_loss.csv file. </p> <p> </p> <p>(vi) <em>mse_lr2p5e-4.gnu</em> : This file consists the codes required to output a png image from the data provided in train_loss.csv and val_loss.csv<em>. </em></p> <p><strong>(B) grain_area_prediction.zip:</strong></p> <p>Inside this folder, there is a folder named "prediction_of_grain_area" consisting of the following files:</p> <p><em>initial_area.csv file</em>: This file consists the value of the initial grain area of grain 4. It is a constant at all orientation angle.</p> <p><em>predicted_result_00_5e4.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 0<sup>o</sup> and 0<sup>o </sup>respectively, and for applied current density of 5.0E+4 J/m<sup>2</sup> .</p> <p><em>predicted_result_00_5e5.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 0<sup>o</sup> and 0<sup>o </sup>respectively, and for applied current density of 5.0E+5 J/m<sup>2</sup> .</p> <p><em>predicted_result_9090_5e4.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 90<sup>o</sup> and 90<sup>o </sup>respectively, and for applied current density of 5.0E+4 J/m<sup>2</sup> .</p> <p><em>predicted_result_9090_5e5.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 90<sup>o</sup> and 90<sup>o </sup>respectively, and for applied current density of 5.0E+5 J/m<sup>2</sup> .</p> <p><em>area_00_adj.gnu</em> : This gnu file contains the code to produce the png image from the data contained in <em>predicted_result_00_5e4.csv </em>and<em> predicted_result_00_5e5.csv </em>. The information about the initial area of grain 4 is obtained from <em>initial_area.csv</em> file by the code.</p> <p><em>area_9090_adj.gnu</em> : This gnu file contains the code to produce the png image from the data contained in <em>predicted_result_9090_5e4.csv </em>and<em> predicted_result_9090_5e5.csv </em>. The information about the initial area of grain 4 is obtained from <em>initial_area.csv</em> file by the code.</p> <p> </p>
Data_Discrimination between the effects of pulsed electrical stimulation and electrochemically conditioned medium on human osteoblasts
<p>This publication contains the original data supporting our results concerning the application of biphasic pulsed electrical stimulation on MG-63 cells. We aim to discriminate between effects of the electric field and electrochemically conditioned medium. The dataset consists of the original output files, excel sheets to consolidate the information for the experiments as well as Readme.txt files with most important information concerning the data. For additional information on the material and methods as well as results see the corresponding research article.</p>
Norwegian hourly residential electricity demand data with consumer characteristics during the European energy crisis
<p>This dataset was collected to understand how Norwegian households responded to the electricity price shock due to the European energy crisis. It consists of consumer characteristics and their self-reported responses to the extraordinarily high electricity prices which were collected by a survey of 4,446 consumers. The consumer characteristics contain information about socio-demographics such as income, age, education, number of residents, residence type, residence size, and how conscious the respondents are about their electricity consumption. Furthermore, major electricity-consuming appliances are identified, such as whether the residents have an electric vehicle and how they heat their homes, and if they have a variable electricity tariff. In addition, hourly metered electricity consumption data covering October 2020 to March 2022 from a subset of 1,136 residential consumers of the surveyed households and the total hourly residential electricity consumption per Norwegian bidding area from July 2019 to July 2022as well as the hourly day-ahead electricity prices are included in the dataset. These data are interesting to researchers that aim to gain insight into the electricity consumption behaviour of the residential sector and the impact of different socio-demographic variables.</p> <p>A detailed description is available as a data article in Data in Brief: <a href="https://www.sciencedirect.com/science/article/pii/S2352340923007667">Norwegian hourly residential electricity demand data with consumer characteristics during the European energy crisis - ScienceDirect</a></p> <p>Supplementary figures containing the survey results are available here: <a href="../records/11580541">Supplementary result diagrams from household surveys on implicit demand response (zenodo.org)</a></p> <p>Survey answers in Norwegian are available here: <a href="https://zenodo.org/records/15063303">iFleks-prosjekt: Spørreundersøkelser med husholdninger og næringsliv om forbruksrespons på elektrisitetspriser</a></p>
Data from: KCNB1-Leptin receptor complexes couple electric and endocrine function in the melanocortin neurons of the hypothalamus
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Data for: Isolation of targeted hypothalamic neurons for studies of hormonal, metabolic, and electrical regulation
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Data from: Electrical transport in tunably-disordered metamaterials
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Data from: Electrical imaging of cortical and spinal axons using high-density microelectrode arrays
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Data from: Do ditch-side electrical fences improve the breeding productivity of ground-nesting waders?
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Data from: Electrical properties of alkaline earth sulfides and implications for the interior of mercury
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Data from: Precise control of neural activity using dynamically optimized electrical stimulation
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Data from: Internal injuries in marine fishes caught in beam trawls using electrical versus mechanical stimulations
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Data from: Gill evolution in Neotropical electric fishes: Comparative phylogenetic evidence for hypoxia-driven adaptation
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Data from: A hierarchical model for external electrical control of an insect, accounting for inter-individual variation of muscle force properties
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Data from: US-Mexico second-hand electric vehicle trade: Battery circularity and end-of-life policy implications
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Data from: Organic electro-scattering antenna: Wireless and multisite probing of electrical potentials with high spatial resolution
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Data from road demonstration of a PEMFC range extender for electric buses
<p>Prototype will comprise the construction of a range extender powered by hydrogen fuel cells. The range extender will be tested in an emulated relevant environment with long-term and accelerated tests. Robust hybridisation strategies will be developed to interface buses with range extenders</p>
STEG data of manuscript "Electrical Generation of a Ground Level Solar Thermoelectric Generator: Experimental Tests and One-year Cycle Simulation" submitted to Energies
<p>Figure_7_data: laboratory data of TEG output power working at low temperature differences. Data used in Figure 7 of manuscript "Electrical Generation of a Ground Level Solar Thermoelectric Generator: Experimental Tests and One-year Cycle Simulation" submitted to Energies.</p> <p>Figure_9_data: experimental data of TEG temperature differences from July 24 to July 31, 2017. Data used in Figure 9 of manuscript "Electrical Generation of a Ground Level Solar Thermoelectric Generator: Experimental Tests and One-year Cycle Simulation" submitted to Energies.</p> <p>Figures_11_14_data: input and output data of the STEG model. One-year cycle data. Used to obtain figures 11 to 14 of manuscript "Electrical Generation of a Ground Level Solar Thermoelectric Generator: Experimental Tests and One-year Cycle Simulation" submitted to Energies</p>
Result data related to "Tröndle (2020) -- Supply-side options to reduce land requirements of fully renewable electricity in Europe"
<p>The dataset contains the optimisation model and aggregated result data of my study. See `README.md` for more information.</p> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T. (2020). Supply-side options to reduce land requirements of fully renewable electricity in Europe. PLOS ONE.</p> </blockquote>
Data related to Sasse et al. (2020) "Regional impacts of electricity system transition in Central Europe until 2035"
<p>This is the accompanying dataset for the publication "Regional impacts of electricity system transition in Central Europe until 2035". </p> <p>The dataset contains the following input data files:</p> <ul> <li><strong>Capacity_Factor_EXPANSE.csv</strong>: annual capacity factors per NUTS-3 region and electricity generation technology, used by the EXPANSE model, in MWh/MWh</li> <li><strong>Demand_EXPANSE.csv</strong>: annual electricity demand per NUTS-3 region, used by the EXPANSE model, in MWh and in TWh</li> <li><strong>LCOE_EXPANSE.csv</strong>: levelized electricity generation costs per NUTS-3 region and electricity generation technology, used by the EXPANSE model, in EUR/MWh</li> <li><strong>Emax_EXPANSE.csv</strong>: maximum annual electricity generation potential per NUTS-3 region and electricity generation technology, used by the EXPANSE model, in MWh</li> <li><strong>Emin_EXPANSE.csv</strong>: minimum annual electricity generation potential per NUTS-3 region and electricity generation technology, used by the EXPANSE model, in MWh</li> <li><strong>Costs_PyPSA.csv</strong>: Cost assumptions, used by the PyPSA model, units defined in units column</li> </ul> <p>The dataset contains the result data files:</p> <ul> <li><strong>Aggregated_Impacts.csv</strong>: Aggregated impacts for all MGA scenarios and distinct scenarios, units defined in each column</li> <li><strong>Regional_Impacts.csv</strong>: Regional impacts for all distinct scenarios, units defined in each column</li> </ul>
the supplemental data of 'The frequency-dependent effect of electrical fields on the mobility of intracellular vesicles in astrocytes'
<p>This data set is the supplemental data of the manuscript ‘The frequency-dependent effect of electrical fields on the mobility of intracellular vesicles in astrocytes’. Due to the size limitation of online storage, this data set contains partial data collected in this work, which consists of one experiment per condition. The full data that support the findings of this study are available from the corresponding author upon reasonable request.</p>
ScienceDex guides
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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.