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213 results for “electric field”
Data set for "Electrical detection of the flat band dispersion in van der Waals field-effect structures"
<p>Data set for "Electrical detection of the flat band dispersion in van der Waals field-effect structures" paper published in doi:10.1038/s41565-023-01489-x</p>
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>
Datasets and code for "Multi-site transfer function approach for real-time modeling of the ground electric field induced by laterally-nonuniform ionospheric source" by Kruglyakov et al. (2023)
<ol> <li>Archive calculate_weights_for_rt.tgz contains the code for calculation of weights used for computation of electric fields based on multi-site transfer function approach following Kruglyakov et al. (2023). The code is written in Fortran 2003 and the only external dependency is LAPACK/BLAS -compatible library, for example OpenBLAS from https://www.openblas.net. See READ.ME for details.</li> <li>Files GICs*.dat contain observed and modelled geomagnetically induced currents (GICs) at Mäntsälä compressor station in southern Finland (60.6 N, 25.2 E) (https://space.fmi.fi/gic/) for three events (in 2000, 2001, and 2003).</li> <li>Files E_x*. E_y* contain corresponding components of measured (detrended and downsampled from 1s to 10s) and modeled electric fields at sites M02 and M05 from 05:15 to 06:15 UT, 11 Sep 2005.</li> <li>Files MS_TF*.dat contain multi-site transfer functions for different sets of IMAGE magnetometers (based on the data availability during the simulated events) in the frequency domain and the corresponding weights for calculation of electric field in the time domain.</li> <li>File E_to_GICs_W.dat contains coefficients for computation of GICs at Mäntsälä station from electric fields at 18 sites used in the simulation. See Equation (15) of Kruglyakov et al. (2023) for details.</li> </ol> <p> </p> <p> </p>
Percutaneous Electrical Nerve Field Stimulation for Adults With Irritable Bowel Syndrome
ClinicalTrials.gov study NCT04428619. IPD Sharing: NO. Countries: 1. Publications: 2.
Auricular Percutaneous Electrical Nerve Field Stimulation for Postoperative Pain Control in Adults
ClinicalTrials.gov study NCT02892513. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Electric Field Navigated 1hz Rtms for Post-stroke Motor Recovery Trial
ClinicalTrials.gov study NCT03010462. IPD Sharing: NO. Countries: 1. Publications: 1.
Navigating the 16-dimensional Hilbert space of a high-spin donor qudit using magnetic and electric fields
Open the record for dataset details and reuse information.
The electrical activity of Saharan dust as perceived from surface electric field observations: Field Mill Datasets
<p>Electric Field Mill time series used for the reproduction of the results in the following manuscript:</p> <p><strong>"The electrical activity of Saharan dust as perceived from surface electric field observations" - accepted in ACP</strong></p> <p>Authors: Vasiliki Daskalopoulou, Sotirios A. Mallios, Zbigniew Ulanowski, George Hloupis, Anna<br> Gialitaki, Ioanna Tsikoudi, Konstantinos Tassis and Vassilis Amiridis</p>
GROMACS simulations of unfolding of ubiqutin in a strong electric field
<p>The simulations were performed in GROMACS 4.6.7. The dataset contains binary run input files (.tpr) and simulated trajectory files (.trr). Here are 100 statistically independent runs for the field values: E = 3e4 V/nm, E = 5e4 V/nm, E = 7e4 V/nm, E = 11e4 V/nm. The names of files denote the value of the electric field in [V/nm] and the number of independent runs.</p>
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>
Ferroelectricity in a nematic liquid crystal under a direct current electric field - dataset
<p>The presented data were used in the following publication: Mrukiewicz, M., Perkowski, P., Karcz, J. & Kula, P. Ferroelectricity in a nematic liquid crystal under a direct current electric field. Phys. Chem. Chem. Phys. 25, 13061–13071 (2023).</p>
Data of "Modulating Electric Field Distribution by Alkali Cations for CO2 Electroreduction in Strongly Acidic Medium"
<p>Data of the paper "Modulating Electric Field Distribution by Alkali Cations for CO2 Electroreduction in Strongly Acidic Medium"</p>
Selected Electric Field Measurement Campaigns data
<p>This presents the selected electric field measurement data acquired under several measurement campaigns in indoor and outdoor environments. </p> <p>This work was supported by the EU project 5GRFEX entitled – ‘Metrology for RF exposure from Massive MIMO 5G base station: Impact on 5G network deployment’ (this project has received funding from the support for impact (SIP) programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme), under European Association of National Metrology Institutes (EURAMET) Reference 18SIP02.</p> <p> </p>
Data from: Laminar microcircuitry of visual cortex producing attention-associated electric fields
<p>Cognitive operations are widely studied by measuring electric fields through EEG and ECoG. However, despite their widespread use, the neural circuitry giving rise to these signals remains unknown because the functional architecture of cortical columns producing attention-associated electric fields has not been explored. Here, we detail the laminar cortical circuitry underlying an attention-associated electric field measured over posterior regions of the brain in humans and monkeys. First, we identified visual cortical area V4 as one plausible contributor to this attention-associated electric field through inverse modeling of cranial EEG in macaque monkeys performing a visual attention task. Next, we performed laminar neurophysiological recordings on the prelunate gyrus and identified the electric-field-producing dipoles as synaptic activity in distinct cortical layers of area V4. Specifically, activation in the extragranular layers of cortex resulted in the generation of the attention-associated dipole. Feature selectivity of a given cortical column determined the overall contribution to this electric field. Columns selective for the attended feature contributed more to the electric field than columns selective for a different feature. Last, the laminar profile of synaptic activity generated by V4 was sufficient to produce an attention-associated signal measurable outside of the column. These findings suggest that the top-down recipient cortical layers produce an attention-associated electric field that can be measured extracortically with the relative contribution of each column depending upon the underlying functional architecture.</p>
Perylenetetracarboxylic Acid Nanosheets with Internal Electric Fields and Anisotropic Charge Migration for Photocatalytic Hydrogen Evolution
<p> Source data for Perylenetetracarboxylic Acid Nanosheets with Internal Electric Fields and Anisotropic Charge Migration for Photocatalytic Hydrogen Evolution</p>
Effects of radial electrical field on kinetic ballooning mode in toroidal plasma
<p>Figures in the paper titled ‘Effects of radial electrical field on kinetic ballooning mode in toroidal plasma’</p>
Wide-field optical imaging of electrical charge and chemical reactions at the solid-liquid interface
<p>Data availability for silica measurements</p>
Data for "A New Method of Three-Dimensional Location for Low-frequency Electric Field Detection Array"
<p>In a manuscript entitled “A New Method of Three-Dimensional Location for Low-frequency Electric Field Detection Array”, the lightning location data obtained by the low-frequency electric field detection array (LFEDA) were analyzed. The data of our results are including in the Data_for_Fig_0x.ogw. These files can be opened by Origin 9.0(or later). The data supports the aforementioned manuscript and can be used freely for scientific purposes with appropriate citation.</p>
Dataset for the manuscript "pH drives electron density fluctuations that enhance electric field-induced liquid flow"
Open the record for dataset details and reuse information.
Data for "Effect of Electric Fields on the Decomposition of Phosphate Esters"
<p>Data from nonequilibrium molecular dynamics simulation of tri(n-butyl)phosphate confinid between two iron oxide surfaces at 1100 K, with and without an electric field, from "Effect of Electric Fields on the Decomposition of Phosphate Esters". Other conditions, and simulations with nascent iron surfaces, are available on request.</p> <div> <p>The data contains the reaxff bonding information from reaxff (bonds_*.txt), the trajectories (dump_*.lammpstrj). Files with the name "comp" are used for production run calculations.</p> </div>
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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)
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.