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68 results for “electrostatics”
Data from: An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles
<p><strong> Data from: An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles</strong></p> <p>- Authors: Pedro Jimenez, Luis Chacon, Mario Merino</p> <p>- Contact email: pejimene@ing.uc3m.es</p> <p>- Date: 2024-02-09</p> <p>- Keywords: electric propulsion, plasma simulation, magnetic nozzles, implicit particle-in-cell (PIC)</p> <p>- Version: 1.2</p> <p>- Digital Object Identifier (DOI): 10.5281/zenodo.8081962</p> <p>- License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0">Open Data Commons Attribution License</a></p> <p><strong>Abstract</strong></p> <p>This dataset contains the data found in the plots of the journal article:</p> <p><a href="https://www.sciencedirect.com/science/article/pii/S0021999124000755?via%3Dihub">Pedro Jimenez, Luis Chacon, Mario Merino, "An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles"</a></p> <p>The data in this repository are the results of kinetic plasma simulations as described in the reference. For further information on the setup for the simulation please refer to the article.</p> <p><strong>Data Files</strong></p> <p>The data files are in .csv format. They were produced in Julia using <a href="http://csv.juliadata.org/stable/)">CSV.jl</a> and <a href="https://dataframes.juliadata.org/stable/">DataFrames.jl</a> libraries.</p> <p>The files are organised following the order of the figures in the article. All the plots are 1D series, the first column corresponding to the x-axis data. Y-axis data is presented in the following columns, the total number of additional columns is equal to the number of line series. The title of each series is found in the first row of the .csv files. Please find below some specificalities in certain figures:</p> <p>- The columns for the time evolution in <strong>fig6_left.csv</strong> and<strong> fig6_right.csv </strong>(corresponding to the actual left and right columns in the figure i.e. cases A and B) contain a field tag followed by the corresponding time step (e.g. phi_500).</p> <p>- Due to the different number of nodes, steady state fields for cases A and B are saved in <strong>fig8_a-f.csv</strong> while cases AF and BF are saved in <strong>fig8_a-f_fine.csv</strong>.</p> <p>The rest of the data files should be self descripting</p> <p><strong>Citation</strong></p> <p>Any works using this dataset or any part of it in any form shall cite it as follows:</p> <p>The prefered means of citation is to reference the publication asociated to the jounal article with DOI: <a href="https://doi.org/10.1016/j.jcp.2024.112826">10.1016/j.jcp.2024.112826</a></p> <p>The BibTex is also provided for the sake of convinience:</p> <pre>@article{jimenez2024implicit, title={An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles}, author={Jim{\'e}nez, Pedro and Chac{\'o}n, Luis and Merino, Mario}, journal={Journal of Computational Physics}, pages={112826}, year={2024}, publisher={Elsevier} }</pre> <p>Optionally the dataset can be cited by referencing the corresponding DOI:</p> <p><a href="https://doi.org/10.5281/zenodo.8081962">https://doi.org/10.5281/zenodo.8081962</a></p> <p><strong>Acknowledgments</strong></p> <p>This dataset was created by the [ERC-ZARATHUSTRA project](https://erc-zarathustra.uc3m.es/).</p> <p>The ERC-ZARATHUSTRA project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 950466).</p>
Molecular mechanism for the synchronized electrostatic coacervation and co-aggregation of alpha-synuclein and tau
<p><strong><em>The following metadata refers exclusively to electron paramagnetic resonance (EPR) measurements, which represent the contribution of the PARACAT students to this work</em></strong></p> <ul> <li><strong>Data type</strong>: EPR spectroscopic measurements and simulations</li> <li>Files are in <strong>.DTA, .DSC, .m, .mat, and .xlxs, </strong>formats</li> <li>Information on <strong>origin of the data</strong>: <ul> <li>EPR spectroscopic measurements in <strong>.DTA </strong>and<strong> .DSC</strong> formats</li> <li>EPR spectroscopic simulation and analyses in .<strong>m </strong>and<strong> .mat</strong> format</li> <li>“Ready-to-plot”, processed EPR spectra are in <strong>.xlxs</strong> format.</li> </ul> </li> <li>The data are <strong>generated</strong> by: <ul> <li>CW-EPR measurements were performed with a Bruker ELEXSYS E580 X-band spectrometer equipped with a Bruker ER4118 SPT-N1 resonator operating at a microwave (MW) frequency of ∼9.7 GHz. The temperature was set to 25 °C and controlled by a liquid nitrogen cryostat.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP3_20221219_EPR </strong>folder includes EPR spectroscopic measurements and computer simulations/analyses, original data are in <strong> .DTA/.DSC</strong> formats; files in .<strong>m</strong> format were used to process the data.</li> </ul> </li> </ul> <p>NB. See the “READ ME” text file for more detailed information on files organization.</p> <p> </p> <ul> <li><strong>Information on</strong>: <ul> <li>Abbreviations: <ul> <li><strong>avg</strong> = averaged</li> <li><strong>aS_24</strong> = alpha-synuclein protein with TEMPOL spin label at position 24 of the polypeptidic chain</li> <li><strong>aS_122</strong> = alpha-synuclein protein with TEMPOL spin label at position 122 of the polypeptidic chain</li> <li><strong>pLK</strong> = poly-lysine</li> <li><strong>Tau441</strong> = Tau protein with complete amino-acid sequence</li> <li><strong>Tau_DNt</strong> = truncated Tau protein lacking N-terminal (see paper methods for further details)</li> </ul> </li> <li>Units of measurement: <ul> <li>Temperature: <strong>°</strong><strong>C</strong> (Celsius)</li> <li>Microwave Frequency: <strong>GHz</strong> (Giga-Hertz), <strong>MHz</strong> (Mega-Hertz), <strong>kHz</strong> (kilo-Hertz)</li> <li>Microwave Power: <strong>mW</strong> (milli-Watt)</li> <li>Magnetic Field: <strong>mT</strong> (milli-Tesla)</li> <li>Time: <strong>s</strong> (seconds)</li> <li>Concentration: <strong>μM</strong> (micro-Molar), <strong>% w/v</strong> (percentage weight-volume)</li> </ul> </li> </ul> </li> </ul>
FIB cleaned and reshaped Electrostatic electron vortex beam generators - dataset2
<p>Here is a second set of SEM images of Electrostatic electron vortex beam generators fabricated at the CNR-IMM in Bologna and FIB cleaned and reshaped at the CNR-NANO in Modena. The difference between this batch of devices and the first one is in the thickness of the electrode (the z-dimension), this batch has a electrode thickness of 30um, while the first batch is 10um thick.</p>
FIB cleaned an reshaped Electrostatic Electron Vortex Beam Generators
<p>Here are collected the SEM images (top-view and tilted-view) of the Electrostatic Electron Vortex Beam Generators fabricated at the CNR-IMM in Bologna and then cleaned and reshaped by FIB milling at the CNR-NANO in Modena. Different shapes of the main paired needles have been fabricated to try to optimize the reproduction the ideal phase profile as reported in https://doi.org/10.1103/PhysRevResearch.2.013185</p>
Quantitative electrostatic force tomography for virus capsids in interaction with an approaching nanoscale probe
<p>This repository contains the simulated data of a simple electrostatic model, based on the Poisson-Boltzmann equation, that quantifies the subnanometric electrostatic interactions between an AFM tip and a proteinaceous capsid (Zika Virus) from molecular snapshots. This allows us to describe the contributions of specific amino acids and atoms to the interaction force.</p> <p>The contains of this repository can be easily visualized through Jupyter Notebooks contained here:</p> <p>https://github.com/pyF4all/eTipVirusForce</p>
Research data in support of: An Interplay of Mechanical and Structural Properties of DNA Determines Its Electrostatic Interactions with Lipids
<p>The data collected and reported for the publication entitled: An Interplay of Mechanical and Structural Properties of DNA Determines Its Electrostatic Interactions with Lipids. by Diana Morzy et al.</p> <p>Data is divided by the technique used, with folders named accordingly. Each dataset has a readme file, explaining the basic technical details, as well as how to open each file type.</p> <p>Please do not hesitate to contact the corresponding author (MMCB) for further details.</p>
Supplementary files for paper: "Design and fabrication of an electrostatic precipitator for infrared spectroscopy" in Atmospheric Measurement Techniques, 2022.
<ol> <li>File of absorbance spectra and hypothetical thickness for each sample.</li> <li>MATLAB function to perform clean crystal spectrum subtraction and baseline correction (described in the paper).</li> </ol>
Datasets from "Electrostatic Embedding of Machine Learning Potentials"
<p>Data required to reproduce results in "Electrostatic Embedding of Machine Learning Potentials" <a href="https://doi.org/10.26434/chemrxiv-2022-rknwt">article</a>. See <a href="https://github.com/emedio/embedding">https://github.com/emedio/embedding</a> for details.</p> <ul> <li>QM7_B3LYP_cc-pVTZ.tgz - outputs of single point B3LYP/cc-pVTZ calculations of structures in <a href="http://quantum-machine.org/data/qm7.mat">QM7 dataset</a> with ORCA 5. Include molecular dipolar polarizabilities.</li> <li>QM7_B3LYP_cc-pVTZ_horton.tgz - MBIS partitioning of the B3LYP/cc-pVTZ densities with <a href="https://github.com/theochem/horton">Horton 2.1.0</a>.</li> <li>mpro_xyz.tgz - coordinates of the ligand and surrounding point charges from 100 snapshots of SARS-CoV-2 Mpro complex with PF-00835231.</li> <li>mpro_*.tgz - DFT and semiempirical single point calculations with ORCA 5 for the coordinates from mpro_xyz.tgz, <em>in vacuo </em>and in presence of point charges.</li> <li>mlmm.mat - learned parameters and SOAP feature vectors of reference atomic environments</li> </ul>
Cold Ion Measurements Enabled by Electrostatic Instrument Biasing: Implementation and Modeling Results
<p><strong>Cold Ion Measurements Enabled by Electrostatic Instrument Biasing: Implementation and Modeling Results </strong></p> <p> </p> <p>This archive contains the data file for the five CPIC simulations run for paper Larsen et al 2019. [1, 2]. The data files contents and format are described.</p> <p> </p> <p>Each data file is stored in HDF5 format written with h5py [3, 4].</p> <p>The files named field_potential_bias_XX_data.h5 contain the electric field components and electric potential with the following structure:</p> <blockquote> <p>+</p> <p>:|____Author (str [50])</p> <p>:|____Bias (str [3])</p> <p>:|____DOI (str [22])</p> <p>:|____Date (str [14])</p> <p>:|____Description (str [81])</p> <p>:|____File_Creator (str [44])</p> <p>:|____License (str [2140])</p> <p>|____E (h5py._hl.dataset.Dataset (3, 101, 101, 101))</p> <p> :|____Description (str [41])</p> <p> :|____Format (str [94])</p> <p> :|____Units (str [3])</p> <p>|____XYZ (h5py._hl.dataset.Dataset (3, 101))</p> <p>|____phi (h5py._hl.dataset.Dataset (101, 101, 101))</p> <p> </p> </blockquote> <p>See the metadata within the file for units of each variable.</p> <p> </p> <p>The files named detector_bias_XX_data.h5 contain the particles collected at the simulated detector in a mix of physical and CPIC units with the following structure:</p> <blockquote> <pre>+</pre> <pre>:|____Author (str [50])</pre> <pre>:|____Bias (str [4])</pre> <pre>:|____DIO (str [22])</pre> <pre>:|____Date (str [14])</pre> <pre>:|____Description (str [81])</pre> <pre>:|____Detector_area (str [11])</pre> <pre>:|____File_Creator (str [44])</pre> <pre>:|____License (str [2140])</pre> <pre>:|____Timestep (str [12])</pre> <pre>:|____Timestep_description (str [114])</pre> <pre>:|____electron_temperature (str [6])</pre> <pre>:|____photoemission_electron_energy (str [4])</pre> <pre>:|____potential_at_boundary (str [3])</pre> <pre>:|____proton_electron_mass_ratio (str [4])</pre> <pre>:|____proton_temperature (str [7])</pre> <pre>:|____reference_density (str [9])</pre> <pre>|____Energy (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [32])</pre> <pre> :|____Units (str [2])</pre> <pre>|____Velocity (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [59])</pre> <pre> :|____Units (str [3])</pre> <pre>|____Velocity_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [59])</pre> <pre> :|____Units (str [4])</pre> <pre>|____Vx (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [3])</pre> <pre>|____Vx_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [4])</pre> <pre>|____Vy (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [3])</pre> <pre>|____Vy_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [4])</pre> <pre>|____Vz (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [3])</pre> <pre>|____Vz_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [4])</pre> <pre>|____phi (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [41])</pre> <pre> :|____Units (str [3])</pre> <pre>|____q (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [47])</pre> <pre> :|____Units (str [4])</pre> <pre>|____species (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [26])</pre> <pre>|____theta (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [37])</pre> <pre> :|____Units (str [3])</pre> <pre>|____timestep (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [41])</pre> <pre>|____weight (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [38])</pre> <pre> :|____Units (str [4])</pre> </blockquote> <p> </p> <p>The information in parentheses are the datatype and size, square brackets, [], denote a single element of that many characters and parentheses, (), denote the stored array size. Lines with a leading colon, :, are metadata fields to make the data files more usable.</p> <p> </p> <p>1. Delzanno, G.L., et al., <em>CPIC: a curvilinear particle-in-cell code for plasma–material interaction studies.</em> IEEE Transactions on Plasma Science, 2013. <strong>41</strong>(12): p. 3577-3587.</p> <p>2. Meierbachtol, C.S., et al., <em>An electrostatic Particle-In-Cell code on multi-block structured meshes.</em> Journal of Computational Physics, 2017. <strong>350</strong>: p. 796-823.</p> <p>3. Folk, M., A. Cheng, and K. Yates. <em>HDF5: A file format and I/O library for high performance computing applications</em>. in <em>Proceedings of supercomputing</em>. 1999.</p> <p>4. Collette, A., et al., <em>h5py/h5py 2.9.0.</em> 2018.</p> <p> </p> <p> </p>
Imaging the Breaking of Electrostatic Dams in Graphene for Ballistic and Viscous Fluids
<p>These datasets can be used to reproduce the experimental and theoretical results in the manuscript "Imaging the Breaking of Electrostatic Dams in Graphene for Ballistic and Viscous Fluids," and corresponding supplementary material. </p> <p>"STP-measurement1" and "STP-measurement2" contain the raw scanning tunneling potentiometry (STP) data used to generate Figures 1-4 in the main text and Figures S2-5 in the supplement. Each ".dat" file contains the tunneling I-V curve used to solve for the electrochemical potential at a single pixel in each STP dataset. </p> <p>"Estimation of c_G.nb" contains the raw Mathematica code to generate Figure 5 in the main text and Figures S7-8 in the supplement.</p> <p>"device-resistance_gate-sweep_T=4.5K.txt" and "device-resistance_gate-sweep_T=77K.txt" can be used to reproduce Figure S1 in the supplement. </p> <p> </p>
Data from: Emergent electrostatics in planar XY spin models: The bridge connecting topological order with broken U(1) symmetry
Open the record for dataset details and reuse information.
Underlying data for "The role of electrostatics in enzymes: do biomolecular force fields reflect protein electric fields?"
<p>This dataset contains code, data, trajectories, and figures used in the article "The role of electrostatics in enzymes: do biomolecular force fields reflect protein electric fields?".</p> <p> </p> <p>Contents:</p> <p>code/* - Code used to calculate electric fields from simulation trajectories with either polarizable or additive force fields</p> <p>data/* - Electric fields calculated for the CypA WT cis, WT trans, R55A cis, and R55A trans systems, with AMOEBA, Amber, or Charmm force fields. Each subfolder also includes a set of structural coordinates extracted at 2.5 ns intervals from the first simulation trajectory and used to calculate ONETEP DFT electric fields.</p> <p>figures/* - Underlying data and scripts used to create all figures and movies used in the article.</p> <p>trajectories/* - Simulation trajectories of the CypA WT cis, WT trans, R55A cis, and R55A trans systems</p> <p> </p> <p>Where appropriate, README files include instructions for regenerating data used in the article, and details of the Python packages and other software used to generate data are available in Dependencies.txt</p>
The linear dispersion relation of the electrostatic waves in magnetized anisotropic dysty plasma
<p>The linear and weakly nonlinear dust-ion-acoustic wave propagation obliquely with respect to an external magnetic field is studied in magnetized dusty plasma, which consists of magnetized fluid ions bearing with anisotropic pressure, suprathermal electrons, and static dust grains. In the linear regime, the magnetized dusty plasma supports the propagation of fast dust-ion-cyclotron (EDIC) mode and the slow electrostatic dust-ion-acoustic (DIA) mode.</p>
Development of Ensemble Steric and Electrostatic Chirality (ESEC) descriptors for modelling chromatographic enantioseparations
<p>For example for the Excel file with the title "Borate_chiral_explicit_biased_charged_uncharged.xlsx":</p> <p>- In the first tab, titled "Exp_water_acn_biased_charge", you can find the biased chiral descriptors for the molecules in their pH 9 state, simulated in explicit solvent. </p> <p>- In the second tab, titled "Exp_water_acn_biased_uncharged", you can find the biased chiral descriptors for the molecules in their uncharged state, simulated in explicit solvent. </p> <p>- The third tab, titled "Chiral log alfaRS charge uncharge", includes the responses (log αRS and αRS), along with the retention times and retention factors (k) for each molecule. In addition, this tab contains the descriptors from both the first and the second tab.</p> <p>- The fourth and the fifth tabs present the experimental and predicted responses (log αRS in the fourth tab and αRS in the fifth tab) for the models that were built. </p> <p>The structure of the other files follows a similar pattern: the first tabs provide the descriptor values, followed by a tab containing the modelling information (response(s) and various descriptor sets) and finally, a tab is included with the predicted response values. </p>
Research Data Supporting "Electrostatic co-assembly of nanoparticles with oppositely charged small molecules into static and dynamic superstructures"
<p>This repository contains the set of data shown in the paper "<strong>Electrostatic co-assembly of nanoparticles with oppositely charged small molecules into static and dynamic superstructures</strong>", published on <strong>Nature Chemistry </strong>(DOI: 10.1038/s41557-021-00752-9).</p> <p>The files in the folders are organized as follow:</p> <p><strong>AA_models/ : </strong>contains all the files needed to run the Atomistic simulations discussed in the paper (including the topologies and starting configurations).</p> <p><strong>CG_models/ : </strong>contains all the files needed to run the Coarse Grained simulations discussed in the paper (including the topologies and starting configurations).</p> <p><strong>citrate_analysis/ : </strong>contains all the files needed to reproduce the analysis of the CV and SOAP+PCA+PAMM (including input files and python scripts).</p> <p>Additional details are available in the methods section and in the Supporting Information of the main paper.</p>
Electrostatic OAM generator with boundary condition - Phase images of the boundary conditions
<p>Here reported is the dataset that can be used to reconstruct the phase of the electron beam (process already done for the files which have a C_P in the name) after it has interacted with the electrodes that are used as boundary condition for our electrostatic variable electron vortex beam generator that allow to improve its functioning.</p> <p> </p>
Molecular Dynamics of hACE2 Receptor and SARS-CoV-2 Omicron-RBD (Receptor Binding Domain) in Electrostatics View
<p>Molecular Dynamics of hACE2 Receptor and SARS-CoV-2 Omicron-RBD (Receptor Binding Domain) in Electrostatics View.</p> <p>Molecular Dynamics performed with NAMD in Frontera supercomputer for 8 nanoseconds at 37 degrees Celsius. Electrostatics is visualized with ChimeraX (red is negative and blue is positive). By Victor Padilla-Sanchez, PhD; Texas Advanced Computing Center.</p>
Electrostatically Driven Polarization Flop and strain-induced Curvature in free-standing Ferroelectric Superlattices
<p>Supporting data for publication: "Electrostatically Driven Polarization Flop and strain-induced Curvature in free-standing Ferroelectric Superlattices"</p> <p>DOI: 10.1002/adma.202106826</p> <p>This repository contains higher resolution STEM images published in the paper.</p>
Second dataset on diffraction patterns of electron vortex beams generated by the electrostatic chopstic/MINEON device- CL340
<p>Franhofer diffraction dataset of electron vortex beams (EVBs) generated by the chopstic/MINEON device observed with a Camera Length of 340m on the K2 camera of the Titan Holo present in the Ernst-Ruska Centre at FZ-Julich. In this dataset, which can be accessed through Digital Micrograph and STEM_Cell it is possible to observe how by changing the potential difference between the two tips (main electrodes of the device) the radius of the EVB increases. A striking feature is that the radius increases almost linearly with the Orbital Angular Momentum. The analisys of this dataset can be found.at <strong>https://arxiv2203.00477.org/abs/</strong></p>
Third dataset on diffraction patterns of electron vortex beams generated by the electrostatic chopstic/MINEON device- CL23
<p>Franhofer diffraction dataset of electron vortex beams (EVBs) generated by the chopstic/MINEON device observed with a Camera Length of 23m on the K2 camera of the Titan Holo present in the Ernst-Ruska Centre at FZ-Julich. In this dataset, which can be accessed through Digital Micrograph and STEM_Cell it is possible to observe how by changing the potential difference between the two tips (main electrodes of the device) the radius of the EVB increases. A striking feature is that the radius increases almost linearly with the Orbital Angular Momentum. The analisys of this dataset can be found.at https://arxiv.org/abs/2203.00477</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.