Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
68
datasets available to search
ShareScore release 0.9.0
Dataset results
68 results for “electrostatics”
Structural Dynamics Support Electrostatic Interactions in the Active Site of Adenylate Kinase - A55G and V135G mutants
<p>Tinker MD trajectories of AdK WT and mutants as described in the paper published in ChemBioChem, e202200097.</p>
Neural-Parareal dataset JOREK blob runs with electrostatic model batch_0001-0500
<p>Please refer to journal paper from S.J.P.Pamela, titled</p> <p>"Neural-Parareal: Self-improving acceleration of fusion MHD simulations using time-parallelisation and neural operators"</p> <p>Available on ArXiV and on Comp.Phys.Comm.: https://doi.org/10.1016/j.cpc.2024.109391</p> <p> </p> <p>Data produced by the JOREK code, https://jorek.eu</p> <p>All runs created using the electrostatic model, model-ID "model003"</p> <p>Data downsampled by saving every 10th timestep, on a regular 2D grid of 100x100.</p> <p>To reproduce full runs, use corresponding input files.</p> <p> </p> <p>Note: variables names are the same as in the JOREK code:</p> <p>u = electric potential</p> <p>omega = toroidal vorticity</p> <p>rho = density</p> <p>T = temperature</p> <p> </p> <p>The create_gif.py can be used to convert data into movies.</p> <p> </p>
Neural-Parareal dataset JOREK blob runs with electrostatic model batch_0501-1000
<p>Please refer to journal paper from S.J.P.Pamela, titled</p> <p>"Neural-Parareal: Self-improving acceleration of fusion MHD simulations using time-parallelisation and neural operators"</p> <p>Available on ArXiV and on Comp.Phys.Comm.: https://doi.org/10.1016/j.cpc.2024.109391</p> <p> </p> <p>Data produced by the JOREK code, https://jorek.eu</p> <p>All runs created using the electrostatic model, model-ID "model003"</p> <p>Data downsampled by saving every 10th timestep, on a regular 2D grid of 100x100.</p> <p>To reproduce full runs, use corresponding input files.</p> <p> </p> <p>Note: variables names are the same as in the JOREK code:</p> <p>u = electric potential</p> <p>omega = toroidal vorticity</p> <p>rho = density</p> <p>T = temperature</p> <p> </p> <p>The create_gif.py can be used to convert data into movies.</p>
Neural-Parareal dataset JOREK blob runs with electrostatic model batch_1501-2000
<p>Please refer to journal paper from S.J.P.Pamela, titled</p> <p>"Neural-Parareal: Self-improving acceleration of fusion MHD simulations using time-parallelisation and neural operators"</p> <p>Available on ArXiV and on Comp.Phys.Comm.: https://doi.org/10.1016/j.cpc.2024.109391</p> <p> </p> <p>Data produced by the JOREK code, https://jorek.eu</p> <p>All runs created using the electrostatic model, model-ID "model003"</p> <p>Data downsampled by saving every 10th timestep, on a regular 2D grid of 100x100.</p> <p>To reproduce full runs, use corresponding input files.</p> <p> </p> <p>Note: variables names are the same as in the JOREK code:</p> <p>u = electric potential</p> <p>omega = toroidal vorticity</p> <p>rho = density</p> <p>T = temperature</p> <p> </p> <p>The create_gif.py can be used to convert data into movies.</p>
Neural-Parareal dataset JOREK blob runs with electrostatic model batch_1001-1500
<p>Please refer to journal paper from S.J.P.Pamela, titled</p> <p>"Neural-Parareal: Self-improving acceleration of fusion MHD simulations using time-parallelisation and neural operators"</p> <p>Available on ArXiV and on Comp.Phys.Comm.: https://doi.org/10.1016/j.cpc.2024.109391</p> <p> </p> <p>Data produced by the JOREK code, https://jorek.eu</p> <p>All runs created using the electrostatic model, model-ID "model003"</p> <p>Data downsampled by saving every 10th timestep, on a regular 2D grid of 100x100.</p> <p>To reproduce full runs, use corresponding input files.</p> <p> </p> <p>Note: variables names are the same as in the JOREK code:</p> <p>u = electric potential</p> <p>omega = toroidal vorticity</p> <p>rho = density</p> <p>T = temperature</p> <p> </p> <p>The create_gif.py can be used to convert data into movies.</p> <p> </p>
Density-Based Long-Range Electrostatic Descriptors - Datasets
<p>Datasets in the VASP ML_AB format.</p> <p>ML_AB_NaCl_liqiud: 64 Na atoms, 64 Cl atoms, 1014 configurations<br>ML_AB_NaCl_solid: 64 Na atoms, 64 Cl atoms, 1151 configurations<br>ML_AB_point_charges: 32 +1 charged atoms, 32 -1 charged atoms, 2000 configurations<br>ML_AB_ZrO2: 32 Zr atoms, 64 O atoms, 592 configurations</p> <p>For more details, see preprint: <br>https://arxiv.org/abs/2406.17595</p>
Quantitative Virus-Plane electrostatics
<p>Virus plane electrostatic interactions</p>
Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC) and Electrostatic PIPAC (ePIPAC) With Paclitaxel In Patients With Peritoneal Carcinomatosis
ClinicalTrials.gov study NCT05395910. IPD Sharing: NO. Countries: 1. Publications: 10.
Clinical Trial to Monitor the Efficacy of the Elosan Treatment by Applying Electrostatic
ClinicalTrials.gov study NCT04818294. IPD Sharing: NO. Countries: 1. Publications: 1.
Electrostatic interactions contribute to the control of intramolecular thiol–disulfide isomerization in a protein
Open the record for dataset details and reuse information.
Experimental Estimation of Gap Thickness and Electrostatic Forces Between Contacting Surfaces Under Electroadhesion
<p>Electroadhesion (EA) is a promising technology with potential applications in robotics, automation, space missions, textiles, tactile displays, and some other fields where efficient and versatile adhesion is required. However, a comprehensive understanding of the physics behind it is lacking due to the limited development of theoretical models and insufficient experimental data to validate them. This article proposes a new and systematic approach based on electrical impedance measurements to infer the electrostatic forces between two dielectric materials under EA. The proposed approach is applied to tactile displays, where skin and voltage-induced touchscreen impedances are measured and subtracted from the total impedance to obtain the remaining impedance to estimate the electrostatic forces between the finger and the touchscreen. This approach also marks the first instance of experimental estimation of the average air gap thickness between a human finger and a voltage-induced capacitive touchscreen. Moreover, the effect of electrode polarization impedance on EA is investigated. Precise measurements of electrical impedances confirm that electrode polarization impedance exists in parallel with the impedance of the air gap, particularly at low frequencies, giving rise to the commonly observed charge leakage phenomenon in EA.</p>
Effects of the electrostatic environment on superlattice Majorana nanowires (dataset)
<p><strong>Description</strong></p> <p>This repository contains the dataset required to reproduce all the figures of the article "Samuel D. Escribano, Alfredo Levy Yeyati, Yuval Oreg, and Elsa Prada,<em> Effects of the electrostatic environment on superlattice Majorana nanowires</em>, arXiv:1904.10289 (2019)".</p> <p> </p> <p><strong>Structure of the dataset</strong></p> <p>All the data is saved in Matlab file-format .mat. Their file names correspond to the figure that they plot. Together with each dataset, there is a Python script .py which plots the corresponding figure. The output of every script is the corresponding figure in PDF-format .pdf. Please, read "reedme.txt" file for further information.</p> <p><br> </p>
Arabidopsis heat stress-induced proteins are enriched in electrostatically charged amino acids and intrinsically disordered regions
GEO Series GSE116592. Arabidopsis thaliana. 12 samples. Type: Expression profiling by array.
Electrostatic properties of disordered regions control transcription factor search and pioneer activity [ATAC-Seq]
GEO Series GSE290873. Mus musculus. 3 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Electrostatic properties of disordered regions control transcription factor search and pioneer activity [ChIP-Seq]
GEO Series GSE290874. Mus musculus. 10 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Electrostatic drum machine
18th/19th century, England **Jagiellonian University Museum Collegium Maius** Inventory number: 17035; 2151/V Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Structural Dynamics Support Electrostatic Interactions in the Active Site of Adenylate Kinase - WT and A37G mutant
<p>Tinker MD trajectories of AdK WT and mutants as described in the paper published in ChemBioChem, e202200097.</p>
Electrostatic control of the proximity effect in the bulk of semiconductor-superconductor hybrids
<p>The proximity effect in semiconductor-superconductor nanowires is expected to generate an in-<br> duced gap in the semiconductor. The magnitude of this induced gap, together with the semicon-<br> ductor properties like the spin-orbit coupling and g - factor, depends on the coupling between the<br> materials. It is predicted that this coupling can be adjusted through the use of electric fields. We<br> study this phenomena in InSb/Al/Pt hybrids using nonlocal spectroscopy. We show that these<br> hybrids can be tuned such that the semiconductor and superconductor are strongly coupled. In this<br> case, the induced gap is similar to the superconducting gap in the Al/Pt shell and closes only at<br> high magnetic fields. In contrast, the coupling can be suppressed which leads to a strong reduction<br> of the induced gap and critical magnetic field. At the crossover between the strong-coupling and<br> weak-coupling regimes, we observe the closing and reopening of the induced gap in the bulk of a<br> nanowire. Contrary to expectations, it is not accompanied by the formation of zero-bias peaks in<br> the local conductance spectra. As a result, this cannot be attributed conclusively to the anticipated<br> topological phase transition and we discuss possible alternative explanations.</p>
Variation of Electron Lifetime due to Scattering by Electrostatic Electron Cyclotron Harmonic Waves in the Inner Magnetosphere with Electron Distribution Parameters
<p>Dataset that supports the Results, Tables and Figures of "Variation of Electron Lifetime due to Scattering by Electrostatic Electron Cyclotron Harmonic Waves in the Inner Magnetosphere with Electron Distribution Parameters".</p>
iCn3D shows electrostatic potential
<p>PDD ID 3GVU</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.