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.
33
datasets available to search
ShareScore release 0.7.1
Dataset results
33 results for “magnetic reconnection”
Labelled magnetic reconnection simulation data set
<p>Numerical simulations have been performed on Marconi at CINECA (Italy) under the ISCRA initiative. The corresponding data can be found at: <a href="https://doi.org/10.5281/zenodo.3935887">https://doi.org/10.5281/zenodo.3935887</a></p>
Reconnection rates of the paper "Simulation of plasmaspheric plume impact on dayside magnetic reconnection"
<p>This repository contains the dataset needed for the paper "Simulation of plasmaspheric plume impact on dayside magnetic reconnection", i.e. the magnetic reconnection rates at each time and the quantities needed to normalize them. All the data are stored in the file "rates_norm.h5". The file "si_content.pdf" explain how the data are stored and how you can extract them.</p>
Three-dimensional magnetic reconnection in particle-in-cell simulations of anisotropic plasma turbulence (Simulation Data)
<p>This folder contains the output of the following simulation: </p> <p>We use the explicit Plasma Simulation Code (PSC, Germaschewski et al.2016) to simulate eight anisotropic counter-propagating Alfvén waves in an ion-electron plasma. The anisotropy of the initial fluctuation is set up according to the theory of critical balance by Sridhar & Goldreich (1994) and Goldreich & Sridhar (1995) at the small scale end of the inertial range: <span class="math-tex">\(k_{\parallel} d_{i} = C (|k_{\perp}|d_{i})^{2/3}\)</span>, where <span class="math-tex">\(C= 10^{-4/3}\)</span>. The normalization parameters are the speed of light <span class="math-tex">\(c = 1\)</span>, the vacuum permittivity <span class="math-tex">\(\epsilon_{0} = 1\)</span>, the magnetic permeability <span class="math-tex">\(\mu_{0} = 1\)</span>, the Boltzmann constant <span class="math-tex">\(k_{b}=1\)</span>, the elementary charge <span class="math-tex">\(q=1\)</span>, the ion mass <span class="math-tex">\(m_{i}=1\)</span>, the density of ions and electrons <span class="math-tex">\(n_{i}=n_{e}=1\)</span> and the ion inertial length <span class="math-tex">\(d_{i}=c/\omega_{pi}\)</span> where <span class="math-tex">\(\omega_{pi}=\sqrt{n_{i}q^{2}/m_{i}\epsilon_{0}}\)</span> is the ion plasma frequency. We set <span class="math-tex">\(\beta_{s,\parallel}=1\)</span> and <span class="math-tex">\(T_{s,\parallel}/T_{s,\perp}=1\)</span>, where <span class="math-tex">\(\beta_{s,\parallel}=2 n_s \mu_{0} k_{B}T_{s,\parallel}/B_{0}^{2}\)</span> is the ratio between the plasma pressure parallel to the background magnetic field <span class="math-tex">\(\mathbf{B}_{0}\)</span> and the magnetic pressure and $T_{s,\parallel}$ is the parallel temperature. The magnetic field is normalised to <span class="math-tex">\(B_{0}=V_{A}/c\)</span>, where <span class="math-tex">\(V_{A}=B_{0} / \sqrt{\mu_{0}n_{i}m_{i}}\)</span> is the ion Alfvén speed. We use 100 particles per cell (100 ions and 100 electrons), a mass ratio of <span class="math-tex">\(m_{i}/m_{e} = 100\)</span> so that <span class="math-tex">\(d_e = 0.1 d_{i}\)</span> where <span class="math-tex">\(m_{e}\)</span> is the electron mass and <span class="math-tex">\(d_{e}\)</span> is the electron inertial length. The simulation box size is <span class="math-tex">\(L_{x} \times L_{y} \times L_{z} = 24d_{i}\times24d_{i}\times125d_{i}\)</span> and the spatial resolution is <span class="math-tex">\(\Delta x =\Delta y = \Delta z = 0.06d_{i}\)</span>. We use a time step <span class="math-tex">\(\Delta t =0.06/ \omega_{pi}\)</span>. In our normalisation, the Debye length <span class="math-tex">\(\lambda_{D}=d_{i}\sqrt{\beta_{i}/2}V_{A}/c\)</span> defines the minimum spatial distance that needs to be resolve in the simulation and <span class="math-tex">\(\lambda_D=0.07d_i\)</span>.</p> <p>This output corresponds to <span class="math-tex">\(t=120 \omega_{pi}\)</span>. </p> <p>These data were produced using the Data Intensive at Leicester (DIaL) facility provided by the DiRAC project<br> dp126 "Identifying and Quantifying the Role of Magnetic Reconnection in Space Plasma Turbulence".</p>
Data for "Impact of the Out-of-Plane Flow Shear on Magnetic Reconnection at the Flanks of Earth's Magnetopause"
<p>Data for Figures 3-8 in the paper (data for Figures 5 has been updated on 2024-09-20). The data is compatible with all data-analysis software. Here are the guidelines for reading and visualizing the data:</p> <p>(1) The filenames "noshear", "MA0p7", and "MA2p3" correspond to the simulation runs with no flow shear, Mach number M_A=0.7 flow shear, and M_A=2.3 flow shear.</p> <p>(2) The "upper" and "lower" mean upper and lower current sheet, corresponding to dusk-side and dawn-side reconnection respectively. For the "noshear" case, only the "upper" is considered.</p> <p>(3) Each data file (*.dat) is written in ASCII format and has multiple columns. The first row is the header.</p> <ul> <li>The first column is always the x-coordinates of the figure. </li> <li>For the line plots, all columns starting from the second column are the y-coordinates for different variables. The variables names can be found at the header. </li> <li>For the 2D image plots, the second column is the y-coordinates, and the third column is the value of the variable at a given (x,y) location. </li> </ul> <p>(4) The files "fig4_*_field_*.dat" are the magnetic potential in the x-y domain. The contour of this potential gives the in-plane field line configurations.</p>
Data and Software for "Determining the orientation of a magnetic reconnection X line and implications for a 2D coordinate system"
<p>Supporting information for "Determining the orientation of a magnetic reconnection X line and implications for a 2D coordinate system", by Denton et al. Includes a copy of the paper and previous relevant papers, the simulation data used in the paper, and the reconstruction code used in the paper. See the readme files.</p>
The spreading of magnetic reconnection X-line in particle-in-cell simulations– mechanism and the effect of drift-kink instability
<p>This dataset contains data and Python scripts in "The spreading of magnetic reconnection X-line in particle-in-cell simulations– mechanism and the effect of drift-kink instability" prepared to submit to the Journal of Geophysical Research. </p>
Data files of the paper "Energy conversion by magnetic reconnection in multiple ion temperature plasmas"
<p>Reconnection rate and energy budget files for the simulations used in for the paper "Energy conversion by magnetic reconnection in multiple ion temperature plasmas". The paper has two simulations. The first one, without cold ions, has his reconnection rate data stored in "rate_147.dat" and the energy budget data in "Ebudget_symm_nocold_Xframe.h5". The second one, with cold ions, has his reconnection rate data stored in "rate.dat" and the energy budget data in "Ebudget_symm_cold_Xframe.h5".</p> <p>On top of that is a datafile from simulation 1 at time 144.5 (corresponding to the time of the picture A in figure 1) with all the output fields from the simulation at this given time.</p>
Estimates of the Wavenumber Wavelet Power Spectrum of Magnetic Fluctuations during Magnetic Reconnection Figure Data
<p>This is data for the publication, "Estimates of the Wavenumber Wavelet Power Spectrum of Magnetic Fluctuations during Magnetic Reconnection".</p>
Dataset: 2D particle-in-cell (PIC) simulation of the magnetic reconnection for the paper "Electron mixing and isotropization in the exhaust of asymmetric magnetic reconnection with a guide field"
<p>This repository contains pubilicly available numerical data of a 2D magnetic reconnection event, which includes the field data and plasma moment data. The simulation is performed with the VPIC code. The simulated data are used for the paper "Electron mixing and isotropization in the exhaust of asymmetric magnetic reconnection with a guide field". </p>
Intermittent Lobe Reconnection under Prolonged Northward Interplanetary Magnetic Field Condition: Insights from Cusp Spot Event Observations
<p>SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America. <br>We would like to thank British Antarctic Survey (https://www.bas.ac.uk/project/superdarn) and the University of Saskatchewanan (https://superdarn.ca) for hosting the SuperDARN data mirrors access. <br>The EISCAT dataset is available from the Madrigal database (http://millstonehill.haystack.mit.edu). <br>We acknowledge the use of DMSP/SSUSI data provided by the Johns Hopkins University Applied Physics Laboratory (https://cdaweb.gsfc.nasa.gov). <br>Additionally, the OMNI dataset is available from the OMNIWeb service online of NASA/GSFC's Space Physics Data Facility's (https://spdf.gsfc.nasa.gov/pub/data/omni/).</p>
Data and Analysis Files for Simulations of Radiatively-Cooled Magnetic Reconnection on the Z machine
<p>This dataset contains processed simulation data, representative simulation output files, and Python code for analysis of simulations presented in Datta, Rishabh, et al. "Simulations of Radiatively Cooled Magnetic Reconnection Driven by Pulsed Power." J. Plasma Phys. (2024).</p> <p>The simulations were run using GORGON, a radiative resistive MHD code with van Leer advection.<br><br>Details on GORGON can be found in Chittenden et al. (2004) 10.1063/1.1643756, and Ciardi et al. (2007) 10.1063/1.2436479.</p>
Configuration of magnetotail current sheet prior to magnetic reconnection onset
<p>Data repository for "Configuration of magnetotail current sheet prior to magnetic reconnection onset". This repository contains the following files: (1) "xyarray" is the main dataset; (2) "read_pritchett_pic_2d.py" is the python module that reads xyarray; (3) "simulation_params2.py" is the auxiliary python module that stores simulation parameters; (3) The python scripts with prefix "prod2_" plot the production figures; (4) "plt_style.py" is the plotting style sheet; (5) "movie-prod2_ratio-force.mp4" shows the evolution of different terms in the momentum equation prior to magnetic reconnection (The gray lines stand for the sum of all terms).</p>
Data for Three-dimensional X-line spreading in asymmetric magnetic reconnection
<p>The data and IDL scripts for all the figures of our 2019 JGR are organized in a self-explanatory way.</p>
Simulation data in "Intense magnetic reconnection process embedded in three-dimensional turbulent current sheet"
<p>Simulation data and program used for the research "Intense magnetic reconnection process embedded in three-dimensional turbulent current sheet".</p>
Supplemental Material for the paper "Hamiltonian model for electron heating by electromagnetic waves during magnetic reconnection with a strong guide field"
<p>Video clip showing the trajectories of two close particles in the (x,px) phase space while interacting with a wave.</p> <p>Red and green dots are the particle positions, superimposed to the instantaneous energy levels.</p>
Files to recreate figures in "Magnetic Reconnection on a Klein Bottle" by Xia and Swisdak (2024)
Open the record for dataset details and reuse information.
Kinetic Alfvén waves excited in two-dimensional magnetic reconnection
<p>Simulation data used in the paper ”Kinetic Alfvén waves excited in two-dimensional magnetic reconnection", submitted to JGR-Space Physics.</p> <p>The directory contains the input file, output data and post-processing script from running iPIC3D for investigating the kinetic Alfvén waves in magnetic reconnection, while the code iPIC3D is available on <a href="https://bitbucket.org/bopkth/ipic3d-klm">https://bitbucket.org/bopkth/ipic3d-klm</a>.</p> <p>Each case represents different thickness of the current sheet. The details are described in the paper.</p>
Example Data for the MARZ Magnetic Reconnection Experiments
<p>This dataset contains example data used in the MARZ magnetic reconnection expeirments.</p> <p>Experimental details are in:</p> <p>[1] R. Datta et al. (2024) PRL. <a href="https://doi.org/10.1103/physrevlett.132.155102" rel="nofollow">https://doi.org/10.1103/physrevlett.132.155102</a> <br>[2] R. Datta, et al. (2024). Phys. Plasmas. <a href="https://doi.org/10.1063/5.0201683" rel="nofollow">https://doi.org/10.1063/5.0201683</a></p> <p>Analysis code can be downloaded from <a href="https://github.com/ridatta/MARZ_Analysis_Tools.git">https://github.com/ridatta/MARZ_Analysis_Tools.git </a></p> <p>Diagnostics details are in Webb, Timothy Jay, et al. Review of Scientific Instruments 94.3 (2023). <a href="https://doi.org/10.1063/5.0123448" rel="nofollow">https://doi.org/10.1063/5.0123448</a></p> <p>The diagnostics in MARZ include:</p> <p>(1) B-Dots/ Inductive Probes.</p> <p>Data from each probe is stored as .CSV files. </p> <p>(2) Visible Spectroscopy (SVS)</p> <p>Data is stored in HDF files. For pre-processing, we need shot data, as well as calibration data, which includes pre-shot laser images, LDLS fast and slow images, and Tungsten lamp images. </p> <p>(3) XRS3 (X Ray Spectroscopy)</p> <p>Data is stored in TIFF files. </p> <p>(4) Ultra-Fast X Ray Pinhole Cameras</p> <p>Data is stored in TIFF files. </p> <p>(5) Self Emission Gated Optical Imager (SEGOI)</p> <p>SEGOI outputs 2D time-resolved optical emission data, and 1-D space- and time-resolved streak image data, stored in HDF5 format.</p> <p>(6) X Ray Diodes (TADPoles and LOS 170 Silicon Diodes)</p> <p>Data is stored in CSV files. </p>
Simulation dataset for Off-diagonal Ion Pressure Linked to Hall Fields in Collisionless Magnetic Reconnection
<p>The dataset contains simulation data used in the paper of "Off-diagonal Ion Pressure LInked to Hall Fields in Collisionless Magnetic Reconnection". Data are generated from a particle-in-cell simulation using the VPIC code. Please see the paper for the descriptions of the simulation setup.</p> <p>The .gda files in fiels_moments_gda_files.zip are fields and plasma moments data from the time step presented in the paper. Each file contains float-type data arrays with a size of 6720x1x2240, corresponding to x-y-z dimensions, respectively. Data can be read by softwares like IDL, python, matlab, etc., using the standard data reading methods.</p> <p>The file of harris_m100_x60_Hparticle.89586 contains ion particle data for this time step, for the domain of x=[0,60]di, z=[-8,8]di. The file is written in BINARY, for the information of indivial particles. Each data chunk constitues of the following variables in order: x, z, ux, uy, uz, q. Here x and z are positions of the particles, in unit of de; ux, uy, uz are relativistic velocities in unit of the speed of light (c), i.e., ux = gamma*vx, gamma=sqrt(1+ux^2+uy^2+uz^2); q represents the weight of the particles, so the contribution of a particle to the phase space density needs to multiply by this q factor (In the paper, q factors are re-normalized while the relative weights between particles remain the same).</p> <p>An example IDL sentence of reading particle data: readu, lun, x, z, ux, uy, uz, q. Repeat this sentence for different particles.</p>
Dataset for "Identifying The Growth Phase of Magnetic Reconnection using Pressure-Strain Interaction"
<p>Simulation dataset for the "Identifying The Growth Phase of Magnetic Reconnection using Pressure-Strain Interaction"</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.