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.
691
datasets available to search
ShareScore release 0.9.0
Dataset results
691 results for “magnetic field”
Total magnetic field measurements Piton de la Fournaise 2017-2019-2020
<p>Piton de la Fournaise northern profile</p> <p>* File number:<br> nT_yyyy_mm_dd.txt</p> <p>* File format (5 columns): <br> Coordinates (in m) Total magnetic field <br> X Y Z pre-filtered magnetic data (in nT) index of quality (>80)</p> <p><br> * Magnetic reiteration campaigns funded by:<br> ANR contract 16-CE04-0004-01 SlideVOLC<br> ANR contract 21-CE49-0015 Scan4Volc<br> Centre National d'Études Spatiales (CNES)<br> Laboratory of Excellence ClerVolc </p>
Fatiando a Terra Data: Lightning Creek Sill Complex, Australia - Airborne total-field magnetic anomaly grid
<p>Regular grid of total-field magnetic anomaly data from the Lightning Creek Sill Complex, featuring a text-book dipolar anomaly. This is a gridded version of the Lightning Creek anomaly from <a href="https://github.com/fatiando-data/osborne-magnetic">our Osborne Mine dataset</a>.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Slice to area of interest. Project data to UTM. Interpolate to a regular 50 m grid at 500 m height. Add CF-compliant metadata to the grid. Export to compressed netCDF 4.</p> <p><strong>Source: </strong>Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia, Canberra. <a href="http://pid.geoscience.gov.au/dataset/ga/142419">http://pid.geoscience.gov.au/dataset/ga/142419</a></p> <p><strong>Source license: </strong><a href="http://pid.geoscience.gov.au/dataset/ga/142419">CC-BY</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/lightning-creek-magnetic-grid">https://github.com/fatiando-data/lightning-creek-magnetic-grid</a></p>
Reproduction package for the paper "The effects of surface fossil magnetic fields on massive star evolution: IV. Grids of models at Solar, LMC, and SMC metallicities"
<p>This is a reproduction package for the paper "The effects of surface fossil magnetic fields on massive star evolution - IV. Grids of models at Solar, LMC, and SMC metallicities" by <a href="https://doi.org/10.1093/mnras/stac2598">Keszthelyi et al. (2022).</a></p>
MESSENGER magnetic field data with Mercury's magnetic main field removed through application of the Chapman-Miller method
<p>The MESSENGER (Mercury Surface, Space Environment, Geochemistry and Ranging) spacecraft followed a highly elliptical orbit about Mercury. Therefore, attenuation with radial distance of the dipole and higher order terms of Mercury’s core-generated, steady main field led to MESSENGER’s low-noise, triaxial ring-core fluxgate magnetometer registering magnetic field variations of several hundred nanoteslas. These variations swamp Mercury’s significantly smaller time-varying induction signal. Generally, the steady main field of a planetary body can be removed using a model derived through spherical harmonic analysis. However, MESSENGER’s highly eccentric orbit with near-polar perihermian leads to models of Mercury’s magnetic main field that are inadequately characterised for this purpose. Instead, novel application of the Chapman-Miller method, a geophysical processing technique, better models and removes Mercury’s magnetic main field from MESSENGER data. Three-component magnetic field time series sampled at 10 s intervals were downloaded from NASA’s Planetary Data System (Korth and Anderson, 2016) and processed by applying the Chapman-Miller method to 20 pairs of MESSENGER orbits, yielding 40 events of 256 data points per magnetic component that provide a basis for studying electromagnetic induction in Mercury’s deep crust and mantle.</p>
3D co-registration of ultra-low-field and high-field magnetic resonance images (data)
<p>Dataset used for "3D co-registration of ultra-low-field and high-field magnetic resonance images" submitted to PlosOne.</p>
Figure 1 in The orientation of earthworms is influenced by magnetic fields
Figure 1. The coiled vivarium with tool for camera (a); the experimental setup, I indicates current (b); the measured MF intensities and directions of application setting (c).
Figure 4 in The orientation of earthworms is influenced by magnetic fields
Figure 4. The direction and mean vectors of earthworms during 60-min application. Each color indicates the 5-min intervals indicated in the color panel.
Figure 3 in The orientation of earthworms is influenced by magnetic fields
Figure 3. The distribution and mean angles of earthworms in the control and experimental groups exposed to MF during the 60-min period repeated over 7 days.
Direct comparison of the tsunami-generated magnetic field with sea level change for the 2009 Samoa and 2010 Chile tsunamis
<p>This is the dataset for the paper of</p> <p>“<strong>Direct comparison of the tsunami-generated magnetic field with sea level change for the 2009 Samoa and 2010 Chile tsunamis</strong>”</p> <p>in Journal of Geophysical Research: Solid Earth.</p> <p> </p> <p>This dataset includes three zips:</p> <p><strong>1. Processed Observation Tsunami Data</strong></p> <p>-- In this zip, there have the observation tsunami magnetic field and sea level change data of 2009 Samoa and 2010 Chile earthquakes which extracted from the data of the TIARES experiment (Suetsugu et al., 2012).</p> <p><strong>2. Simulated Tsunami Data</strong></p> <p>-- This is the simulated tsunami sea level change and magnetic field of 2009 Samoa and 2010 Chile earthquakes. The tsunami sea level was simulated by JAGURSv5.2 (Baba et al., 2017) and the tsunami magnetic field was simulated by TMTGEMv1.1 (Minami et al., 2017).</p> <p><strong>3. Converted Tsunami Sea Level Change</strong></p> <p>-- The converted sea level changes were calculated by the 2-D analytical solution of tsunami magnetic field (Minami et al., 2021) using the filtered tsunami magnetic vertical component Bz.</p>
Linking detected gravity modes to axisymmetric internal magnetic fields
<p>The spectropolarimetric techniques used to characterize surface magnetic fields do not probe the stellar interior, and therefore cannot be used to constrain internal magnetic fields. The most promising way to detect/characterize such fields is to consider their effect on stellar oscillations that probe deep stellar layers, the gravity modes. Conventional asteroseismic modelling ignores any effect of a (internal) magnetic field, however, Prat et al. (2019) developed a formalism that takes the perturbative effect into account for axisymmetric, dipolar, fossil magnetic fields confined to the stellar interior, allowing one to perform magneto-asteroseismic modeling for the first time. Most detected gravity modes in intermediate-mass main-sequence pulsating stars are dipolar, and propagate in the direction of rotation (e.g. Li et al. (2020)). In this talk I will discuss the typical signatures expected in period spacing patterns of dipolar gravity modes, according to the Prat et al. (2019) formalism, for a range of stellar models covering fundamental parameters typical for this mass range. We vary the initial metallicity and mass, the magnetic field strength, the rotation rate, and the degree of mixing in the envelope and near-core regions, throughout the evolution of the main sequence. We find detectable signatures that are significantly different from those due to rotation in the period spacing patterns of gravity modes in terminal age main sequence models if the near-core magnetic field strength is higher than 10^5 Gauss. Such signatures can be used in future magneto-asteroseismic modeling based on photometry from space missions such as Kepler (Borucki et al. (2010)), TESS (targets in the continuous viewing zone; Ricker et al. (2015)) and PLATO (Rauer et al. (2014)).</p>
A Spherical Harmonic model of Earth's lithospheric magnetic field up to degree 1050
<p>This model describes the Earth's vector lithospheric (or crustal ) magnetic field to about 40-km spatial resolution. It is provided in the form of Spherical Harmonic (SH) Gauss coefficients using the norm commonly used in geomagnetism (the Schmidt normalization). The file is in (zipped) ascii with 4 columns: the SH degree, the SH order, the Gnm and the Hnm parameters. The model was obtain after selecting and processing magnetic field measurements from the German CHAMP and ESA Swarm satellites that were merged with worldwide near-surface scalar anomaly data compiled over decades. A description of the scientific procedure and the model assessments are described in a paper under publication at Geophysical Research Letters. Please consider citing the GRL paper: Thébault E., Hulot G., Langlais B., and Vigneron P., A Spherical Harmonic model of Earth’s lithospheric magnetic field up to degree 1050, Geophys. Res. Lett., 2021</p>
Data for paper "Constructing precisely quasi-isodynamic magnetic fields"
<p>This archive contains data and source code used for the paper "Constructing precisely quasi-isodynamic magnetic fields".</p>
Magnetic field reversal in the turbulent environment around a repeating fast radio burst
<p>All the bursts of FRB 20190520B detected from the Green Bank Telescope (GBT) that was used in the Anna-Thomas et al (2022) paper. This dataset contains both L-Band (1.4 GHz) and C-Band (6 GHz) bursts. The data is in pulse archive format, which can be read using the software suite PSRCHIVE or the package PyPulse. The L-band bursts are not calibrated. The C-Band bursts which has an extension .calib is calibrated for both flux and polarization and those which has an extension .calibP is only calibrated for polarization. </p>
Cosmological Initial Conditions (3D magnetic fields for an alfa=1.0 magnetic spectrum) for 85Mpc^3
<p>Files representing the initial conditions at z=40 for ENZO-MHD cosmological simulation of a comoving 85Mpc^3 volume, for tangled magnetic fields from an alfaB=-1.0 initial spectrum of magnetic fluctuations. The simulation has 1024^3 cells and 1024^3 DM particles. These data are in binary format and can be read by the ENZO code.</p> <p>More details of the simulations and on it cosmological parameter can be found at:</p> <ul> <li>https://ui.adsabs.harvard.edu/abs/2021Galax...9..109V/abstract</li> <li>https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.5350V/abstract</li> <li>https://ui.adsabs.harvard.edu/abs/2017CQGra..34w4001V/abstract</li> </ul>
Cosmological Initial Conditions (3D magnetic fields for an alfa=1.0 magnetic spectrum) for 85Mpc^3
<p>Files representing the initial conditions at z=40 for ENZO-MHD cosmological simulation of a comoving 85Mpc^3 volume, for tangled magnetic fields from an alfaB=1.0 initial spectrum of magnetic fluctuations. The simulation has 1024^3 cells and 1024^3 DM particles. These data are in binary format and can be read by the ENZO code.</p> <p>More details of the simulations and on it cosmological parameter can be found at:</p> <ul> <li>https://ui.adsabs.harvard.edu/abs/2021Galax...9..109V/abstract</li> <li>https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.5350V/abstract</li> <li>https://ui.adsabs.harvard.edu/abs/2017CQGra..34w4001V/abstract</li> </ul>
Cosmological Initial Conditions (3D magnetic fields for an alfa=2.0 magnetic spectrum) for 85Mpc^3
<p>Files representing the initial conditions at z=40 for ENZO-MHD cosmological simulation of a comoving 85Mpc^3 volume, for tangled magnetic fields from an alfaB=2.0 initial spectrum of magnetic fluctuations. The simulation has 1024^3 cells and 1024^3 DM particles. These data are in binary format and can be read by the ENZO code.</p> <p>More details of the simulations and on it cosmological parameter can be found at:</p> <ul> <li>https://ui.adsabs.harvard.edu/abs/2021Galax...9..109V/abstract</li> <li>https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.5350V/abstract</li> <li>https://ui.adsabs.harvard.edu/abs/2017CQGra..34w4001V/abstract</li> </ul>
IMU, magnetometer, and motion capture data from a UAV used for indoor magnetic field mapping and localization
<p>IMU, magnetometer, and motion capture data from a UAV used for indoor magnetic field mapping and localization. </p> <p>This data is split up into two folders. </p> <p>"stationary_magnetometer_data" 0.1Hz samples of a RM3100 magnetometer that was kept in one position from July 2022 through February 2023. The data is partitioned into separate files for analytical convenience of our research. Each file here is a CSV with a timestamp (synchronized with chrony to a central computer) and the three components of the measured magnetic field. We did not calibrate this stationary magnetometer.</p> <p>"UAV_and_mocap_data" has many subfolders. Each subfolder is labeled by a date and a small description of the goals for that test segment. There is a single "EXPLANATION" file in each subfolder that gives more detail on the provided data. The data here includes the trajectory flown by the UAV, outdoor calibration data to adjust the raw magnetometer measurements, and IMU/motion capture data for each listed flight test. The EXPLANATION file should explain what trajectory was flown for each individual flight test. </p>
Experimental data for "Magnetic field control of light-induced spin accumulation in monolayer MoSe2"
<p>This repository contains the experimental data related to the article "Magnetic field control of light-induced spin accumulation in monolayer MoSe<sub>2</sub>" and is provided to the reader under the “data availability” directive. The files have been organized following the figure notation of the main text.</p>
Solar wind plasma, magnetic field parameters and geomagnetic storm index SYM-H from 2000 to 2020
<p>SYM-H index is used to quantify the intensity of geomagnetic storm. Its temporal variation is related to the solar wind plasma and magnetic field parameters. This dataset offers time series of solar wind density, solar wind velocity and solar wind magnetic field, SYM-H index. The python and matlab code files for processing and plotting data are also included. </p>
Three-dimensional modelling of shock-turbulence interaction. Magnetic field and temperature datasets.
<p>Simulation snapshots for hybrid-kinetic model of a perpendicular shock interacting with pre-existing plasma turbulence. This dataset allows to reproduce the analyses presented in Trotta et al. 2023. The filenames are organised in <level of turbulence>-Variable-time.txt *(e.g., dB00_B_mag-14.txt is the magnetic field magnitude at T\Omega_ci = 14 for the case with turbulence level dB/B = 0.0). These textfiles are stored as one-dimensional array, which need to be reshaped to a 256x256x256 grid (which has a resolution of 0.5 di).</p> <p> </p> <p>For support in reading the simulation data or if further variables/snapshots are needed for scientific purposes, please be in touch.</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.