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”
Simulation data of "Controlling the chirping of chorus waves via magnetic field inhomogeneity"
<p>Simulation data of "Controlling the chirping of chorus waves via magnetic field inhomogeneity", including waveform recorded at certain locations, part of 2D wave field and wave spectrogram obtained with 2D FFT. </p>
Nanoscale Imaging of High-Field Magnetic Hysteresis in Meteoritic Metal Using X-Ray Holography
<p>Data of magnetisation (two datasets) of the cloudy zone of Tazewell IIICD iron meteorite. Data was obtained using X-ray holography. Magnetization data is a 3D matrix containing magnetisation data in form of data[x location][y location][applied field], applied field values is provided in a separate file.</p> <p>Further details about this dataset and conditions of measurements can be found in Blukis et al., 2020 submitted to Geochemistry, Geophysics, Geosystems</p>
Reproduction package for the paper "The effects of surface fossil magnetic fields on massive star evolution - II. Implementation of magnetic braking in MESA and implications for the evolution of surface rotation in OB stars "
<p>This is a reproduction package for the paper "The effects of surface fossil magnetic fields on massive star evolution - II. Implementation of magnetic braking in MESA and implications for the evolution of surface rotation in OB stars" by Keszthelyi et al. (2020), https://doi.org/10.1093/mnras/staa237</p>
Detecting axisymmetric magnetic fields using gravity modes in intermediate-mass stars
<p>Typical MESA and GYRE inlists associated with <a href="https://ui.adsabs.harvard.edu/abs/2020arXiv200502411V/abstract">Van Beeck et al. (2020)</a>. MESA version 10398 and GYRE version 5.2.</p> <p>Context: Angular momentum (AM) transport models of stellar interiors require improvements to explain the strong extraction of AM from stellar cores that is observed with asteroseismology. One of the often invoked mediators of AM transport are internal magnetic fields, even though their properties, observational signatures and influence on stellar evolution are largely unknown.</p> <p>Aims: We study how a fossil, axisymmetric internal magnetic field affects period spacing patterns of dipolar gravity mode oscillations in main-sequence stars with masses of 1.3, 2.0 and 3.0 <span class="math-tex">\(\mathrm{M}_{\odot}\)</span> . We assess the influence of fundamental stellar parameters on the magnitude of pulsation mode frequency shifts.</p> <p>Methods: We compute dipolar gravity mode frequency shifts due to a fossil, axisymmetric poloidal-toroidal internal magnetic field for a grid of stellar evolution models, varying stellar fundamental parameters. Rigid rotation is taken into account using the traditional approximation of rotation and the influence of the magnetic field is computed using a perturbative approach.</p> <p>Results: We find magnetic signatures for dipolar gravity mode oscillations in terminal-age main-sequence stars that are measurable for a near-core field strength larger than 10<sup>5</sup> G. The predicted signatures differ appreciably from those due to rotation.</p> <p>Conclusions: Our formalism demonstrates the potential for the future detection and characterization of strong fossil, axisymmetric internal magnetic fields in gravity-mode pulsators near the end of core-hydrogen burning from Kepler photometry, if such fields exist.</p> <blockquote> <p>The publication date is the date of acceptance.</p> </blockquote> <p>J. Van Beeck would like to thank researchers M. Michielsen, C. Johnston, and dr. M. G. Pedersen for their valuable input in the MESA and GYRE computations.</p>
Spherical harmonic model of the magnetic field of Mars from Morschhauser et al. (2014)
<p><strong>Morschhauser2014.txt.gz</strong> is a gzipped file of the magnetic potential coefficients of Mars as published by Morschhauser et al. (2014). This is the same as the file ts01.txt in the supplemental materials of this manuscript.</p>
Optimum spacing of thin rectangular magnetic field coils
<p>The data used to generate each of the figures in a pending submission to Reviews of Scientific Instruments.</p>
Replication Data for: "Parabolic Diamond Scanning Probes for Single-Spin Magnetic Field Imaging
<p>Data repository for: <strong>Parabolic Diamond Scanning Probes for Single-Spin Magnetic Field Imaging</strong></p> <ul> <li><em>DataDescription.pdf</em><strong><em>: </em></strong>describes the uploaded data</li> <li><em>Data (folder): </em>folder containing <em>data.xlsx</em>, which summarizes all the data plotted in the paper as well as additional imaging and simulation data sets</li> <li><em>Code (folder): </em> contains Matlab code for converting and plotting certain data sets</li> </ul>
Electric field driven switching of individual magnetic skyrmions
<p>Data related to the publication</p>
Dataset: Maurel et al. "Hayabusa 2 returned samples reveal a weak to null magnetic field during aqueous alteration of Ryugu's parent body"
<p>Samples: C0005 and A0154a from asteroid Ryugu (JAXA Hayabusa 2 mission), CI chondrite Orgueil, CM2 chondrite Daoura 003</p> <ul> <li>C0005: NRM, ARM, IRM demagnetization and anisotropy of ARM (AARM), IRM acquisition</li> <li>A0154a: NRM demagnetization, AARM</li> <li>Orgueil: NRM and ARM demagnetization</li> <li>Daoura 003: NRM demagnetization</li> </ul>
Magnetic field impact on ferronanofluid laminar flow
Open the record for dataset details and reuse information.
Magnetic field influence on heat transfer in inclined laminar ferronanofluid flow
Open the record for dataset details and reuse information.
The impact of solar wind magnetic field fluctuations on the magnetospheric energetics
<p>This dataset provides the results and analysis tools of the manuscript by Ala-Laht et al. "The impact of solar wind magnetic field fluctuations on the magnetospheric energetics". In addition, relevant SWMF simulation input files are included. See ReadMe.txt for information.</p>
Datasets for producing figures in "Intergalactic medium rotation measure of primordial magnetic fields" (https://doi.org/10.3847/1538-4357/ad8dc5)
<p>These are hdf5 files for producing figures 2, 3 and 4 from "Intergalactic medium rotation measure of primordial magnetic fields" (Mtchedlidze et al. 2024, see for more details: https://ui.adsabs.harvard.edu/abs/2024arXiv240616230M/abstract). The data is produced by analysing Enzo simulations with yt astrophysics analysis tool (light cones).</p>
Data for "The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment due to their Propagation with MHD Modes" by Strack & Saur
<div>This dataset contains data from the publication Strack & Saur, 2024 (<a href="https://doi.org/10.1029/2024JA033235">https://doi.org/10.1029/2024JA033235</a>), including the output of our MHD model as well as processed data used in Figures 4, 5, and 6.<br> <div> </div> <div>We use a Cartesian and a spherical coordinate system, both with the origin at the geometric center of Callisto. In the Cartesian system, the z-axis is parallel to Jupiter’s rotation axis, the y-axis points to the center of Jupiter and the x-axis, which completes the right-handed coordinate system, is approximately in direction of Callisto's orbital motion. In the spherical coordinate system, phi=0° is defined on the Jupiter-facing meridian (positive y-axis) and is counted in an easterly direction, i.e., phi=90° is the upstream direction (negative x-axis). Theta is taken from the positive z-axis.<br><br></div> <div> <div> <h2>Simulation Output</h2> <br> <div>The PLUTO simulation code (v4.4, Mignone et al. 2007, http://plutocode.ph.unito.it) was used for the numerical solution of the MHD model. A description of the model equations, boundary conditions and simulation process is given Strack & Saur, 2024.</div> <br> <div>The simulations were performed in spherical geometry (r, theta, phi). Each "*.flt" output file contains the model variables on the simulation grid for a single time step. The respective simulation grid is specified in the "grid.out" file. The model variables are:</div> <ul> <li>rho: Plasma mass density</li> <li>vx1: Plasma bulk velocity, r component</li> <li>vx2: Plasma bulk velocity, theta component</li> <li>vx3: Plasma bulk velocity, phi component</li> <li>Bx1: Magnetic field, r component</li> <li>Bx2: Magnetic field, theta component</li> <li>Bx3: Magnetic field, phi component</li> <li>prs: Thermal plasma pressure</li> </ul> <div> <div>Each simulation output file also contains the following additional variables:</div> <ul> <li>Bpx1: In our case, this is the same as Bx1</li> <li>Bpx2: In our case, this is the same as Bx2</li> <li>Bpx3: In our case, this is the same as Bx3</li> <li>Jx1: Electric current density, r component</li> <li>Jx2: Electric current density, phi component</li> <li>Jx3: Electric current density, theta component</li> </ul> <div>In the output files, all values are in normalized units. The normalization factors (in CGS units) are:</div> <ul> <li>norm_r = 2410e3 cm</li> <li>norm_t = 1.255e1 s</li> <li>norm_rho = 1.594e-24 g/cm^3</li> <li>norm_v = 1.92e7 cm/s</li> <li>norm_B = 8.593e-05 Gauss</li> <li>norm_prs = 5.877e-10 dyne/cm^3</li> <li>norm_J = 8.508e-04 statA/cm^2</li> </ul> <div>Since the simulation output files are in PLUTO's binary ".flt" format, we provide the Python script "read_data.py" to read the simulation data and grid specifications.</div> <br> <div>We provide the following simulation data:</div> <br> <div>For Section 4 in Strack & Saur, 2024</div> <ul> <li>`./symmetric_model_reference`: The reference simulation, i.e., moon-magnetosphere interactions only<br>`./symmetric_model_full_A075`: The (main) full simulation with A=0.75, i.e., moon-magnetosphere interactions and induced magnetic field<br>`./symmetric_model_full_A025`: The full simulation with A=0.25<br>`./symmetric_model_full_A050`: The full simulation with A=0.50<br>`./symmetric_model_full_A100`: The full simulation with A=1.00</li> </ul> <div>For Section 5 in Strack & Saur, 2024</div> <div> <ul> <li>`./C03_high_density_reference`: The reference simulation for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_high_density_full`: The full simulation with A=0.85 for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_low_density_reference`: The reference simulation for the C03 flyby with the lower initial plasma mass density</li> <li>`./C03_low_density_full`: The full simulation with A=0.85 for the C03 flyby with the lower initial plasma mass density</li> <li>`./C09_high_density_reference`: The reference simulation for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_high_density_full`: The full simulation with A=0.85 for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_low_density_reference`: The reference simulation for the C09 flyby with the lower initial plasma mass density</li> <li>`./C09_low_density_full`: The full simulation with A=0.85 for the C09 flyby with the lower initial plasma mass density</li> </ul> </div> <br> <div>Note that in the simulation data that is provided for the symmetric model (Section 4), the output numbers of the data files are different. This is because a higher output frequency was used for the reference simulation and the A=0.75 full simulation. All output files for the symmetric full simulations refer to the end of the propagation time span shown in Figure 4. For the reference simulation, the output is provided at the beginning and end of this time span.</div> <div> </div> <div> <div> <h2>Processed Data</h2> <p>In addition to the simulation output, we provide processed data used in Figures 4, 5 and 6 of Strack & Saur, 2024.</p> <p>The directory `./data_figure_4_and_5` contains the following files for each of the four panels in Figure 4:</p> <ul> <li>`fig4_panel_*_reference.csv`: The magnetic field of the reference simulation for the respective profile. Provided are the mean, minimum, and maximum values of each component (Bx, By, Bz) in the analyzed time period.</li> <li>`fig4_panel_*_full_Bx.csv`: The time series of the Bx magnetic field component of the full simulation for the respective profile. Each column contains values for a different position (given in the first row) and each row contains values for a different point in time (given in the first column).</li> <li>`fig4_panel_*_full_By.csv`, `fig4_panel_*_full_Bz.csv`: The time series of the By and Bz magnetic field components, respectively.</li> </ul> <p>The data given for panels a and b are also used in Figure 5.</p> <p>The directory `./data_figure_6` contains a single file `fig6_sample_data.csv` with the data used for Figure 6.</p> <ul> <li>The first three columns of the file give the Cartesian coordinates of the sample points</li> <li>"B_sec_infinity" is the magnitude of the induced magnetic dipole field in a vacuum environment with A=1.0 (Equation 1)</li> <li>"dB_reference" is the numerical variability of the reference simulation in its approximately stationary state</li> <li>The last four columns (e.g. "B_sec_A025") contain the transport altered induced magnetic field magnitudes in the plasma environment for a true dipole amplitude of A=0.25, A=0.50, A=0.75, and A=1.00</li> </ul> <p>Note that length, time and magnetic field in the processed data are given in units of Callisto radii (Rc), seconds and nanotesla.</p> </div> <h2>References:</h2> <div> <div>Mignone, A., Bodo, G., Massaglia, S., Matsakos, T., Tesileanu, O., Zanni, C., & Ferrari, A. (2007). PLUTO: A Numerical Code for Computational Astrophysics. The Astrophysical Journal Supplement Series, 170(1), 228–242. https://doi.org/10.1086/513316</div> <br> <div>Strack, D., Saur, J. (2024). The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment Due to Their Propagation With MHD modes. Journal of Geophysical Research: Space Physics, 129(12), https://doi.org/10.1029/2024JA033235</div> </div> </div> </div> </div> </div> </div>
Bow shock crossing list at Mercury (from MESSENGER magnetic field data)
<div> <div> <div> <div> <p><strong>Version 2 (latest): </strong></p> <p>This file contains a list of bow shock crossings at Mercury, identified from magnetic field data of the MESSENGER mission with 1 second resolution (not aberrated), (https://pds-ppi.igpp.ucla.edu/). </p> <p>For a pre-selection of intervals containing bow shock crossings, we use the list of Philpott (2020): "MESSENGER bowshock and magnetopause crossings", https://doi.org/10.5683/SP2/1U6FEO. There, the first and last crossings of the inbound/outbound orbit segments are listed (boundary numbers 1 and 2 for inbound, 7 and 8 for outbound segments). For the bow shock identification, an automatic detection algorithm is applied to these intervals, extended by <strong>45</strong> seconds in either direction.</p> <p>The algorithm calculates running averages and variances of the magnetic field magnitude within adjacent 15 second intervals, separated by two seconds (gap interval). Whenever the ratio between the average magnetic fields exceed a threshold (default: 1.4), a bow shock crossing is selected. Should there be more than one ratio maximum within 10 seconds, then only the maximum is selected that corresponds to the minimal sum of the variances within the adjacent intervals. Crossings are classified with respect to detection quality depending on the ratios of the average magnetic fields and the corresponding variances. Details can be found in the algorithm script that is published alongside this file.</p> <p>Columns of the list file: <br>['time', 'orbit_number', 'x_mso_km', 'y_mso_km', 'z_mso_km', 'jump_ratio', 'indicator']</p> <p>'time': date and time in YYYY-MM-DD HH:mm:SS<br>'orbit_number': orbit number (MESSENGER Mission)<br>'x_mso_km': position (x-coordinate) in MSO coordinate system in km, not aberrated<br>'y_mso_km': position (y-coordinate) in MSO coordinate system in km, not aberrated<br>'z_mso_km': position (z-coordinate) in MSO coordinate system in km, not aberrated<br>'in/out': inbound or outbound segment of the orbit (apoherm towards periherm or periherm towards apoherm)<br>'jump_ratio': ratio of the average magnetic fields before and after selected crossings<br>'indicator': 1, 2 or 3 (1: very clear crossings, high quality, 2: clear crossings, good quality, 3: unclear crossings, poor quality)</p> <p>For further analysis it is recommended to only use the crossings with the indicators 1 and 2.<br>The uncertainty in the determination of the times is +/- 2 seconds. </p> <p>Number of analyzed orbits: 3982<br>Number of total crossings found: 13502<br>Number of crossings with indicator 1 (very clear crossings, best quality): 1765<br>Number of crossings with indicator 2 (clear crossings, good quality): 5027<br>Number of crossings with indicator 3 (unclear crossings, poor quality): 6710</p> <p> </p> <p><strong>Version 1: </strong></p> <p><br>This file contains a list of bow shock crossings at Mercury, identified from magnetic field data of the MESSENGER mission with 1 second resolution (not aberrated), (https://pds-ppi.igpp.ucla.edu/). </p> <p>For a pre-selection of intervals containing bow shock crossings, we use the list of Philpott (2020): "MESSENGER bowshock and magnetopause crossings", https://doi.org/10.5683/SP2/1U6FEO. There, the first and last crossings of the inbound/outbound orbit segments are listed (boundary numbers 1 and 2 for inbound, 7 and 8 for outbound segments). For the bow shock identification, an automatic detection algorithm is applied to these intervals, extended by 15 seconds in either direction.</p> <p>The algorithm calculates running averages and variances of the magnetic field magnitude within adjacent 15 second intervals, separated by two seconds (gap interval). Whenever the ratio between the average magnetic fields exceed a threshold (default: 1.4), a bow shock crossing is selected. Should there be more than one ratio maximum within 10 seconds, then only the maximum is selected that corresponds to the minimal sum of the variances within the adjacent intervals. Crossings are classified with respect to detection quality depending on the ratios of the average magnetic fields and the corresponding variances. Details can be found in the algorithm script that is published alongside this file.</p> <p>Columns of the list file: <br>['time', 'orbit_number', 'x_mso_km', 'y_mso_km', 'z_mso_km', 'in/out', 'jump_ratio', 'indicator']</p> <p>'time': date and time in YYYY-MM-DD HH:mm:SS<br>'orbit_number': orbit number (MESSENGER Mission)<br>'x_mso_km': position (x-coordinate) in MSO coordinate system in km, not aberrated<br>'y_mso_km': position (y-coordinate) in MSO coordinate system in km, not aberrated<br>'z_mso_km': position (z-coordinate) in MSO coordinate system in km, not aberrated<br>'in/out': inbound or outbound segment of the orbit (apoherm towards periherm or periherm towards apoherm)<br>'jump_ratio': ratio of the average magnetic fields before and after selected crossings<br>'indicator': 1, 2 or 3 (1: very clear crossings, high quality, 2: clear crossings, good quality, 3: unclear crossings, poor quality)</p> <p>For further analysis it is recommended to only use the crossings with the indicators 1 and 2.<br>The uncertainty in the determination of the times is +/- 2 seconds. </p> <p>Number of analyzed orbits: 3982<br>Number of total crossings found: 65666<br>Number of crossings with indicator 1 (very clear crossings, best quality): 4291<br>Number of crossings with indicator 2 (clear crossings, good quality): 16652<br>Number of crossings with indicator 3 (unclear crossings, poor quality): 44723</p> </div> <div> </div> <div> </div> </div> </div> </div>
NASA's Voyager 1 Refined Magnetic Field Data (1-10 au)
<p>This dataset contains NASA's Voyager 1 spacecraft in situ measurements of the solar wind for the Cartesian RTN magnetic field (nT), heliocentric distance (au), proton density (cm^-3), and bulk flow speed (km/s), with time resolution 1.92 seconds.<br> The original source for this refined dataset can be found via the Goddard Space Flight Center Space Physics Data Facility (SPDF), for 2 second MAG data and hourly averaged plasma data.<br> The range of heliocentric distance covered in this dataset is from 1-10 au and each file contains a time series of magnetic field measurements over two days.<br> Each file is labeled with the start and end day covered by the magnetic field time series.<br> Information regarding heliocentric distance, proton density, and bulk flow speed in each file are represented by averages over the corresponding two-day interval.<br> These averages were taken from the hourly averaged data, provided by SPDF, over the appropriate time covered by the magnetic field time series.<br> The CDF files have global attributes: 'CREATOR', 'AFFILIATION', 'S/C NAME', and 'DATE CREATED'.<br> Variable keys are 'SSEPOCH' (time), 'BR' (radial magnetic field), 'BT' (tangential magnetic field), 'BN' (normal magnetic field), 'R' (heliocentric distance), 'N' (proton density), and 'VSW' (bulk flow speed), with variable attribute 'UNITS'.<br> The 'SSEPOCH' variable are timed measurements in units of seconds since Epoch (January 1, 1970).<br> The CDF files were generated in Python using the spacepy.pycdf library.<br> For more specifics regarding the magnetic field cleaning methods utilized to produce this dataset, please refer to the Master's Thesis "Voyager 1: Cleaning Magnetic Field Data for Turbulence Studies" and contact the author for any questions related to the thesis or access.<br> Please send any questions/concerns to Manuel Enrique Cuesta at mecuesta@udel.edu for any problems related to downloading/accessing the data from the CDF files.</p>
Decoupling of Spin Decoherence Paths near Zero Magnetic Field
<p>We demonstrate a method to quantify and manipulate nuclear spin decoherence mechanisms that are active in zero to ultralow magnetic fields. These include (i) nonadiabatic switching of spin quantization axis due to residual background fields and (ii) scalar pathways due to through-bond couplings between <sup>1</sup>H and heteronuclear spin species, such as <sup>2</sup>H used partially as an isotopic substitute for <sup>1</sup>H. Under conditions of free evolution, scalar relaxation due to <sup>2</sup>H can significantly limit nuclear spin polarization lifetimes and thus the scope of magnetic resonance procedures near zero field. It is shown that robust trains of pulsed dc magnetic fields that apply π flip angles to one or multiple spin species may switch the effective symmetry of the nuclear spin Hamiltonian, imposing decoupled or coupled dynamic regimes on demand. The method should broaden the spectrum of hyperpolarized biomedical contrast-agent compounds and hyperpolarization procedures that are used near zero field.</p> <p>Entry contains processed experimental data for Figures 4-5 of the main paper, and Figures 2-5 of the Supporting Information.</p>
Fatiando a Terra Data: Britain - Airborne total-field magnetic anomaly
<p>This is a digitized version of an airborne magnetic survey of Britain. Data are sampled where flight lines crossed contours on the archive maps. Contains only the total field magnetic anomaly, not the magnetic field intensity measurements or corrections.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's mean 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>Datum was changed to WGS8; Year was separated from the survey name; Some fields were dropped; Exported to compressed CSV format.</p> <p><strong>Source: </strong><a href="https://www.bgs.ac.uk/datasets/gb-aeromagnetic-survey/">British Geological Survey</a></p> <p><strong>Source license: </strong><a href="https://www.bgs.ac.uk/bgs-intellectual-property-rights/open-government-licence/">Open Government Licence</a></p> <p><strong>Repository:</strong> <a href="https://github.com/fatiando-data/britain-magnetic">https://github.com/fatiando-data/britain-magnetic</a></p> <p>Contains British Geological Survey materials © UKRI 2021.</p>
Fatiando a Terra Data: Osborne Mine, Australia - Airborne total-field magnetic anomaly
<p>This is a section of a survey acquired in 1990 by the Queensland Government, Australia. The data are good quality with approximately 80 m terrain clearance and 200 m line spacing. The anomalies are very visible and present interesting processing and modelling challenges, as well as plenty of literature about their geology.</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>Change the horizontal datum from GDA94 to WGS84. Convert terrain clearance to flight height using an SRTM grid. Keep only the coordinates, AWAGS leveled magnetic anomaly, and flight line ID. Cut to a smaller region containing only the 2 anomalies of interest.</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/osborne-magnetic">https://github.com/fatiando-data/osborne-magnetic</a></p>
Novel polarimetric technique to constrain the magnetic field structure and strength of Gamma-ray burst jets
<p>Gamma-ray bursts (GRBs) are extremely energetic events of cosmological origin. Observed GRBs have high luminosity and rapid variability that requires ultra-relativistic motion in the production mechanism which drive the synchrotron radiation associated with the relativistic jets and their shocked interactions with the local ambient medium. They are broadly divided into two types based on the gamma-ray duration; long GRBs (>2 seconds), and short GRBs (<2 seconds). Long GRBs are thought to be originated from explosions of very massive stars and short GRBs are thought to be produced by the merger of compact binaries. Several key open questions about our understanding of GRB physics remain: What is the driving mechanism of GRB jets? What is the origin and role of magnetic fields in driving the explosion? Since these events happen at cosmological distances, they can not be resolved using traditional astronomical techniques. However, polarimetric observations of GRBs have allowed us to start the exploration of the structure and magnetic field configurations of their relativistic jets. Generally, polarization is measured via the ratio of fluxes by taking consecutive exposures, however for rapidly varying objects such as GRBs, it is not an effective way to observe polarization. Liverpool Telescope (LT) has utilized rapidly rotating polaroids to overcome this problem and created a series of polarimeters that have successfully detected early-time optical polarimetry of various GRBs. I will present photometric and polarimetric results of various GRBs observed by RINGO3. 10 GRBs were bright enough to perform analysis and we were able to perform polarimetric analysis for 7 GRBs. I will discuss how polarimetric detection for a long GRB 191016A along with photometric data constraint the energy injection mechanism for the central engine. In addition, I will present how polarization depends on various properties of GRBs such as photometric decay index, isotropic energy of GRBs, redshift etc.</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.