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.
10
datasets available to search
ShareScore release 0.9.0
Dataset results
10 results for “fast radio burst”
Multiwavelength Constraints on the Origin of a Nearby Repeating Fast Radio Burst Source in a Globular Cluster (Public Data Release)
<p>This Zenodo dataset contains the data for radio bursts B1-B9 from FRB 20200120E, as described in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024) (see: https://doi.org/10.1038/s41550-024-02386-6).</p> <p>The following data products are included:</p> <ul> <li>Channelized total intensity (Stokes I) data containing radio bursts B1-B5 from FRB 20200120E, recorded using the Effelsberg radio telescope during Pinpointing Repeating CHIME Sources with the EVN (PRECISE) VLBI observations. These data have a time resolution of 8 μs and were used in Figure 1 in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024). <ul> <li>frb20200120e_b1_8us_burst_data.npy</li> <li>frb20200120e_b2_8us_burst_data.npy</li> <li>frb20200120e_b3_8us_burst_data.npy</li> <li>frb20200120e_b4_8us_burst_data.npy</li> <li>frb20200120e_b5_8us_burst_data.npy</li> </ul> </li> <li>Channelized total intensity (Stokes I) data containing radio bursts B6-B9 from FRB 20200120E, recorded using the Effelsberg radio telescope. These data have a time resolution of 64 μs and were used in Figure 1 in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024). <ul> <li>frb20200120e_b6_64us_burst_data.npz</li> <li>frb20200120e_b7_64us_burst_data.npz</li> <li>frb20200120e_b8_64us_burst_data.npz</li> <li>frb20200120e_b9_64us_burst_data.npz</li> </ul> </li> <li>Frequency-summed total intensity (Stokes I) burst profiles of radio burst B4. The frequency range and time resolution of the data are listed below. These data were used in Extended Data Figure 2 (panels b, c, and d) in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024).<br> <ul> <li>frb20200120e_b4_8us_1254-1510mhz_burst_profile.npz; (frequency range, time resolution) = (1254-1510 MHz, 8 μs)</li> <li>frb20200120e_b4_1us_1302-1478mhz_burst_profile.npy; (frequency range, time resolution) = (1302-1478 MHz, 1 μs)</li> <li>frb20200120e_b4_31.25ns_1398-1414mhz_burst_profile.npy; (frequency range, time resolution) = (1398-1414 MHz, 31.25 ns)</li> </ul> </li> </ul> <p>We also provide the following Python code containing functions that can be used to load and plot the radio data. The plots generated by this code are similar to those shown in Figure 1 and Extended Data Figure 2 (panels b, c, and d) in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024).</p> <ul> <li>plot_frb20200120e_radio_data_pearlman+2024_nature_astronomy.py</li> </ul> <p>The X-ray data (from <em>NICER</em>, <em>XMM-Newton</em>, <em>Chandra</em>, and <em>NuSTAR</em>) used in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024) are publicly available and can be accessed through NASA's High Energy Astrophysics Science Archive Research Center (HEASARC) archive.</p> <p>If the data or Python code included in this Zenodo repository are used, please include the following two citations in your work:</p> <ol> <li>Pearlman, A. B., Scholz, P., Bethapudi, S. <em>et al.</em> Multiwavelength constraints on the origin of a nearby repeating fast radio burst source in a globular cluster. <em>Nature Astronomy</em> (2024). <a href="https://doi.org/10.5281/zenodo.13359005">https://doi.org/10.1038/s41550-024-02386-6</a></li> <li>Pearlman, A. B., Scholz, P., Bethapudi, S. <em>et al.</em> Multiwavelength constraints on the origin of a nearby repeating fast radio burst source in a globular cluster (public data release). <em>Zenodo</em> (2024). <a href="https://doi.org/10.5281/zenodo.13359005">https://doi.org/10.5281/zenodo.13359005</a></li> </ol> <p>If you have questions about the contents of this Zenodo repository, please contact the lead author: Dr. Aaron B. Pearlman (<a href="mailto:aaron.b.pearlman@physics.mcgill.ca">aaron.b.pearlman@physics.mcgill.ca</a>)</p>
Reproduction package for the paper "Constraining a neutron star merger origin for localized fast radio bursts"
<p>This is a reproduction package for the paper <a href="https://academic.oup.com/mnras/article/497/3/3131/5875920">"Constraining a neutron star merger origin for localized fast radio bursts"</a> by Gourdji et al. (2020) and published in MNRAS. This package provides a Jupyter notebook and the necessary information to reproduce the figures and main results of this paper.</p>
Data from "A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154"
<p>This includes the data and scripts used to generate the plots in the paper "A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154."</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>
Reproduction package for the paper "The Apertif Radio Transient System (ARTS): Design, Commissioning, Data Release, and Detection of the first 5 Fast Radio Bursts"
<p>This is a basic reproduction package for the paper "The Apertif Radio Transient System (ARTS): Design, Commissioning, Data Release, and Detection of the first 5 Fast Radio Bursts" by van Leeuwen et al. (2023).</p> <p>* arXiv:<a href="https://arxiv.org/abs/2205.12362">arXiv:2205.12362</a><br> * DOI: <a href="https://doi.org/10.1051/0004-6361/202244107">10.1051/0004-6361/202244107</a></p> <p> </p>
Reproduction package for the paper: "Detection of ultra-fast radio bursts from FRB 20121102A"
<p><br># Reproduction package for the paper "Detection of ultra-fast radio bursts from FRB 20121102A"<br>Authors: Mark P. Snelders, K. Nimmo, J.W.T. Hessels, Z. Bensellam, L.P. Zwaan, P. Chawla, O.S. Ould-Boukattine, F. Kirsten, J.T. Faber and V. Gajjar.<br>arXiv link: https://arxiv.org/abs/2307.02303<br>DOI published article: Nature Astronomy, 19 October 2023, https://doi.org/10.1038/s41550-023-02101-x<br><br>This work has been made possible by an NWO Vici grant (Principal investigator, J.W.T.H.). <br><br>## Raw Data<br><br><strong>- The data are 100% publicly available and are explained in great detail in the following post: http://seti.berkeley.edu:8000/frb-data/</strong><br><strong>- The data are available from the Breakthrough Initiatives Open Data Portal with target name FRB121102: https://breakthroughinitiatives.org/opendatasearch</strong><br><br>In this paper we have re-processed and re-analysed data from the Green Bank Telescope that made use of the Breakthrough Listen digital backend. I will call this the GBT BL data. Below you can find links to multiple papers, GitHub repositories and blogposts that explain the GBT BL data.</p><p>- My paper describing the search and analysis of the ultra-fast radio bursts:<br> * https://ui.adsabs.harvard.edu/abs/2023arXiv230702303S/abstract<br> * https://www.nature.com/articles/s41550-023-02101-x<br>- First detection of the bursts at 8 GHz: https://ui.adsabs.harvard.edu/abs/2018ApJ...863....2G/abstract<br>- More bursts from the same dataset with machine learning detections: https://ui.adsabs.harvard.edu/abs/2018ApJ...866..149Z/abstract<br>- Explaining the Breakthrough Listen project: https://ui.adsabs.harvard.edu/abs/2017AcAau.139...98W/abstract<br>- Explaining the GBT breakthrough listen recorder: https://ui.adsabs.harvard.edu/abs/2018PASP..130d4502M/abstract<br>- Explaining the data formats: https://ui.adsabs.harvard.edu/abs/2019PASP..131l4505L/abstract<br>- Python 2 code to work with the baseband data: https://github.com/greghell/extractor (NOTE THAT IT IS PYTHON 2!!) (I recommend using Python 2.7 if you make use of that repo)<br>- Structure of the baseband data: https://github.com/UCBerkeleySETI/breakthrough/blob/master/doc/RAW-File-Format.md<br>- More information: https://github.com/UCBerkeleySETI/breakthrough/blob/master/GBT/waterfall.md<br>- A version of dspsr, called bl-dspsr, that can work with the GBT BL baseband data: https://github.com/UCBerkeleySETI/bl-dspsr<br><br>## Software<br><br>- The data was processed on multiple machines with various operating systems, which include, but are not limited to, macOS, Ubuntu and centOS.<br>- All the used software is open source, see the section above for more information, and also see the 'software' section in the paper.<br><br>## Figures and Tables</p><p>The files in this Zenodo package should be self-explanatory. E.g. `table_1.tar` contains all the scripts/notebooks/files needed to make table_1, and also contains table 1 itself. <br>The file: 'general_info.tar' is basically a txt file with the same info as provided here and it contains an offline version of the Breakthrough Listen blogpost that that explains the raw data. <br>The file: `helper_functions.tar` is a tarball that contains a Python file with a collection of helper functions that are used in the Jupyter notebooks (and the figures are made in the Jupyter notebooks). It also contains some files that are needed to e.g. remove the instrumental delay from the data.<br> <br>## End-to-End analysis scripts<br>The Python code/Jupyter notebooks in the tarfiles are end-to-end. </p><p>## Intermediate data products </p><p>The file `data_and_data_info.tar` contains two intermediate data products (both several gigabytes in size) and a txt file explaining the files and how they were made. Due to the Zenodo file size limitations I cannot upload everything. Please contact me at snelders@astron.nl or m.p.snelders@uva.nl or via ORCID to request any other files. <br><br><br><br> </p>
FRB Mock Catalog and Reproduction Package for "Birth and Evolution of Fast Radio Bursts: Strong Population-Based Evidence for a Neutron-Star Origin"
<h3>Quickstart: FRB Mock Catalog</h3> <p>A simulated 1-day catalog of one-off FRBs, that allows users to access the FRB population without installing the entire frbpoppy package. Download and unzip 1_Day_FRB_Sky_on_Earth.txt.zip (175 MB). This human and machine readable file contains 3.5E6 FRBs that are brighter than 0.01 Jy ms, the best limit in one-off FRB detection currently. The simulated catalog is produced by the perfect telescope in frbpoppy, free of selection effects, that observed 4pi of sky for 24 hrs, with minimum detectable fluence 0.01 Jy ms, for the best-fit no-delay SFR model. This file can be read using the accompanying jupyter notebook "starting_with_mock_catalog.ipynb".</p> <p>If you use this, please cite Wang & van Leeuwen 2024 (A&A), <a href="https://doi.org/10.1051/0004-6361/202450673">https://doi.org/10.1051/0004-6361/202450673</a></p> <h3>Reproduction package for the paper "Birth and Evolution of Fast Radio Bursts: Strong Population-Based Evidence for a Neutron-Star Origin"</h3> <p>ReproductionPackage.zip is a basic reproduction package for the paper "Birth and Evolution of Fast Radio Bursts: Strong Population-Based Evidence for a Neutron-Star Origin" by Wang & van Leeuwen (2024).</p> <p> * arXiv: [<a href="https://arxiv.org/abs/2405.06281">2405.06281</a>] <br> * DOI: [<a href="https://doi.org/10.1051/0004-6361/202450673">10.1051/0004-6361/202450673</a>] </p> <h3>Installation</h3> <p>First pull or download and `frbpoppy` from <https://github.com/TRASAL/frbpoppy>.<br>Then download `ReproductionPackage.zip` and extract it starting in the frbpoppy/ base directory.<br>The scripts to produce the Figures are found in folder `frbpoppy/tests/markov_chain_monte_carlo/`.<br>The data used for these Figures resides in folder `frbpoppy/data/populations/mcmc/`.</p> <h3>Software</h3> <p>The methods and software packages used to produce the results are listed in the paper (including links to the relevant publications and/or packages):<br> FRBPOPPY: <https://github.com/TRASAL/frbpoppy><br> TRASAL: <https://github.com/TRASAL></p> <h3>Raw Data</h3> <p>The data are publicly available at<br> https://www.wis-tns.org/</p> <p> </p>
Reproduction package for the paper "Multi-dimensional population modelling using frbpoppy: Magnetars can produce the observed fast radio burst sky"
<p>This is a basic reproduction package for the paper "Multi-dimensional population modelling using frbpoppy: Magnetars can produce the observed fast radio burst sky" by David Gardenier & Joeri van Leeuwen (2021).</p> <p>* arXiv: <a href="https://arxiv.org/abs/2012.06396">arXiv:2012.06396</a><br> * DOI: <a href="https://doi.org/10.1051/0004-6361/202040119">10.1051/0004-6361/202040119</a></p> <p> </p>
Burst timescales and luminosities as links between young pulsars and fast radio bursts Dataset
<p>The dataset to reproduce the results and plots in Nimmo et al. 2021b (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210511446N/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv210511446N/abstract</a>). The scripts to produce the plots can be found here: <a href="https://github.com/KenzieNimmo/FRB20200120E_timescales">https://github.com/KenzieNimmo/FRB20200120E_timescales</a></p> <p>The software used to make the data products are:</p> <ul> <li>SFXC (Keimpema et al. 2015;<a href="https://github.com/aardk/sfxc/"> https://github.com/aardk/sfxc/</a>) </li> <li>DSPSR (van Straten & Bailes 2011; <a href="http://dspsr.sourceforge.net/">http://dspsr.sourceforge.net/</a>)</li> <li>PSRCHIVE (Hotan et al. 2004; <a href="http://psrchive.sourceforge.net">http://psrchive.sourceforge.net</a>)</li> <li>numpy (Harris et al. 2020; <a href="https://numpy.org/install/">https://numpy.org/install/</a>)</li> </ul> <p>A complete list of the data products:</p> <ul> <li>8us/125kHz full polarisation archive files (dspsr) for all 5 bursts presented in the work (coherently and incoherently dedispersed to 87.75pc/cc). Created from filterbank data made using SFXC. Use load_file.load_archive from the above github link to load data into python as a numpy array. <ul> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor2_Ef.ar.calib</li> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib</li> <li>pr143a_corr_no0015_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib</li> <li>pr143a_corr_no0057_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib</li> <li>pr158a_corr_no0017_8us_125kHz_FullPol_FullDedisp_dm87.75.ar.calib</li> </ul> </li> <li>31.25ns/16MHz Stokes I filterbank data for B2, B3 and B4. Coherently and incoherently dedispersed to 87.7527pc/cc. Created using SFXC. <ul> <li>pr141a_corr_no0069_31.25ns_16MHz_StokesI_FullDedisp_dm87.7527_SFXC.cor2_Ef.fil</li> <li>pr143a_corr_no0015_31.25ns_16MHz_StokesI_FullDedisp_dm87.7527_SFXC.cor_Ef.fil</li> <li>pr143a_corr_no0057_31.25ns_16MHz_StokesI_FullDedisp_dm87.7527_SFXC.cor_Ef.fil</li> </ul> </li> <li>1us/500kHz Stokes I filterbank data for B2, B3 and B4. Coherently and incoherently dedispersed to 87.7527pc/cc. Created using SFXC. <ul> <li>pr141a_corr_no0069_1us_500kHz_StokesI_FullDedisp_dm87.7527_SFXC.cor2_Ef.fil</li> <li>pr143a_corr_no0015_1us_500kHz_StokesI_FullDedisp_dm87.7527_SFXC.cor_Ef.fil</li> <li>pr143a_corr_no0057_1us_500kHz_StokesI_FullDedisp_dm87.7527_SFXC.cor_Ef.fil</li> </ul> </li> <li>125ns/4MHz full pol archive file of burst B3. Coherently and incoherently dedispersed to 87.7527pc/cc. Created using dspsr from a filterbank file made by SFXC. <ul> <li>pr143a_corr_no0015_125ns_4000kHz_FullPol_FullDedisp_dm87.7527_SFXC.cor_Ef.calib</li> </ul> </li> </ul> <p>We also provide a number of numpy files containing analysis products for ease of reproducing the figures. </p> <ul> <li>2D autocorrelation functions of the dynamic spectra of all 5 bursts. Additionally we give the 2D Gaussian fits to the ACFs and the Lorentzian fits to the frequency ACFs (for measuring the scintillation bandwidth) <ul> <li>ACF_b*_8us_f8.npy</li> <li>fitACF_b*_8us_f8.npy</li> </ul> </li> <li>Peak signal-to-noise ratio (S/N) of burst B3 profile at 1us resolution as a function of dispersion measure (DM) with a Gaussian fit to the result <ul> <li>pr143a_corr_no0015_500ns_1000kHz_StokesI_FullDedisp_dm87.7527_DS.npy (500ns dynamic spectrum)</li> <li>DM_vs_peakSN_sfxc_IF1-11.npy</li> <li>DM_vs_peakSN_sfxc_IF1-11_fit.npy</li> </ul> </li> <li>Power spectrum (PS) of 31.25ns profile of B2, B3 and B4 with the power law (PL) fits and power law+lorentzian (PL_lor) fit for B3 <ul> <li>PS_B2_IF4678_31.25ns.npy</li> <li>PS_B3_IF3568_31.25ns.f8.npy (downsampled by a factor of 8)</li> <li>PS_B3_IF3568_31.25ns.npy</li> <li>PS_B4_IF67910_31.25ns.npy</li> <li>PL_fit_B2.npy</li> <li>PL_fit_B3.npy</li> <li>PL_fit_B4.npy</li> <li>PL_lor_fit_B3.npy</li> </ul> </li> <li>Faraday spectra of B1, B2, B3, B4 <ul> <li>no0015_8us_125kHz_faradayspec.npy</li> <li>no0057_8us_125kHz_faradayspec.npy</li> <li>no0069_8us_125kHz_faradayspec.npy</li> <li>no0069_8us_125kHz_faradayspec_2.npy</li> </ul> </li> <li>Stokes Q and U spectra for B1, B2, B3, B4 and the corresponding joint QU fits <ul> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor2_Ef.ar.calib_QUdata.npy</li> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor2_Ef.ar.calib_QUfit.npy</li> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUdata.npy</li> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUfit.npy</li> <li>pr143a_corr_no0015_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUdata.npy</li> <li>pr143a_corr_no0015_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUfit.npy</li> <li>pr143a_corr_no0057_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUdata.npy</li> <li>pr143a_corr_no0057_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUfit.npy</li> </ul> </li> </ul> <p> </p> <p> </p>
The dispersion measure and rotation measure from fast radio burst host galaxies based on the TNG50 simulation
<p>Supplementary data to the paper "The dispersion measure and rotation measure from fast radio burst host galaxies based on the TNG50 simulation" accepted at A&A.</p> <p>The public TNG50 simulation data of the IllustrisTNG project are obtained through the website: https://www.tng-project.org/data/</p> <p>We placed a total of 16.5 million FRBs in 16 500 TNG50 galaxies at redshifts of 0<z<2, with stellar masses in the range 9<log(M*/M_sun)<12. We calculated the DM and RM host galaxy values, which we publish together with the galaxy IDs in TNG50, positions of the FRBs, galaxy inclinations, and the electron density and gas density at the position of the FRBs.</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.