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.
142
datasets available to search
ShareScore release 0.7.1
Dataset results
142 results for “Gravitational Waves”
Milky Way Satellites Shining Bright in Gravitational Waves: dataset release
<p>Data release relative to the analysis presented in:<br> Milky Way Satellites Shining Bright in Gravitational Waves (<a href="https://arxiv.org/abs/2002.10465">arxiv:2002.10465</a>)<br> Folders and subfolders are arranged by source properties</p> <ul> <li>Satellite name <ul> <li>Frequency <ul> <li>Component Masses <ul> <li>Inclination</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p>as described in the paper.<br> Posterior samples, parameter estimation configuration files, sky localisation and SNR estimates are released.</p>
Datasets for "Circular polarization of gravitational waves from early-universe helical turbulence"
<pre>This directory contains an index.html file with links to the run directories for Figs. 1-3 and idl plotting routines with secondary data for the other figures for the paper "Circular polarization of gravitational waves from early universe helical turbulence".</pre>
Prospects of gravitational-waves detections from common-envelope evolution with LISA
<p>Input and output files for the <a href="https://cosmic-popsynth.github.io/">COSMIC</a> simulations used in <a href="https://arxiv.org/abs/2102.00078">arXiv:2102.00078</a>.</p>
Data Release: "A New Probe of Gravitational Parity Violation Through (Non-)Observation of the Stochastic Gravitational-Wave Background"
<p>This dataset contains the results presented in "<strong>A New Probe of Gravitational Parity Violation Through (Non-)Observation of the Stochastic Gravitational-Wave Background</strong>" (<a href="https://arxiv.org/abs/2312.12532">arXiv:2312.12532</a>).</p> <p>The code used to generate this data can be found in the repository <a href="https://github.com/tcallister/stochastic-birefringence/">https://github.com/tcallister/stochastic-birefringence/</a>. This repository includes <a href="https://github.com/tcallister/stochastic-birefringence/tree/main/data">jupyter notebooks</a> that can be used to open, explore, and plot the files contained in this data set. Additional information about reproducing and/or using this dataset can be found in <a href="https://tcallister.github.io/stochastic-birefringence/">our associated documentation</a>.</p> <p>Further notes:</p> <ul> <li>The file <em>o1o2o3_mass_c_iid_mag_iid_tilt_powerlaw_redshift_result.json</em>, used for figure generation, was published by the LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration in support of the paper "<a href="https://arxiv.org/abs/2111.03634">The population of merging compact binaries inferred using gravitational waves through GWTC-3</a>" (see <a href="../record/5655785">https://zenodo.org/record/5655785</a>).</li> <li>The file <em>matlab_orfs.dat</em> is created via running the script <a href="https://github.com/tcallister/stochastic-birefringence/blob/main/input/generate_matlab_orfs.m">generate_matlab_orfs.m</a>, which requires a local installation of LIGO matapps tools to rerun.</li> </ul>
Insights from GRBs for optical follow-up of gravitational-wave counterparts
<p>Public data release for the paper "Insights from GRBs for optical follow-up of gravitational-wave counterparts". Includes afterglow lightcurves, skymaps from GW simulations, and scripts for using the scheduler and making figures.</p>
Data release: "The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background"
<p>This data release contains the data to reproduce the results of "<strong>The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background</strong>" (<a href="https://arxiv.org/abs/2310.17625">arXiv:2310.17625</a>, published version <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ad3d5c">here</a>).</p> <p>The code that was used to generate this data can be found on <a href="https://github.com/kevinturbang/bbh_gwb_time_delay_inference">this GitHub repository</a>. Jupyter notebooks to reproduce the figures of the paper are also included, and can be found <a href="https://github.com/kevinturbang/bbh_gwb_time_delay_inference/tree/main/figures">here</a>.</p>
Simulation of HLVK Configuration for O5 and O6 Observation Runs: Gravitational Wave Detection Rates, Skymaps, and Multimessenger Parameters (November 2024 edition)
<p>We conducted a simulation of the<strong> HLVK </strong>configuration for the upcoming O5 and O6 observing runs, estimating the expected gravitational wave (GW) detection rates based on a signal-to-noise (S/N) <strong>threshold of 10</strong>. This dataset includes skymap files and essential parameters for multimessenger studies. Additionally, a Python script is provided to facilitate the separation of BNS, BBH, and NSBH events based on mass, with a default recommendation of setting neutron star (NS) mass to <3 solar masses and black hole (BH) mass to ≥3 solar masses. Previous simulations for the O4 and O5 runs used an <strong>S/N of 8</strong> and can be found here: <a href="https://doi.org/10.5281/zenodo.7026209" target="_new" rel="noopener">https://doi.org/10.5281/zenodo.7026209</a>. </p>
Datasets for "Low frequency tail of gravitational wave spectra from hydromagnetic turbulence''
<pre>This directory contains an index.html file with links to the run directories with secondary data for Figures 1-12 of the paper "Low frequency tail of gravitational wave spectra from hydromagnetic turbulence" by Ramkishor Sharma and Axel Brandenburg. The extensions "c" or "(cont)" refer to continued runs with higher cadence, while "_nophase" or "(nophase)" refers to the corresponding run with fixed phase. If anything turns out to be incomplete, please email brandenb@nordita.org. </pre>
Reconstruction of eccentric binary black hole gravitational wave signals using deep learning
<p>Dataset of deep learning-based reconstruction of simulated binary black hole gravitational wave signals with non-zero eccentricity. The details of the deep learning models and studies conducted can be found in the following papers: 1. https://arxiv.org/abs/2403.01559 and 2. https://arxiv.org/abs/2406.06324.<br><br>The data consists of 1 sec long whitened BBH injections ('Injection_signal') sampled at 2048 Hz, and their corresponding reconstructions ('Mean_reconstruction'), alongwith 50% ('Lower_50, Upper_50') and 90% ('Lower_90, Upper_90') C.I. of the reconstructions. The eccentricity values are labelled 'Eccentricity'. The masses and SNR of these signals are 30+30 solar mass and 12 respectively.</p>
Optimal neutron-star mass ranges to constrain the equation of state of nuclear matter with electromagnetic and gravitational-wave observations: Animated Fig. 1
<p>This repository includes the animated version of Fig. 1 in https://arxiv.org/abs/1905.04900.</p>
The proper way to spatially decompose the gravitational-wave origin in stellar collapse simulations
<p>This data release contains a jupyter notebook and data that are necessary to reproduce all the figures in the corresponding paper at https://arxiv.org/abs/2405.09729. The readme file explain the nature of the files. You can contact the author for futher information.</p>
Parameter estimation catalogs for binary neutron star mergers detected with next-generation gravitational wave detectors
<div> <p>Next-generation gravitational wave (GW) observatories, such as the Einstein Telescope (ET) and the Cosmic Explorer, will provide access to the population of binary neutron star (BNS) mergers throughout cosmic history and yield precise parameter estimates. Here, we publish the results of a comprehensive study evaluating BNS merger detection prospects using the ET alone or in a network of current or next-generation detectors up to redshift equal to 1. We publicly release all the parameter estimation for 10 years of observations of BNSs in the form of catalogs. These catalogs are made available to the community for multi-messenger studies, multi-probe cosmology, and nuclear study to constrain the neutron star (NS) equation of state (EOS). They can be used to focus on specific events (for example golden events with high signal-to-noise ratio) or for statistical studies on the BNS populations. </p> <p>Our simulations assessed the perspectives for detecting the optical emission of BNS mergers in the era of next-generation detectors, considering how uncertainties in BNS population properties, NS mass distribution, and the EOS might affect the detection rate and parameter estimation. The study is published in <a href="https://arxiv.org/abs/2411.02342" target="_blank" rel="noopener">Loffredo, Hazra, Dupletsa, Branchesi et al. 2024</a> arXiv:2411.02342 (submitted to A&A).</p> </div> <h3>BNS merger rate</h3> <p>As shown in <a href="https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.4877S/abstract" target="_blank" rel="noopener">Santoliquido et al. (2021)</a>, the common envelope ejection efficiency parameter, α, determines one of the main sources of uncertainty for the number of BNS mergers per year. In order to evaluate the impact of the uncertainties of the BNS merger rate normalization on our results, we generate two catalogues of BNS mergers assuming α to be either <strong>0.5</strong> or <strong>1.0</strong>. </p> <h3>NS mass distribution</h3> <p>We draw the component masses of the NS binaries, M_1 and M_2, from two different mass distributions: <strong>Gaussian</strong> and<br><strong>uniform</strong> mass distributions. The Gaussian distribution is centred at 1.33 M⊙ with a standard deviation of 0.09 M⊙. The uniform mass distribution ranges in [1.1 M⊙, M_max], where M_max depends on the selected EOS.</p> <h3>Equation of state (EOS)</h3> <p>Since the NS EOS affects both the GW and EM signals expected from BNS mergers, we consider<br>two different EOSs, namely the <strong>APR4</strong> and <strong>BLh</strong> microscopic EOSs.</p> <h3>Detector configuration</h3> <p>Given the two values of α (0.5 and 1.0), the two mass distributions (uniform and Gaussian), and the two EOSs (BLh and APR4), we have a total of 8 different population sets, which constitute our injections for the gravitational signal analysis. For each of these datasets, we consider the following GW detector configurations:</p> <ul> <li>ET in its triangular design of 10 km arms, located in Sardinia, alone and operating together with (<strong>ET_delta_10_cryo</strong>): <ul> <li>the current ground-based network LIGO-Hanford, LIGO-Livingston, Virgo, KAGRA, LIGO-India (<strong>LVKI</strong>) </li> <li>one L-shaped CE with 40 km arms, located in the USA (<strong>1CE</strong>)</li> <li>2 CEs, both with 40 km arms, one in the USA and one in Australia (<strong>2CE</strong>)</li> </ul> </li> <li>ET in its 2L-shaped interferometer configuration of 15 km arms misaligned at 45 deg (one located in Sardinia and the other in the Netherlands); we consider the same networks as above, using the 2L-configuration instead of the triangular one (<strong>ET_2L_15_cryo_45deg</strong>). </li> </ul> <p>We thus have eight different detector networks giving a total of 64 simulations available in this repository. </p> <h3>Catalog description</h3> <p>The parameter estimation of the injected GW signals by the various detector networks is obtained through the Fisher matrix software <strong>GWFish</strong> (<a href="https://ui.adsabs.harvard.edu/abs/2023A%26C....4200671D/abstract" target="_blank" rel="noopener">Dupletsa et al. 2023</a>). The Fisher analysis method approximates the likelihood with a multivariate Gaussian distribution. All the parameters [M_1, M_2, dL, ι, RA, DEC, Ψ, phase, tc, Λ_1, Λ_2] are considered for the Fisher matrix derivation. The uncertainties on parameters coming from the covariance matrix (the inverse of the Fisher matrix) are given at 1σ. We implement a duty cycle of 85% for each of the L-shaped detectors, and for each of the three nested detectors composing the triangle. </p> <ul> <li><strong>Signals_<em>{BNS_merger_rate}</em>_<em>{EOS}</em>_<em>{NS_mass_distribution}</em>_<em>{Detector_configuration}</em>.txt </strong>contains the parameters describing a GW event and the corresponding network signal-to-noise ratio (SNR) <ul> <li><strong>mass_1: </strong>primary mass of the binary in [Msol] (in detector frame) (M_1)</li> <li><strong>mass_2:</strong> secondary mass of the binary in [Msol] (in detector frame) (M_2)</li> <li><strong>luminosity_distance:</strong> the luminosity distance of the merger in [Mpc]</li> <li><strong>dec:</strong> declination angle in [rad]. It varies in [−𝜋/2,+𝜋/2]</li> <li><strong>ra:</strong> right ascension in [rad]. It varies in [0,2/𝑝𝑖]</li> <li><strong>theta_jn:</strong> the angle between the line of observation and the total angular momentum (orbital, spin and GR corrections) of the binary [rad] (it reduces to the so-called inclination angle or <strong>iota</strong> if the spin component is absent); it ranges in [0,𝜋]</li> <li><strong>psi:</strong> the polarization angle in [rad]; it ranges in [0,𝜋]</li> <li><strong>geocent_time:</strong> merger time as GPS time in [s]</li> <li><strong>phase:</strong> the initial phase of the merger in [rad]; it ranges in [0,2𝜋]</li> <li><strong>redshift: </strong>the redshift of the merger</li> <li><strong>lambda_1: </strong>dimensionless tidal polarizabilty of primary component</li> <li><strong>lambda_2:</strong> dimensionless tidal polarizabilty of secondary component</li> <li><strong>network_SNR:</strong> network SNR for a the given event</li> </ul> </li> <li><strong>Errors_<em>{BNS_merger_rate}</em>_<em>{EOS}</em>_<em>{NS_mass_distribution}</em>_<em>{Detector_configuration}</em>.txt </strong>contains the <div> <div>parameter errors for each event. The first column is <strong>network_SNR</strong>, the following columns repeat the injected parameters as above and the relative errors <strong>err_<em>{parameter}</em></strong><em>. </em>The last column is the error on sky localisation (<strong>err_sky_location</strong>) at 90% credible interval. </div> </div> </li> </ul> <h3>Further details </h3> <p>Further details on the assumptions we made to produce these catalogs can be found in <a href="https://arxiv.org/abs/2411.02342" target="_blank" rel="noopener">Loffredo et al. 2024</a>, while further details on GWFish can be found on <a href="https://colab.research.google.com/github/janosch314/GWFish/blob/main/gwfish_tutorial.ipynb" target="_blank" rel="noopener">this tutorial</a>. We also provide the jupyter notebook <strong>paper_plots.ipynb</strong>, to reproduce Figs. 10, 11, 13, D.1, D.5, D.6. </p>
Datasets of "Modified propagation of gravitational waves from the early radiation era"
<p>This directory contains an index.html file with links to the run<br> directories with secondary data for the other<br> figures for the paper "Modified propagation of gravitational waves<br> from the early radiation era" by Y. He, A. Roper Pol, and A. Brandenburg.<br> If anything turns out to be incomplete,<br> please email yutong.he@su.se.</p>
Test sets and results for paper 'Rapid localization of gravitational wave sources from compact binary coalescences using deep learning'
<p>This folder contains the input data (signal-to-noise ratio time series for gravitational wave detections) and results (sky localization areas) obtained from the deep learning based sky localization model 'GW-SkyLocator', and the rapid online gravitational wave sky localization tool 'BAYESTAR' on a set of injections of gravitational wave signals from compact binary mergers.</p> <p>For details of the work, please refer to the paper, 'Rapid localization of gravitational wave sources from compact binary coalescences using deep learning' (https://arxiv.org/abs/2207.14522).</p>
Gravitational wave parameter estimation dataset
<p>Dataset for 'Gravitational Wave Parameter Estimation Using Machine Learning' workshop at OzGrav Winter School - 2023, held in University of Western Australia, Perth.</p>
KDE Representations of the Gravitational Wave Background Free Spectra Present in the NANOGrav 12.5-Year Dataset
<p><em><strong>OVERVIEW</strong></em></p> <p><em><strong>----------------</strong></em></p> <p>This is a downloadable file of probability densities from KDEs (Kernel Density Estimator) of the CRN free spectrum analysis of the <a href="https://nanograv.org/science/data/125-year-pulsar-timing-array-data-release">NANOGrav 12.5yr Dataset</a> that can be used with the <a href="https://github.com/astrolamb/ceffyl">Ceffyl</a> and <a href="https://github.com/andrea-mitridate/PTArcade">PTArcade</a> packages. Please see the GitHub page for Ceffyl/PTArcade installation details and usage information.<br><br>Details on how this data product was produced can be found in <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.108.103019"><em>Lamb, Taylor & van Haasteren 2023 (DOI 10.1103/PhysRevD.108.103019).</em></a></p> <p><em><strong>DIRECTORY AND FILE STRUCTURE</strong></em></p> <p><em><strong>----------------------------------------------------</strong></em></p> <p>The directory `ng12p5_ceffyl` contains a file with log-pdfs representing their corresponding free spectra (`density.npy`), the frequencies at which they were analysed (`freqs.npy`), the grid-points at which each log-pdf was computed (`log10rhogrid.npy`), frequency labels (`log10rholabels.txt`), analysis label (`pulsar_list.txt`), an array of bandwidths computed using the Sheather-Jones method (see Lamb et al. 2023; 'bandwidths.npy'), and a file with some metadata about the data (`log.txt`).</p> <p>Note: there are two copies of `ng12p5_ceffyl` in here by accident! Both are correct versions.</p> <p>The free spectrum was ran with a 30 frequency common uncorrelated free spectrum model (CRN FS) and a 30 frequency intrinsic red noise. <a href="https://nanograv.org/science/data/125-year-stochastic-gravitational-wave-background-search">The free spectrum can be found here</a>.</p> <p><em><strong>SOFTWARE</strong></em></p> <p><em><strong>------------------</strong></em></p> <p>This data should ideally be used with the latest versions of:</p> <ul> <li><em><strong>ceffyl</strong></em> (https://github.com/astrolamb/ceffyl)</li> <li><em><strong>PTArcade </strong></em>(https://github.com/andrea-mitridate/PTArcade)</li> </ul> <p><em><strong>PLANNED REVISIONS</strong></em></p> <p><em><strong>---------------------------------</strong></em></p> <p>None</p> <p><em><strong>CHANGE LOG</strong></em></p> <p><em><strong>----------------------</strong></em></p> <p>16/01/2024 - updated KDE representations</p> <p>A bug was found that produced a poor reflection at the lower prior boundary. Hence, data was being represented well at the lower prior boundary. This has now been corrected.</p>
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders: BNS Dataset
<p>Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders: BNS Dataset</p>
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders: BBH Dataset
<p>Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders: BBH Dataset</p>
Datasets for "Gravitational wave signal from primordial magnetic fields in the Pulsar Timing Array frequency band'
<p>The tar archive run_directories.zip contains the run directories for each run in Table 1 and the figures of the paper "Gravitational wave signal from primordial magnetic fields in the Pulsar Timing Array frequency band", by A. Roper Pol, C. Caprini, A. Neronov, D. Semikoz. The run directories, the plots, and the code to generate the plots can be found in https://github.com/AlbertoRoper/GW_turbulence.</p>
Movies for Figures in "In LIGO's Sight? Vigorous Coherent Gravitational Waves from Cooled Collapsar Disks"
<p>Full resolution videos for Figures 1, 3, 4 and 6 in publication: "In LIGO’s Sight? Vigorous Coherent Gravitational Waves from Cooled Collapsar Disks".</p> <p>The movies for Figure 1 (GW_cooled_disk and GW_noncooled_disk) present 3D rendering movie of the density in cooled and non-cooled disks. </p> <p>The movie for Figure 3 (GW_polarizations) shows 3D rendering of the plus polarization in model C.</p> <p>The movie for Figure 4 (Spectrum) shows the evolution of the characteristic strain in the frequency domain in model C.</p> <p>The movie for Figure 6 (Post_merger_disk_GW) presents 3D rendering of density in the post-merger disk.</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.