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142 results for “gravitational waves”

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zenodo40/100

BHBH simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers

The data for all BHBH simulations shown in "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers".  Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)   Contents:  <ul> <li>18 zip files that each contain an hdf5 file with the raw data for one of the simulations from Table 1 in the paper. The only exception is the fiducial.zip file and the unstableCaseBB.zip file, which contain both the fiducial (model A) and 'optimistic CE' (model K) data file and the "unstable case BB" (model E)  and "unstable case BB + optimistic CE" (model F) files. These zip files are:  <ul> <li>fiducial.zip,  the Fiducial model (A) and Optimistic CE model (K)</li> <li>massTransferEfficiencyFixed_0_25.zip, the \(\beta\) = 0.25 model (B) </li> <li>massTransferEfficiencyFixed_0_5.zip, the \(\beta\) = 0.5 model (C) </li> <li>massTransferEfficiencyFixed_0_75.zip, the \(\beta\) = 0.75 model (D)</li> <li>unstableCaseBB.zip, the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li>alpha0_1 zip, the \(\alpha = 0.1\) model (G) </li> <li>alpha0_5.zip, the \(\alpha = 0.5\) model (H) </li> <li>alpha2_0.zip, the \(\alpha = 2.0\) model (I) </li> <li>alpha10_0.zip, the \(\alpha = 10.0\) model (J) </li> <li>rapid.zip, the rapid SN model (L) </li> <li>maxNSmass2_0.zip, the max \(m_{\rm{NS}} = 2\, \rm{M}_{\odot}\) model (M) </li> <li>maxNSmass3_0.zip, the max \(m_{\rm{NS}} = 3\, \rm{M}_{\odot}\) model (N)</li> <li>noPISN.zip, the no PISN model (O) </li> <li>ccSNkick_100km_s.zip, the \(\sigma_{\rm{cc}}\)= 100 km/s model (P) </li> <li>ccSNkick_30km_s.zip, the \(\sigma_{\rm{cc}}\)= 30 km/s model (Q)</li> <li> noBHkick.zip, the no BH SN kick model (R)</li> <li>wolf_rayet_multiplier_0_1.zip, the model with Wolf-Rayet wind factor \(f_{\rm{WR}} = 0.1\) (S)</li> <li>wolf_rayet_multiplier_5.zip, the model with Wolf-Rayet wind factor \(f_{\rm{WR}} = 5\) (T)  </li> </ul> </li> <li>2 more zip files containing csv files with the summarized rates to create Figures 1, 2 and 3, which do not require downloading the entire dataset, but instead use these csv files with the summarized rates:  <ul> <li>csvFilesForFigure1_DCOpaper.zip # contains the files to recreate figure 1 with the merger rates per metallicity for BH-BH, BH-NS and NS-NS: <ul> <li>formationRatesTotalAndPerChannel_BHBH_.csv</li> <li>formationRatesTotalAndPerChannel_BHNS_.csv</li> <li>formationRatesTotalAndPerChannel_NSNS_.csv</li> </ul> </li> <li>csvFilesForFigure2_and_3_DCOpaper.zip # contains the files to recreate figure 2 with the merger rates for intrinsic and GW detection weighted, containing the csv files with names:  <ul> <li>rates_MSSFR_Models_BHBH_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_NSNS_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_BHNS_AllDCOsimulation.csv</li> </ul> </li> </ul> </li> </ul>   Details of how to use the data (a readme),  as well as scripts to reproduce all results, plots, and figures from the paper are given in the accompanying Github repository <a href="https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers">https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers</a>  If you use this data, please cite  Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a>

opencc-by-4.0Nov 2021View details →

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Reproduction package for the paper "Investigating the detection rates and inference of gravitational-wave and radio emission from black hole-neutron star mergers"

This is a basic reproduction package for the paper "Investigating the detection rates and inference of gravitational-wave and radio emission from black hole neutron star mergers".

opencc-by-4.0May 2022View details →

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Data release associated with ``Search for Coincident Gravitational Wave and Long Gamma-Ray Bursts from 4-OGC and the Fermi-GBM/Swift-BAT Catalog"

This is associated data release for the paper https://arxiv.org/abs/2208.03279. It contains the skymaps from potential gravitational-wave candidates from binary neutron star or neutron star-black hole merger. The notebook showcases how to use it. More information can be found in the github repository: https://github.com/gwastro/gw-longgrb <pre>  </pre> <pre>  </pre>

opencc-by-4.0Sep 2022View details →

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The Missing Link Between Black Holes in High-Mass X-ray Binaries and Gravitational-Wave Sources: Observational Selection Effects

Data tables containing the calculated binary parameters used to acquire all results in <a href="https://arxiv.org/abs/2210.01825v1">arXiv:2210.01825v1</a>. The file "xrb_params_illustris_z0.05_sample.csv" contains data for the z<0.05 sampled population and the file xrb_params_illustris_z20_sample.csv contains data for the z<20 sampled population.

opencc-by-4.0Oct 2022View details →

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Train and test datasets used for the paper "Neural network time-series classifiers for gravitational-wave searches in single-detector periods"

This repository contains the datasets used for training and testing during the work discussed in the paper "<a href="https://iopscience.iop.org/article/10.1088/1361-6382/ad40f0" target="_blank" rel="noopener">Neural network time-series classifiers for gravitational-wave searches in single-detector periods</a>". Please refer to this paper for more details on how the dataset was produced and cite it if you use these data: A. Trovato et al "Neural network time-series classifiers for gravitational-wave searches in single-detector periods", Class. Quant. Grav. 2024 DOI 10.1088/1361-6382/ad40f0. In this repository you will find six files in format npz, three of which refer to the test dataset and three to the train dataset. Each file name is of the type {label}_{train or test}.npz where "label" can be "glitch", "noise" or "signal", while the second part of the name indicates whether the file was used for training or testing. Each file is a collection of numpy arrays so it should be read with python. It contains 3 numpy arrays: 'X', 'Y' and 'metadata'. 'X' is a matrix containing 1-second segments of data sampled at 2048 Hz of the LIGO-Livingston detector, so it has shape: (number of samples, 2048). 'Y' contains the label for each segment, which is 0 for noise, 1 for signal and 2 for glitch, so it has shape: (number of samples,). In this case, the information on 'Y' is redundant since it's given directly by the filename. The 'metadata' matrix contains 17 metadata for each sample only for the case of signals, for glitch or noise it contains just 17 zeros for each sample. The shape of 'metadata' is thus: (number of samples, 17). For the signal files, for each sample the metadata is an array with these components: <ol> <li>GPS start of the file from which this segment comes</li> <li>starting GPS time of this segment</li> <li>duration of the segment [s]</li> <li>mass1 [solar masses]</li> <li>mass2 [solar masses]</li> <li>spin1z</li> <li>spin2z</li> <li>inclination [radians]</li> <li>coalescence phase [radians]</li> <li>distance [Mpc]</li> <li>right_ascension [radians]</li> <li>declination [radians]</li> <li>polarization [radians]</li> <li>SNR (signal to noise ratio)</li> <li>shift of the signal w.r.t. the timeseries [s]</li> <li>length of the signal [s]</li> <li>fraction of the signal contained in the time window</li> </ol> Number of samples: <ul> <li>80000 for the file glitch_test.npz</li> <li>69998 for the file glitch_train.npz</li> <li>500000 for the file noise_test.npz</li> <li>250000 for the file noise_train.npz</li> <li>500000 for the file signal_test.npz</li> <li>250000 for the file signal_train.npz</li> </ul> An example of few lines of python code to read each file is: <pre><code>import numpy as np f = np.load("filename.npz") X = f['X'] Y = f['Y'] m = f['metadata'] </code></pre> For the preparation of these data, we acknowledge the use of the following software packages: GWpy [1], PyCBC [2] and LALSuite [3].  This research has made use of data or software obtained from the Gravitational Wave Open Science Center (<a href="https://gwosc.org/" target="_blank" rel="noopener">gwosc.org</a>), a service of the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation, as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. KAGRA is supported by Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS) in Japan; National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea; Academia Sinica (AS) and National Science and Technology Council (NSTC) in Taiwan. [1] https://gwpy.github.io [2] https://pycbc.org [3] https://lscsoft.docs.ligo.org/lalsuite

opencc-by-4.0Apr 2024View details →

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Data from "A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154"

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."

opencc-by-4.0Oct 2024View details →

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Simulating relic gravitational waves from inflationary magnetogenesis

<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Simulating relic gravitational waves from inflationary magnetogenesis" by Axel Brandenburg and Ramkishor Sharma. If anything turns out to be incomplete, please email brandenb@nordita.org. See also the notes.pdf file in the paper directory with information about the importance of the f'/f term in the expression for the E field.</pre>

opencc-by-4.0Jun 2021View details →

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Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders: H1 detector

Gravitational Wave signals and random noise datasets, generated with the PyCBC library (https://pycbc.org).  Data consists of 8 sec time sequences for a single detector (the H1 detector, mimicking LIGO Hanford), sampled at 2048 Hz and  represented as a one-dimensional array with 16,384 entries.  Signal samples are provided, corresponding to Binary Black Hole and Binary Neutron Star mergers overlapped to noise.

opencc-by-4.0Jul 2021View details →

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Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders: L1 detector

Gravitational Wave signals and random noise datasets, generated with the PyCBC library (https://pycbc.org).  Data consists of 8 sec time sequences for a single detector (the L1 detector, mimicking LIGO Livingston), sampled at 2048 Hz and  represented as a one-dimensional array with 16,384 entries.  Signal samples are provided, corresponding to Binary Black Hole and Binary Neutron Star mergers overlapped to noise.

opencc-by-4.0Jul 2021View details →

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NSNS simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers

The data for all NSNS simulations shown in "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers".  Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)   Contents:  <ul> <li>18 zip files that each contain an hdf5 file with the raw data for one of the simulations from Table 1 in the paper. The only exception is the fiducial.zip file and the unstableCaseBB.zip file, which contain both the fiducial (model A) and 'optimistic CE' (model K) data file and the "unstable case BB" (model E)  and "unstable case BB + optimistic CE" (model F) files. These zip files are:  <ul> <li>fiducial.zip,  the Fiducial model (A) and Optimistic CE model (K)</li> <li>massTransferEfficiencyFixed_0_25.zip, the \(\beta\) = 0.25 model (B) </li> <li>massTransferEfficiencyFixed_0_5.zip, the \(\beta\) = 0.5 model (C) </li> <li>massTransferEfficiencyFixed_0_75.zip, the \(\beta\) = 0.75 model (D)</li> <li>unstableCaseBB.zip, the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li>alpha0_1 zip, the \(\alpha = 0.1\) model (G) </li> <li>alpha0_5.zip, the \(\alpha = 0.5\) model (H) </li> <li>alpha2_0.zip, the \(\alpha = 2.0\) model (I) </li> <li>alpha10_0.zip, the \(\alpha = 10.0\) model (J) </li> <li>rapid.zip, the rapid SN model (L) </li> <li>maxNSmass2_0.zip, the max \(m_{\rm{NS}} = 2\, \rm{M}_{\odot}\) model (M) </li> <li>maxNSmass3_0.zip, the max \(m_{\rm{NS}} = 3\, \rm{M}_{\odot}\) model (N)</li> <li>noPISN.zip, the no PISN model (O) </li> <li>ccSNkick_100km_s.zip, the \(\sigma_{\rm{cc}}\)= 100 km/s model (P) </li> <li>ccSNkick_30km_s.zip, the \(\sigma_{\rm{cc}}\)= 30 km/s model (Q)</li> <li> noBHkick.zip, the no BH SN kick model (R)</li> <li>wolf_rayet_multiplier_0_1.zip, the model with Wolf-Rayet wind factor \(f_{\rm{WR}} = 0.1\) (S)</li> <li>wolf_rayet_multiplier_5.zip, the model with Wolf-Rayet wind factor \(f_{\rm{WR}} = 5\) (T)  </li> </ul> </li> <li>2 more zip files containing csv files with the summarized rates to create Figures 1, 2 and 3, which do not require downloading the entire dataset, but instead use these csv files with the summarized rates:  <ul> <li>csvFilesForFigure1_DCOpaper.zip # contains the files to recreate figure 1 with the merger rates per metallicity for BH-BH, BH-NS and NS-NS: <ul> <li>formationRatesTotalAndPerChannel_BHBH_.csv</li> <li>formationRatesTotalAndPerChannel_BHNS_.csv</li> <li>formationRatesTotalAndPerChannel_NSNS_.csv</li> </ul> </li> <li>csvFilesForFigure2_and_3_DCOpaper.zip # contains the files to recreate figure 2 with the merger rates for intrinsic and GW detection weighted, containing the csv files with names:  <ul> <li>rates_MSSFR_Models_BHBH_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_NSNS_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_BHNS_AllDCOsimulation.csv</li> </ul> </li> </ul> </li> </ul>   Details of how to use the data (a readme),  as well as scripts to reproduce all results, plots, and figures from the paper are given in the accompanying Github repository <a href="https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers">https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers</a>  If you use this data, please cite  Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a>

opencc-by-4.0Aug 2021View details →

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BHNS simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers

The data for all BHNS simulations shown in "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers".  Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)   Contents:  <ul> <li>18 zip files that each contain an hdf5 file with the raw data for one of the simulations from Table 1 in the paper. The only exception is the fiducial.zip file and the unstableCaseBB.zip file, which contain both the fiducial (model A) and 'optimistic CE' (model K) data file and the "unstable case BB" (model E)  and "unstable case BB + optimistic CE" (model F) files. These zip files are:  <ul> <li>fiducial.zip,  the Fiducial model (A) and Optimistic CE model (K)</li> <li>massTransferEfficiencyFixed_0_25.zip, the \(\beta\) = 0.25 model (B) </li> <li>massTransferEfficiencyFixed_0_5.zip, the \(\beta\) = 0.5 model (C) </li> <li>massTransferEfficiencyFixed_0_75.zip, the \(\beta\) = 0.75 model (D)</li> <li>unstableCaseBB.zip, the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li>alpha0_1 zip, the \(\alpha = 0.1\) model (G) </li> <li>alpha0_5.zip, the \(\alpha = 0.5\) model (H) </li> <li>alpha2_0.zip, the \(\alpha = 2.0\) model (I) </li> <li>alpha10_0.zip, the \(\alpha = 10.0\) model (J) </li> <li>rapid.zip, the rapid SN model (L) </li> <li>maxNSmass2_0.zip, the max \(m_{\rm{NS}} = 2\, \rm{M}_{\odot}\) model (M) </li> <li>maxNSmass3_0.zip, the max \(m_{\rm{NS}} = 3\, \rm{M}_{\odot}\) model (N)</li> <li>noPISN.zip, the no PISN model (O) </li> <li>ccSNkick_100km_s.zip, the \(\sigma_{\rm{cc}}\)= 100 km/s model (P) </li> <li>ccSNkick_30km_s.zip, the \(\sigma_{\rm{cc}}\)= 30 km/s model (Q)</li> <li> noBHkick.zip, the no BH SN kick model (R)</li> <li>wolf_rayet_multiplier_0_1.zip, the model with Wolf-Rayet wind factor \(f_{\rm{WR}} = 0.1\) (S)</li> <li>wolf_rayet_multiplier_5.zip, the model with Wolf-Rayet wind factor \(f_{\rm{WR}} = 5\) (T)  </li> </ul> </li> <li>2 more zip files containing csv files with the summarized rates to create Figures 1, 2 and 3, which do not require downloading the entire dataset, but instead use these csv files with the summarized rates:  <ul> <li>csvFilesForFigure1_DCOpaper.zip # contains the files to recreate figure 1 with the merger rates per metallicity for BH-BH, BH-NS and NS-NS: <ul> <li>formationRatesTotalAndPerChannel_BHBH_.csv</li> <li>formationRatesTotalAndPerChannel_BHNS_.csv</li> <li>formationRatesTotalAndPerChannel_NSNS_.csv</li> </ul> </li> <li>csvFilesForFigure2_and_3_DCOpaper.zip # contains the files to recreate figure 2 with the merger rates for intrinsic and GW detection weighted, containing the csv files with names:  <ul> <li>rates_MSSFR_Models_BHBH_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_NSNS_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_BHNS_AllDCOsimulation.csv</li> </ul> </li> </ul> </li> </ul>   Details of how to use the data (a readme),  as well as scripts to reproduce all results, plots, and figures from the paper are given in the accompanying Github repository <a href="https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers">https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers</a>  If you use this data, please cite  Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a>

opencc-by-4.0Aug 2021View details →

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Reproduction package for the paper "Prospects of Gravitational Wave Follow-up Through a Wide-field Ultra-violet Satellite: a Dorado Case Study"

This is a basic reproduction package for the paper "Prospects of Gravitational Wave Follow-up Through a Wide-field Ultra-violet Satellite: a Dorado Case Study" by [Dorsman et al. (2022)](https://doi.org/10.48550/arXiv.2206.09696). This package provides (1) data and software to reproduce the results from the Bayesian inference, (2) the pre-computed results from the Bayesian inference for the purpose of reproducing the plots, and (3) the plots.

opencc-by-4.0Jan 2023View details →

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Precision Ephemerides for Gravitational-wave Sources+

Zenodo repository containing datasets and ephemeris predictions from the Precision Ephemerides for Gravitational-wave Searches project. New ephemerides will be published here, and updated incrementally as new data is acquired. Version history <ul> <li>1.0.0-release: first data release based on the PEGS IV MNRAS publication</li> <li>1.0.1: t_asc column now correctly corresponds to the neutron star time of ascending node.</li> <li>1.0.2: now provide files tabulating our measured velocities, and velocity errors for each dataset.</li> </ul>

opencc-by-4.0Jan 2023View details →

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Datasets for "Relic gravitational waves from the chiral plasma instability in the standard cosmological model"

<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Relic Gravitational Waves from the Chiral Plasma Instability in the Standard Cosmological Model". If anything turns out to be incomplete, please email brandenb@nordita.org.</pre>

opencc-by-4.0Jul 2023View details →

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The second data release from the European Pulsar Timing Array III. Search for gravitational wave signals

We present the results of the search for an isotropic stochastic gravitational wave background (GWB) at nanohertz frequencies using the second data release of the European Pulsar Timing Array (EPTA) for 25 millisecond pulsars and a combination with the first data release of the Indian Pulsar Timing Array (InPTA). A robust GWB detection is conditioned upon resolving the Hellings-Downs angular pattern in the pairwise cross-correlation of the pulsar timing residuals. Additionally, the GWB is expected to yield the same (common) spectrum of temporal correlations across pulsars, which is used as a null hypothesis in the GWB search. Such a common-spectrum process has already been observed in pulsar timing data. We analysed (i) the full 24.7-year EPTA data set, (ii) its 10.3-year subset based on modern observing systems, (iii) the combination of the full data set with the first data release of the InPTA for ten commonly timed millisecond pulsars, and (iv) the combination of the 10.3-year subset with the InPTA data. These combinations allowed us to probe the contributions of instrumental noise and interstellar propagation effects. With the full data set, we find marginal evidence for a GWB, with a Bayes factor of four and a false alarm probability of 4%. With the 10.3-year subset, we report evidence for a GWB, with a Bayes factor of 60 and a false alarm probability of about 0.1% (≳ 3σ significance). The addition of the InPTA data yields results that are broadly consistent with the EPTA-only data sets, with the benefit of better noise modelling. Analyses were performed with different data processing pipelines to test the consistency of the results from independent software packages. The latest EPTA data from new generation observing systems show non-negligible evidence for the GWB. At the same time, the inferred spectrum is rather uncertain and in mild tension with the common signal measured in the full data set. However, if the spectral index is fixed at 13/3, the two data sets give a similar amplitude of (2.5 ± 0.7) × 10−15 at a reference frequency of 1 yr−1 . Further investigation of these issues is required for reliable astrophysical interpretations of this signal. By continuing our detection efforts as part of the International Pulsar Timing Array (IPTA), we expect to be able to improve the measurement of spatial correlations and better characterise this signal in the coming years.

opencc-by-4.0Jun 2023View details →

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Data of Two-fluid Modeling of Acoustic Wave Propagation in Gravitationally Stratified Isothermal Media

Fully data of the paper "Two-fluid Modeling of Acoustic Wave Propagation in Gravitationally Stratified Isothermal Media" in the Astrophysical Journal. The Astrophysical Journal, 911:119 (18pp), 2021 April 20.

opencc-by-4.0Jul 2023View details →

zenodo40/100

Fermi-GBM and Swift-BAT Data Release Related to Analysis of Gravitational-Wave Candidates from the Third Gravitational-wave Observing Run

This material contains the data products associated with A Joint Fermi-GBM and Swift-BAT Analysis of Gravitational-Wave Candidates from the Third Gravitational-wave Observing Run [1]. It is based, in part, on data products associated with GWTC-2.1 [2][3] and GWTC-3 [4][5] from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration provided under a Creative Commons Attributional 4.0 International license. For more information, see the paper (<a href="https://arxiv.org/abs/2308.13666">https://arxiv.org/abs/2308.13666</a>) and the included README.txt. The data release includes the following directories: <ul> <li>3sigma_upper_limit_maps: 3 sigma flux upper limits reported by GBM as a function of sky position over a 10-1000 keV energy range.</li> <li>5sigma_upper_limit_maps: 5 sigma flux upper limits reported by GBM and BAT as a function of sky position over a 15-350 keV energy range.</li> <li>gbm_targeted_results: GRB candidates from the GBM Targeted Search.</li> <li>gbm_temporal_offset_analysis: input files to the time offset analysis applied to GBM on-board triggers and candidates from the GBM Untargeted Search.</li> <li>bbh_model_fluxes: predicted gamma-ray fluxes over the 10-1000 keV energy range for likely BBH mergers.</li> <li>gw_localizations: GW localization files used to overlay the 90% credible area onto GBM upper limits as a function of sky position.</li> <li>m1_m2_contours: 90% credible region paths for the GW component masses m1, m2.</li> </ul> The data release also includes a set of example scripts to show how the contents of the data files are used. Refer to the included README.txt for more details. References: <a href="https://arxiv.org/abs/2308.13666">[1] Fletcher, C. et al 2023, arXiv, 2308.13666</a> <a href="https://doi.org/10.5281/zenodo.6513631">[2] LIGO Scientific Collaboration and Virgo Collaboration. 2022,  Zenodo, 6513631</a> <a href="https://doi.org/10.5281/zenodo.5759108">[3] LIGO Scientific Collaboration and Virgo Collaboration. 2021, Zenodo, 5759108</a> <a href="https://doi.org/10.5281/zenodo.5546663">[4] LIGO Scientific Collaboration and Virgo Collaboration and KAGRA Collaboration. 2021, Zenodo, 5546663</a> <a href="https://doi.org/10.5281/zenodo.5546665">[5] LIGO Scientific Collaboration and Virgo Collaboration and KAGRA Collaboration 2021, Zenodo, 5546665</a>

opencc-by-4.0Sep 2023View details →

zenodo40/100

A Unified pastro for Gravitational Waves: Consistently Combining Information from Multiple Search Pipelines

This repository is made to contain the data products released with the paper " <a href="https://arxiv.org/abs/2305.00071">A Unified p-astro for Gravitational Waves: Consistently Combining Information from Multiple Search Pipelines</a>". In this paper, we showed how information from multiple GW pipelines can be combined to calculate a single probability of astrophysical origin - p-astro for GW triggers. This repository contains the joint probability distributions trained using injection (simulated signal) and noise triggers from the PyCBC and gstLAL pipelines from the O3 observing run of LIGO and Virgo. Using these distributions, a unified p-astro can be calculated for the on-source triggers from GWTC-2.1, as we show in the paper. If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get)">zenodo_get</a>:pip install zenodo_get zenodo-get RECORD_ID_OR_DOI There are two data products in this repository.1. A text file "pastro_calcs.txt" which has the unified p-astro of all triggers from GWTC-2.12. A pickle file containing the injection KDEs and the noise distribution files, along with the values of the normalization constants for the various cases. The pickle file can be read in python by,import pickle pdf = pickle.load(open('zenodo_pdf.pickle', 'rb')) The injection KDEs were created using search pipelines run on simulated signals done by the LIGO-Virgo-KAGRA collaboration. The injection files can be found in <a href="https://zenodo.org/record/5546676">this zenodo</a>

opencc-by-4.0Oct 2023View details →

zenodo36/100

Submitted Completed pointings to the Gravitational Wave Treasure Map for event S191205ah

Attached in a .json file is the completed pointing information for 12 observation(s) for the EM counterpart search associated with the gravitational wave event S191205ah.

opencc-zeroDec 2019View details →

zenodo36/100

Submitted Completed pointings to the Gravitational Wave Treasure Map for event S191216ap

Attached in a .json file is the completed pointing information for 30 observation(s) for the EM counterpart search associated with the gravitational wave event S191216ap. These observations were taken on the Sinistro, and Spectral instruments.

opencc-zeroDec 2019View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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Allen Brain Atlas

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Last verified 2026-04-30Open record

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Last verified 2026-04-30Open record

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.

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Last verified 2026-04-29Open record

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.

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neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record