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951 results for “Data release”
Greengenes2 training data formatted for DADA2 (Greengenes2 release version 2024.09)
<p>These DADA2-formatted training fasta files were derived from the Greengenes2 version 2024.09 release. https://ftp.microbio.me/greengenes_release/2024.09/</p> <p>These fastas were generated by the following commands (using the dada2 R package version 1.35.4):</p> <blockquote> <p>path <- "~/tax/GG2/2024_09"<br>fn <- file.path(path, "5b42d9b6-2f24-4f01-b989-9b4dafca7d5e/data/dna-sequences.fasta")<br>txfn <- file.path(path, "b7c3e691-ea51-4547-94dd-f79f49e41a36/data/taxonomy.tsv")</p> <p>fn.out.gg <- "~/Desktop/gg2_2024_09_toGenus_trainset.fa.gz"<br>dada2:::makeTaxonomyFasta_GG2(fn, txfn, fn.out.gg, include.species=FALSE, compress=TRUE)</p> <p>fn.out.spc.gg <- "~/Desktop/gg2_2024_09_toSpecies_trainset.fa.gz"<br>dada2:::makeTaxonomyFasta_GG2(fn, txfn, fn.out.spc.gg, include.species=TRUE, compress=TRUE)</p> </blockquote>
RDP taxonomic training data formatted for DADA2 (RDP release 19 - update 2023-08-23)
<p>These DADA2-formatted training fasta files were derived from the Ribosomal Database Project's Training Set 19 and the 2023-08-23 release of the RDP database. https://sourceforge.net/projects/rdp-classifier/files/RDP_Classifier_TrainingData/</p> <p>These fastas were generated by the following commands using the dada2 R package version 1.35.4:</p> <blockquote> <p>## RDP data: https://sourceforge.net/projects/rdp-classifier/files/RDP_Classifier_TrainingData/</p> <p>path <- "~/tax/rdp/v19"<br>fn.out.rdp <- "~/Desktop/rdp_19_toGenus_trainset.fa.gz"<br>dada2:::makeTaxonomyFasta_RDP(file.path(path, "trainset19_072023_speciesrank.fa"), <br> file.path(path, "trainset19_db_taxid.txt"), <br> fn.out.rdp, include.species=FALSE,<br> compress=TRUE)</p> <p>fn.out.spc.rdp <- "~/Desktop/rdp_19_toSpecies_trainset.fa.gz"<br>dada2:::makeTaxonomyFasta_RDP(file.path(path, "trainset19_072023_speciesrank.fa"), <br> file.path(path, "trainset19_db_taxid.txt"), <br> fn.out.spc.rdp, include.species=TRUE,<br> compress=TRUE)</p> </blockquote>
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>
Supplementary data to "GD-1 STELLAR STREAM AND COCOON IN THE DESI EARLY DATA RELEASE"
<p>Supplementary material to DESI's publication "GD-1 STELLAR STREAM AND COCOON IN THE DESI EARLY DATA RELEASE" to comply with the data management plan. Data associated with revised version of paper submitted to The Astrophysical Journal. Directory contains data points for all figures with file names indicating the figure to which the data corresponds as well as a FITS file for Table 4. Data for 13 figures and one table (46 FITS files) included in .zip directory</p>
Inferring cosmology from gravitational waves using non-parametric detector-frame mass distribution: Data Release
<p>Dataset release accompanying Inferring cosmology from gravitational waves using non-parametric detector-frame mass distribution.</p>
Data release: Searching for binary black hole sub-populations in gravitational wave data using binned Gaussian processes
<p>The data required to reproduce the analyses of "Searching for binary black hole sub-populations in gravitational wave data using binned Gaussian processes" (<a href="https://arxiv.org/abs/2404.03166" target="_blank" rel="noopener">arxiv:2404.03166</a>). The main inference code can be found at <a href="https://github.com/AnaryaRay1/gppop/tree/spin-dev" target="_blank" rel="noopener">https://github.com/AnaryaRay1/gppop/tree/spin-dev </a> (commit: <a href="https://github.com/AnaryaRay1/gppop/commit/ee5ffc421e2c96eeed15a0e0d3839da42b982842">ee5ffc</a>). To reproduce the analyses, follow the instructions at <a href="https://github.com/AnaryaRay1/bbh-subpopulations-scripts">https://github.com/AnaryaRay1/bbh-subpopulations-scripts</a> (commit <a href="https://github.com/AnaryaRay1/bbh-subpopulations-scripts/commit/de88f931d8c1a2cb31ad2fa9d6fdf9a5a00a3c3b">de88f93</a>). Frozen versions of these repositories that were used to generate all the results are available as part of this data release, in the files "gppop_spin_dev_ee5ffc421.tar.gz" and "bbh-subpopulations-scripts_de88f931.tar.gz" respectively.</p>
SemanticPriming/SPAML: SPAML v1.0.2 Data Release
<h2>What's Changed</h2> <ul> <li>Add information on computationally reproducing the analyses by @suchow in https://github.com/SemanticPriming/SPAML/pull/6</li> <li>Use data URL that always resolves to latest by @suchow in https://github.com/SemanticPriming/SPAML/pull/5</li> <li>Add checksum file for data files by @suchow in https://github.com/SemanticPriming/SPAML/pull/4</li> <li>Copyedit the various READMEs by @suchow in https://github.com/SemanticPriming/SPAML/pull/3</li> <li>Updated manuscript to final version for submission by updating reliability, adding raw response time tables to descriptives, creating raw response latency figures, and remove unused files</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/SemanticPriming/SPAML/compare/v1.0.1...v1.0.2</p> <p>This data release includes all data from the SPAML project as of February 14, 2024. Please see documentation in the main github for information on the project and data. You can use the <code>semanticprimeR</code> package to help you process and load the data: https://github.com/SemanticPriming/semanticprimeR.</p>
FM-Tools Release 2.2: Data Set of Metadata about Tools for Formal Methods (SV-COMP 2025, Test-Comp 2025)
<h1>Collection of Information about Formal-Methods Tools</h1> <h2>Motivation</h2> <p>There are many tools available that implement formal-methods approaches. This repository collects meta data about the tools, such that it becomes easier to reuse, integrate, and cooperate with formal-methods tools.</p> <p>A <a href="https://www.sosy-lab.org/research/pub/2024-Podelski65.Find_Use_and_Conserve_Tools_for_Formal_Methods.pdf">description</a> of the structure of this repository can be found in an article.</p> <p>A <a href="https://fm-tools.sosy-lab.org/">formatted listing</a> of some of the data in this repository can be found on a generated web site.</p> <p>A <a href="https://fm-tools.sosy-lab.org/schema.html">schema definition</a> of the data files in this repository can be found on a generated web site.</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Candidate data release
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog 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. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><strong>Candidate data release</strong></p> <p>Data associated with candidates in GWTC-3. These are gravitational-wave candidates from the the third observing run (O3) of the Advanced LIGO and Advanced Virgo detectors that pass a false alarm rate threshold of 2/day. We upload a tar file (search_data.tar.gz) containing all the data and a python notebook (GWTC3_search_data.ipynb) that provides a description on how to use the files contained in the dataset.</p> <p>Associated with each candidate are the search analysis results and a localization (assuming that the source is astrophysical). Four search analysis pipelines have been used: the templated-based <a href="https://lscsoft.docs.ligo.org/gstlal/">GstLAL</a>, <a href="https://doi.org/10.1088/1361-6382/abe913">MBTA</a> and <a href="https://pycbc.org/">PyCBC</a>, plus the template-free <a href="https://gwburst.gitlab.io/">cWB</a>. Localizations from the template-based pipelines are calculated using <a href="https://lscsoft.docs.ligo.org/ligo.skymap/bayestar/index.html">Bayestar</a>, while cWB candidates are calculated by cWB itself.</p> <p>This release is primarily composed of results from the second part of O3 (O3b), but also includes a subset of results from the first part (O3a). A similar release was made for the previous <a href="https://doi.org/10.5281/zenodo.5117761">GWTC-2.1</a> that contained candidates from the first part of O3 (O3a) from GstLAL, MBTA and PyCBC. We include updated probabilities of astrophysical origin for these candidates: each search analysis has a o3a_pastro directory that contains these results. Since GWTC-2.1 did not include cWB results, this release includes cWB O3a candidates in addition to O3b: the cWB directory contains a subdirectory called o3a_events that contains the O3a results.</p> <p>The probability of astrophysical origin is calculated assuming a compact binary coalescence source, which may not always be appropriate for the template-free cWB analysis.</p> <p> </p> <p>For more general background on gravitational-wave search analysis and sky maps, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Candidate Data Release
<p>Data associated with candidates in <a href="https://dcc.ligo.org/LIGO-P2100063/public">GWTC-2.1</a>. These are gravitational-wave candidates from the first half of the third observing run (O3a) of the Advanced LIGO and Virgo detectors that pass a false alarm rate threshold of 2/day. We upload a tar file (search_data_GWTC2p1.tar.gz) containing all the data and a python notebook (search_data.ipynb) which provides description on how to use the files contained in the dataset.</p>
Supplementary Material 1: Original dataset collected during the tracking and mark-release-recapture study and R script used to analyse the data
<p>The original dataset collected in northern Serbia during butterfly behavioural study on two species, <em>Phengaris teleius</em> and <em>Polyommatus icarus</em>. The dataset is provided in two separate CSV files for mark-release-recapture study and for butterfly tracking study. In addition, R script used to preopare the dataset and fit the models is given.</p>
Spatial patterns of extreme precipitation and their changes under ~2 °C global warming: A large-ensemble study of the western US: Data Release
<p>This dataset supports the analysis in Rupp et al. (2022). The dataset consists of 17,223 data files containing the water year (WY) maximum of the daily-averaged precipitation rate simulated with the HadRM3p regional climate model configured for the western United States. Each file contains the WY maxima across the model domain for a single WY, single model parameterization, and single set of initial conditions. Please refer to Hawkins et al. (2019) and Rupp et al. (2022) for a description of how the climate model data were generated.</p>
LiBforSecUse Data Release - Impedance spectra of life cycle tests of commercial 18650 cells
<p>The EMPIR project LiBforSecUse aimed to develop empirical measurement models to estimate the residual capacity of second-use Li-ion battery cells with impedance-based measurement and evaluation methods. The models have been established based on a series of life cycle tests of commercial 18650 (graphite/NMC) cells including regular impedance spectroscopy and capacity measurements. The measured data are made publicly available here. They can be downloaded to verify the models established within the project and they may be used for further investigations. However, the user is asked to pay tribute to the project and the researchers providing the data by citing this data source. A pdf file is added to give more detailed information on the data.</p>
Development and evaluation of a method to identify potential release areas of snow avalanches based on watershed delineation - source data
<p>Full data files for the paper:</p> <p>Duvillier, C., Eckert, N., Evin, G., and Deschâtres, M.: Development and evaluation of a method to identify potential release areas of snow avalanches based on watershed delineation, Nat. Hazards Earth Syst. Sci., 23, 1383–1408, https://doi.org/10.5194/nhess-23-1383-2023, 2023.</p> <p>Can be used to reproduce all the results of the paper and for further benchmarking of snow avalanche potential release area detection methods.</p>
Fermi-GBM Data Release Related to Searches for Neutrinos from Gamma-Ray Bursts using the IceCube Neutrino Observatory
<p>This data release includes Fermi Gamma-ray Burst Monitor (GBM) localizations used in searches for neutrinos from gamma-ray bursts (GRB) by the IceCube Neutrino Observatory. These localizations are provided publicly to the community since they are generally useful for any analysis that needs the Fermi-GBM localization for a GRB.</p> <p><strong>Full Details:</strong></p> <p>The files contained herein are HEALPix representations of GRB localizations from the Fermi-GBM stored as FITS files and produced according to the automated method described in [1]. Each file represents the probability density (statistical + systematic) for the true source location. By definition, this excludes the Earth occulted region of the sky, which is set to 0 due to the fact that real sources are not visible through the Earth. These files cover a time range spanning the first detection of GRBs by GBM in July 2008 through July 2019 and should be considered preliminary. The files are preliminary in the sense that they contain some key differences to the official files hosted at HEASARC FTP server through the Fermi Science Support Center (FSSC; <a href="https://fermi.gsfc.nasa.gov/ssc/data/access/gbm/">https://fermi.gsfc.nasa.gov/ssc/data/access/gbm/</a>). We list the key differences here:</p> <ul> <li>Fermi began production HEALPix FITS files in early 2018, and files prior to that have not been officially provided. The files in this archive are currently the only version of HEALPix files pre-2018.<br> </li> <li>These files were not produced via the standard GBM operational pipeline; however they were produced with the same functional code that is used to make the files. The result of this is that the standard quality checks on the FITS headers by uploading to the FSSC were skipped. The primary header is most affected, with some null values, but these null values do not affect the HEALPix data.<br> </li> <li>These localizations may have centroids that are slightly different than reported in the online catalog. This is because an automated algorithm for localization (RoboBA) was used to localize the GRBs and produce these files as opposed to the manual Human-in-the-Loop localization performed for every GRB prior to 2016, and ~15% of GRBs thereafter [1].<br> </li> <li>These localizations contain an updated and improved systematic uncertainty model compared to the pre-July 2019 localizations at the FSSC. The new systematic uncertainty model is explained in [1], while the older localizations at the FSSC contain a systematic uncertainty model from [2].<br> </li> <li> In general, the official localizations hosted at the FSSC currently do not remove localization probability that overlaps the Earth, but these files do remove the probability that overlaps the Earth and renormalizes the remaining PDF. This encodes the assertion that the localization is indeed of an astrophysical nature.</li> </ul> <p>The FITS files are organized with two HDUs:</p> <ul> <li> PRIMARY HDU with some basic metadata about the mission from which the data originated<br> </li> <li> HEALPIX HDU containing header information about the GBM detector pointings, as well as the Sun and Geocenter localizations with respect to Fermi. There are two data fields contained in the extension: <ul> <li> PROBABILITY: the differential localization probability per pixel (NSIDE=128)</li> <li> SIGNIFICANCE: integrated probability for estimating confidence intervals (NSIDE=128)</li> </ul> </li> </ul> <p>Furthermore, we provide images of each localization. The images are a Mollweide projection of the sky, with the 50% and 90% localization confidence regions marked in shaded purple. The location of the Earth from Fermi's perspective is marked in shaded blue.</p> <p>The GBM trigger number associated with each FITS file and image is listed in the filename.</p> <p><strong>References:</strong></p> <p><a href="https://iopscience.iop.org/article/10.3847/1538-4357/ab8bdb">[1] Goldstein, A. et al. 2020, ApJ, 895, 40</a><br> <a href="https://iopscience.iop.org/article/10.1088/0067-0049/216/2/32/meta">[2] Connaughton, V. et al. 2015, ApJS, 216, 32</a></p>
Gaia Data Release 3: BP/RP split-epoch validation dataset
<p>This dataset includes mean BP/RP spectra for about 43.6 thousand sources for which two mean spectra per source were generated using only a random selection of the available epoch spectra.</p> <p>More details about this dataset are given in Appendix D in the paper "Gaia Data Release 3: Processing and validation of BP/RP low-resolution spectral data", De Angeli, F., et al. A&A (2022). Results obtained from this dataset are published in the same paper and in "Gaia Data Release 3: The Galaxy in your preferred colours. Synthetic photometry from Gaia low-resolution spectra", Gaia Collaboration, Montegriffo, P., et al. A&A (2022).</p>
Correlative 3D SBFSEM data from: Intermittent bulk release of human cytomegalovirus
<p>Human Cytomegalovirus (HCMV) can infect a variety of cell types by using virions of varying glycoprotein compositions. It is still unclear how this diversity is generated, but spatio-temporally separated envelopment and egress pathways might play a role. So far, one egress pathway has been described in which HCMV particles are individually enveloped into small vesicles and are subsequently exocytosed continuously. However, some studies have also found enveloped virus particles inside multivesicular structures but could not link them to productive egress or degradation pathways.<br>We used a novel 3D-CLEM workflow allowing us to investigate these structures in HCMV morphogenesis and egress at high spatio-temporal resolution. We found that multiple envelopment events occurred at individual vesicles leading to multiviral bodies (MViBs), which subsequently traversed the cytoplasm to release virions as intermittent bulk pulses at the plasma membrane to form extracellular virus accumulations (EVAs). Our data support the existence of a novel bona fide HCMV egress pathway, which opens the gate to evaluate divergent egress pathways in generating virion diversity.</p>
Data Release: Population properties and multimessenger prospects of neutron star-black hole mergers following GWTC-3
<p>Neutron star-black hole (NSBH) mergers detected in gravitational waves have the potential to shed light on supernova physics, the dense matter equation of state, and the astrophysical processes that power their potential electromagnetic counterparts. We use the population of four candidate NSBH events detected in gravitational waves so far with a false alarm rate ≤1 yr−1 to constrain the mass and spin distributions and multimessenger prospects of these systems. We find that the black holes in NSBHs are both less massive and more slowly spinning than those in black hole binaries. We also find evidence for a mass gap between the most massive neutron stars and least massive black holes in NSBHs at 98.6% credibility. We consider both a Gaussian and a power-law pairing function for the distribution of the mass ratio between the neutron star and black hole masses but find no statistical preference between the two. Using an approach driven by gravitational-wave data rather than binary simulations, we find that fewer than 14% of NSBH mergers detectable in gravitational waves will have an electromagnetic counterpart. Finally, we propose a method for the multimessenger analysis of NSBH mergers based on the nondetection of an electromagnetic counterpart and conclude that, even in the most optimistic case, the constraints on the neutron star equation of state that can be obtained with multimessenger NSBH detections are not competitive with those from gravitational-wave measurements of tides in binary neutron star mergers and radio and X-ray pulsar observations.</p>
Data release for Tests of General Relativity with GWTC-3
<p>This is the full posterior samples release of the following analyses reported in the paper <a href="https://arxiv.org/abs/2112.06861">Tests of General Relativity with GWTC-3</a> from the LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration:</p> <ul> <li>Inspiral-merger-ringdown consistency test (Sec IV B): <strong><em>IGWN-GWTC3-TGR-v1-imr.zip</em></strong></li> <li>Lorentz invariance violation test (Sec VI): <strong><em>IGWN-GWTC3-TGR-v1-liv.zip</em></strong></li> <li>Parametrized tests of general relativity (Sec V A): <strong><em>IGWN-GWTC3-TGR-v1-par.zip</em></strong></li> <li>Ringdown test (Sec VIII A): <em><strong>IGWN-GWTC3-TGR-v1-rin.zip</strong></em></li> <li>Spin-induced quadrupole moment test (Sec V B): <em><strong>GWN-GWTC3-TGR-v1-sim.zip</strong></em></li> </ul> <p>Each zip file contains HDF5 files that can either be read directly with standard HDF5 tools or using PESummary (<a href="https://docs.ligo.org/lscsoft/pesummary/">https://docs.ligo.org/lscsoft/pesummary/</a>). The curated data set used for producing the figures and table in the paper can be found in <a href="https://dcc.ligo.org/LIGO-P2100456/public">https://dcc.ligo.org/LIGO-P2100456/public</a>.</p> <p>If you make use of these data or software in your own work, please include the following acknowledgment.</p> <blockquote> <p>LIGO Laboratory and Advanced LIGO are funded by the United States National Science Foundation (NSF) 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. The construction and operation of KAGRA are funded by Ministry of Education, Culture, Sports, Science and Technology (MEXT), and Japan Society for the Promotion of Science (JSPS), National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea, Academia Sinica (AS) and the Ministry of Science and Technology (MoST) in Taiwan. Unless otherwise specified, the contents of this release are licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.</p> </blockquote>
Data release associated with ``Search for Coincident Gravitational Wave and Long Gamma-Ray Bursts from 4-OGC and the Fermi-GBM/Swift-BAT Catalog"
<p>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</p> <pre> </pre> <pre> </pre>
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.