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Dataset results
216 results for “gravitation”
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>
Datasets for ``Leading-order nonlinear gravitational waves from reheating magnetogeneses''
<pre>This directory contains an index.html file with links to the run directories with secondary data for Table II of the paper "Leading-order nonlinear gravitational waves from reheating magnetogeneses" by Yutong He, Axel Brandenburg, and Alberto Roper Pol. If anything turns out to be incomplete, please email brandenb@nordita.org.</pre>
Reproduction package for "Searching for low radio-frequency gravitational wave counterparts in wide-field LOFAR data"
<p>This is a basic reproduction package for the paper "Searching for low radio-frequency gravitational wave counterparts in wide-field LOFAR data" by Gourdji et al. (2021) published in MNRAS. It describes the software and settings used to obtain the final data products of the analysis. It also includes a Jupyter notebook and required data to reproduce the tables and figures of this paper.</p>
Datasets for ``Big bang nucleosynthesis limits and relic gravitational waves detection prospects''
<pre>This directory contains an index.html file with links to the run directories with secondary data for Table I of the paper "Big bang nucleosynthesis limits and relic gravitational waves detection prospects" by T. Kahniashvili, E. Clarke, J. Stepp, & Axel Brandenburg. If anything turns out to be incomplete, please email brandenb@nordita.org. </pre>
Dataset from: Gravitational wave sources in our Galactic backyard - Predictions for BHBH, BHNS and NSNS binaries in LISA
<p>The data from all simulations used in "<em><strong>Gravitational wave sources in our Galactic backyard: Predictions for BHBH, BHNS and NSNS binaries in LISA</strong></em>"</p> <p>Contents:</p> <ul> <li><strong>detections_and_totals.zip</strong> <ul> <li>Contains for .npy files that contain tables of the detections and total DCOs in Milky Way. These were calculated with <a href="https://github.com/TomWagg/detecting-DCOs-in-LISA/blob/main/simulation/postprocessing_notebooks/get_detection_rates.ipynb">this</a> and <a href="https://github.com/TomWagg/detecting-DCOs-in-LISA/blob/main/simulation/postprocessing_notebooks/get_total_DCOs_in_MW.ipynb">this</a> notebook and are included for convenience so you don't have to re-run these notebooks</li> </ul> </li> <li><strong>simulations_4yr.zip</strong> <ul> <li>Contains 60 .h5 files that contain the main simulations for a 4-year LISA mission. Each file contains the results for a single DCO type (BHBH, BHNS or NSNS) and model variation (20 variations) are labeled as <em>{DCO_type}_{variation}_all.h5</em><strong><em>. </em></strong></li> </ul> </li> <li><strong>simulations_10yr.zip</strong> <ul> <li>As simulations_4yr.zip but for a 10-year LISA mission</li> </ul> </li> <li><strong>simple_mw_simulations.zip</strong> <ul> <li>As simulations_4yr.zip but using a simple model for the Milky Way (discussed in Appendix D) and only for models A and F (hence only contains 6 .h5 files)</li> </ul> </li> </ul> <p>For a description of how to use these files to reproduce figures and results see the README.md in the associated GitHub repository: <a href="https://github.com/TomWagg/detecting-DCOs-in-LISA">https://github.com/TomWagg/detecting-DCOs-in-LISA</a></p> <p>Version 0.0.1 - Changes model E to E' as discussed in paper (now we allow HeHG donors to survive common-envelopes)</p> <ul> </ul>
Datasets for "Dynamo effect in unstirred self-gravitating turbulence"
<pre>This directory contains an index.html file with links to the run directories with secondary data for Table I of the paper "Dynamo effect in unstirred self-gravitating turbulence" by Axel Brandenburg and Evangelia Ntormousi. If anything turns out to be incomplete, please email brandenb@nordita.org. </pre>
BHBH simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers
<p>The data for all <strong>BHBH </strong>simulations shown in<em><strong> "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers". </strong>Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)</em></p> <p> </p> <p><strong>Contents: </strong></p> <ul> <li><strong>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.</strong><br> <strong>These zip files are: </strong> <ul> <li><em>fiducial.zip, </em> the Fiducial model (A) and Optimistic CE model (K)</li> <li><em>massTransferEfficiencyFixed_0_25.zip, </em>the <span class="math-tex">\(\beta\)</span> = 0.25 model (B) </li> <li><em>massTransferEfficiencyFixed_0_5.zip</em>, the <span class="math-tex">\(\beta\)</span> = 0.5 model (C) </li> <li><em>massTransferEfficiencyFixed_0_75.zip,</em> the <span class="math-tex">\(\beta\)</span> = 0.75 model (D)</li> <li><em>unstableCaseBB.zip, </em>the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li><em>alpha0_1 zip</em>, the <span class="math-tex">\(\alpha = 0.1\)</span> model (G) </li> <li><em>alpha0_5.zip</em>, the <span class="math-tex">\(\alpha = 0.5\)</span> model (H) </li> <li><em>alpha2_0.zip</em>, the <span class="math-tex">\(\alpha = 2.0\)</span> model (I) </li> <li><em>alpha10_0.zip</em>, the <span class="math-tex">\(\alpha = 10.0\)</span> model (J) </li> <li><em>rapid.zip</em>, the rapid SN model (L) </li> <li><em>maxNSmass2_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 2\, \rm{M}_{\odot}\)</span> model (M) </li> <li><em>maxNSmass3_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 3\, \rm{M}_{\odot}\)</span> model (N)</li> <li><em>noPISN.zip</em>, the no PISN model (O) </li> <li><em>ccSNkick_100km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 100 km/s model (P) </li> <li><em>ccSNkick_30km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 30 km/s model (Q)</li> <li> <em>noBHkick.zip, </em>the no BH SN kick model (R)</li> <li><em>wolf_rayet_multiplier_0_1.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 0.1\)</span> (S)</li> <li><em>wolf_rayet_multiplier_5.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 5\)</span> (T)<br> <br> </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><strong>csvFilesForFigure1_DCOpaper.zip </strong># 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><strong>csvFilesForFigure2_and_3_DCOpaper.zip </strong># 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> <p> </p> <p>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> </p> <p>If you use this data, please cite </p> <p>Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a></p>
Spherical harmonic models of the gravitational field and shape of (101955) Bennu
<p>Provided are (i) three types of spherical harmonic models of the gravitational field of Bennu, (ii) reference gravitational data and (iii) a spherical harmonic model of Bennu's shape. The gravitational field models and the reference data were obtained by gravity forward modelling using a degree-15 spherical harmonic expansion of Bennu's shape and a constant mass density.</p> <p>The datasets have been published in Bucha, B., Sanso, F., 2021. <em>Gravitational field modelling near irregularly shaped bodies using spherical harmonics: a case study for the asteroid (101955) Bennu</em>. Journal of Geodesy, 95, 56, <a href="https://link.springer.com/article/10.1007/s00190-021-01493-w">https://link.springer.com/article/10.1007/s00190-021-01493-w</a></p>
Pixelated Reconstruction of Gravitational Lenses using Recurrent Inference Machine
<p>Modeling strong gravitational lenses to quantify the distortions of the background sources and reconstruct the mass density in the foreground lens has traditionally been a major computational challenge. As the quality of gravitational lens images increases, the task of fully exploiting the information they contain becomes computationally and algorithmically more difficult. </p> <p>In order to tackle this challenge, we have trained a Recurrent Inference Machine (RIM) on a large dataset of realistic gravitational lenses simulated from galaxy images from the COSMOS survey and convergence maps from the IllustrisTNG simulation. This dataset contains the dataset used to train the RIM and the Variational AutoEncoder (VAE) used in our work, <a href="https://arxiv.org/abs/2207.01073">Pixelated Reconstruction of Gravitational Lenses using Recurrent Inference Machine</a>, presented at the Machine Learning for Astrophysics Workshop at the Thirty-ninth International Conference on Machine Learning (ICML 2022), as well as the checkpoint files for the neural networks. </p> <p>The source code for our work is published under the package name <a href="https://github.com/AlexandreAdam/Censai">Censai</a>.</p>
Reproduction package for the paper "Investigating the detection rates and inference of gravitational-wave and radio emission from black hole-neutron star mergers"
<p>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".</p>
A suite of 2-arcsec surface gravitational maps of the Slovak Republic up to the full gravitational tensor
<p><em>grav-sr-2arcsec</em> is a suite of 2-arcsec surface gravitational maps of the Slovak Republic in terms of</p> <ul> <li>the gravitational potential (<em>V</em>),</li> <li>the full gravitational vector (<em>V</em><sub><em>x</em></sub>, <em>V</em><sub><em>y</em></sub>, <em>V</em><sub><em>z</em></sub><em>)</em> in the local north-oriented reference frame (LNOF), and</li> <li>the full gravitational tensor in LNOF (<em>V</em><sub><em>x</em><em>x</em></sub>, <em>V</em><sub><em>x</em><em>y</em></sub>, …, <em>V</em><sub><em>z</em><em>z</em></sub>).</li> </ul> <p>The suite was computed from a high-resolution gravity field model of Slovakia developed by <a href="http://doi.org/10.1093/gji/ggw311">Bucha et al. (2016)</a>. The maps rely on three sources of gravitational information:</p> <ul> <li>the Zero-Tide version of the global geopotential model EIGEN-6C4 (Forste, C. et al., 2014) up to degree 2190 (~5 arcmin resolution, ~9 km),</li> <li>the expansion of the residual gravitational signal up to degree 21600 in terms of spherical radial basis functions (<a href="http://doi.org/10.1093/gji/ggw311">Bucha et al. 2016</a>) (~30 arcsec, ~950 m), and</li> <li>residual terrain modelling (<a href="http://doi.org/10.1093/gji/ggw311">Bucha et al. 2016</a>) (2 arcsec, ~60 m).</li> </ul> <p>An independent validation of grav-sr-2arcsec revealed the standard deviation of 0.789 mGal in terms of the gravity.</p> <p>Bucha, B., Janak, J., Papco, J., Bezdek, A., 2016. <em>High-resolution regional gravity field modelling in a mountainous area from terrestrial gravity data</em>. Geophysical Journal International 207, 949-966, <a href="http://doi.org/10.1093/gji/ggw311">http://doi.org/10.1093/gji/ggw311</a></p>
Spherical harmonic models of the gravitational field implied by the Moon's topographic masses
<p>Provided are 12 spherical harmonic models of the gravitational field implied by the Moon's topographic masses. The models mitigate the divergence effect of spherical harmonics on the Moon's topography when compared with spectral gravity forward modelling methods. The topographic masses are expanded up to degrees 90, 180, 360 and 720, and the maximum degree of the gravitational models varies from 360 up to 2160. All models are available in the <a href="http://icgem.gfz-potsdam.de/ICGEM-Format-2011.pdf">gfc</a> format as defined by <a href="http://icgem.gfz-potsdam.de">ICGEM</a>. One of the models, <em>STU_Moon_topography_to720_gravity_to2160</em>, can also be accessed from <a href="http://icgem.gfz-potsdam.de/tom_celestial">ICGEM</a>, where it can be find under a shortened name <em>STU_MoonTopo720</em>.</p> <p>The models rely on the Runge-Krarup theorem and enable generally a more accurate evaluation of the gravitational field in the proximity to the lunar topography as compared to the models from spectral gravity forward modelling. This is because the latter ones may suffer from the divergence effect when evaluating the spherical harmonic series on or below the limit sphere encompassing all the gravitating masses (that is, also on the topography).</p> <p>The datasets have been published in Bucha, B., Hirt, C., Kuhn, M., 2019. <em>Divergence-free spherical harmonic gravity field modelling based on the Runge—Krarup theorem: a case study for the Moon</em>. Journal of Geodesy 93, 489-513, <a href="https://doi.org/10.1007/s00190-018-1177-4">https://doi.org/10.1007/s00190-018-1177-4</a></p>
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>
The Missing Link Between Black Holes in High-Mass X-ray Binaries and Gravitational-Wave Sources: Observational Selection Effects
<p>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.</p>
Train and test datasets used for the paper "Neural network time-series classifiers for gravitational-wave searches in single-detector periods"
<p>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:</p> <p><em>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.</em></p> <p>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.</p> <p>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:</p> <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> <p>Number of samples:</p> <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> <p>An example of few lines of python code to read each file is:</p> <pre><code>import numpy as np f = np.load("filename.npz") X = f['X'] Y = f['Y'] m = f['metadata'] </code></pre> <p>For the preparation of these data, we acknowledge the use of the following software packages: GWpy [1], PyCBC [2] and LALSuite [3]. </p> <p>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.</p> <p>[1] https://gwpy.github.io<br>[2] https://pycbc.org<br>[3] https://lscsoft.docs.ligo.org/lalsuite</p>
ERT geophysical surveys for detecting gravitational morpho-structures in the Becca France area (Aosta Valley, Italy)
<p>This dataset contains the raw and processed data of an Electrical Resistivity Tomography (ERT) geophysical survey conducted on November 2023 in the Becca France area (Aosta Valley, Italy), in order to help the geological reconstruction of gravitational morpho-structures.</p> <p>It represents the supplementary materials of a publication submitted on Geohazards journal: Forno et al., 2024. Deep electrical resistivity tomography for detecting gravitational morpho-structures in the Becca France area (Aosta Valley, Italy).</p> <p> </p> <p>For details, please refer to the above mentioned publication and to the readme file.</p> <p> </p> <p>7z software (free and open source) can be used to unzip the dataset.</p> <p> </p>
Gravitational lensing: towards combining the multi-messengers (data sharing)
<div> <div># Gravitational lensing: towards combining the multi-messengers (data sharing)</div> <br> <div>This repository contains the data and code to plot Fig. 1 and Fig. 4 from the article titled - "Gravitational lensing : towards combining the multi messengers". The two folders included here are described below.</div> <br> <div>## 1. delta-psi</div> <br> <div>- This folder contains the data and code to plot Fig. 1 in the paper.</div> <div>- To generate plot for Fig. 1, run python plot_fig1.py</div> <div>- It computes the y-axis ($\delta \Delta \psi$ : uncertainty in the determination of the relative Fermat potential) for each combination of image pairs (x-axis), for 3 mock lenses, for each of the 4 configuration which are stored in "PLpert_fig_data"</div> <br> <div>## 2. grb-gw-lensing</div> <br> <div>- This folder contains the data and code to plot Fig. 4 in the paper.</div> <div>- data-gererator.ipynb generates the data for the plot. This data is stored in "ler_data" folder. Parameters for the GRB-GW lensing system are stored there.</div> <div>- grb-gw-lensing-plot.ipynb generates the plot for Fig. 4 (stored as 'combined-final.png'). It uses the data generated by data-gererator.ipynb. This plot shows, i) detectable lensed GRBs and the associated detectable lensed GW events, and ii) detectable lensed GW events and their</div> <div>associated detectable lensed GRBs.</div> </div>
Simulated data from "Gravitational instability in a planet-forming disk"
<h2>Repository Contents</h2> <blockquote> <p><strong>Simulated data from:</strong> Speedie et al. (2024)<br>"Gravitational instability in a planet-forming disk", J. Speedie, R. Dong, C. Hall, C. Longarini, B. Veronesi, T. Paneque-Carreño, G. Lodato, Y. Tang, R. Teague, & J. Hashimoto, <a href="https://www.nature.com/articles/s41586-024-07877-0"><em>Nature</em></a>, (2024).</p> <p><strong>Paper DOI:</strong> <a href="https://doi.org/10.1038/s41586-024-07877-0">https://doi.org/10.1038/s41586-024-07877-0</a></p> </blockquote> <p>This repository contains synthetic ALMA 13CO (J=2-1) image cubes and moment maps generated from 3D smoothed-particle hydrodynamic (SPH) simulations of a gravitationally unstable disk and a Keplerian counterpart. The following products are available:</p> <ul> <li>PHANTOM products</li> <li>MCFOST products</li> <li>Synthetic ALMA image cubes & moment maps</li> </ul> <p>The README provides a more detailed description. In the article, these synthetic data were compared with observed ALMA 13CO (J=2-1) data of the AB Aurigae disk from program 2021.1.00690.S (PI: R. Dong). Those ALMA data can be found here: <a href="https://doi.org/10.11570/24.0087">https://doi.org/10.11570/24.0087</a></p>
General relativistic self-gravitating equilibrium disks around rotating neutron stars: dataset
<p><strong>Dataset containing the results from <em>arXiv:2406.00945 (Y.Kim et al. 2024)</em></strong></p> <ul> <li>`model_list.asc` contains the name of the model (can be looked up from the Table 4 in the manuscript) and basic information.</li> </ul> <p>All equilibrium solutions are generated with (N_s x N_mu) = (801 x 401) resolution. See section 3.4 of the manuscript.</p> <ul> <li>`radial_grid.dat` contains the radial grid in the unit of the neutron star coordinate radius. A zero value at the end of the file corresponds to the endpoint (infinity) of the radial grid, which can be ignored.</li> <li>Angular grid is simply a uniformly divided interval of `mu = cos (theta)`, which can be easily generated and is not included in this dataset. Note that the angle `theta` is measured from the polar axis (z) toward the equatorial plane.</li> </ul> <p>Main data files (`<model_name>_<quantity_name>.dat`) include a flattened 2D array of the four metric functions, rest mass density, and the angular velocity of the fluid. They all contain a single long column of numbers, and can be opened with normal text editors or loaded with other packages (e.g. numpy) without difficulty. Data value at a grid point (mu_i, r_j) is located as a (801 * i + j)th entry with zero-based indexing. For example, the first 801 numbers correspond to the data on the equator (mu=0), then a radial profile along `mu=1/400`, then `mu=2/400`, and so on.</p> <ul> <li>The metric function `rho` and `gamma` correspond to `nu - beta` and `nu + beta` (see Eq 8-9 of Komatsu+1989: https://ui.adsabs.harvard.edu/abs/1989MNRAS.237..355K/abstract).</li> <li>Other metric functions `alpha` and `omega` have the same definition.</li> <li>`restenergydensity` is the rest energy density, and `angvel` is the coordinate angular velocity (see Eq 5 of the manuscript) of the fluid.</li> <li>Neutron star (r<=r_e) is modeled with K=100, Gamma=2 polytropic EoS, where the disk is modeled with K=0.468, Gamma=4/3 polytropic EoS. See the section 4 of the paper.</li> </ul> <p> </p> <p>If you use this dataset, please cite the Zenodo DOI and the companion manuscript <em>Y.Kim et al. 2024 (arXiv:2406.00945).</em></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>
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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.