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5,462 results for “binary”
Chronogram or phylogram for ancestral state estimation? Model-fit statistics indicate the branch lengths underlying a binary character’s evolution: R scripts and simulated trees
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Orbital Stability of Circumstellar Planets in Binary Systems
<p>Tar archives of simulations for circumstellar planets orbiting a range of host binary stars, where the archives are delineated by the mutual inclination in the filename. Each file in the archive has the assumed mass ratio, mu, and binary eccentricity, e_bin, in the file name as a tuple (mu,e_bin). Tools in python to manipulate and plot the data are provided through a GitHub repository, https://github.com/saturnaxis/ThreeBody_Stability. Inside each file contains a comma delimited text file, where the columns are the initial semimajor axis ratio, the initial phase of the planet, the maximum planetary eccentricity attained over the simulation time, the minimum planetary eccentricity attained over the simulation time, and the collision/escape time. Collisions are defined as collisions with either star, where the escape is when the radial distance of the planet exceeds 200 AU.</p>
Tagged original datasets for 'Genetically Optimized Massively Parallel Binary Neural Networks for Intrusion Detection Systems'
<p>Tagged, non-formatted, original datasets used in 'Genetically Optimized Massively Parallel Binary Neural Networks for Intrusion Detection Systems', T. Murovič, A. Trost.</p> <p>Available from the original authors:</p> <p>1. <a href="https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/">https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/</a> (UNWS-NB15 dataset)</p> <p>2. <a href="https://www.unb.ca/cic/datasets/nsl.html">https://www.unb.ca/cic/datasets/nsl.html</a> (NSL-KDD dataset)</p>
Pulsational pair-instability supernovae in very close binaries
<p>Models of pulsational pair-instability supernovae arising from the evolution of metal poor helium stars. These simulations have been made with the MESA software instrument for stellar evolution, and are used in the paper <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...882...36M/abstract">"Pulsational pair-instability supernovae in very close binaries"</a>. Data includes machine-readable tables, both history and profile files from the simulations, as well as the terminal output and a movie for each. All MESA output files have been post-processed to reduce their size. All the code used to process the MESA simulations and produce figures is provided in a jupyter-notebook.</p> <p>WARNING: Simulations were done using a development MESA version (11123) rather than an official release. </p> <p>List of files:</p> <p>- template.tar.xz: MESA work folder to reproduce the simulations.</p> <p>- xxx_xx.tar.xz: Each of these files contains stellar profiles at different moments of evolution for one simulation.</p> <p>- histories.tar.xz: MESA history files for each simulation, containing the time evolution of quantities that are single-valued at each moment in time.</p> <p>- preSN.tar.xz: profiles for stars before the onset of pulsations (see paper for definition).</p> <p>- movies.tar: Movies for all simulations, produced using MESAs pgstar.</p> <p>- terminal_output.tar.xz: Terminal output from the simulations.</p> <p>- masses_table.txt: Machine readable version of Table 1 in the paper.</p> <p>- pulse_tables.tar.xz: machine readable tables containing mass lost, time, and kinetic energy of each pulse.</p> <p>- notebook.tar.xz: jupyter-notebook plus complementary files used to post-process the data and produce Figures.</p> <p>This version of the files has an updated notebook.tar.xz to produce the figures in the published version of the paper.</p>
Towards a Realistic Explosion Landscape for Binary Population Synthesis
<p>Data from:</p> <p> Towards a Realistic Explosion Landscape for Binary Population Synthesis</p> <p> by</p> <p> Patton, Rachel A. and Sukhbold, Tuguldur<br> (2020).</p> <p> (Submitted to MNRAS)</p> <p>- Detailed descriptions of calculations are provided in the paper, but<br> please feel free to get in touch with any of us with questions, or<br> if you need something that is not provided here.</p> <p>Contacts:<br> Rachel Patton = patton[dot]502[at]osu[dot]edu<br> Tuguldur Sukhbold = tuguldur[dot]s[at]gmail[dot]com<br> - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -</p> <p>Below are brief descriptions of each dataset. More details can be found in the paper.</p> <p>kepler_table.tar.gz (1.0GB)<br> -These are tables for our entire set of 3496 KEPLER CO-core models, containing the final presupernova structure and composition quantities for each zone. Core masses range from 2.5 to 10.0 in increments of 0.1 Msun and the initial composition ranges from 5% carbon and 95% oxygen to 50% carbon and oxygen, in incrmeents of 1%. Each file has the naming convention "mA_B.ktbl" where A is the core mass in Msun (2.5, 2.6, etc.) and B represents the initial carbon mass fraction as the number of increments of 0.01 taken from 0.05 (5%). For example, the file 'm8.0_27.ktbl' would be for an 8 Msun core with an initial carbon mass fraction of 0.32 (= 0.05 + 27*0.01).</p> <p>kepler_datatset.tar.gz (30.4KB)<br> -This folder contains four tables listing the presupernova parameters evaluated from all of our KEPLER CO-cores: M_fe, the iron core mass, M4, the mass enclosed where the entropy per baryon reaches 4 Kb, mu_4, the radial gradient of mass at the location of M4, and xi_2.5, the compactness parameter. See section#3 of our paper for details on how these were evaluated. Columns represent each CO-core mass and rows represent starting carbon mass fraction.</p> <p>inlist (2.8KB)<br> -This is the inlist used to create 760 MESA (version 7624) CO-core models, presented in section#4.1. The only thing that changes between models is the CO-core mass, set by 'initial_mass' and the composition, set by 'accretion_species_xa(1)' and 'accretion_species_xa(2)'. In our grid, we covered the same mass and composition range as the KEPLER models, keeping the same mass increment but increasing the carbon mass increment from 0.01 to 0.05.</p>
All Luminosity Templates Associated with "Classifying Single Stars and Spectroscopic Binaries Using Optical Stellar Templates"
<p>Zip files for the luminosity normalized individual stellar templates, and all combinations of SB2 templates. The templates are in fits format. The first table extension contains the template (wavelength, luminosity, variance, error). These luminosity templates have units of erg /s /angstrom.</p>
Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs
<p>This dataset and pre-trained models are released as a companion to our OOPSLA '20 publication: "Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs":</p> <ol> <li>The dataset file (<a href="https://zenodo.org/api/files/0e98c909-50b4-43e3-8ab2-bb9673eed786/nero_dataset_binaries.tar.gz?versionId=b943752a-e89e-43c1-b32f-19d4a7420e00">nero_dataset_binaries.tar.gz</a>) is composed from packages of binary executables created by compiling several GNU source-code packages. We used these executables to evaluate our approach as implemented in our prototype "Nero" and compare it to other approaches. All executables contain debug information which serves as the ground truth for the procedure name predictions. The packages are split into three sets: training, validation and test. <ol> <li>The executable file name structure is: "<compiler>-<compiler version>__O<Optimization level(u for default)>__<Package name>[-<optional package version>]__<Executable name>". For example "gcc-5__Ou__cssc__sccs".</li> </ol> </li> <li>The procedure representation file (<a href="https://zenodo.org/api/files/0e98c909-50b4-43e3-8ab2-bb9673eed786/procedure_representations.tar.gz?versionId=fef2380c-c869-4a3f-8e32-bc4972dbb219">procedure_representations.tar.gz</a>) contains: <ol> <li>The raw representations for all the binary procedures in the above dataset. Each procedure is represented by one line in the relevant file for each set (training.json, validation.json and test.json) </li> <li>The above representations preprocessed for training.</li> </ol> </li> <li>The pre-trained model file (<a href="https://zenodo.org/api/files/0e98c909-50b4-43e3-8ab2-bb9673eed786/nero_gnn_model.tar.gz">nero_gnn_model.tar.gz</a>) was created using the above preprocessed dataset and contains: <ol> <li>Pre-trained model.</li> <li>Training log.</li> <li>Prediction results log.</li> </ol> </li> </ol> <p>For the code of the "Nero" prototype, and more information about the above artifacts see <a href="https://github.com/tech-srl/Nero">our Github repo</a></p>
Stars Stripped in Binaries: The Living Gravitational-wave Sources
<p>Binary interaction can cause stellar envelopes to be stripped, which significantly reduces the radius of the star. The orbit of a binary composed of a stripped star and a compact object can therefore be so tight that the gravitational radiation the system produces reaches frequencies accessible to the Laser Interferometer Space Antenna (LISA). Two such stripped stars in tight orbits with white dwarfs are known so far (ZTF J2130+4420 and CD−30°11223), but many more are expected to exist. These binaries provide important constraints for binary evolution models and may be used as LISA verification sources. We develop a Monte Carlo code that uses detailed evolutionary models to simulate the Galactic population of stripped stars in tight orbits with either neutron star or white dwarf companions. We predict 0–100 stripped star + white dwarf binaries and 0–4 stripped star + neutron star binaries with a signal-to-noise ratio >5 after 10 yr of observations with LISA. More than 90% of these binaries are expected to show large radial velocity shifts of <span class="math-tex">\(\gtrsim\)</span>200 km s<sup>-1</sup>, which are spectroscopically detectable. Photometric variability due to tidal deformation of the stripped star is also expected and has been observed in ZTF J2130+4420 and CD−30°11223. In addition, the stripped star + neutron star binaries are expected to be X-ray bright with L<sub>X</sub> <span class="math-tex">\(\gtrsim\)</span> 10<sup>33</sup>–10<sup>36</sup> erg s<sup>-1</sup>. Our results show that stripped star binaries are promising multimessenger sources for the<br> upcoming electromagnetic and gravitational wave facilities.</p> <p> </p> <p>We provide detailed information about the run presented in Figures 2, 4, and 5 in the file named "entire_population_64.txt". We also provide information about sources with SNR > 4 in "pop_SNR4.txt" for 1000 runs of our standard model. The Jupyter notebook "Reading_stripped_star_compact_object_population.ipynb" reproduces figures of the manuscript and the Jupyter notebook "living_population.ipynb" shows how we model the Galactic population of stripped stars in tight orbit with compact objects.</p>
Wide binaries from Gaia eDR3
<p>This dataset contain the catalogs accompanying the paper "A million binaries from Gaia eDR3: sample selection and validation of Gaia parallax uncertainties" by Kareem El-Badry, Hans-Walter Rix, and Tyler M. Heintz, submitted to MNRAS in January 2021. </p> <p>There are two data files:</p> <p><strong>all_columns_catalog.fits.gz</strong>: fits table with 1,817,594 rows and 217 columns<br> <strong>all_columns_catalog_shift.fits.gz: </strong>fits table with 517,993 rows and 201 columns</p> <p>There are also two code files: </p> <p><strong>num_neighbors_edr3.py:</strong> python code to count the number of neighbors for each source <br> <strong>find_binaries_edr3.py</strong>: python code to assemble the binary catalog</p>
Dataset from: Fallback Supernova Assembly of Heavy Binary Neutron Stars and Light Black Hole-Neutron Star Pairs and the Common Stellar Ancestry of GW190425 and GW200115
<p>The results of the simulations shown in "Fallback Supernova Assembly of Heavy Binary Neutron Stars and Light Black Hole-Neutron Star Pairs and the Common Stellar Ancestry of GW190425 and GW200115" (<a href="https://arxiv.org/abs/2106.12381">arXiv:2106.12381</a>).</p> <p>Contents:</p> <ol> <li>Run_Details_COMPAS</li> <li>COMPAS_Output_*.hdf5</li> <li>MESA.zip</li> <li>GADGET.zip</li> </ol> <p>If you use any of these data please kindly include a citation to:<br> Alejandro Vigna-Gómez <em>et al</em> 2021 <em>ApJL</em> <strong>920</strong> L17 <a href="https://iopscience.iop.org/article/10.3847/2041-8213/ac2903">doi:10.3847/2041-8213/ac2903</a></p> <p>If you use the MESA profile or history files please also cite:</p> <p>Aguilera-Dena, D.R., et al., in prep.</p> <p>Additionally, we point the reader towards the following GitHub repositories:<br> 1) <a href="https://github.com/aldobatta/fallback-supernova">aldobatta/fallback-supernova</a><br> 2) <a href="https://github.com/avigna/heavy-BinaryNeutronStars">avigna/heavy-BinaryNeutronStars</a></p>
Binary Pulsar PSR B1913+16 arrival time and associated files. Ref: Weisberg & Huang APJ 2016
<p>These files contain arrival time and associated data for PSR B1913+16, which are usable as input to the tempo software program. They were used for the paper "Relativistic Measurements from Timing the Binary Pulsar PSR B1913+16," Weisberg & Huang, Astrophysical Journal, 2016, in press. </p>
Synthetic J-H-K-W1-W2-W3-G-B-V photometry for random population of binary & single stars
<p>Table of synthetic photometry for ~800k stars, randomly distributed in the following ranges:</p> <ul> <li>mass_A = (0.2, 2.0)</li> <li>log10(age) = (8.0, 9.9)</li> <li>feh = N(0, 0.2)</li> <li>distance = (50, 2000) [pc]</li> <li>A_V = (0, 1)</li> <li>q (mass ratio) = (0.2, 1); mass_B = q * mass_A</li> </ul> <p>The table has columns for all the synthetic photometry as well as columns for the true properties and a boolean column for "is_binary"; the synthetic photometry is only a sum of the two stars if is_binary is True; otherwise the photometry is just for the primary star. Synthetic photometry is done using the MIST model grids, implemented by isochrones (github.com/timothydmorton/isochrones)</p> <p>The purpose of this table is to act a synthetic training set for single/binary classification algorithms.</p>
Tracing the evolution of short-period binaries with super-synchronous fast-rotators
<p>Dataset for the triple scenario (see Sec. 5 in Britavskiy et al.)<br><br>*_triple.txt contain zero age main sequence triple configurations compatible with the "triple merger scenario" described in the paper.<br>*.npy are binary version of the corresponding txt for faster loading. <br><br>The txt files where generated with <a href="https://zenodo.org/api/records/10028333/draft/files/ML_stability.py/content">ML_stability.py.</a> <br><a href="https://zenodo.org/api/records/10028333/draft/files/plot_P_unstable.py/content">plot_P_unstable.py</a> generates fig. 10 and <a href="https://zenodo.org/api/records/10028333/draft/files/plot_min_a_in.py/content">lot_min_a_in.py</a> fig. 9, the other python files are libraries of functions called by these.<br><a href="https://zenodo.org/api/records/10028333/draft/files/mlp_model_trip_ghost.pkl/content">mlp_model_trip_ghost.pkl</a> is the dynamical stability classifier from <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.525.2388V/abstract">Vynatheya et al. 2023</a> (ghost orbit method), used by <a href="https://zenodo.org/api/records/10028333/draft/files/classify_trip.py/content">classify_trip.py</a> to determine the probability of dynamical stability of a given system.</p>
Crystalline Morphology Formation in Phase-Field Simulations of Binary Mixtures
<p>Simulation data used for the publication "Crystalline Morphology Formation in Phase-Field Simulations of Binary Mixtures" in Journal of Materials Chemistry C, Royal Society of Chemistry (2023). See README file for more details.</p>
Neutron Star - White Dwarf Binaries: Probing Formation Pathways and Natal Kicks with LISA
<p>We present supplementary datasets accompanying our publication<em> </em><a href="https://arxiv.org/abs/2310.06559">Neutron Star - White Dwarf Binaries: Probing Formation Pathways and Natal Kicks with LISA</a>.<em> </em>These catalogues reperesent the Galactic population of double white dwarf (DWD) and neutron star - white dwarf (NSWD) binaries emitting gravitational waves (GWs) in the <em>Laser Interferometer Space Antenna</em> (LISA) frequency band (0.1 mHz - 1 Hz). The catalogues have been constructed based on binary evolution models <a href="https://arxiv.org/abs/1208.6446">Toonen et al. 2012</a> for DWDs and <a href="https://arxiv.org/abs/1804.01538">Toonen et al. 2018</a> for NSWD binaries, obtained using SeBa binary population synthesis code.</p> <p><strong>Data contents</strong></p> <p>The dataset consists of <strong>12 catalogues </strong>representing Galactic populations of NSWD and/or DWD binaries, which are expected to be the most numerous types of binaries amongt LISA's Galactic sources. Each catalogue is distinguished by its model ID, which specifies the presence of NSWD and/or DWD binaries, the CE model used, CE efficiency values, and the NS natal kick prescription applied (see table below).</p> <p>Each catalogue is structured to describe a binary systems with the following attributes:</p> <ul> <li><strong>Name*</strong>: binary identifier; this consist of a prefix indicating the binary type (<code>'MW_DWD'</code> for a DWD binary, <code>'MW_NSWD_ecc0'</code> for a circular NSWD bianry, or <code>'MW_NSWD_ecc1'</code> for an eccentric NSWD binary) followed by a unique ID number. For example, <code>'MW_DWD 28713637'</code>.</li> <li><strong>Frequency</strong>: present-day GW frequency (Hz).</li> <li><strong>Frequency Derivative</strong>: rate of change of GW frequency over time (Hz^2).</li> <li><strong>Ecliptic Latitude</strong>: in radians (rad).</li> <li><strong>Ecliptic Longitude</strong>: in radians (rad).</li> <li><strong>Amplitude</strong>: GW amplitude (dimensionless).</li> <li><strong>Inclination</strong>: angle between the binary's orbital plane and our line of sight, in radians (rad).</li> <li><strong>Polarization</strong>: Orientation of the GW's polarization, in radians (rad).</li> <li><strong>Initial Phase</strong>: initial phase (rad).</li> <li><strong>Eccentricity</strong>: orbital eccentricity (dimensionless).</li> </ul> <p><strong>*</strong>Note that the <strong>Name </strong>field for eccentric NS+WD binaries (staring with <code>'MW_NSWD_ecc1'</code>) is not unique because these binaries are represented by multiple harmonics sharing the same name ID. The number of harmonics included varies for each binary to ensure that at least 99% of the binary's total GW power is represented. Thus, for each binary, we added harmonics incrementally until this threshold is reached.</p> <table> <tbody> <tr> <td>Model ID</td> <td>WD+WD</td> <td>NS+WD</td> <td>CE model</td> <td>CE efficiency</td> <td>NS natal kick</td> </tr> <tr> <td>1_0</td> <td>Yes</td> <td>No</td> <td>αα</td> <td>αλ=2.00</td> <td>N/A</td> </tr> <tr> <td>1_1</td> <td>Yes</td> <td>Yes</td> <td>αα</td> <td>αλ=2.00</td> <td>Verbunt</td> </tr> <tr> <td>1_2</td> <td>Yes</td> <td>Yes</td> <td>αα</td> <td>αλ=2.00</td> <td>Arzoumanian</td> </tr> <tr> <td>1_3</td> <td>Yes</td> <td>Yes</td> <td>αα</td> <td>αλ=2.00</td> <td>Hobbs</td> </tr> <tr> <td>1_4</td> <td>Yes</td> <td>Yes</td> <td>αα</td> <td>αλ=2.00</td> <td>Blaauw</td> </tr> <tr> <td>2_0</td> <td>Yes</td> <td>No</td> <td>αα2</td> <td>αλ=0.25</td> <td>N/A</td> </tr> <tr> <td>2_1</td> <td>Yes</td> <td>Yes</td> <td>αα2</td> <td>αλ=0.25</td> <td>Verbunt</td> </tr> <tr> <td>2_2</td> <td>Yes</td> <td>Yes</td> <td>αα2</td> <td>αλ=0.25</td> <td>Arzoumanian</td> </tr> <tr> <td>2_3</td> <td>Yes</td> <td>Yes</td> <td>αα2</td> <td>αλ=0.25</td> <td>Hobbs</td> </tr> <tr> <td>2_4</td> <td>Yes</td> <td>Yes</td> <td>αα2</td> <td>αλ=0.25</td> <td>Blaauw</td> </tr> <tr> <td>3_0</td> <td>Yes</td> <td>No</td> <td>αγ</td> <td>αλ=2.00, γ=1.75</td> <td>N/A</td> </tr> <tr> <td>3_1</td> <td>Yes</td> <td>Yes</td> <td>αγ</td> <td>αλ=2.00, γ=1.75</td> <td>Verbunt</td> </tr> </tbody> </table> <p> </p> <h4><strong>Citing the Dataset</strong></h4> <p>When utilising these catalogues in your research, please cite <a href="https://arxiv.org/abs/2310.06559">Korol et al. 2024.</a> We also note our companion data-analysis-focused paper <a href="https://arxiv.org/abs/2310.06568">Moore et al. 2024</a>.</p>
LAMMPS atomistic simulation data of a binary LJ glass
<p>Simulation data associated with the published papers:</p> <ul> <li>Correlated disorder in a model binary glass through a local SU (2) bonding topology, P. M. Derlet, Phys. Rev. Mater. 4, 125601 (2020).</li> <li>Micro-plasticity in a model binary glass, PM Derlet and R. Maass, Acta. Mater. 209, 116771 (2021).</li> <li>Viscosity and transport in a model fragile metallic glass, P. M. Derlet, H. Bocquet, and R. Maass, Phys. Rev Mater 5, 125601 (2021).</li> </ul>
Cosmic Cousins: Identification of a Subpopulation of Binary Black Holes Consistent with Isolated Binary Evolution
<p>The accompanying data release for the analyses presented in "Cosmic Cousins: Identification of a Subpopulation of Binary Black Holes Consistent with Isolated Binary Evolution". See the github paper repository at https://github.com/jaxengodfrey/CosmicCousins and the arXiv release of the paper at: https://arxiv.org/abs/2304.01288</p>
Eclipsing binary systems with beta Cephei components. V1216 Sco
<p>A set of files for MESA 23.05.1 and GYRE 7.1, used to compute a grid of binary evolutionary tracks and stellar oscillation models used in the paper "Eclipsing binary systems with beta Cephei components. V1216 Sco".</p>
Threatened endemic arthropods and vertebrates partition their diets with non-native ants in an isolated island ecosystem - binary diet dataset and stats code
<p>The dietary dataset associated to <strong>"Threatened endemic arthropods and vertebrates partition their diets with non-native ants in an isolated island ecosystem"</strong>. We also provide the novel code (in R) used for the statistical analyses.</p>
Data for 'Cosmology with Binary Neutron Stars: Does Mass-Redshift Correlation Matter?'
<p>The code is available at the following GitHub link: https://github.com/SoumendraRoy/RedevolBNS</p> <p>To generate the plots in the paper, see: https://github.com/SoumendraRoy/RedevolBNS/tree/main/Make_Plots</p> <p>A frozen version of the Make_Plots is included here.</p> <p>Description of the files:</p> <ol> <li><a href="https://zenodo.org/api/records/14704635/draft/files/Cosmo_Plots.ipynb/content" target="_blank" rel="noopener noreferrer">Cosmo_Plots.ipynb</a>, <a href="https://zenodo.org/api/records/14704635/draft/files/Pop_Plots.ipynb/content" target="_blank" rel="noopener noreferrer">Pop_Plots.ipynb</a> : Jupyter notebooks containing all plots in the main text.</li> <li><a href="https://zenodo.org/api/records/14704635/draft/files/Appendix_Plots.ipynb/content" target="_blank" rel="noopener noreferrer">Appendix_Plots.ipynb</a>, <a href="https://zenodo.org/api/records/14704635/draft/files/All_Contours.ipynb/content" target="_blank" rel="noopener noreferrer">All_Contours.ipynb</a> : Jupyter notebooks containing all plots in Appendix.</li> <li><a href="https://zenodo.org/api/records/14704635/draft/files/inference_marginal_fiducial.h5/content" target="_blank" rel="noopener noreferrer">inference_marginal_fiducial.h5</a>, <a href="https://zenodo.org/api/records/14704635/draft/files/inference_full_fiducial.h5/content" target="_blank" rel="noopener noreferrer">inference_full_fiducial.h5</a> : The samples of the Hubble constant and dark matter density for the fiducial injected population, with uncorrelated and correlated mass-redshift populations, respectively.</li> <li><a href="https://zenodo.org/api/records/14704635/draft/files/inference_marginal_MM.h5/content" target="_blank" rel="noopener noreferrer">inference_marginal_MM.h5</a>, <a href="https://zenodo.org/api/records/14704635/draft/files/inference_full_MM.h5/content" target="_blank" rel="noopener noreferrer">inference_full_MM.h5</a> : The samples of the Hubble constant and dark matter density for the Mandel-Müller injected population, with uncorrelated and correlated mass-redshift populations, respectively.</li> <li><a href="https://zenodo.org/api/records/14704635/draft/files/inference_full_result_Uncorrelated.h5/content" target="_blank" rel="noopener noreferrer">inference_full_result_Uncorrelated.h5</a>, <a href="https://zenodo.org/api/records/14704635/draft/files/inference_full_result_Injected.h5/content" target="_blank" rel="noopener noreferrer">inference_full_result_Injected.h5</a> : The samples of the Hubble constant and dark matter density for the fiducial injected population, with uncorrelated and correlated mass-redshift populations, respectively for varying number of detections.</li> <li><a href="https://zenodo.org/api/records/14704635/draft/files/simulation.h5/content" target="_blank" rel="noopener noreferrer">simulation.h5</a> : The injected mass, redshift samples for different population synthesis variations.</li> <li><a href="https://zenodo.org/api/records/14704635/draft/files/compare_pop.h5/content" target="_blank" rel="noopener noreferrer">compare_pop.h5</a>, <a href="https://zenodo.org/api/records/14704635/draft/files/variant_pop.h5/content" target="_blank" rel="noopener noreferrer">variant_pop.h5</a> : Comparison of different population synthesis variations.</li> <li><a href="https://zenodo.org/api/records/14704635/draft/files/gmm.h5/content" target="_blank" rel="noopener noreferrer">gmm.h5</a> : Gaussian mixture model fit of the fiducial and Mandel-Müller injected population.</li> </ol>
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.