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44 results for “star formation”
Evolution of cosmic star formation in the SCUBA-2 Cosmology Legacy Survey
<p>This dataset consists of tabulated data from the figures included in the referenced publication. The following datasets are included:</p> <p>Stacked SFR obscuration (IRX=IR/UV) of UVJ-selected star-forming galaxies:</p> <ul> <li>Weighted mean IRX as a function of Muv & stellar mass (Figure 12): MUV_irx1.dat</li> <li>Weighted mean IRX as a function of beta, over all masses and redshifts: beta_irx.dat</li> <li>Weighted mean IRX as a function of beta, binned by stellar mass (Figure 13): beta_irx_mstar.dat</li> <li>Weighted mean IRX as a function of beta, binned by redshift (Figure 14): beta_irx_z.dat</li> </ul> <p>Cosmic SFR density as a function of redshift for massive galaxies log(Ms/Msol)>10 (Figure 15):</p> <ul> <li>All mass-selected galaxies: sfrd_massive.dat</li> <li>UV-luminous galaxies Muv<M*; log(Ms/Msol)>10: sfrd_hiLUV.dat</li> <li>IR-luminous galaxies detected at 450µm: sfrd_IRdet.dat</li> </ul> <p> </p> <p>Cosmic SFR density as a function of redshift corrected to all stellar masses (Figure 16):</p> <ul> <li>All mass-selected galaxies: sfrd_uvlfcorr.dat</li> <li>UV-luminous galaxies Muv<M*; log(Ms/Msol)>10: sfrd_hiLUV_uvlfcorr.dat</li> </ul> <p>Full details of the binning and stacking methodology are explained in the paper.</p>
Quenching of star formation from a lack of inflowing gas to galaxies
<p>This dataset provides HST and ALMA mosaics of the REQUIEM-ALMA survey of six strong gravitationally lensed quiescent galaxies at z=1.6 to z=3.2 (MRG-M1341, MRG-M0138, MRG-M2129, MRG-M0150, MRG-M0454, MRG-M1423) from Whitaker et al. (2021). The HST mosaics were produced with the <a href="https://github.com/gbrammer/grizli">grizli</a> software module. The ALMA data products include the full spectrally-averaged continuum data weighted for optimum sensitivity (with MRG-M2129 and MRG-M0138 including a correction for the primary beam response).</p>
Science ready spectra of star clusters and their best-fitting models described in the research paper "Using Star Clusters as Tracers of Star Formation and Chemical Evolution: the Chemical Enrichment History of the Large Magellanic Cloud" by Chilingarian & Asa'd
<p>Science ready spectra of star clusters in the Large Magellanic Cloud and their best-fitting templates (alpha-enhanced MILES based simple stellar population models) obtained using the NBursts full spectrum fitting code. Each spectrum is presented as a binary FITS table, which contains a spectrum (wavelength, flux, uncertainties), best-fitting template, best-fitting parameters (radial velocity, age, metallicity), and a pixel mask used in the fitting procedure. For each cluster, 5 spectra are provided, which correspond to [alpha/Fe] values from 0.0 to 0.4 dex with a step of 0.1 dex. The only exception is NGC2249, for which only 3 models are provided. The alpha-enhancement value of a model grid used in the fitting procedure is given in the FITS keyword MGFEGRID.</p>
Data Release: "LIGO-Virgo-KAGRA's Oldest Black Holes: Probing star formation at cosmic noon with GWTC-3"
<p>This repository contains the data behind the figures presented in v2 of "LIGO-Virgo-KAGRA's Oldest Black Holes: Probing star formation at cosmic noon with GWTC-3" (<a href="https://ui.adsabs.harvard.edu/link_gateway/2023arXiv230715824F/arxiv:2307.15824">arXiv:2307.15824</a>), to appear in ApJL.</p><p>The csv files (in Output.zip) and the h5 files contain the data products. The three Jupyter notebooks include code for plotting the figures and calculating the summary statistics that appear in the paper. </p><p> </p>
Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership. SPCM Atlas Dataset.
<p>The SPCM (SFiNCs Possible Cluster Member) Atlas dataset accompanies the article entitled ``Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership,'' by Getman, Broos, Kuhn, Feigelson, Richert, Ota, Bate, and Garmire, to appear in The Astrophysical Journal Supplement Series. The paper is also available on-line on astro-ph at: https://arxiv.org/abs/1612.05282 . SPCM Atlas is a collection of 25 PDF files. Four pdf files are associated with the SFiNCs star forming region (SFR) Cep OB3b, and 21 pdf files are associated with the remaining 21 SFiNCs SFRs. Full description of SPCM Atlas is given in the Appendix B section of the article. This upload is superseded by a new version, http://doi.org/10.5281/zenodo.345398 .</p>
The NGDEEP NIRIS calibration files for 'The Next Generation Deep Extragalactic Exploratory Public Near-Infrared Slitless Survey Epoch 1 (NGDEEP-NISS1): Extra-Galactic Star-formation and Active Galactic Nuclei at 0.5 < z < 3.6
<p>GRISMCONF configurations files used in Pirzkal et al. 2024. These contain the full field calibrated solution for the dispersion solution, trace as well as wavelength calibration. They provide a mean to extract NIRISS WFSS spectra obtained using the F115W, F150W, or F200W to within an acccuracy better than 0.25 pixel over most of the field of view. Wavelength calibration of both grism was verified to be accurate to within 15A over most of the field of view. Details can be found in Appendix A of Pirzkal et al. 2024.</p>
Dataset for "From Halos to Galaxies. X: Decoding Galaxy SEDs with Physical Priors and Accurate Star Formation History Reconstruction"
<p>This deposit contains the data related to the manuscript "<em>From Halos to Galaxies. X: Decoding Galaxy SEDs with Physical Priors and Accurate Star Formation History Reconstruction</em>" submitted to the Astrophysical Journal. It includes the basic SDSS identifier, stellar mass, star formation rate, fractional formation time, and their errors. A detailed description can be found in Table 1 of the manuscript.</p> <p>The data is stored in a CSV file. It can be read with standard data analysis packages like <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html">Pandas</a> in Python. </p> <p>This deposit has also be updated to include a machine-readable table that follows the standards of the AAS Journals and Vizier (<span>datafile1_ApJ57534.mrt). More information on this standard can be found in the <a href="https://journals.aas.org/mrt-overview/">AAS</a> or <a href="http://cds.u-strasbg.fr/doc/catstd.htx">CDS</a> documentation. This format can be read in Python with packages like <a href="https://docs.astropy.org/en/stable/api/astropy.io.ascii.Mrt.html">astropy</a>. </span></p>
Dataset for Bate (2022): Dust coagulation during the early stages of star formation: molecular cloud collapse and first hydrostatic core evolution
<p>This data set contains 12 smoothed particle hydrodynamics (SPH) dump files that were used to produce some of the figures in the journal paper:</p> <p>Bate, Matthew. R., 2022, Monthly Notices of the Royal Astronomical Society, accepted 13 May 2022</p> <p>Each of the SPH dump files is from a different calculation of the early stages of star formation: the gravitational collapse of a molecular cloud core, including dust coagulation. Each SPH dump file gives the state of the SPH calculation when the maximum temperature reached 1500 K, except for the beta=0.05 cases which give the state when the maximum hydrogen number density reaches 10^{14} cm^{-3}. The calculations were each performed using 3 million SPH particles and differed by their initial rotation rate, which was parameterised by beta=0, 0.0025, 0.005, 0.01, 0.02, and 0.05 (the magnitude of the ratio of the rotational and gravitational potential energies). Dump files from calculations that include and exclude envelope turbulence are provided (both are used for Figure B1). The dump files associated with each calculation are:</p> <p>beta=0: B1M0123 (does not include envelope turbulence)<br> beta=0.0025: B1M2123 (does not include envelope turbulence)<br> beta=0.005: B1M5123 (does not include envelope turbulence)<br> beta=0.01: B1M1128 (does not include envelope turbulence)<br> beta=0.02: B1M2126 (does not include envelope turbulence)<br> beta=0.05: B1M5109_b05_NoEnvTurb (does not include envelope turbulence)</p> <p>beta=0.0: B1M0123_b0_EnvTurb<br> beta=0.0025: B1M2177_b0025_EnvTurb<br> beta=0.005: B1M5209_b005_EnvTurb<br> beta=0.01: B1M1219_b01_EnvTurb<br> beta=0.02: B1M2221_b02_EnvTurb<br> beta=0.05: B1M5321_b05_EnvTurb</p> <p>The SPH dump files are Fortran binary files written in big endian format and generated by the sphNG code (Benz 1990; Bate 1995; Bate & Keto 2015). They can be read, visualised, and manipulated using the free, publicly available SPLASH visualisation code (which reads sphNG dump files), written by Daniel J. Price, that can be downloaded from: </p> <p>http://users.monash.edu.au/~dprice/splash/ </p> <p>The SPLASH configuration files used to produce Figs. 10,11,12,and B1 in Bate (2022) are included with this dataset in a gzipped tar file.</p> <p> </p>
Text-fig. 7. Geology of the Muaredzi-Muanza sector of the Cheringoma Plateau showing the location of fossil occurrences. White stars – fossiliferous localities mapped by Pickford (2012, 2013), Black stars – fossil sites mapped by Habermann et al. (2019) and d'Oliveira Coelho et al. (2021) (GPL 12 and GPL 12b correspond to the White Patch sites). TTI – Cheringoma Formation, TTs1 – Mazamba Formation, TTs1a – Palaeopan facies, TTs2 – Inhaminga Formation, Qc – Quaternary sediments. The base map is modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 7. Geology of the Muaredzi-Muanza sector of the Cheringoma Plateau showing the location of fossil occurrences. White stars – fossiliferous localities mapped by Pickford (2012, 2013), Black stars – fossil sites mapped by Habermann et al. (2019) and d'Oliveira Coelho et al. (2021) (GPL 12 and GPL 12b correspond to the White Patch sites). TTI – Cheringoma Formation, TTs1 – Mazamba Formation, TTs1a – Palaeopan facies, TTs2 – Inhaminga Formation, Qc – Quaternary sediments. The base map is modified from Google Earth.
NFFA-Europe|Pilot proporsal "NANO meets ASTRO: simulating the formation of silicon oxide nanoparticles in the atmosphere of dying stars" (PID: 140).
<p>XPS, IRRAS, QMS and OES data of the nanoparticles synthesized within the NFFA-Europe|Pilot proporsal "NANO meets ASTRO: simulating the formation of silicon oxide nanoparticles in the atmosphere of dying stars" (PID: 140).</p>
Turbulence pattern files used for star cluster formation in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<p>** these files are automatically downloaded by Phantom on running the code **</p> <p>The files here are sample cubes containing turbulent driving patterns for the velocity field (vx, vy and vz) used to initiate star cluster formation simulations in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code</p> <p>These can be used to set up initial conditions for a set of simulations similar to those shown in <a href="http://adsabs.harvard.edu/abs/2003MNRAS.339..577B">Bate, Bonnell & Bromm (2003)</a>. The files here are not the original driving patterns used in the BBB03 simulations, but have the same structure, and give a default driving pattern that can be used without having to re-generate the files. A similar set of files was used for the simulations published in <a href="https://ui.adsabs.harvard.edu/abs/2017MNRAS.465..105L">Liptai et al. (2017)</a>.</p> <p>The files were generated with a piece of code written by Volker Bromm, which was originally part of Matthew Bate's sphNG simulation code.</p> <p>For details of how to read these files, see the Phantom source code (<a href="https://github.com/danieljprice/phantom/blob/master/src/setup/velfield_fromcubes.f90">src/setup/velfield_fromcubes.f90</a>)</p>
Stellar mass and star formation rate within a billion light-years
<p>Tables 1-5 of the paper entitled "Stellar Mass and Star Formation Rate within a Billion Light-Years" are available in machine readable format, together with a ReadMe file in CDS format. The python script loads Table 5 and displays the stellar mass density inferred in a volume of 200 Mpc size. Interactive versions of the six figures shown in Appendix C of the above mentioned article are also available in html format, as generated from a similar script.</p>
Text-fig. 1. Map of westernmost part of Rio Grande do Sul showing the position of the sampling locality (star). Adapted from Oliveira and Kerber (2009). in A New Fossil Fabaceae Wood From The Pleistocene Touro Passo Formation Of Rio Grande Do Sul, Brazil
Text-fig. 1. Map of westernmost part of Rio Grande do Sul showing the position of the sampling locality (star). Adapted from Oliveira and Kerber (2009).
Text-fig. 1. Location of fossiliferous localities in the Ashawq Formation in south-western Oman (the star labelled Thaytiniti shows the area depicted in Text-fig. 2). in Large Mammals From The Rupelian Of Oman - Recent Finds
Text-fig. 1. Location of fossiliferous localities in the Ashawq Formation in south-western Oman (the star labelled Thaytiniti shows the area depicted in Text-fig. 2).
Dataset from: Common–Envelope Episodes that lead to Double Neutron Star formation
<p>The results of all simulations shown in "Common–Envelope Episodes that lead to Double Neutron Star formation" (<a href="https://arxiv.org/abs/2001.09829">arXiv:2001.09829</a>)</p> <p>Contents:</p> <p>COMPASOutput.h5<br> MATLABscripts.zip<br> README</p> <p>All simulations made using <a href="https://compas.science/">COMPAS</a> (internally referred to as COMPAS Legacy).</p> <p>If you use these data please kindly include a citation to:<br> A. Vigna-Gómez, M. MacLeod, C. J. Neijssel, F. S. Broekgaarden, S. Justham, G. Howitt, S. E. de Mink, S. Vinciguerra, and I. Mandel. Common envelope episodes that lead to doubleneutron star formation. PASA, 37:e038, Jan. 2020 (<a href="https://ui.adsabs.harvard.edu/abs/2020PASA...37...38V/abstract">ADS</a>)</p>
Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership. SPCM Atlas Dataset (Version 2).
<p>The SPCM (SFiNCs Possible Cluster Member) Atlas dataset accompanies the article entitled ``Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership,'' by Getman, Broos, Kuhn, Feigelson, Richert, Ota, Bate, and Garmire, to appear in The Astrophysical Journal Supplement Series. The paper is also available on-line on astro-ph at: https://arxiv.org/abs/1612.05282 . SPCM Atlas is a collection of 25 PDF files. Four pdf files are associated with the SFiNCs star forming region (SFR) Cep OB3b, and 21 pdf files are associated with the remaining 21 SFiNCs SFRs. Full description of SPCM Atlas is given in the Appendix B section of the article. This is an update of the previous zenodo.231216 upload. This update includes revised PDF atlases for Be59 and IC348 (with revised vales of the SED slope and [3.6] and [4.5]-band magnitudes).</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>
Source Data for 'The imprint of star formation on stellar pulsations'
<p>This repository holds the source data and plotting routines for all Figures and Tables of the artice 'The imprint of star formation on stellar pulsations'.</p> <p><a href="https://zenodo.org/api/files/2ff2ea54-edc1-4fdc-9af8-4f839fbb089e/source_data.zip">source_data.zip </a>holds the source data. Each file provides a data set. Different data sets used in this publication are i.e. one parameter of a evolutionary track (i.e. 2Msun_classic_history_star_age.txt), one parameter of a structure model (i.e. 2Msun_classic_different_ov_profile_zams_mass.txt), or one parameter of a theoretical frequency set (i.e. GYRE_summary_classic_pre_ms_stage_n_pg.txt). All files have one column and are directly loaded in <a href="https://zenodo.org/api/files/2ff2ea54-edc1-4fdc-9af8-4f839fbb089e/plot_utils.py">plot_utils.py </a>.</p> <p><a href="https://zenodo.org/api/files/2ff2ea54-edc1-4fdc-9af8-4f839fbb089e/plot_utils.py">plot_utils.py </a>holds the plotting Routines for Figures 1-7 and Figures A1-A43.</p> <p><a href="https://zenodo.org/api/files/2ff2ea54-edc1-4fdc-9af8-4f839fbb089e/figures.py">figures.py </a>executes the plotting routines from <a href="https://zenodo.org/api/files/2ff2ea54-edc1-4fdc-9af8-4f839fbb089e/plot_utils.py">plot_utils.py</a>.</p> <p><a href="https://zenodo.org/api/files/2ff2ea54-edc1-4fdc-9af8-4f839fbb089e/Figures.zip">Figures.zip </a>holds the resulting Figures.<br> </p> <p>Requirements to execute the plotting routines are:</p> <pre>Python 3.7.5 numpy 1.19.5 matplotlib 3.3.3 pandas 1.2.2 cmcrameri 1.4 scipy 1.2.3 seaborn 0.11.1</pre>
Modeling the Extragalactic Background Light and the Cosmic Star Formation History (data)
<p>Paper: Modeling the Extragalactic Background Light and the Cosmic Star Formation History</p> <p>Authors: Justin D. Finke, Marco Ajello, Alberto Dominguez, Abhishek Desai, Dieter H. Hartmann, Vaidehi S. Paliya, Alberto Saldana-Lopez</p> <p>Description: These are the luminosity densities, EBL energy density/intensities, and gamma-ray absorption optical depths for "Model A" from this publication.</p> <p>Contents:</p> <p>lumdens.tar.gz: The model luminosity density, with redshift given in the title of the file. Each file contains the wavelength in microns, and the luminosity density in Watts/Mpc^3.</p> <p>EBL_energydensity.tar.gz: The model EBL energy density, with redshift given in the name of the file. Each file contains the photon energy in eV, and the energy density in erg/cm^3.</p> <p>EBL_intensity.tar.gz: The model EBL intensity, with redshift given in the name of the file. Each file contains the wavelength in angstroms, and the intensity in nW/(m^2 srad).</p> <p>tau.tar.gz: The model gamma-gamma absorption optical depths, with redshift given in the name of the file. Each file contains the photon energy in TeV, and the absorption optical depth.</p> <p>Distribution Statement A. Approved for public release. Distribution is unlimited.<br> </p>
The C-flame Quenching by Convective Boundary Mixing in Super-AGB Stars and the Formation of Hybrid C/O/Ne White Dwarfs and SN Progenitors
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2013ApJ...772...37D/abstract">Denissenkov et al. (2013)</a>. MESA version 4631.</p> <p>Publication DOI: <a href="https://doi.org/10.1088/0004-637X/772/1/37">10.1088/0004-637X/772/1/37</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.