Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
11
datasets available to search
ShareScore release 0.9.0
Dataset results
11 results for “stellar parameter”
The stellar parameters and the quantities of the residual emissions of the detected active stars in the LAMOST-K2 survey
<p>The full Table 1 in <em>Investigation of stellar magnetic activity using variational autoencoder based on low-resolution spectroscopic survey</em> (Xiang, Gu & Cao, 2022, MNRAS, 514, 4781; <a href="https://arxiv.org/abs/2206.07257">arXiv:2206.07257</a>). The columns are LAMOST obsid, K2 ID, Teff, logg, [Fe/H], EW_res_Halpha, EW_res_Ca II 8498, EW_res_Ca II 8542, EW_res_Ca II 8662, log F_Halpha, log F_Ca, log R'_Halpha, log R'_Ca. The chromospheric emissions were detected and measured with the spectral subtraction technique, which removes the inactive template spectra (photospheric contribution) from the observed stellar spectra. In this work, we used the variational autoencoder neural networks to efficiently generate the proper template spectra in a data-driven manner. More details can be found in the associated paper (<a href="https://arxiv.org/abs/2206.07257">https://arxiv.org/abs/2206.07257</a>). The demo code can be found on GitHub (<a href="https://github.com/xylib/vae-for-spectroscopic-survey">https://github.com/xylib/vae-for-spectroscopic-survey</a>).</p>
A join of the Huber et al. (2014) catalog of stellar parameters and the Kepler Input Catalog
<p>This a join of the <a href="http://arxiv.org/abs/1312.0662">Huber et al. (2014)</a> and the <a href="http://arxiv.org/abs/1102.0342">Kepler Input Catalog</a></p>
Parameter catlogs of "Photometric Stellar Parameters for 195,478 KIC Stars"
<h1>Data Abstract</h1> <p>Here are the parameter catalogs of KIC (Kepler Input Catalog) stars from the paper "Photometric Stellar Parameters for 195,478 KIC Stars." The paper includes two separate catalogs: one for A, F, G, and K type stars, and another for M type stars. Please note that the reliability of M-type star parameters is lower compared to that of AFGK stars, and caution is advised when using these values for further analysis. For this reason, we do not provide isochrone-fitting parameters for M-type stars. For detailed information on each catalog, please refer to the "Catalog Description" section. Each catalog is available in both FITS and CSV formats, with identical data content across these formats.</p> <h1>Article Abstract</h1> <p>The stellar atmospheric parameters and physical properties of stars in the Kepler Input Catalog (KIC) are of great significance for the study of exoplanets, stellar activity, and asteroseismology. However, despite extensive effort over the past decades, accurate spectroscopic estimates of these parameters are available for only about half of the stars in the full KIC catalog. In our work, by training relationships between photometric colors and spectroscopic stellar parameters from Gaia DR3, the Kepler Issac-Newton Survey, LAMOST DR10, and APOGEE DR17, we have obtained atmospheric-parameter estimates for over 195,000 stars, accounting for 97$\%$ of the total sample of KIC stars. We obtain 1$\sigma$ uncertainties of 0.1\,dex on metallicity [Fe/H], 100\,K on effective temperature $T_{\rm eff}$, and 0.2\,dex on surface gravity log $g$. In addition, based on these atmospheric parameters, we estimated the ages, masses, radii, and surface gravities of these stars using the commonly adopted isochrone-fitting approach. The resulting precision for turn-off stars is 20$\%$ in age; for dwarf stars, it is 0.07 $M_{\odot}$ in mass, 0.05 $R_{\odot}$ in radius, and 0.12 dex in surface gravity; and for giant stars, it is 0.14 $M_{\odot}$ in mass, 0.73 $R_{\odot}$ in radius, and 0.11 dex in surface gravity.</p> <h1>Status of Paper Publication</h1> <p>Paper ‘Photometric Stellar Parameters for 195,478 KIC Stars’ is published by <em>ApJS</em>.</p> <h1>Notes of the Data</h1> <div>Please note the following two points:</div> <ol> <li> <div>The A, F, G and K-type star parameter table contains the complete rows of KIC, with a total of 200,038 rows. The M-type star table includes the stars that have been filtered with $(BP-RP)_0$ > 1.8.</div> </li> <li> <div> <p>The total number of stars with parameter measurements is determined as follows:</p> <p>1) For A-, F-, G-, and K-type stars, we include those with metallicity measurements derived from either photometric data synthesized from Gaia XP spectra or KIS photometry, resulting in a total of 190,226 stars.</p> <p>2) For M-type stars, only 5,252 stars with metallicity measurements based on Gaia XP spectra are considered, as the UV radiation of M-type stars is weak, making KIS-based measurements unreliable. Although these KIS-derived values are listed in the table, they are not included in the final count.</p> <p>Thus, the total number of stars with reliable parameter measurements presented in this article is 195,478.</p> </div> </li> </ol> <h1>Catalog Description</h1> <table> <tbody> <tr> <td>Column Name</td> <td>Description</td> </tr> <tr> <td>kepid</td> <td>ID of the KIC star</td> </tr> <tr> <td>degree$\_$ra</td> <td>RA of KIC star</td> </tr> <tr> <td>degree$\_$dec</td> <td>DEC of KIC star</td> </tr> <tr> <td>$G$</td> <td>$G$-band photometry from Gaia DR3</td> </tr> <tr> <td>$G$$\_$err</td> <td>Uncertainty of $G$-band photometry from Gaia DR3</td> </tr> <tr> <td>$BP$</td> <td>$BP$-band photometry from Gaia DR3</td> </tr> <tr> <td>$BP$$\_$err</td> <td>Uncertainty of $BP$-band photometry from Gaia DR3</td> </tr> <tr> <td>$RP$</td> <td>$RP$-band photometry from Gaia DR3</td> </tr> <tr> <td>$RP$$\_$err</td> <td>Uncertainty of $RP$-band photometry from Gaia DR3</td> </tr> <tr> <td>$BP-RP$</td> <td>$BP-RP$ color from Gaia DR3</td> </tr> <tr> <td>$v$</td> <td>$v$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$v$$\_$err</td> <td>Uncertainty of $v$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$b$</td> <td>$b$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$b$$\_$err</td> <td>Uncertainty of $b$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$y$</td> <td>$y$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$y$$\_$err</td> <td>Uncertainty of $y$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$U$</td> <td>$U$-band photometry from KIS DR2</td> </tr> <tr> <td>$U$$\_$err</td> <td>Uncertainty of $U$-band photometry from KIS DR2</td> </tr> <tr> <td>$E(BP-RP)$</td> <td>$BP-RP$ excess from our 3-D extinction map</td> </tr> <tr> <td>FeH$\_$KIS$\_$PHOT/MH$\_$KIS$\_$PHOT$^a$</td> <td>[Fe/H]/[M/H] from KIS colors</td> </tr> <tr> <td>FeH$\_$KIS$\_$PHOT$\_$err/MH$\_$KIS$\_$PHOT$\_$err$^a$</td> <td>Uncertainty of [Fe/H]/[M/H] from KIS colors</td> </tr> <tr> <td>FeH$\_$GaiaSyn$\_$PHOT/MH$\_$GaiaSyn$\_$PHOT$^a$</td> <td>[Fe/H]/[M/H] from Gaia synthesized colors</td> </tr> <tr> <td>FeH$\_$GaiaSyn$\_$PHOT$\_$err/MH$\_$GaiaSyn$\_$PHOT$\_$err$^a$</td> <td>Uncertainty of [Fe/H]/[M/H] from Gaia synthesized colors</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$PHOT</td> <td>Photometric Effective temperature</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$PHOT$\_$err</td> <td>Uncertainty of photometric effective temperature</td> </tr> <tr> <td>log $g$$\_$PHOT</td> <td>Photometric Surface gravity</td> </tr> <tr> <td>log $g$$\_$PHOT$\_$err</td> <td>Photometric Uncertainty of surface gravity</td> </tr> <tr> <td>Age$\_$ISO$^b$</td> <td>Age by isochrone-fitting method</td> </tr> <tr> <td>Age$\_$ISO$\_$low$^b$</td> <td>16th percentile of age posterior by isochrone-fitting method</td> </tr> <tr> <td>Age$\_$ISO$\_$up$^b$</td> <td>84th percentile of age posterior by isochrone-fitting method</td> </tr> <tr> <td>$M$$\_$ISO$^b$</td> <td>Mass by isochrone-fitting method</td> </tr> <tr> <td>$M$$\_$ISO$\_$err$^b$</td> <td>Uncertainty of mass fitted by isochrone-fitting method</td> </tr> <tr> <td>log $g$$\_$ISO$^b$</td> <td>Surface gravity by isochrone-fitting method</td> </tr> <tr> <td>log $g$$\_$ISO$\_$err$^b$</td> <td>Uncertainty of surface gravity by isochrone-fitting method</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$ISO$^b$</td> <td>Effective temperature by isochrone-fitting method</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$ISO$\_$err$^b$</td> <td>Uncertainty of effective temperature by isochrone-fitting method</td> </tr> <tr> <td>log $L$$\_$ISO$^b$</td> <td>Luminosity by isochrone-fitting method</td> </tr> <tr> <td>log $L$$\_$ISO$\_$err$^b$</td> <td>Uncertainty of luminosity by isochrone-fitting method</td> </tr> <tr> <td>$R$$\_$ISO$^b$</td> <td>Radius by isochrone-fitting method</td> </tr> <tr> <td>$R$$\_$ISO$\_$err$^b$</td> <td>Uncertainty of radius fitted by isochrone-fitting method</td> </tr> <tr> <td>FeH$\_$LAMOST/MH$\_$LAMOST$^{a}$</td> <td>[Fe/H]/[M/H] from LAMOST DR10</td> </tr> <tr> <td>FeH$\_$LAMOST$\_$err/MH$\_$LAMOST$\_$err$^{a}$</td> <td>Uncertainty of [Fe/H]/[M/H] from LAMOST DR10</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$LAMOST</td> <td>Effective temperature from LAMOST DR10</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$LAMOST$\_$err</td> <td>ncertainty of effective temperature from LAMOST DR10</td> </tr> <tr> <td>log $g$$\_$LAMOST</td> <td>Surface gravity from LAMOST DR10</td> </tr> <tr> <td>log $g$$\_$LAMOST$\_$err</td> <td>Uncertainty of surface gravity from LAMOST DR10</td> </tr> <tr> <td>FeH$\_$APOGEE</td> <td>[Fe/H] from APOGEE DR17</td> </tr> <tr> <td>FeH$\_$APOGEE$\_$err</td> <td>Uncertainty of [Fe/H] from APOGEE DR17</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$APOGEE</td> <td>Effective temperature from APOGEE DR17</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$APOGEE$\_$err</td> <td>Uncertainty of effective temperature from APOGEE DR17</td> </tr> <tr> <td>log $g$$\_$APOGEE</td> <td>Surface gravity from APOGEE DR17</td> </tr> <tr> <td>log $g$$\_$APOGEE$\_$err</td> <td>Uncertainty of surface gravity from APOGEE DR17</td> </tr> <tr> <td>MH$\_$APOGEE</td> <td>[M/H] from APOGEE DR17</td> </tr> <tr> <td>MH$\_$APOGEE$\_$err</td> <td>Uncertainty of [M/H] from APOGEE DR17</td> </tr> <tr> <td>pmra</td> <td>Proper motion in R.A. direction from Gaia DR3</td> </tr> <tr> <td>pmra$\_$err</td> <td>Uncertainty of proper motion in R.A. direction from Gaia DR3</td> </tr> <tr> <td>pmdec</td> <td>Proper motion in decl. direction from Gaia DR3</td> </tr> <tr> <td>pmdec$\_$err</td> <td>Uncertainty of proper motion in decl. direction from Gaia DR3</td> </tr> <tr> <td>rgeo</td> <td>Geometric distance from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>rgeo$\_$low</td> <td>16th percentile of the geometric distance posterior from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>rgeo$\_$up</td> <td>84th percentile of the geometric distance posterior from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>rpgeo</td> <td>Photogeometric distance from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>rpgeo$\_$low</td> <td>16th percentile of the photogeometric distance posterior from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>rpgeo$\_$up</td> <td>84th percentile of the photogeometric distance posterior from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>StarType</td> <td>Type of KIC star, including TO (turn-off), Giant, MS (main sequence), Binary, HS (hot star), Mgaint (M-type giant) and Mdwarf (M-type dwarf)</td> </tr> </tbody> </table> <p><br>a: [Fe/H] for A, F, G and K type stars and [M/H] for M type stars.<br>b: Only for A, F, G and K type stars.</p>
The APO-K2 Catalog. I. ~7,500 Red Giants with Fundamental Stellar Parameters from APOGEE DR17 Spectroscopy and K2-GAP Asteroseismology
<p><strong>Abstract: </strong>We present a catalog of fundamental stellar properties for ~7,500 evolved stars, including stellar radii and masses, determined from the combination of spectroscopic observations from the Apache Point Observatory Galactic Evolution Experiment (APOGEE), part of the Sloan Digital Sky Survey IV (SDSS), and asteroseismology from K2. The resulting APO-K2 catalog provides spectroscopically derived temperatures and metallicities, asteroseismic global parameters, evolutionary states, and asteroseismically-derived masses and radii. Additionally, we include kinematic information from <em>Gaia</em>. We investigate the multi-dimensional space of abundance, stellar mass, and velocity with an eye toward applications in Galactic archaeology. The APO-K2 sample has a large population of low metallicity stars (~288 at [M/H] ≤ -1), and their asteroseismic masses are larger than astrophysical estimates. We argue that this may reflect offsets in the adopted fundamental temperature scale for metal-poor stars rather than metallicity-dependent issues with interpreting asteroseismic data. We characterize the kinematic properties of the population as a function of α-enhancement and position in the disk and identify those stars in the sample that are candidate components of the <em>Gaia-Enceladus</em> merger. Importantly, we characterize the selection function for the APO-K2 sample as a function of metallicity, radius, mass, νmax, color, and magnitude referencing Galactic simulations and target selection criteria to enable robust statistical inferences with the catalog.</p> <p><strong>Included Files:</strong></p> <ul> <li>The publicly available APO-K2 catalog, the is provided in the publication.</li> <li>The APO-K2 catalog without truncation to any numbers. </li> <li>The selection function relative density tables for the mass-radius parameter space. </li> <li>The selection function relative density tables for the metallicity-mass parameter space. </li> <li>The selection function relative density tables for the magnitude-color parameter space. </li> <li>The selection function relative density tables for the ν<sub>max</sub>-mag parameter space. </li> </ul>
Output Dataset from "Stellar Atmospheric Parameters From Gaia BP/RP Spectra using Uncertain Neural Networks"
<p>Output dataset of stellar parameters and associated uncertainties derived by the machine learning approach, as decribed in <a href="https://doi.org/10.1093/mnras/stae1303" target="_blank" rel="noopener"><em>"Stellar Atmospheric Parameters From Gaia BP/RP Spectra using Uncertain Neural Networks", Fallows & Sanders</em> (</a><em><a href="https://doi.org/10.1093/mnras/stae1303" target="_blank" rel="noopener">2024)</a>. </em></p> <p>Included parameters: [Fe/H] (fe_h), effective temperature (teff), surface gravity (logg), [C/Fe] (c_fe), [N/Fe] (n_fe), and [a/M] (a_m). Gaia DR3 source ids are included for matching purposes, alongside Gaia bp_rp_excess_factor and ruwe metrics for quality filtering.</p> <p><em>We include predictions for only Gaia objects with radial velocity measurements ('output_0.0_360.0_v2.txt'), and for all* Gaia objects with XP spectra ('gaiaFull_output_0.0_360.0.zip'). Note our full catalogue has a total size of 30.9GB once uncompressed.</em></p> <p> </p> <p><em>* Not truly all objects with XP spectra; stars with spurious or unreliable measurements in our requred inputs (Gaia, 2MASS, WISE) have been removed.</em></p>
Forward Asteroseismic Modeling of Stars with a Convective Core from Gravity-mode Oscillations: Parameter Estimation and Stellar Model Selection
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/arXiv:1806.06869">Aerts et al. (2018)</a>. MESA version 10108.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/1538-4365/aaccfb">10.3847/1538-4365/aaccfb</a></p>
Stellar Parameter Estimation for Half a Million M Dwarfs based on Cycle-StarNet
Open the record for dataset details and reuse information.
MESA inlists for 'Revisiting the Red Giant Branch Hosts KOI-3886 and $\iota$ Draconis. Detailed Asteroseismic Modeling and Consolidated Stellar Parameters' by Campante et al.
<p>MESA inlists used in preparation of the paper 'Revisiting the Red Giant Branch Hosts KOI-3886 and $\iota$ Draconis. Detailed Asteroseismic Modeling and Consolidated Stellar Parameters' by Campante et al. Two folders are provided in the .zip file:</p> <ul> <li>TL_Pipeline — inlists (MESA release version 12115) used to build the grid of stellar models used in Sect. 4.1 of the paper.</li> <li>JO_Pipeline — inlists (MESA release version 12778) used to build both grids of stellar models used in Sect. 4.2 of the paper.</li> </ul>
The young massive SMC cluster NGC 330 seen by MUSE. III. Stellar parameters and rotational velocities - diagnostic plots
<p>This Zenodo repository hosts additional diagnostic plots for the article "<em>The young massive SMC cluster NGC 330 seen by MUSE. III. Stellar parameters and rotational velocities</em>" by Bodensteiner et al. 2023. It provides three diagnostic figures for each of the 282 stars in the sample described in the paper. Three example figures are given for one star (#654) in the Figures 2, 3, and 4 in the paper.</p> <p>Here, we provide a .zip archive with three additional figures for all other stars that are similar to the ones in the paper:</p> <ol> <li>the <strong>spectroscopic fit</strong> ("<em>id###_spec.pdf</em>"): Each panel, one for each of the six diagnostic spectral regions, shows the combined spectrum (black) and the best-fitting tlusty model (red). Grayed-out regions are only shown for comparison but were not included in the fit, which may vary from star to star.</li> <li>the <strong>photometric fit</strong> ("<em>id###_phot.pdf</em>"): The top panel shows a comparison between the observed HST fluxes (black crosses) and the flux (colored circles) computed from the best-fit model (gray line) by convolution with the HST filters (colored lines). The bottom panel gives residuals.</li> <li>the <strong>combined fit results</strong> ("<em>correlationplot_comb_id###.pdf</em>"): The diagonal gives the χ2-distribution as a function of each parameter and the panels below show 2-dimensional χ2 -maps. The red line marks the 95% confidence level.</li> </ol> <p>The star ids (###) correspond to the ones provided in Table 1 (also available at the CDS).</p>
Data for J-PLUS: Beyond spectroscopy III. Stellar parameters and elemental-abundance ratios for five million stars from DR3
<p>The table contains stellar atmospehric parameters (Teff, logg, [Fe/H]), luminosity classification, element abundance ratios ([C/Fe], [Mg/Fe] and [alpha/Fe]), distance and age for over five million stars derived using J-PLUS DR3 + Gaia EDR3.</p><p>It includes the following parameters:</p><p>ra: Right Ascension from J-PLUS DR3 (J2000)</p><p>dec: Declination from J-PLUS DR3 (J2000)</p><p>gl: Galactic longitude derived from ICRS coordinates</p><p>gb: Galactic latitude derived from ICRS coordinates</p><p>mag[12]: Magnitudes of J-PLUS twelve bands (r, g, i, z, u, J0378, J0395, J0410, J0430, J0515, J0660, J0861)</p><p>err_mag[12]&Uncertainties of magnitudes of J-PLUS twelve bands (r, g, i, z, u, J0378, J0395, J0410, J0430, J0515, J0660, J0861)</p><p>gp1/rp1/ip1: Magnitudes from Pan-STARRS1</p><p>err_gp1/rp1/ip1: Uncertainties of magnitudes from Pan-STARRS1</p><p>G/BP/RP: Magnitudes for the Gaia three bands from EDR3; note G represents a calibration-corrected G magnitude</p><p>err_G/BP/RP: Uncertainties of magnitudes for the three Gaia bands from EDR3</p><p>ebv_sfd: Value of E(B - V) from the extinction map of SFD98, corrected for a 14% systematic</p><p>[Fe/H]: Photometric metallicity</p><p>err_[Fe/H]: Uncertainty of photometric metallicity</p><p>flg_[Fe/H]: Quality flag of [Fe/H]</p><p>[C/Fe]: Photometric carbon-to-iron abundance ratio</p><p>err_[C/Fe]:Uncertainty of photometric carbon-to-iron abundance ratio</p><p>flg_[C/Fe]: Quality flag of [C/Fe]</p><p>[Mg/Fe]: Photometric magnesium-to-iron abundance ratio</p><p>err_[Mg/Fe]: Uncertainty of photometric magnesium-to-iron abundance ratio</p><p>flg_[Mg/Fe]: Quality flag of [Mg/Fe]</p><p>[alpha/Fe]: Photometric alpha-to-iron abundance ratio</p><p>err_[alpha/Fe]: Uncertainty of photometric alpha-to-iron abundance ratio</p><p>flg_[alpha/Fe]: Quality flag of [alpha/Fe]</p><p>Teff: Effective temperature</p><p>err_Teff: Uncertainty of effective temperature</p><p>logg: Surface gravity</p><p>err_logg: Uncertainty of surface gravity</p><p>dist: Distance </p><p>err_dist: Uncertainty of distance</p><p>flg_dist: Flag to indicate the method used to derive distance, which takes the values ``parallax", ``CMF", and ``NO"</p><p>age: Stellar age</p><p>err_age: Uncertainty of stellar age</p><p>rv: Radial velocity</p><p>err_rv: Uncertainty of radial velocity</p><p>flg_rv: Flag to indicate the source of radial velocity, which takes the values ``GALAH", ``APOGEE'', ``Gaia",``RAVE", ``LAMOST", ``SEGUE"</p><p>parallax: Parallax from {it Gaia} EDR3</p><p>err_parallax: Uncertainty of parallax from {it Gaia} EDR3</p><p>pmra: Proper motion in Right Ascension direction from Gaia EDR3</p><p>err_pmra: Uncertainty of proper motion in Right Ascension direction from Gaia EDR3</p><p>pmdec: Proper motion in Declination direction from Gaia EDR3</p><p>err_pmdec&Uncertainty of proper motion in Declination direction from Gaia EDR3</p><p>ruwe: Renormalised unit weight error from Gaia EDR3</p><p>type: Flag to indicate classifications of stars, which takes the values ``dwarf" and ``giant" </p><p>subtype: Flag to indicate further sub-classifications of dwarf stars, which takes the values ``TO", ``MS" and ``Binary"</p>
Stellar Parameter Estimation for Half a Million LAMOST M Dwarfs based on Cycle-StarNet
Open the record for dataset details and reuse information.
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.