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1,584 results for “stars”
Asteroseismic Inversions for Internal Sound Speed Profiles of Main-sequence Stars with Radiative Cores
<p>This repository contains the accompanying inlists and run_star_extras files for Buchele et al. (2024): Asteroseismic Inversions for Internal Sound Speed Profiles of Main-sequence Stars with Radiative Cores.</p> <p> </p> <p>The MESA version used is r22.05.1. We have modified the /star/private/pulse_fgong.f90 file in the MESA source code to output the Gamma1 derivatives that are necessary to compute u-Y kernels by replacing the file with the version from <a href="https://github.com/MESAHub/mesa/pull/431">https://github.com/MESAHub/mesa/pull/431</a>. Additionally, we use the MESA SDK versions 22.6.1 and the GYRE version 6.0 that is included with the r22.05.1 release of MESA. </p> <p> </p> <p>The directory grid_base contains the inlist which set the parameters that do not change across the tracks of our grid</p> <p> </p> <ul> <li> <p>Inlist_all includes parameters that are constant across all tracks and evolutionary stages </p> </li> <li> <p>Inlist_prems includes parameters for the pre-main sequence evolution of all tracks </p> </li> <li> <p>Inlist_ms includes parameters for the main sequence evolution of all tracks </p> </li> <li> <p>run_star_extras.f90 includes the code which stops the evolution of a track when the central hydrogen abundance reaches the target value </p> </li> <li> <p>gyre.in includes parameters for calculating of frequencies using GYRE</p> </li> </ul> <p> </p> <p>The directory reference_models provides the inlists to generate our reference model for each star in our sample. This directory also includes the FGONG structure files of the computed reference models. Each file is labeled with the identifier used in the text, typically the KIC number. </p> <p> </p> <p>The physics_tweaks directory provides the inlists to generate the models with modified input physics. The suffixes refer to the physics that was changed in each model, as follows: </p> <ul> <li> <p>core_kap: Core opacity</p> </li> <li> <p>pp2: change to the He3+He4->Be7 reaction rate</p> </li> <li> <p>cno: change to the N14 + p -> O15. </p> </li> </ul>
WACCM Test File for the Variable Star Phase Curve Package
<p>This is a Whole Atmosphere Community Climate Model (WACCM) dataset that is used to test WACCM integration for the Variable Star Phase Curve (VSPEC) code.</p>
A seven-Earth-radius helium-burning star inside a 20.5-min detached binary
<p><strong>2024-02-19 updated for including the full MESA inlists of evolutionary models.</strong></p> <p><strong>MESA_inlist.zip</strong> -<strong> </strong>the full MESA inlists of evolutionary models (including evolutions of single stars and binaries).</p> <p>_____________________________________________________________________________________________________________________</p> <p>This record contains the data of spectra, photometries and models used for the paper <em><strong>A seven-Earth-radius helium-burning star inside a 20.5-min detached binary</strong>,<a href="https://www.nature.com/articles/s41550-023-02188-2"> click here</a></em></p> <p><strong>J0526_fchart.png</strong> - the finder chart of J0526;</p> <p><strong>J0526_Keck_[0-5].spec </strong> (in Keck_ori_spectra.zip)<strong> </strong>- Keck I/LRIS spectra of J0526;</p> <p><strong>J0526_GTC_[0-6].spec </strong> (in GTC_ori_spectra.zip)<strong> </strong>- GTC/OSIRIS spectra of J0526;</p> <p><strong>J0526_GTC_syn_[0-6].spec </strong> (in GTC_spectra_models.zip)<strong> </strong>- GTC/OSIRIS spectra and their best-fit TLUSTY/Synspec spectra;</p> <p><strong>J0526_RV_bary_corrected.dat</strong> - the barycenter-corrected radial velocities derived from LRIS and OSIRIS spectra;</p> <p><strong>J0526_SED.dat</strong> - the photometric fluxes in the broad-band SED and corresponding synthetic fluxes from the best-fit model spectrum;</p> <p><strong>ZTF_J0526_[r,g].dat </strong>(in light_curves.zip) - ZTF <em>r</em>-/<em>g</em>-band light cuvrs of J0526, the raw data were provided by NASA/IPAC Infrared Science Archive (<a href="https://irsa.ipac.caltech.edu/">https://irsa.ipac.caltech.edu</a> );</p> <p><strong>Lijiang_J0526_[r,g].dat </strong>(in light_curves.zip) - LJT <em>r</em>-/<em>g</em>-band light cuvrs of J0526;</p> <p><strong>TMTS_J0526_L.dat </strong>(in light_curves.zip) - LJT <em>L</em>-band light cuvrs of J0526;</p> <p><strong>CHeB_c[0.32,0.33,0.34]+H1e-6.track</strong> (in MESA_models.zip) - the evolutionary tracks for core helium-burning stars with He-core mass of [0.32,0.33,0.34] M<sub>sun</sub> and H-envelope mass of 1e-6 M<sub>sun</sub>;</p> <p><strong>ZAHeMS_H0_sequence.dat</strong> (in MESA_models.zip) - the zero-age He-star sequence (without H envelope);</p> <p><strong>ZAHeMS_H1e-6_sequence.dat</strong> (in MESA_models.zip) - the zero-age He-star sequence (with H-envelope mass of 1e-6 M_sun );</p> <p><strong>binary_0.33+0.735.dat </strong> (in MESA_models.zip) - the binary evolutionary track for a core helium-burning star (M<sub>core</sub>=0.33 M_sun, M<sub>env</sub>=1e-6 M_sun ) and a CO WD (M=0.735 M_sun);</p> <p><strong>binary_0.36+0.735.dat </strong> (in MESA_models.zip) - the binary evolutionary track for a core helium-burning star (M<sub>core</sub>=0.36 M_sun, M<sub>env</sub>=1e-6 M_sun ) and a CO WD (M=0.735 M_sun);</p> <p><strong>J0526_[SED, RV, lc]_fit_corner.png</strong> - the corner plots for [SED, RV, light curve] MCMC fit.</p>
Dynamical Parameters and Clustering Results for Four-hundred Very Metal-Poor Stars Studied with LAMOST and Subaru
<p>This is the data associated with the paper "Four-hundred Very Metal-Poor Stars Studied with LAMOST and Subaru. III. Dynamically Tagged Groups and Chemodynamical Properties" by Zhang, Matsuno, Li et al. 2024. Table "LSVMP_HRdata_dynamics.csv" contains the dynamical parameters and clustering results of the HR sample in this paper. File "readme.txt" describes the meaning of each column in the table and the notes for the flag of stars. </p> <p>This sample is obtained by the LAMOST/Subaru joint project. See our paper I (DOI: 10.3847/1538-4357/ac6515) for detailed descriptions of target selection and observations, paper II (DOI: 10.3847/1538-4357/ac6514) for the chemical abundance analysis, and paper III (DOI: 10.3847/1538-4357/ad31a6) for clustering and chemodynamical analysis.</p> <p>If you have any questions about this data, please contact lhn@nao.cas.cn or rz.richie.zhang@gmail.com for more information.</p>
The data catalog for Metallicity and alpha-abundance for 48 million stars in low-extinction regions in the Milky Way
<p>Stellar chemistry contains information on the environment in which the star was born. Therefore, measuring the chemical abundances of stars in the Milky Way, such as the overall metallicity [M/H] and the alpha-abundance [alpha/M], is essential in Galactic astronomy.</p> <p>We estimate ([M/H], [alpha/M]) for giants and dwarfs in low dust extinction region from the Gaia DR3 XP spectra by using tree-based machine-learning models trained on APOGEE DR17 (Abdurro’uf et al. 2022) and the metal-poor star sample of Li et al. (2022).</p> <p>Here, we upload the catalogues of ([M/H], [alpha/M]) for 182 million stars. The data are divided into 10 fits files. The i-th file (i=1,2,...,10) contains stars with E(B-V) value between 0.1*(i-1) and 0.1*i. Because our machine-learning models are trained on stars with low dust extinction (E(B-V)<0.1), we recommend using 48 million stars with low-dust extinction region with 0<E(B-V)<0.1 (table_light_mh_am_0p0ebv0p1.fits). The description for each column of the data is shown in column_description.png. </p> <p>The source paper of this catalog:</p> <ul> <li>Kohei Hatori "Metallicity and alpha-abundance for 48 million stars in low-extinction regions in the Milky Way" <br>https://iopscience.iop.org/article/10.3847/1538-4357/ad9686</li> </ul> <p>References:</p> <div> <div> <div> <ul> <li>Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 1026 35, doi: 10.3847/1538-4365/ac4414</li> </ul> </div> </div> </div> <ul> <li>Li, H., Aoki, W., Matsuno, T., et al. 2022, ApJ, 931, 147, doi: 10.3847/1538-4357/ac6514</li> </ul> <p> </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>
Resolving Pleiades Binary Stars with Gaia and Speckle Interferometric Observations
<p>These supplementary data accompany the paper "Resolving Pleiades Binary Stars with Gaia and Speckle Interferometric Observations", <a href="https://iopscience.iop.org/article/10.3847/1538-3881/ada564" target="_blank" rel="noopener">published by the Astronomical Journal</a>. Table data are also available through the <a href="https://vizier.cds.unistra.fr/viz-bin/VizieR?-source=J/AJ/169/145" target="_blank" rel="noopener">VizieR service</a>. <br><a href="https://ui.adsabs.harvard.edu/abs/2025AJ....169..145C/abstract" target="_blank" rel="noopener">ADS: 2025AJ....169..145C</a>, <a href="https://arxiv.org/abs/2412.20986" target="_blank" rel="noopener">arXiv: 2412.20986</a></p> <p>Observations were obtained with the Speckle Polarimeter (SPP) instrument of the 2.5-m telescope of the Caucasian Observatory of the SAI MSU.</p> <p>Full versions of Table 5 and Table 6, containing binarity information and detection limits, are stored in "table5.mrt" and "table6.mrt". Contrast curves and autocorrelation functions for all observed objects are stored in "acfs/" folder inside "SPP_contrastCurves_ACFs_Pleiades.zip". These plots can be accessed directly with filename search by Gaia DR3 source identifier. The csv-table "contrastCurves_ACFs_filenames_info.csv" contains additional information about observations - date of observation, passband, seeing, comments etc. We provide a small Jupyter notebook script "display_contrastCurves_ACFs.ipynb" to display available observations along with fragments of Table 5 and Table 6 for specified object.</p>
Reproduction package for the paper "Constraining a neutron star merger origin for localized fast radio bursts"
<p>This is a reproduction package for the paper <a href="https://academic.oup.com/mnras/article/497/3/3131/5875920">"Constraining a neutron star merger origin for localized fast radio bursts"</a> by Gourdji et al. (2020) and published in MNRAS. This package provides a Jupyter notebook and the necessary information to reproduce the figures and main results of this paper.</p>
Fig. 6 in A New Record of Brittle Star Ophiopsila cf. polyacantha (Echinodermata: Ophiuroidea) from Southwestern Japan, with Notes on its Bioluminescence
Fig. 6. Ophiopsila cf. polyacantha (NSMT E-13189), SEM photographs of vertebrae from middle (A–C) and distal (D–G) portions of arms. A, F, ventral views; B, G, dorsal views; C, lateral view; D, proximal view; E, distal view. Arrowheads indicate orientation: do, dorsal side; dis, distal side; pro, proximal side; v, ventral side. Abbreviations: DF, dorsal muscle flange; DT, depression for tentacle; H, hole; LaS, lateral saddle; LoF, longitudinal furrow; VF, ventral muscle flange. Scale bars=100 µm.
Fig. 2 in A New Record of Brittle Star Ophiopsila cf. polyacantha (Echinodermata: Ophiuroidea) from Southwestern Japan, with Notes on its Bioluminescence
Fig. 2. Ophiopsila cf. polyacantha, living, in situ (A, B), anesthetized (C, D) and fixed (E–H) states of a smaller specimen (A, G, H; NSMT E-13190) and a larger specimen (B–F; NSMT E-13189). A, with burrowed disc and extended arms; B, exposed from sand ground, dorsal view; C, E, G, dorsal views; D, F, H, ventral views. Scale bars=1 cm.
Fig. 8 in A New Record of Brittle Star Ophiopsila cf. polyacantha (Echinodermata: Ophiuroidea) from Southwestern Japan, with Notes on its Bioluminescence
Fig. 8. Ophiopsila cf. polyacantha (NSMT E-13189), SEM photographs of arm ossicles: A–C, arm spines from middle (A, B) and distal (C) portion of arms, ventral most (A, C) and middle (B) one; D–F, adradial tentacle scales, from proximal (D), middle (E) and distal (F) portion of arms; G–L, dorsal arm plate from proximal (G, H), middle (I, J) and distal (K, L) portion of arms, internal (G, J, L) and external (H, I, K) views; M–R, ventral arm plates from proximal (M, N), middle (O, P) and distal (Q, R) portion of arms, internal (M, O, Q) and external (N, P, R) views. Arrowheads indicate orientations: ba, basal side; dis, distal side; ex, external side; pro, proximal side. Scale bars=100 µm.
Fig. 5 in A New Record of Brittle Star Ophiopsila cf. polyacantha (Echinodermata: Ophiuroidea) from Southwestern Japan, with Notes on its Bioluminescence
Fig. 5. Ophiopsila cf. polyacantha (NSMT E-13189), SEM photographs of vertebrae from proximal (A–E) and middle (F, G) portions of arms. A, F, distal views; B, G, proximal views; C, ventral view; D, dorsal view; E, lateral view. Arrowheads indicate orientation: do, dorsal side; dis, distal side; pro, proximal side; v, ventral side. Abbreviations: DF, dorsal muscle flange; DT, depression for tentacle; LaS, lateral saddle; LoF, longitudinal furrow; TrF, transverse furrow; VF, ventral muscle flange. Scale bars=100 µm.
Fig. 4 in A New Record of Brittle Star Ophiopsila cf. polyacantha (Echinodermata: Ophiuroidea) from Southwestern Japan, with Notes on its Bioluminescence
Fig. 4. Ophiopsila cf. polyacantha (NSMT E-13189): A–C, dorsal views of arms, proximal (A), middle (B) and distal (C) portions; D–F, lateral views of arms, proximal (D), middle (E) and distal (F) portions. Arrows indicate orientations: do, dorsal side; v, ventral side. Arrowheads indicate arm spines. Abbreviations: DAP, dorsal arm plate; T, tentacle; TS, tentacle scale. Scale bars=1 mm.
Fig. 3 in A New Record of Brittle Star Ophiopsila cf. polyacantha (Echinodermata: Ophiuroidea) from Southwestern Japan, with Notes on its Bioluminescence
Fig. 3. Ophiopsila cf. polyacantha (NSMT E-13189): A, dorsal disc; B, dorsal central part of disc; C, dorsal peripheral part of disc, arrowheads indicate internal edges of radial shields; D, jaws; E, lateral interradial part of disc; F–H, oral views of arms, proximal (F), middle (G) and distal (H) portions. Abbreviations: AdS, adradial shield; AS, arm spine; ASS, adoral shield spine; GS, genital slit; IP, infradental papilla; OS, oral shield; ORS, oral ridge spine; TS, tentacle scale; VAP, ventral arm plate. Scale bars=1 mm.
Fig. 7 in A New Record of Brittle Star Ophiopsila cf. polyacantha (Echinodermata: Ophiuroidea) from Southwestern Japan, with Notes on its Bioluminescence
Fig. 7. Ophiopsila cf. polyacantha (NSMT E-13189), SEM photographs of arm ossicles from distal portion (A, H–J), proximal portion (B–D, K, L) and middle portion (E–G): A, vertebra, lateral view; B–J, lateral arm plates, external views (B, E, H), internal views, arrowheads indicate perforations (C, F, I) and distal views (D, G, J); K, L, arm spines, ventral most (K) and middle (L) one. Arrowheads indicate orientations: ba, basal side; dis, distal side; do, dorsal side, ex, external side; v, ventral side; pro, proximal side. Abbreviations: DL, dorsal lobe; DT, depression for tentacle; K, knob; MO, muscle opening; NO, nerve opening; R, ridge; TN, tentacle notch; VL, ventral lobe. Scale bars=100 µm.
Reproduction package for the paper "A search for radio emission from double-neutron star merger GW190425 using Apertif"
<p>This is a basic reproduction package for the paper "A search for radio emission from double-neutron star merger GW190425 using Apertif".</p>
STAR NDSI collection: A cloud-free MODIS NDSI dataset (2001–2020) for China
<p><strong>If you used our dataset, please cite our reference:</strong></p> <p><strong>Jing, Y., Li, X., and Shen, H.: STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) </strong></p> <p><strong>for China, Earth Syst. Sci. Data, 14, 3137–3156, https://doi.org/10.5194/essd-14-3137-2022, 2022.</strong></p> <p>1. A recent 20-year cloud-free MODIS normalized difference snow index (NDSI) collection for </p> <p>China (excluding sea area) is generated using a Spatio-Temporal Adaptive fusion method with </p> <p>erroR correction (STAR).</p> <p>2. The STAR NDSI collection is derived from daily 1-km MODIS NDSI datasets (MOD10A1 and </p> <p>MYD10A1).</p> <p>3. It is provided using a WGS_1984_UTM_48N projection, with the data format of TIFF images.</p> <p>4. Each ZIP contains NDSI data for one hydrological year (August to next July). After </p> <p>uncompressing into the TIFF format, the files are named as "NDSI_ yyyymmdd_Daily_500M_</p> <p>V01.tif" for NDSI data and "QA_yyyymmdd_Daily_500M_V01.tif" for quality assessment (QA) data. </p> <p>5. The accuracy of this collection has been well validated by the in-situ snow depth observations </p> <p>and Landsat OLI NDSI maps. The detailed information can be found in the published paper.</p> <p>6. The NDSI value ranges from 0, 1-100. The value of water is set to 255. The fill value is set to 127.</p> <p>7. Disclaimer: The spatial scope of the dataset is for research convenience. Please refer to official</p> <p>release for a standard map of China, http://211.159.149.56/index.html.</p> <p>8. The new version includes data from 2001 to 2020, while the old version includes data from</p> <p>2012 to 2020 (https://doi.org/10.5281/zenodo.5644386).</p> <p>9. In case any questions arise, please do not hesitate to contact us (yhjing@whu.edu.cn).</p>
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>
Spectro-photometric distances and self-calibrated abundances for Apogee DR16 RGB stars within the Milky Way disk
<p>The data file contains 48,853 RGB stars from Apogee DR16 within the Milky Way disk, for which we determine spectro-photometric parallax estimates (as described in Hogg et al. 2019, AJ, 158, 147), as well as self-calibrated stellar element abundances. The data set is described in detail and analyzed in Eilers et al. 2022 (arXiv: 2112.03295).</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.