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.
264
datasets available to search
ShareScore release 0.9.0
Dataset results
264 results for “Stellarator”
Dataset of hydrogen Balmer line indexes for stellar chromospheric activity of solar-like stars based on the LAMOST spectroscopic surveys
<p><span>This dataset consists of three catalogs of the data of the hydrogen Balmer line indexes of solar-like stars for stellar chromospheric activity based on the LAMOST Low-Resolution Spectroscopic Survey (LRS) and Medium-Resolution Spectroscopic Survey (MRS): (1) LRS spectral catalog, (2) MRS spectral catalog, and (3) stellar source catalog. These catalogs are stored as three CSV-format data files. The columns contained in the catalogs are described below. The observational and spectroscopic parameters of the spectral samples in the dataset are from LAMOST DR9 v2.0. Unavailable values in the dataset are marked as −9999.0.</span></p> <p><span> </span></p> <p><span>(1) LRS spectral catalog (Balmer_line_indexes_LRS_spectral_catalog_LAMOST_DR9.csv)</span></p> <p><span>obsid - unique LAMOST observation identifier of LRS</span></p> <p><span>obsdate - date of LRS observation (UTC)</span></p> <p><span>uid - unique LAMOST source identifier</span></p> <p><span>ra - right ascension of stellar source in LRS catalog (unit: degree)</span></p> <p><span>dec - declination of stellar source in LRS catalog (unit: degree)</span></p> <p><span>sn_g - LRS g-band signal-to-noise ratio</span></p> <p><span>sn_r - LRS r-band signal-to-noise ratio</span></p> <p><span>teff - stellar effective temperature determined from LRS data (unit: K)</span></p> <p><span>teff_err - uncertainty of teff (unit: K)</span></p> <p><span>logg - stellar surface gravity determined from LRS data (unit: dex)</span></p> <p><span>logg_err - uncertainty of logg (unit: dex)</span></p> <p><span>feh - stellar metallicity determined from LRS data (unit: dex)</span></p> <p><span>feh_err - uncertainty of feh (unit: dex)</span></p> <p><span>rv - radial velocity determined from LRS data (unit: km/s)</span></p> <p><span>rv_err - uncertainty of rv (unit: km/s)</span></p> <p><span>L_halpha - Hα activity index of LRS data</span></p> <p><span>L_halpha_err - uncertainty of L_halpha</span></p> <p><span>L_hbeta - Hβ activity index of LRS data</span></p> <p><span>L_hbeta_err - uncertainty of L_hbeta</span></p> <p><span>L_hgamma - Hγ activity index of LRS data</span></p> <p><span>L_hgamma_err - uncertainty of L_hgamma</span></p> <p><span>L_hdelta - Hδ activity index of LRS data</span></p> <p><span>L_hdelta_err - uncertainty of L_hdelta</span></p> <p><span> </span></p> <p><span>(2) MRS spectral catalog (Balmer_line_indexes_MRS_spectral_catalog_LAMOST_DR9.csv)</span></p> <p><span>obsid - unique LAMOST observation identifier of MRS</span></p> <p><span>obsdate - date of MRS observation (UTC)</span></p> <p><span>uid - unique LAMOST source identifier</span></p> <p><span>ra - right ascension of stellar source in MRS catalog (unit: degree)</span></p> <p><span>dec - declination of stellar source in MRS catalog (unit: degree)</span></p> <p><span>sn_B - MRS blue-band signal-to-noise ratio</span></p> <p><span>sn_R - MRS red-band signal-to-noise ratio</span></p> <p><span>teff - stellar effective temperature determined from MRS data (unit: K)</span></p> <p><span>teff_err - uncertainty of teff (unit: K)</span></p> <p><span>logg - stellar surface gravity determined from MRS data (unit: dex)</span></p> <p><span>logg_err - uncertainty of logg (unit: dex)</span></p> <p><span>feh - stellar metallicity determined from MRS data (unit: dex)</span></p> <p><span>feh_err - uncertainty of feh (unit: dex)</span></p> <p><span>rv_r0 - radial velocity determined from the red band data of MRS (unit: km/s)</span></p> <p><span>rv_r0_err - uncertainty of rv_r0 (unit: km/s)</span></p> <p><span>M_halpha – Hα activity index of MRS data</span></p> <p><span>M_halpha_err - uncertainty of M_halpha</span></p> <p><span> </span></p> <p><span>(3) stellar source catalog (Balmer_line_indexes_stellar_source_catalog_LAMOST_DR9.csv)</span></p> <p><span>uid - unique LAMOST source identifier</span></p> <p><span>ra - right ascension of stellar source from LRS catalog (unit: degree)</span></p> <p><span>dec - declination of stellar source from LRS catalog (unit: degree)</span></p> <p><span>teff - stellar effective temperature from LRS catalog (unit: K)</span></p> <p><span>teff_err - uncertainty of teff (unit: K)</span></p> <p><span>logg - stellar surface gravity from LRS catalog (unit: dex)</span></p> <p><span>logg_err - uncertainty of logg (unit: dex)</span></p> <p><span>feh - stellar metallicity from LRS catalog (unit: dex)</span></p> <p><span>feh_err - uncertainty of feh (unit: dex)</span></p> <p><span>L_halpha - Hα activity index of LRS data</span></p> <p><span>L_halpha_err - uncertainty of L_halpha</span></p> <p><span>L_hbeta - Hβ activity index of LRS data</span></p> <p><span>L_hbeta_err - uncertainty of L_hbeta</span></p> <p><span>L_hgamma - Hγ activity index of LRS data</span></p> <p><span>L_hgamma_err - uncertainty of L_hgamma</span></p> <p><span>L_hdelta - Hδ activity index of LRS data</span></p> <p><span>L_hdelta_err - uncertainty of L_hdelta</span></p> <p><span>M_halpha - Hα activity index of MRS data</span></p> <p><span>M_halpha_err - uncertainty of M_halpha</span></p> <p><span>l_halpha - Hα absolute flux index of LRS data (unit: 1/</span><span>Å</span><span>)</span></p> <p><span>l_halpha_err - uncertainty of l_halpha (unit: 1/</span><span>Å</span><span>)</span></p> <p><span>l_hbeta - Hβ absolute flux index of LRS data (unit: 1/</span><span>Å</span><span>)</span></p> <p><span>l_hbeta_err - uncertainty of l_hbeta (unit: 1/</span><span>Å</span><span>)</span></p> <p><span>l_hgamma - Hγ absolute flux index of LRS data (unit: 1/</span><span>Å</span><span>)</span></p> <p><span>l_hgamma_err - uncertainty of l_hgamma (unit: 1/</span><span>Å</span><span>)</span></p> <p><span>l_hdelta - Hδ absolute flux index of LRS data (unit: 1/</span><span>Å</span><span>)</span></p> <p><span>l_hdelta_err - uncertainty of l_hdelta (unit: 1/</span><span>Å</span><span>)</span></p> <p><span>m_halpha</span><span> - Hα absolute flux index of MRS data (unit: 1/</span><span>Å</span><span>)</span></p> <p><span>m_halpha_err - uncertainty of m_halpha (unit: 1/</span><span>Å</span><span>)</span></p> <p><span> </span></p> <p><span> </span></p>
The data for Time-dependent Stellar Flare Models of Deep Atmospheric Heating
<p>This repository contains model output (within two .tar.gz files) and a pdf document (analysis_tools_mdwarfradyngrid-v1.0.pdf) that explains the contents and use. The models are described in Kowalski, A.F., Allred, J.C., & Carlsson, M. <em>Time-dependent Stellar Flare Models of Deep Atmospheric Heating, </em>published 2024 July 5 in <em>The Astrophysical Journal</em> Volume 969, Number 2 (DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ad4148">10.3847/1538-4357/ad4148</a>).</p> <p>The Jupyter notebook demo, radyn_xtools_Demo.ipynb is included in the PyPI package installation that is described in analysis_tools_mdwarfradyngrid-1.0.pdf.</p>
Light Curves from a Tri-chord Stellar Occultation by Jupiter's Trojan Diomedes in 2020
<p>Light curves for the three occultation chords. </p> <p> </p> <p>bender_lc.dat : occultation light curve dataset from the data provided by observer K. Bender.</p> <p>kitting_lc.dat : occultation ligth curve dataset from the data provided by observer C. Kitting.</p> <p>oesper_lc.dat : occultation light curve dataset from the data provided by observer D. Oesper. </p> <p>README.txt : column label of all light curves dataset. </p> <p> </p>
Data associated with 'Closing the stellar labels gap: Stellar label independent evidence for [α/M] information in Gaia BP/RP spectra'
<p>We present a stellar label independent model for Gaia BP/RP (XP) spectra, which does not rely on stellar labels to simulate XP spectra. Here, we provide all of the data required to reproduce <a href="https://github.com/AlexLaroche7/xp_vae">our results</a>. All of the data files should be placed in the data folder. Below we brieflty summarize the contents of each:</p> <p>APOGEE/GAIA XP DATA:</p> <ul> <li>xp_apogee_cat.h5: Contains all Gaia XP data and APOGEE stellar labels used in this work.</li> <li>xp_corrs.npz: Contains Gaia XP covariance matrices for a subset of the test data to compute reconstruction errors.</li> </ul> <p>INTERMEDIATE DATA PRODUCTS:</p> <p><em>(All files below can be reproduced with our codebase. We simply include them to speed up runtime for reproducing our results.)</em></p> <ul> <li>lb23_xp_est_labels.npy: <a href="https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.1494L/abstract">Leung & Bovy (2023)</a> XP coeffficient spectra reconstructions (stellar label dependent implementaton)</li> <li>lb23_xp_est_no_labels.npy: Leung & Bovy (2023) XP coeffficient spectra reconstructions (stellar label independent implementaton)</li> <li>xp_wavelength_space.npy: Observed Gaia XP spectra in wavelength space</li> <li>err_wavelength_space.npy: Observed Gaia XP uncertainties in wavelength space</li> <li>apogee_norm.npz: Means and standard deviations which are used to pre-process Gaia XP data before inputting into our model</li> <li>zhang_stellar_params_xmatch.npz: <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.1855Z/abstract">Zhang, Green, Rix (2023)</a> stellar parameter estimates for subset of XP spectra (to compute reconstructions)</li> <li>vae_wavelength_space.npy: Our model XP coefficient reconstructions in wavelength space</li> </ul> <p>If you have any questions please reach out via email: alex.laroche@mail.utoronto.ca</p>
Accurate and Robust Stellar Rotation Periods catalog for 82771 Kepler stars using deep learning
<p>This repository is for the paper "Rotation Period for 83022 Kepler Stars: A Deep Learning Approach" by I. Kamai and H. B. Perets. It is associated with manuscript number AAS56501. It consists a frozen repository and the published catalog</p>
Data and Software for: Resolved Near-infrared Stellar Photometry from the Magellan Telescope for 13 Nearby Galaxies: JAGB Method Distances
<p><strong>12/17/24 Update: I was made aware that the MRT files had the J-band and H-band column header labels incorrectly switched (this occured when converting the CSV files to MRT). I have fixed this mistake. The CSV files were correct the entire time. No data have been changed. Apologies for any inconvinence this may have caused. </strong></p> <p> </p> <p>Data for Resolved Near-infrared Stellar Photometry from the Magellan Telescope for 13 Nearby Galaxies: JAGB Method Distances</p> <p>The files are provided in two formats. A CSV format with a single line header, and a MRT files (zipped) written in the machine-readable format used by AAS & CDS: <a href="https://journals.aas.org/mrt-overview/" target="_blank" rel="noopener noreferrer">https://journals.aas.org/mrt-overview/</a></p> <p>Column descriptions in photometry catalogs:</p> <p>X/Y: Image coordinates from Fourstar camera</p> <p>J, J_err: J-band magnitudes and photometric errors returned from DAOPHOT/ALLFRAME</p> <p>H, H_er: H-band magnitudes and photometric errors returned from DAOPHOT/ALLFRAME</p> <p>K, K_err: K-band magnitudes and photometric errors returned from DAOPHOT/ALLFRAME</p> <p>chi: chi value returned from DAOPHOT/ALLFRAME</p> <p>sharp: sharpness value returned from DAOPHOT/ALLFRAME</p> <p>ra/dec: RA/Decl </p> <p>RGC: semi-major axis distance</p> <p> </p> <p>Notes on versions: all catalogs from all versions contain the same information, style was just adjusted to adhere to AAS standards. </p> <p> </p>
Precise and efficient modeling of stellar-activity-affected solar spectra using SOAP-GPU
<p>The simulated spectral time series generated using SOAP-GPU is based on the extraction of active regions from Solar Dynamics Observatory (SDO) data. The positions of sunspots and faculae on the solar disk at each time step are obtained from magnetogram and intensity maps, which are then input into SOAP-GPU to simulate the corresponding spectra. Comparison with HARPS-N solar spectra demonstrates that SOAP-GPU can accurately model the solar RV time series with a precision of 0.9 m/s.</p>
Data for the paper "Computing MHD equilibria of stellarators with a flexible coordinate frame"
<p>Data for the revised paper "<span>Computing MHD equilibria of stellarators with a </span><span>flexible coordinate frame"</span></p> <p>Thetitle has changed from the submission title: "A generalized Frenet frame for computing MHD equilibria in stellarators"</p> <p>We provide the input and output files for all GVEC simluations presented at the "JOINT VARENNA - LAUSANNE INTERNATIONAL WORKSHOP: THEORY OF FUSION PLASMAS, 2024" and to be published in PPCF.</p> <p>An ipython script that generates the postprocessing /plots is also provided.</p> <p>New content computing the frame from a boundary surface obtained from quasr is now also part of this compilation.</p> <p>See the README.md file for details.</p>
A Nuclear Equation of State Inferred from Stellar r-Process Abundances: Data
<p>This repository contains the raw MCMC posteriors as H5 files for the three cases presented in "A Nuclear Equation of State Inferred from Stellar <em>r</em>-Process Abundances" (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211006432H/abstract">Holmbeck et al., arXiv:2110.06432</a>).</p> <p>Also included are Python scripts with a variety of functions to read the H5 files and interpret the data with <a href="https://git.ligo.org/lscsoft/lalsuite">LALSuite</a>. These include:</p> <ul> <li>reading the data contained in the H5 file (likelihood, acceptance, and values for each MCMC step)</li> <li>generating a corner plot of the data</li> <li>finding the maximum likelihood in the posterior distribution</li> <li>calculating a neutron star mass-radius curve for a posterior EOS</li> <li>calculating pressure and density for a posterior EOS</li> <li>calculating observables (M_TOV, R_1.4, and L) associated with an EOS</li> </ul> <p>The scripts are written for Python3 compatibility and depend on:</p> <ul> <li><a href="https://pypi.org/project/lalsuite/">lalsuite</a></li> <li><a href="https://docs.h5py.org/en/stable/">h5py</a></li> <li><a href="https://numpy.org/">numpy</a></li> <li><a href="https://scipy.org/">scipy</a></li> <li><a href="https://matplotlib.org/">matplotlib</a></li> <li><a href="https://corner.readthedocs.io/en/latest/">corner</a></li> </ul> <p>For more information and how to use these scripts, see the comments in `example.py` or contact Erika Holmbeck.</p> <p>If any of our posterior samples are used in your work, we ask that you appropriately cite this repository and the original paper (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211006432H/abstract">Holmbeck et al., arXiv:2110.06432</a>).</p>
Dynamics in a stellar convective layer and at its boundary: Comparison of five 3D hydrodynamics codes
<p>Supplementary materials for the paper "Dynamics in a stellar convective layer and at its boundary: Comparison of five 3D hydrodynamics codes". The data contained in the .tar.gz archives can be read and visualised using the Jupyter notebooks available on the CoCoPy repository (<a href="https://github.com/robert-andrassy/CoCoPy">https://github.com/robert-andrassy/CoCoPy</a>) and on the CoCo Hub (<a href="https://www.ppmstar.org/coco">https://www.ppmstar.org/coco</a>). The four parts of the archive 2D-slices.tar.gz need to be concatenated using the standard Unix tool "cat" before decompression.</p> <p>Two minor bugs affecting the 1D profiles are corrected in this version. Everything else is the same as in Version 1.</p>
Supporting information for v2 of ``A Transparent Window into Early-Type Stellar Variability''
<p>These folders contain grids of main-sequence stellar models computed four different metallicities and masses varying from 0.1-60 MSun. Models were computed using MESA r15140 following commit `964e07d` in this GitHub repo: https://github.com/adamjermyn/conv_trends.</p> <p>Changes from the previous version include:</p> <p>- Fixed a bug in calculating certain averages over the convection zone.</p> <p>- Incorporated the effects of radiation pressure on the Rayleigh number.</p> <p>- Increased time resolution of models.</p> <p> </p> <p>The metallicities are as follows:</p> <p>main_Z_time_2022_01_18_13_56_00_sha_ef8c - Z = 0.002 (SMC)</p> <p>main_Z_time_2022_01_18_13_56_06_sha_adbd - Z = 0.006 (LMC)</p> <p>main_Z_time_2022_01_18_13_56_13_sha_e814 - Z = 0.01<br> main_Z_time_2022_01_18_13_56_19_sha_f7ec - Z = 0.014 (MW)</p>
Stellar chromospheric activity spectral database of solar-type stars based on the LAMOST Low-Resolution Spectroscopic Survey(disused)
<p>A stellar chromospheric activity spectral database of solar-type stars is constructed based on the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS). The database contains 1,330,654 high-quality LRS spectra of solar-type stars with <em>T</em><sub>eff</sub> ranging from 4800 K to 6800 K. We measure the mean fluxes at line cores of the Ca II H&K lines using a 1 Å rectangular bandpass as well as a 1.09 Å full width at half maximum (FWHM) triangular bandpass, and the mean fluxes of two 20 Å pseudo-continuum bands on the two sides of the lines. Three chromospheric activity indexes, <em>S</em><sub>rec</sub> using the rectangular bandpass, and <em>S</em><sub>tri</sub> and <em>S</em><sub>MWL</sub> using the triangular bandpass, are evaluated based on the measured fluxes. The uncertainties of all the obtained parameters are estimated. We also produce spectrum diagrams of Ca II H&K lines for all the spectra in the database. This database with more than one million high-quality LAMOST LRS spectra and basal chromospheric activity parameters can be further used for investigating activity characteristics of solar-type stars and solar-stellar connection.</p> <p> </p> <p>The entity of the database is composed of (1) a catalog of spectral sample and activity parameters, and (2) a library of spectrum diagrams.</p> <p>(1) Catalog of Spectral Sample and Activity Parameters<br> CaIIHK_Sindex_LAMOST_DR7_LRS.csv</p> <p>(2) Library of Spectrum Diagrams<br> spectrum_diagrams_000-049.zip (46 subfolders)<br> spectrum_diagrams_050-099.zip (40 subfolders)<br> spectrum_diagrams_100-149.zip (47 subfolders)<br> spectrum_diagrams_150-199.zip (49 subfolders)<br> spectrum_diagrams_200-249.zip (47 subfolders)<br> spectrum_diagrams_250-299.zip (45 subfolders)<br> spectrum_diagrams_300-349.zip (41 subfolders)<br> spectrum_diagrams_350-399.zip (39 subfolders)<br> spectrum_diagrams_400-449.zip (45 subfolders)<br> spectrum_diagrams_450-499.zip (35 subfolders)<br> spectrum_diagrams_500-549.zip (27 subfolders)<br> spectrum_diagrams_550-599.zip (38 subfolders)<br> spectrum_diagrams_600-649.zip (32 subfolders)<br> spectrum_diagrams_650-699.zip (26 subfolders)<br> spectrum_diagrams_700-749.zip (28 subfolders)</p> <p> </p>
Stellar chromospheric activity spectral database of solar-type stars based on the LAMOST Low-Resolution Spectroscopic Survey (disused)
<p>A stellar chromospheric activity spectral database of solar-type stars is constructed based on the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS). The database contains 1,330,654 high-quality LRS spectra of solar-type stars with effective temperature ranging from 4800 K to 6800 K. We measure the mean fluxes at line cores of the Ca II H&K lines using a 1 Å rectangular bandpass as well as a 1.09 Å full width at half maximum (FWHM) triangular bandpass, and the mean fluxes of two 20 Å pseudo-continuum bands on the two sides of the lines. Three chromospheric activity indexes, <em>S</em><sub>rec</sub> based on the 1 Å rectangular bandpass, and <em>S</em><sub>tri</sub> and <em>S</em><sub>MWL</sub> based on the 1.09 Å FWHM triangular bandpass, are evaluated from the measured fluxes. The uncertainties of all the obtained parameters are estimated. We also produce spectrum diagrams of Ca II H&K lines for all the spectra in the database. This database with more than one million high-quality LAMOST LRS spectra and basal chromospheric activity parameters can be further used for investigating activity characteristics of solar-type stars and solar-stellar connection.</p> <p> </p> <p>The entity of the database is composed of (1) a catalog of spectral sample and activity parameters, and (2) a library of spectrum diagrams.</p> <p>(1) Catalog of Spectral Sample and Activity Parameters<br> CaIIHK_Sindex_LAMOST_DR7_LRS.csv</p> <p>(2) Library of Spectrum Diagrams<br> spectrum_diagrams_000-049.zip (46 subfolders)<br> spectrum_diagrams_050-099.zip (40 subfolders)<br> spectrum_diagrams_100-149.zip (47 subfolders)<br> spectrum_diagrams_150-199.zip (49 subfolders)<br> spectrum_diagrams_200-249.zip (47 subfolders)<br> spectrum_diagrams_250-299.zip (45 subfolders)<br> spectrum_diagrams_300-349.zip (41 subfolders)<br> spectrum_diagrams_350-399.zip (39 subfolders)<br> spectrum_diagrams_400-449.zip (45 subfolders)<br> spectrum_diagrams_450-499.zip (35 subfolders)<br> spectrum_diagrams_500-549.zip (27 subfolders)<br> spectrum_diagrams_550-599.zip (38 subfolders)<br> spectrum_diagrams_600-649.zip (32 subfolders)<br> spectrum_diagrams_650-699.zip (26 subfolders)<br> spectrum_diagrams_700-749.zip (28 subfolders)</p>
Stellar chromospheric activity spectral database of solar-type stars based on the LAMOST Low-Resolution Spectroscopic Survey(disused)
<p>A stellar chromospheric activity spectral database of solar-type stars is constructed based on the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS). The database contains 1,330,654 high-quality LRS spectra of solar-type stars with effective temperature ranging from 4800 K to 6800 K. We measure the mean fluxes at line cores of the Ca II H&K lines using a 1 Å rectangular bandpass as well as a 1.09 Å full width at half maximum (FWHM) triangular bandpass, and the mean fluxes of two 20 Å pseudo-continuum bands on the two sides of the lines. Three chromospheric activity indexes, <em>S</em><sub>rec</sub> based on the 1 Å rectangular bandpass, and <em>S</em><sub>tri</sub> and <em>S</em><sub>MWL</sub> based on the 1.09 Å FWHM triangular bandpass, are evaluated from the measured fluxes. The uncertainties of all the obtained parameters are estimated. We also produce spectrum diagrams of Ca II H&K lines for all the spectra in the database. This database with more than one million high-quality LAMOST LRS spectra and basal chromospheric activity parameters can be further used for investigating activity characteristics of solar-type stars and solar-stellar connection.</p> <p> </p> <p>The entity of the database is composed of (1) a catalog of spectral sample and activity parameters, and (2) a library of spectrum diagrams.</p> <p>(1) Catalog of Spectral Sample and Activity Parameters<br> CaIIHK_Sindex_LAMOST_DR7_LRS.csv</p> <p>(2) Library of Spectrum Diagrams<br> spectrum_diagrams_000-049.zip (46 subfolders)<br> spectrum_diagrams_050-099.zip (40 subfolders)<br> spectrum_diagrams_100-149.zip (47 subfolders)<br> spectrum_diagrams_150-199.zip (49 subfolders)<br> spectrum_diagrams_200-249.zip (47 subfolders)<br> spectrum_diagrams_250-299.zip (45 subfolders)<br> spectrum_diagrams_300-349.zip (41 subfolders)<br> spectrum_diagrams_350-399.zip (39 subfolders)<br> spectrum_diagrams_400-449.zip (45 subfolders)<br> spectrum_diagrams_450-499.zip (35 subfolders)<br> spectrum_diagrams_500-549.zip (27 subfolders)<br> spectrum_diagrams_550-599.zip (38 subfolders)<br> spectrum_diagrams_600-649.zip (32 subfolders)<br> spectrum_diagrams_650-699.zip (26 subfolders)<br> spectrum_diagrams_700-749.zip (28 subfolders)</p>
Simulation data for the article "Revisiting a disky origin for the faint branch of the Sagittarius stellar stream"
<p>This archive contains the data shared in the context of the "Revisiting a disky origin for the faint branch of the Sagittarius stellar stream" (Oria et al. 2022) article. It allows one to run the simulation of the Sgr dwarf falling into the joint potential of the MW and the LMC, based on the model of Vasiliev et al. (2021), and extended with our faint branch particles. Furthermore, plots and videos complementary to those shown in the article are shared here as well.</p> <p><strong>best_model_y_axis_rotation</strong></p> <p>contains data pertaining to our best model, put forward in the article: plots of the evolution of our faint branch particles for every snapshot of the simulation and its corresponding video, initial conditions, trajectory of the progenitor, final snapshot with many coordinates and kinematics. </p> <p><strong>other_model_x_axis_rotation</strong></p> <p>contains the same data as before for our other model mentioned in the article</p> <p><strong>potentials_triax</strong></p> <p>contains the evolving MW+LMC potential files as shared by Vasiliev et al. (2021), under DOI 10.5281/zenodo.4300977 necessary to run the simulation</p> <p><strong>scripts</strong></p> <p>contains scripts to create the initial conditions (with Agama) and run the simulation (with Gyrfalcon)</p>
Data for the paper "Optimization of quasisymmetric stellarators with self-consistent bootstrap current and energetic particle confinement"
<p>Data for the paper "Optimization of quasisymmetric stellarators with self-consistent bootstrap current and energetic particle confinement"</p>
Juno Stellar Reference Unit (SRU) image data from Juno's G34 Ganymede flyby on 7 June 2021
<p>This is the Juno Stellar Reference Unit (SRU) image data from Juno's G34 Ganymede flyby on 7 June 2021. The image is discussed in the paper, "Surface features of Ganymede revealed in Jupiter-shine by Juno's Stellar Reference Unit," published in AGU Geophysical Research Letters.</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>
Data for the paper "Mapping the space of quasisymmetric stellarators using optimized near-axis expansion"
<p>Data for the paper "Mapping the space of quasisymmetric stellarators using optimized near-axis expansion"</p>
Data Products for "The Upper Atmosphere of Uranus from Stellar Occultations II: Revised Temperatures in the Upper Stratosphere and Lower Thermosphere"
<p>From the README file:</p> <p>Organization of data products connected to Saunders et al. (2023, PSJ) and Saunders et al. (2024, PSJ).</p> <p>/forward_modeling_results.csv -- Anderson-Darling test values and critical values for each comparison between Voyager 2 profiles and observed stellar occultation light curves. Voyager 2 profiles were forward modeled into stellar occultation light curves to enable a direct comparison to observed, Earth-based stellar occultation profiles using the Anderson-Darling test of normality. Critical values are provided. See Section 3 of Saunders+23 and Section 2 of Saunders+24.</p> <p>/original_occultation_profiles/ -- Previously published atmospheric profiles from Earth-based stellar occultations. Data were extracted using a data extraction software on published papers. The citation for each data source is provided below. <br>/original_occultation_profiles/1977* -- Elliot et al. (1979)<br>/original_occultation_profiles/1981* -- French et al. (1983)<br>/original_occultation_profiles/1982-04* -- Sicardy et al. (1985)<br>/original_occultation_profiles/1982-05* -- French et al. (1987)<br>/original_occultation_profiles/1983* -- Elliot et al. (1987)</p> <p>/reprocessed_occultation_profiles/ -- All atmospheric profiles resulting from reprocessing the 26 occultation profiles.<br>/reprocessed_occultation_profiles/profiles/ -- Only the atmospheric profiles.<br>/reprocessed_occultation_profiles/profiles/* -- Each individual profile, in original vertical resolution. Columns: radius [km] (from center of Uranus), temperature [k], pressure [microbar], number density [m^-3], refractivity, scale_height [km] (pressure scale height H = kT/mg), y [km] (close-approach distance of the line connecting the viewer and the occulted star to the center of Uranus, see Saunders+23 for description). Units are provided in column headers.<br>/reprocessed_occultation_profiles/errors/ -- Only the errors for the atmospheric profiles.<br>/reprocessed_occultation_profiles/errors/* -- 1-sigma srrors for each individual profile.</p> <p>/atmospheric_models/ -- One-dimensional atmospheric model products. See Section 5 of Saunders+24.<br>/atmospheric_models/model_parameters/model_constants -- Values of constants used in the models. See Table 3 of Saunders+24.<br>/atmospheric_models/model_parameters/model_parameters -- Values and uncertainties of model parameters. See Table 3 of Saunders+24.<br>/atmospheric_models/model_profiles/* -- Profiles for the 9 models provided in Appendix C of Saunders+24. "Average" profiles are fit to the average of the 26 reprocessed stellar occultations; "cool" profiles are fit to the lower bound of the reprocessed occultations; "warm" profiles are fit to the upper bound of the reprocessed occultations. "Best-fit" profiles are the best fit model results; "lower" profiles are the lower bound of the family of generated profiles, meant to serve as a range of uncertainty; "upper" profiles are the upper bound. See Table 3 and Appendix C in Saunders+24 for more information. Units are provided in column headers.</p> <p> </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.