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264 results for “Stellarator”
Dark Matter-Induced Stellar Oscillations
<p>Reproduction package for the paper "Dark Matter-Induced Stellar Oscillations".</p>
Stellar models for "A prescription for the asteroseismic surface correction" by Li, Yaguang et al. (2023)
<p>This folder publishes the stellar models utilized in Li et al. (2023)</p> <p> </p> <p>The stellar models presented here encompass a range of parameters: mass (0.7, 2.3) solar mass, Yinit (0.22, 0.32), [M/H] within (-0.94, 0.56) dex, and amlt (1.3, 2.7). All evolutionary tracks are calculated up to the point on the RGB when the p-mode large separtion Dnu equals 2 muHz.</p> <p> </p> <p>Each model entry (row) are presented with classical observables (Teff, radius, luminosity, ...) and radial modes (mode_freq, mode_inertia, mode_n, ...)</p> <p> </p> <p>For a comprehensive description of the stellar models, please refer to the paper.</p> <p> </p> <p>The folder contains three sets of stellar models. These models are stored in the "npy" format. They can be read in Python using the following code: "import numpy as np; data = np.load(filename, allow_pickle=True);"</p> <p><br> </p> <p>- grid_models_surface_effect_corrected/tracks*.npy</p> <p>This set includes stellar models with frequencies that have surface effect removed.</p> <p> </p> <p>- grid_models_surface_effect_uncorrected/tracks*.npy</p> <p>This set includes stellar models with frequencies in the original uncorrected format.</p> <p> </p> <p>- stats_models_for_fDnu_ANN_weights.npy</p> <p>This file is a statistical stellar model fitted by an artificial neural network with fully connected layers. It can be used for calculating fDnu, the correction factor of the Dnu scaling relation, or for doing any stellar parameter inference using global parameters (such as numax, Dnu, Teff, lum, etc.). More details and example use cases can be found at https://github.com/parallelpro/surface/</p>
Supplementary data and code for "Higher order theory of quasi-isodynamicity near the magnetic axis of stellarators"
<p>VMEC equilibria and plotting code for Figure 3 in "Higher order theory of quasi-isodynamicity near the magnetic axis of stellarators".</p>
Dataset for MNRAS Letter: The stellar thermal wind as a consequence of oblateness
<p>This dataset contains temperature anomalies published as figure 2 and table 1 of the MNRAS Letter: The stellar thermal wind as a consequence of oblateness. This dataset also contains the entropy anomalies (not discussed in the paper), the rotation rate and its derivatives (computed from the <a href="https://zenodo.org/record/8171573">Howe 2023 dataset</a>), and the interpolated model S profiles (computed from the dataset at J. Christensen-Dalsgaard's <a href="https://users-phys.au.dk/jcd/solar_models/">personal website</a>). For more information, see the README.pdf document. </p>
Stellar Sources data for the Weltgeist code
<p>Dataset for stellar feedback in the Weltgeist code for simulations of stellar feedback in 1D spherical coordinates. Further information about the software, including source code and installation instructions can be found here: https://github.com/samgeen/Weltgeist</p>
MRExo 4D Fit: Mass-Radius-Stellar-Mass-Insolation Small Planet
<p>A sample of 182 planets from the NASA Exoplanet Archive queried on 2023 March 6 for planets with masses and radii $> 3\sigma$ precision, planetary radii $<$ 4 \earthradius~ and stellar masses $<$ 1.5 \solmass described in Section 2 of Kanodia et al. 2023.</p> <p>We then perform a 4-D fit to the dataset described above, across planetary masses, radii, insolation fluxes, and stellar masses. We use 40 degrees across all four dimensions, and perform 100 trials for Monte-Carlo and Bootstrap simulations each. This is described in Section 3.1 of Kanodia et al. 2023.</p> <p>For ease of download, we have broken up the fit into three separate .zip files -</p> <p>1. Without the bootstrap or Monte-Carlo results (AllPlanet_RpLt4_StMlt1.5_MRSStM_d40_0MC_0BS.zip)</p> <p>2. With the bootstrap but not Monte-Carlo results (AllPlanet_RpLt4_StMlt1.5_MRSStM_d40_0MC_100BS.zip)</p> <p>3. Without the bootstrap but with the Monte-Carlo results (AllPlanet_RpLt4_StMlt1.5_MRSStM_d40_100MC_0BS.zip)</p> <p><em>Beyond 2-D Mass-Radius Relationships: A Nonparametric and Probabilistic Framework for Characterizing Planetary Samples in Higher Dimensions: Kanodia et al. 2023</em></p> <p> </p>
Juno Stellar Reference Unit (SRU) image data for high energy ion detections during perijoves 22-35
<p>This is the Juno Stellar Reference Unit (SRU) image data for high energy ion detections during perijoves 22 to 35.</p>
Study to Evaluate ACDN-01 in ABCA4-related Stargardt Retinopathy (STELLAR)
ClinicalTrials.gov study NCT06467344. IPD Sharing: NO. Countries: 1. Publications: 1.
Safety and Efficacy of TTFields (150 kHz) Concomitant With Pemetrexed and Cisplatin or Carboplatin in Malignant Pleural Mesothelioma (STELLAR)
ClinicalTrials.gov study NCT02397928. IPD Sharing: Not stated. Countries: 7. Publications: 7.
CODEX multiplexed imaging cell datasets used for using STELLAR to transfer cell type annotations to other tissues and donors
Open the record for dataset details and reuse information.
AGN and stellar spectra used to produce model molecular emission lines
Open the record for dataset details and reuse information.
Data associated with "Adjoint methods for stellarator shape optimization and sensitivity analysis"
<p>All data produced for this dissertation and the associated post-processing scripts have been archived. </p>
Magnetic well and Mercier stability of stellarators near the magnetic axis
<p>Data files and plotting scripts for figures in the paper "Magnetic well and Mercier stability of stellarators near the magnetic axis"</p>
BPASS Stellar Models in DataFrames
<p>This data set is made of 26 binary files (.pckl) that contain pandas DataFrames compiling all the BPASS stellar models for the fiducial IMF (img135_300 - see BPASS manual for more info or contact hfstevance@gmail or @sydonahi on Twitter).</p> <p>If you end up using this data set please also cite hoki - which was used to create these data.</p> <p>The BibTex is as follows.</p> <pre>@ARTICLE{2020JOSS....5.1987S, author = {{Stevance}, Heloise and {Eldridge}, J. and {Stanway}, Elizabeth}, title = "{Hoki: Making BPASS accessible through Python}", journal = {The Journal of Open Source Software}, keywords = {Python, galaxies, Batchfile, SED, astronomy, binary stars, Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics}, year = "2020", month = "Jan", volume = {5}, number = {45}, eid = {1987}, pages = {1987}, doi = {10.21105/joss.01987}, archivePrefix = {arXiv}, eprint = {2001.11069}, primaryClass = {astro-ph.SR}, adsurl = {https://ui.adsabs.harvard.edu/abs/2020JOSS....5.1987S}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} }</pre> <p> </p>
Pulsation-driven Mass Loss from Massive Stars behind Stellar Mergers in Metal-poor Dense Clusters
<p>MESA inlists, run_star_extras, and data associated with Nakauchi, Inayoshi, Omukai 2020. MESA version</p> <p>12115.</p>
Data for "Figures of merit for stellarators near the magnetic axis"
<p>Data for figures in the paper "Figures of merit for stellarators near the magnetic axis"</p>
Pickled Stagger-Grid stellar photospheres for Oracle
<p>This dataset contains Python-pickled Stagger-Grid stellar photospheres by Magic et al. (2013).</p>
Calibration Data for Kinematic Stellar Age Models
<p>https://github.com/ssagear/KinematicAgePredictor</p>
Input datasets for the Stellar Discovery (S-Disco) EXPLORE app
Gaia RVS spectra, stellar model grids, Gaia ID catalog
Video Recordings from a Stellar Occultation by Jupiter's Trojan Diomedes on November 1st, 2020
<p>Raw video recordings and their respective observer reports for the three light curves/chords.</p>
ScienceDex guides
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.