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1,584 results for “Star”
TULIPS: a Tool for Understanding the Lives, Interiors, and Physics of Stars
<p>This is a basic reproduction package for the paper "TULIPS: a Tool for Understanding the Lives, Interiors, and Physics of Stars".</p> <p>This package contains inlists for MESA, analysis scripts, animations created with TULIPS, and processed output from MESA.</p>
Constraining the properties of dense neutron star cores: The case of the transient low-mass X-ray binary HETE J1900.1-2455
<p>This is a basic reproduction package for the paper "Constraining the properties of dense neutron star cores: The case of the transient low-mass X-ray binary HETE J1900.1-2455" by <a href="https://doi.org/10.1093/mnras/stab2202">N. Degenaar et al. (2021)</a>. It provides reduced data products, simulated data and scripts to allow the reproduction of the work performed in this paper. It also lists software used and data archives containing the public observational data.</p>
Pop III Star Lyman Band Spectra
<pre>This dataset contains the Lyman band stellar spectra of individual Pop III stars computed and utilized in the paper <br>"Impact of the Primordial Stellar Initial Mass Function on the 21-cm Signal". In addition, we provide the photon number <br>emissivity per stellar baryon for each of our four fiducial IMFs (shown in Figure 6 of the paper). Please consult the <br>README.md for further details about the dataset contents, calculation, and how it is structured.<br> </pre>
Constraining Neutron-Star Matter with Microscopic and Macroscopic Collisions
<p>Data release associated with the preprint "<em>Constraining Neutron-Star Matter with Microscopic and Macroscopic Collisions</em>'' (2021; <a href="https://arxiv.org/abs/2107.06229">arxiv:2107.06229[nucl-th]</a>)</p> <p>Data includes:</p> <p>EOS files:</p> <ol> <li>chiral effective field theory (CEFT) up to 1nsat and extended with speed-of-sound extension (cse)</li> <li>CEFT up to 1.5 nsat and cse</li> <li>CEFT up to 1.5 nsat and extended with piecewise-polytrope</li> <li>CEFT up to 1.0 nsat, cse and enforced a uniform distribution on a radius for 1.4 solar mass neutron star (R14)</li> <li>CEFT up to 1.5 nsat, cse and enforced a uniform distribution on R14</li> </ol> <p>Posterior probability files: details to be found in README.txt<br> <br> Data used in Fig.1 and Fig.2 are included</p>
Impact of the PSR J0740+6620 radius constraint on the properties of high-density matter: Neutron star equation of state posterior samples
<p>Equation of state posterior samples associated with Legred et al., "Impact of the PSR J0740+6620 radius constraint on the properties of high-density matter," Phys. Rev. D 104, 063003 (2021); doi:10.1103/PhysRevD.104.063003</p> <p> </p> <p>Three sets of 1e4 samples from the posterior distribution over equations of state are provided. These sets are drawn from the posterior conditioned on different combinations of radio pulsar observations, gravitational wave data, and NICER x-ray measurements. The data release contains the equation of state table and the corresponding table of neutron star observables for each sample. The posterior distributions one can generate from these samples approximate those plotted in Figs. 1-6 of the accompanying paper.</p> <p> </p> <p>Refer to the readme for usage information.</p>
Simulations from "Using Molecular Gas Observations to Guide Initial Conditions for Star Cluster Simulations"
<p>This dataset contains the simulation results from the article "Using Molecular Gas Observations to Guide Initial Conditions for Star Cluster Simulations" (submitted to MNRAS).</p> <p><br> The data is grouped by simulation and by particle type (gas, sinks and stars). Gas is uploaded with one snapshot per 0.05 Myr, sinks and stars with one snapshot per 0.01 Myr. The data is stored in AMUSE data format, which uses hdf5.</p>
High resolution spectra of the spinning-top Be star Achernar
<p>Achernar, the closest and brightest classical Be star, presents rotational flattening, gravity darkening, occasional emission lines due to a gaseous disk, and an extended polar wind. It is also a member of a close binary system with an early A-type dwarf companion. We aim to determine the orbital parameters of the Achernar system and to estimate the physical properties of the components. We monitored the relative position of Achernar B using a broad range of high angular resolution instruments of the VLT/VLTI over a period of 13 years (2006-2019). These astrometric observations are complemented with a series of more than 700 optical spectra for the period from 2003 to 2016. The present dataset contains the high resolution spectra of Achernar that were included in our study. They were collected using the BESO, BeSS, CHIRON, CORALIE, FEROS, HARPS, PUCHEROS, and UVES instruments. The spectra are provided in the form of standard FITS files, with the continuum flux normalized to unity.</p>
The winds of young Solar-type stars in the Hyades - Quiet Sun model
<p>This is the quiet Sun model from my MNRAS paper "The winds of young Solar-type stars in the Hyades"(https://doi.org/10.1093/mnras/stab1696). Please see the paper for a full description.</p>
Optical polarimetric observations of the black hole binary star Cyg X-1 with RoboPol
<p>The dataset contains raw FITS images of the black hole X-ray binary star <a href="https://simbad.cds.unistra.fr/simbad/sim-id?Ident=%402905066&Name=HD%20226868&submit=submit">Cyg X-1</a>, raw FITS images of a nearby field star used for the interstellar polarization correction and processed measurements of polarimetric standards used for the instrumental polarization correction. The dataset was obtained with the <a href="http://robopol.org">RoboPol</a> optical polarimeter in the R-band mounted at the 1.3 m telescope of the Skinakas Observatory, Greece. The data were collected between 13 May and 1 June 2022.<br> </p>
Data affiliated with "Evolution of Flare Activity in GKM Stars Younger than 300 Myr over Five Years of TESS Observations"
<p>Data and Python scripts affiliated with the publication "Evolution of Flare Activity in GKM Stars Younger than 300 Myr over Five Years of TESS Observations" in the American Astronomical Journals. The manuscript pre-print can be found on <a href="https://arxiv.org/abs/2405.00850">arXiv</a>.</p> <p>This repository contains all of the data used to complete the analysis of the aforementioned manuscript, along with the Python scripts used to create all of the figures in the manuscript. Many of the data products from this manuscript are saved as CSVs, with appropriate column names and units, when applicable.</p> <p>Additionally, we include the light curves for all targets in this sample, along with the 'probability light curves,' which were used to identify flares in the TESS data. These data products can be found in the zip file 'TESS_stella_outputs.zip'. The rest of the data product is structured as it is on the <a href="https://github.com/afeinstein20/young-stellar-flares/tree/paper">associated GitHub repository</a>.</p>
Accelerated Mechanophore Activation and Drug Release in Network Core-Structured Star Polymers Using High-Intensity Focused Ultrasound
<div>Data of the associated manuscript and supporting information sorted after Figures, Schemes, and Tables.</div>
microSPLiT single-cell and bulk transcriptomes analysed with STAR - Pseudomonas putida KT2440/pKJK5
<h3>Description of the data and file structure</h3> <p>Data are displayed as 2 files</p> <p><strong>1. Bulk transcriptomics results (Bulk_STAR.csv)</strong></p> <p>STAR processed data combined in a gene x sample table</p> <p><strong>2. microSPLiT single-cell results (microSPLiT_STARsolo.xlsx)</strong></p> <p>STARsolo processed data combined as sublibraries’ gene associated transcript numbers (UMIs) per cell for the control (E1) and experiment (E2) sublibraries (F1-8) - (1 sublibrary per table).</p> <div> <p> </p> </div>
REsolved ALMA and SMA Observations of Nearby Stars (REASONS)
<p>This is the data release of the REASONS survey, a sample of planetesimal belts around nearby stars resolved interferometrically (see journal article for full details). Every tar file corresponds to a planetary system, and contains:<br>Data:<br>1 - the calibrated continuum visibility data in CASA .ms format<br>2 - a FITS file with the non-primary-beam-corrected image of the system. <br>3 - a PDF image of the system<br>Visibility modelling results:<br>4 - an ASCII file ('*_fitresults.txt') containing the results of the MCMC visibility fitting, as reported in Table X in the article but containing extra parameters fitted (such as background sources, extra astrometry for fits of multiple datasets/pointings, weight-rescaling factors)<br>5 - an ASCII notes ('*_fitnotes.txt') file, which should always be consulted when interpreting the fit results, as it typically points out peculiarities in the posterior probability distributions.<br>6 - a PDF of the triangle ('corner') plot of the N-dimensional posterior probability distribution of the fitted parameters, which should be consulted to get a better idea of the results reported in the ASCII files.<br>7 - a PDF image (targetstar_imagecombo.pdf) showing the data, model, residuals and visibility data+model curves, to visually evaluate the goodness of the visibility fit.<br>8 - a PDF image (targetstar.pdf) showing the multiwavelength photometry for the planetary system and a star+belt modified-blackbody fit, with parameters reported in the journal article.<br>Please refer to the journal article for more details on the methods used to obtain these data and modelling results.</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>
Star Wars social network
<p><strong>Star Wars social network</strong></p> <p>This dataset contains the social network of Star Wars characters extracted from movie scripts. In short, two characters are connected if they speak together within the same scene. The data contain characters and links from episodes I to VII.</p> <p>How the data were created is described in my blog posts:</p> <ul> <li><a href="http://evelinag.com/blog/2015/12-15-star-wars-social-network/index.html">The Star Wars social network</a></li> <li><a href="http://evelinag.com/blog/2016/01-25-social-network-force-awakens/index.html">Star Wars social network: Force Awakens</a></li> </ul> <p>The associated code is available in the main Github repository <a href="https://github.com/evelinag/StarWars-social-network">evelinag/StarWars-social-network</a>.</p> <p>Contents of the files are the following:</p> <ul> <li> <p><code>starwars-episode-N-interactions.json</code> contains the social network extracted from Episode N, where the links between characters are defined by the times the characters speak within the same scene.</p> </li> <li> <p><code>starwars-episode-N-mentions.json</code> contains the social network extracted from Episode N, where the links between characters are defined by the times the characters are mentioned within the same scene.</p> </li> <li> <p><code>starwars-episode-N-interactions-allCharacters.json</code> is the <code>interactions</code> network with R2-D2 and Chewbacca added in using data from <code>mentions</code> network.</p> </li> <li> <p><code>starwars-full-...</code> contain the corresponding social networks for the whole set of 6 episodes.</p> </li> </ul> <p><strong>Description of networks</strong></p> <p>The json files representing the networks contain the following information:</p> <p><strong>Nodes</strong></p> <p>The nodes contain the following fields:</p> <ul> <li>name: Name of the character</li> <li>value: Number of scenes the character appeared in</li> <li>colour: Colour in the visualization</li> </ul> <p><strong>Links</strong></p> <p>Links represent connections between characters. The link information corresponds to:</p> <ul> <li>source: zero-based index of the character that is one end of the link, the order of nodes is the order in which they are listed in the “nodes” element</li> <li>target: zero-based index of the character that is the the other end of the link.</li> <li>value: Number of scenes where the “source character” and “target character” of the link appeared together. Please not that the network is <em>undirected</em>. Which character represents the source and the target is arbitrary, they correspond only to two ends of the link.</li> </ul>
Bright Southern Variable Stars in the bRing Survey
<p>The corresponding data and plots for the 353 variables in the comprehensive survey of bright stars from the bRing telescopes. The paper has been accepted to the Astrophysical Journal Supplemental Series (July 27, 2019). An arXiv pre-print article is now available.</p> <p>If these data are to be used in future works, we ask that a short list of the bRing team be included as co-authors. Please contact Samuel Mellon (smellon@ur.rochester.edu) or Matthew Kenworthy (kenworthy@strw.leidenuniv.nl) for details.</p> <p>Paper Abstract:</p> <p>Besides monitoring the bright star <em>β</em> Pic during the near transit event for its giant exoplanet, the <em>β</em> Pictoris b Ring (bRing) observatories at Siding Springs Observatory, Australia and Sutherland, South Africa have monitored the brightnesses of bright stars (<em>V</em> ≃ 4--8 mag) centered on the south celestial pole (<em>δ</em> ≤ -30∘) for approximately two years. Here we present a comprehensive study of the bRing time series photometry for bright southern stars monitored between 2017 June and 2019 January. Of the 16762 stars monitored by bRing, 353 of them were found to be variable. Of the variable stars, 80% had previously known variability and 20% were new variables. Each of the new variables was classified, including 3 new eclipsing binaries (HD 77669, HD 142049, HD 155781), 26 <em>δ</em> Scutis, 4 slowly pulsating B stars, and others. This survey also reclassified four stars based on their period of pulsation, light curve, spectral classification, and color-magnitude information. The survey data were searched for new examples of transiting circumsecondary disk systems, but no candidates were found.</p>
Data and Code for: 'Stellar Models are Reliable at Low Metallicity: An Asteroseismic Age for the Ancient Very Metal-Poor Star KIC 8144907', Huber et al. 2024.
<p>Data and code to reproduce plots for the paper '<em>Stellar Models are Reliable at Low Metallicity: An Asteroseismic Age for the Ancient Very Metal-Poor Star KIC 8144907'</em>, Huber et al. 2024.</p> <p>Descriptions of the enclosed data files are as follows:</p> <div> <ul> <li>Freqs_best_fit.dat: Best-fitting GARSTEC model frequencies (Figure 3, right)</li> <li>KIC10006158_spec.txt: Reduced and normalized HDS spectrum of KIC10006158 (Figure 1)</li> <li>KIC8144907_ps.txt: Power spectrum of the Kepler light curve of KIC8144907 (Figure 3, left)</li> <li>KIC8144907_spec.txt: Reduced and normalized HDS spectrum of KIC8144907 (Figure 1)</li> <li>apokasc2.tsv: APOKASC sample from Pinsonneault+ 2014 (Figure 2)</li> <li>dwarfs.csv: Asteroseismic sample from Serenelli+ 2017 (Figure 2) </li> <li>freqs.csv: Data in Table 1</li> <li>hosts.tsv: Asteroseismic ages from Silva Aguirre+ 2015 (Figure 4) </li> <li>legacy-t1.tsv: Asteroseismic ages from Silva Aguirre+ 2017 (Figure 4) </li> <li>legacy-t2.tsv: Asteroseismic ages from Silva Aguirre+ 2017 (Figure 4) </li> <li>li-2020.csv: Asteroseismic ages from Li+ 2020 (Figure 4) </li> <li>matsuno.txt: Asteroseismic sample from Matsuno+ 2021 (Figure 2) </li> </ul> </div>
All Stars Plus Pretest and Posttest
<p>These data were collected as part of a research project funded by the National Institute on Drug Abuse.</p>
Reproduction package for the paper "Two waves of massive stars running away from the young cluster R136"
<h2>Reproduction package for the paper "Two waves of massive stars running away from the young cluster R136".</h2> <ul> <li>This reproduction package aims for open science, with the internal API designation of 'Gold'</li> <li>Authors: M. Stoop, A. de Koter, L. Kaper, S. Brands, S. Portegies Zwart, H. Sana, F. Stoppa, M. Gieles, L. Mahy, T. Shenar, D. Guo, G. Nelemans, S. Rieder</li> <li>Paper DOI: https://doi.org/10.1038/s41586-024-08013-8</li> <li>Zenodo DOI: http://doi.org/10.5281/zenodo.10058762</li> <li>Published in Nature (date of publication: 2024/10/09)</li> </ul> <h2>Hardware</h2> <ul> <li>Tested on a MacBook Pro (13-inch, 2020, Four Thunderbolt 3 ports)</li> <li>Processor: 2 GHz Quad-Core Intel Core i5</li> <li>Memory: 32 GB 3733 MHz LPDDR4X</li> <li>Graphics: Intel Iris Plus Graphics 1536 MB</li> </ul> <h2>Required non-standard hardware</h2> <ul> <li>None</li> </ul> <h2>Software dependencies</h2> <ul> <li>Jupyterlab (4.0.8)</li> <li>Notebook (7.0.6)</li> <li>Programming languages used: Python (3.11.7)</li> <li>Python packages used: numpy (1.25.2), pandas (2.1.4), matplotlib (3.8.0), os (comes with Python) scipy (1.11.4), gaiadr3-zeropoint (0.0.4) https://gitlab.com/icc-ub/public/gaiadr3_zeropoint), astroquery (0.4.6), pymc (5.6.1), corner (2.2.2), arviz (0.16.0), pytensor (2.12.3), lmfit (1.2.2), powerlaw (1.5), rpy2 (3.5.16), seaborn (0.12.2), consistencytest (0.0.2)</li> </ul> <h2>Instructions</h2> <ul> <li>The Anaconda conda environment is given should this be needed</li> <li>All Jupyter Notebooks are ready-made to produce the raw data, intermediate and end data products</li> <li>Gaia raw data is downloaded in the Jupyter Notebook "R136_runaway_candidates.ipynb"</li> <li>Data from the literature is given in the subdirectory /tables/ or /input_files/</li> <li>Input images and files are given in the subdirectory /input_files/</li> <li>Intermediate and end data products are given in /output_files/</li> <li>Figures in the paper are produced in the Jupyter Notebooks in the subdirectory /figures/ and stored in the subdirectory /figures/figures_paper/</li> </ul> <h2>Expected Output</h2> <ul> <li>Jupyter Notebooks can be executed by "Run" -> "Run All Cells"</li> <li>The Jupyter Notebook show the expected output in their respective cell</li> <li>Expected runtime are given at the top of each Jupyter Notebook</li> <li>The Jupyter Notebook which takes the longest "R136_runaway_search.ipynb" takes 7-8 hours for the entire dataset</li> <li>A small dataset has been given in this Jupyter Notebook as a proof-of-concept</li> </ul> <h2>Instructions for use</h2> <ul> <li>Jupyter Notebooks can be executed by "Run" -> "Run All Cells"</li> </ul> <h2>Figures</h2> <ul> <li>Figures can be reproduced from the /figures/ folder.</li> <li>All material and data used are available either in the Raw Data or in the Intermediate Data</li> <li>The figures shown in the paper will be saved in ./figures/figures_paper/ folder.</li> </ul> <pre> </pre>
Astrophysical constraints on neutron star f -modes with a nonparametric equation of state representation
<p>Data release for Mohanty et al. "<em>Astrophysical constraints on neutron star f-modes with a nonparametric equation of state representation"</em></p> <p>The data release consists of three files: </p> <ol> <li><a href="https://zenodo.org/api/records/13952437/draft/files/EoS_posterior_samples_PSR.h5/content" target="_blank" rel="noopener noreferrer">EoS_posterior_samples_PSR.h5</a> </li> <li><a href="https://zenodo.org/api/records/13952437/draft/files/EoS_posterior_samples_PSR+GW.h5/content" target="_blank" rel="noopener noreferrer">EoS_posterior_samples_PSR+GW.h5</a> </li> <li><a href="https://zenodo.org/api/records/13952437/draft/files/EoS_posterior_samples_PSR+GW+NICER.h5/content" target="_blank" rel="noopener noreferrer">EoS_posterior_samples_PSR+GW+NICER.h5</a> </li> </ol> <p>Each file contains 9,835 samples of EOS draws. The equation of state id's matches those of Legred et. al. 2022</p> <p>The data structure follows Legred, I. (2022) “<em>Impact of the PSR J0740+6620 radius constraint on the properties of high-density matter: Neutron star equation of state posterior samples</em>”. Zenodo. doi: 10.5281/zenodo.6502467.</p> <p>Samples were generated using stanspy, a general relativistic neutron star code written by Sailesh Ranjan Mohanty. </p> <p>Please see the readme (adapted from Legred et. al. 2022 Zenodo. doi: 10.5281/zenodo.6502467) </p>
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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.