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.
8
datasets available to search
ShareScore release 0.9.0
Dataset results
8 results for “Spectral Energy Distribution”
RCSED - A Value-Added Reference Catalog of Spectral Energy Distributions of 800,299 Galaxies in 11 Ultraviolet, Optical, and Near-Infrared Bands: Morphologies, Colors, Ionized Gas and Stellar Populations Properties
<p>We present RCSED, the value-added Reference Catalog of Spectral Energy Distributions of galaxies, which contains homogenized spectrophotometric data for 800,299 low and intermediate redshift galaxies (0.007 < z < 0.6) selected from the Sloan Digital Sky Survey spectroscopic sample. Accessible from the Virtual Observatory (VO) and complemented with detailed information on galaxy properties obtained with the state-of-the-art data analysis, RCSED enables direct studies of galaxy formation and evolution during the last 5 Gyr. We provide tabulated color transformations for galaxies of different morphologies and luminosities and analytic expressions for the red sequence shape in different colors. RCSED comprises integrated k-corrected photometry in up-to 11 ultraviolet, optical, and near-infrared bands published by the GALEX, SDSS, and UKIDSS wide-field imaging surveys; results of the stellar population fitting of SDSS spectra including best-fitting templates, velocity dispersions, parameterized star formation histories, and stellar metallicities computed for instantaneous starburst and exponentially declining star formation models; parametric and non-parametric emission line fluxes and profiles; and gas phase metallicities. We link RCSED to the Galaxy Zoo morphological classification and galaxy bulge+disk decomposition results by Simard et al. We construct the color-magnitude, Faber-Jackson, mass-metallicity relations, compare them with the literature and discuss systematic errors of galaxy properties presented in our catalog. RCSED is accessible from the project web-site and via VO simple spectrum access and table access services using VO compliant applications. We describe several SQL query examples against the database. Finally, we briefly discuss existing and future scientific applications of RCSED and prospectives for the catalog extension to higher redshifts and different wavelengths.</p>
An updated modular set of synthetic spectral energy distributions for young stellar objects
<p>These are the models released with the following publication:</p> <p><strong><em>An updated modular set of synthetic spectral energy distributions for young stellar objects</em></strong> (<a href="https://ui.adsabs.harvard.edu/abs/2024ApJ...961..188R/abstract" target="_blank" rel="noopener">Richardson et al. 2024</a>).</p> <p>This is a set of young stellar object (YSO) models with associated spectral energy distributions (SEDs) calculated through radiative transfer. It is a significant update to the data published alongside Robitaille (2017, R17). It contains the parameters shaping each model and adds the newly calculated parameters of envelope mass, average dust temperature, disk stability, and line-of-sight extinction. It also makes explicit quantities, such as source luminosity, that were left implicit in the previous release. This set also convolves the SEDs with several new filters, primarily those on the James Webb Space Telescope, and adds a script to facilitate convolution of these models with additional filters as desired by users. All data included in Version 1.1 of the R17 set (the most recent) are included here.</p> <p>Like their predecessors, these models are versioned. Updates will be released as more models are completed or other changes are made.</p> <p>Files unzip to r+24_models-{version}/{geometry}. "files.tar.gz" contains scripts for SED convolution and main sequence comparison, the opacity to absorption of dust used in the radiative transfer calculations, main sequence T/L values used for results in the accompanying work, and reference material for the contents of the dataset and latest version.</p> <p>The primary use of these models is as templates for SED fitting. The R17 models were structured for use with the <a href="https://sedfitter.readthedocs.io/en/stable/" target="_blank" rel="noopener">sedfitter</a> python package, which enables fitting and analysis of the fit results. For a version of sedfitter which accommodates the new additions, use <a href="https://github.com/richardson-t/sedfitter/tree/dev" target="_blank" rel="noopener">this fork</a>.</p>
A modular set of synthetic spectral energy distributions for young stellar objects - Robitaille (2017) - v1.1 [Hyperion files]
<p>These are the input and output files for the radiative transfer code (Hyperion) for the model sets presented in</p> <p><em>A modular set of synthetic spectral energy distributions for young stellar objects</em>, Robitaille (2017)</p> <p>Each model set is provided as a single tar file. Each tar file expands to <strong>grids-1.1/<set name></strong>, so if you expand all tar files in the same folder, you will end up with a single <strong>grids-1.1</strong> folder with 18 sub-folders, one for each model set.</p> <p>For a given model set, the files are as follows:</p> <ul> <li>grids-1.1/<set name>/input - input Hyperion files</li> <li>grids-1.1/<set name>/log - log files from Hyperion</li> <li>grids-1.1/<set name>/output - output Hyperion files</li> <li>grids-1.1/<set name>/par - parameters for each model</li> <li>grids-1.1/<set name>/ranges.conf - ranges of parameters varied in the model set</li> <li>grids-1.1/<set name>/parameters.hdf5 - table of parameters for all models</li> <li>grids-1.1/<set name>/d03_5.5_3.0_A_sub.hdf5 - dust file used for the models</li> </ul> <p>Given the large number of models for some of the model sets, the models are not all stored directly inside the par, input, output or log directories - instead these directories contain folders formed from the first two characters (forced to lowercase) of the names of the models they contain. For example, a3 contains all models whose name starts with a3 or A3. This was done to avoid having too many files in a single folder which can cause issues on certain file systems.</p> <p>For the Hyperion input and output files, in some cases an _sed file is present. In these cases, the output SEDs (and polarization spectra) should be read from the _sed file, not the original output file. This is the case for all models that are in a set for which the ambient medium was present, as described in §4.2.3 of Robitaille (2017). Furthermore, in some cases the SED file is called _sed_noscat to indicate that scattering was not included, as described in §5.1 of Robitaille (2017).</p> <p>To avoid taking up too much disk space, the Hyperion HDF5 input/output files use external links to refer to each other and to the dust file. To make sure the links work, you should do all operations with the input/output files from the directory containing <strong>grids-1.1</strong>. For example, to open a Hyperion output file, you would need to do (in Python):</p> <p> In [1]: from hyperion.model import ModelOutput</p> <p> In [2]: mo = ModelOutput('grids-1.1/s---s-i/output/a3/A3kQmQtj.rtout')</p> <p>A notebook with examples of reading in the output files can be found here:</p> <p>https://github.com/hyperion-rt/paper-2017-sed-models/blob/master/notebook_raw/reading_raw_files.ipynb</p> <p>More information on using Hyperion, including reading input/output files, can also be found at http://docs.hyperion-rt.org</p> <p>For <strong>announcements</strong> of new versions of these models, you can subscribe to the following mailing list:</p> <p>https://groups.google.com/forum/#!forum/protostars</p> <p>For <strong>questions or issues</strong> using these models, you can open a GitHub issue in the companion repository:</p> <p>https://github.com/hyperion-rt/paper-2017-sed-models/issues/new</p>
A modular set of synthetic spectral energy distributions for young stellar objects - Robitaille (2017) - v1.1
<p>These are the models released with the following publication:</p> <p><em>A modular set of synthetic spectral energy distributions for young stellar objects</em>, Robitaille (2017)</p> <p>A companion repository is available on GitHub:</p> <p>https://github.com/hyperion-rt/paper-2017-sed-models</p> <p>In particular, a notebook is provided, demonstrating how the models here can be used:</p> <p>https://github.com/hyperion-rt/paper-2017-sed-models/blob/master/notebook/using_the_models.ipynb</p> <p><strong>Note:</strong> the files here do not include polarization results, nor do they include the SEDs split by components (e.g. scattered versus direct light). The raw Hyperion output files which contain this information will be made available at a later date, and a link will be provided here.</p> <p>For <strong>announcements</strong> of new versions of these models, you can subscribe to the following mailing list:</p> <p>https://groups.google.com/forum/#!forum/protostars</p> <p>For <strong>questions or issues</strong> using these models, you can open a GitHub issue in the companion repository:</p> <p>https://github.com/hyperion-rt/paper-2017-sed-models/issues/new</p> <p><strong>MacOS X users:</strong> there is a bug in the bundled version of tar in MacOS X that causes issues when expanding some of the largest files here (this results in the <em>flux.fits</em> file being empty). To avoid this, you can use the GNU tar version which can be installed e.g. with Homebrew using <em>brew install gnu-tar</em> then using the <em>gtar</em> command instead of <em>tar</em>.</p>
DirtyGrid: 3D dust radiative transfer modeling of spectral energy distributions of dusty stellar populations
<p>Output global SEDs of a large grid of 3D stellar+dust radiative transfer models spanning the range of star formation and dust contents of regions of galaxies.</p> <p>Paper describing the DirtyGrid is Law, Gordo, & Misset (2018, ApJ, submitted)</p> <p>Code to make to access this data at: https://github.com/karllark/pydirtygrid</p>
A set of synthetic spectral energy distributions for "diskless", intermediate-mass young stars
<p>These models were published in conjunction with <em>The Duration of Star Formation in Galactic Giant Molecular Clouds. I. The Great Nebula in Carina, </em>by <a href="https://arxiv.org/abs/1906.01730">Povich et al. (2019)</a>. They will also be used in subsequent papers in that series.</p> <p>The format of these models conforms with the standards of <a href="https://doi.org/10.1051/0004-6361/201425486">Robitaille (2017)</a>, so they are compatible with the <a href="http://sedfitter.readthedocs.io/en/stable/installation.html">python implementation</a> of the <a href="https://doi.org/10.1086/512039">Robitaille et al. (2007)</a> SED fitting tool. We have pre-convolved these models with a number of useful filters, including Johnson/Bessel <em>UB</em><em>VRI, </em>UKIRT <em>ZYJHK, </em>VISTA <em>ZYJHK<sub>S</sub></em>, 2MASS <em>JHK<sub>S</sub></em>, <em>Spitzer/</em>IRAC and MIPS. </p> <p>A software pipeline implementing these models to constrain the age and mass distributions of young stellar populations is also <a href="https://doi.org/10.5281/zenodo.3234101">publicly available</a>.</p> <p><strong>Limitations of these models</strong></p> <p><em>These models produce the best results for stars more massive than the Sun and older than about 0.5 Myr. </em>They employ the pre-main-sequence evolutionary tracks of <a href="https://arxiv.org/abs/astro-ph/0003477">Siess et al. (2000)</a> and <a href="https://ui.adsabs.harvard.edu/abs/1996A&A...307..829B/abstract">Bernasconi & Maeder (1996)</a>. Numerous modern tracks offer significant improvement in the treatment of subsolar-mass stars. In addition, the Kurucz stellar atmospheres used in these synthetic SEDs work best for T<sub>eff </sub>> 4,000 K; for cooler temperatures other models, for example the PHOENIX photospheres, may be more appropriate.</p> <p>Newer evolutionary tracks covering the intermediate-mass range are now available, for example the Geneva pre-MS tracks of <a href="https://doi.org/10.1051/0004-6361/201935051">Haemmerlé et al. (2019)</a>. The principal innovation of these modern models is the treatment of accretion and location of the intermediate-mass stellar birthline. The coolest, most luminous models in this set are likely <em>unphysical</em>, representing fully-convective stars of >2 solar masses and <0.5 Myr isochronal age.</p>
The spectral energy distributions of classical Cepheids in the Magellanic Clouds
<p>The spectral energy distributions (SEDs) of a sample of 142 LMC and 77 SMC fundamental mode classical Cepheids (CCs) were constructed using photometric data in the literature.<br> The sample was build from stars that have a metallicity determination from high-resolution spectroscopy,<br> have been used in Baade-Wesselink type of analysis, have a radial velocity curve published in {\it Gaia} DR3, have Walraven photometry, or have their light- and radial-velocity curves modelled by pulsation codes.<br> <br> The SEDs were fitted with stellar photosphere models to derive the best-fitting luminosity and effective temperature.<br> Distance and reddening were taken from the literature.<br> <br> Only one star with a significant infrared (IR) excess was found in the LMC and none in the SMC, contrary to earlier work on the Milky Way (MW) suggesting that IR excess may be more prominent in MW cepheids than in the Magellanic Clouds.</p> <p>The stars were plotted in a Hertzsprung-Russell diagram (HRD) and compared<br> to evolutionary tracks for CCs and to theoretical instability strips.<br> For the large majority of stars, the position in the HRD is consistent with the instability strip.<br> <br> Period-luminosity ($PL$) and period-radius relations are derived and compared to these relations in the MW.</p>
X-Shooting ULLYSES: Massive Stars at low metallicity VII. Stellar and Wind Properties of B supergiants in the SMC - Appendix F: Spectral energy distributions and spectral fits
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.