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443 results for βgalaxyβ
Data for 'X-ray eruptions every 22 days from the nucleus of a nearby galaxy'
<p>Data used to produce Figure 1 of 'X-ray eruptions every 22 days from the nucleus of a1 nearby galaxy' by Muryel Guolo, Dheeraj R. Pasham, Michal Zajacek, Eric Couglihn, et al. to appear in Nature Astronomy. Units in the files are the same as appear in the Figure 1 of the paper.</p>
GA-SMGPS Galaxy Atlas
<p>An atlas of all galaxy candidates (π=477) discussed in the paper "HI Galaxy Signatures in the SARAO MeerKAT Galactic Plane Survey – I. Probing the richness of the Great Attractor Wall across the inner Zone of Avoidance," by N. Steyn, et al (2024). (MNL, https://doi.org/10.1093/mnrasl/slad196).</p>
The CluMPR Galaxy Cluster Catalogue forΒ DESI Legacy Survey DR9
<p>Galaxy cluster catalog and cluster member galaxy catalogs compiled using the CluMPR cluster-finding algorithm. </p> <p>Paper decribing the CluMPR algorithms and cluster catalogs: The CluMPR Galaxy Cluster-Finding Algorithm and DESI Legacy Survey Galaxy Cluster catalogue (M. J. Yantovski-Barth et al.)</p> <p>File description: </p> <p>DESI_clusters_2024_simple contains the official cluster catalog,</p> <p>DESI_clusters_2024_extended is the official cluster catalog + some clusters which were flagged and removed,</p> <p>north_members_reweighted contains the member galaxies for clusters in the north region of DESI Legacy Survey,</p> <p>south_members_reweighted contains the member galaxies for clusters in the south region of DESI Legacy Survey.</p>
The TELPERION Survey for Extended Emission Regions around AGN: a strongly-interacting and merging galaxy sample
<p>FITS files of narrowband and broadband images used in the search of the TELPERION merging-galaxy sample for AGN-ionized extended [O III] clouds, and confirming longslit spectrum as described in the manuscript. A full list of observation sites and dates, and guide to file names, are in the file 000Readme.txt</p> <p>Analysis is in the paper of the same title submitted to the Monthly Notices of the Royal Astronomical Society.</p> <p> </p>
Observable and ionizing properties of star-forming galaxies with very massive stars and different IMFs
<p>Predicted, observable and ionizing properties of star-forming galaxies with very massive stars and different IMFs.</p> <p>The dataset includes the results from the models described in Table 1 of Schaerer+2024 (https://ui.adsabs.harvard.edu/abs/2024arXiv240712122S/abstract) and additional models.</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>
Galaxy groups and protocluster candidates in the CLAUDS and HSC-SSP joint deep surveys
<p>Using the extended halo-based group finder developed by <a href="http://iopscience.iop.org/article/10.3847/1538-4357/abddb2/pdf">Yang et al. 2021</a>, which is able to deal with galaxies via spectroscopic and photometric redshifts simultaneously, we (<a href="http://arxiv.org/abs/2205.05517">Li et al. 2022</a>) construct galaxy group and candidate protocluster catalogs in a wide redshift range (<span class="math-tex">\(0 < z < 6\)</span>) from the joint CFHT Large Area <em>U</em>-band Deep Survey (CLAUDS) and Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) deep data set. Based on a selection of 5,607,052 galaxies with<em> i</em>-band magnitude m<em><sub>i </sub></em>< 26 and a sky coverage of 34.41 deg<sup>2</sup>, we identify a total of 2,232,134 groups, within which 402,947 groups have at least three member galaxies. By checking the galaxy number distributions within a 5-7 <em>h<sup>-1</sup></em>Mpc projected separation and a redshift difference <span class="math-tex">\(\Delta z \leq 0.1\)</span> around those richest groups at redshift <span class="math-tex">\(z > 2\)</span>, we identified a list of 761, 343 and 43 protocluster candidates in the redshift bins <span class="math-tex">\(2 \leq z < 3\)</span>, <span class="math-tex">\(3 \leq z < 4\)</span> and <span class="math-tex">\(z > 4\)</span>, respectively. </p> <p> </p> <p>More details can be found in our paper (<a href="http://arxiv.org/abs/2205.05517">arXiv: 2205.05517</a>). Our catalogs are also shared at <a href="http://gax.sjtu.edu.cn/data/PFS.html">https://gax.sjtu.edu.cn/data/PFS.html</a>.</p>
Analysis code and data for "End-to-end study of the host galaxy and genealogy of GW170817 with BPASS"
<p>This folder contains the code and data required to reproduce all figures and values presented in the study titled "End-to-end study of the host galaxy and genealogy of GW170817 with BPASS". </p> <p>The running of the data analysis jupyter notebooks will require the installation of the python package "hoki" v1.7 and the download of the BPASS models that are already publically available. The README.md file contains all the information regarding the dependencies of this directory. </p> <p>Should you need assistance please email hfstevance@gmail.com</p>
galaxy-spin-zs-catalog
<p>Galaxy classification catalogs in the paper <em>Galaxy Spin Classification I: Z-wise vs S-wise Spirals With Chirality Equivariant Residual Network</em> by He Jia, Hong-Ming Zhu and Ue-Li Pen. See readme.md for description of the catalogs.</p>
Training data for 'Mapping-by-sequencing' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial that demonstrates mapping-by-sequencing analysis and represent a subsample of the data used in Sun & Schneeberger, 2015 (DOI:10.1007/978-1-4939-2444-8_19).</p>
Data products associated with "Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows"
<p>These are the data products associated with "Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows" by J. F. Crenshaw, et. al. This includes the input catalog and the outputs of the workflow described here https://github.com/jfcrenshaw/pzflow-paper, as well as a gzip of the github repo.</p>
Synthetic Emission Line Catalog and High-Resolution SEDs for SPHEREx Galaxy Simulations
<p><strong>Overview:</strong></p> <p>This repository contains the synthetic galaxy models derived from multi-wavelength photometry in the COSMOS field (166k galaxies over 1.27 sq. deg., 18 < i < 25) and from the GAMA survey (44k galaxies over 217 sq. deg., i < 18). These encompass a representative sample of galaxies SPHEREx will observe, and should be applicable for other surveys as well.</p> <ul> <li>Synthetic emission line strengths in the rest-frame optical/near-infrared (H-alpha, H-beta, Paschen-alpha, [OIII], [OII], [NII], [SII])</li> <li>High-resolution SEDs (0.1 - 8 micron), which are produced by 1) fitting a library of 160 galaxy templates to multi-band photometry and 2) inserting emission lines with strengths predicted by an empirical model that depends on galaxy type, redshift and stellar mass.</li> </ul> <p>These products were made using the modeling framework <strong>C</strong>onditional <strong>LI</strong>ne <strong>P</strong>ainting on <strong>S</strong>ynthetic <strong>S</strong>pectra (<em>CLIPonSS</em>), the details of which are summarized in Feder+2023b (<a href="https://arxiv.org/abs/2312.04636">arXiv link here</a>). The synthetic emission line catalog is validated against a variety of LF measurements, line ratio trends and direct line comparisons. </p> <p><strong>Data description:</strong></p> <p><em><strong>High-resolution SEDs</strong></em>: The SEDs are stored in .FITS files, for which each galaxy SED has its own Header Data Unit (HDU). This format was chosen due to the fact that the wavelength sampling for the 160 galaxy empirical and model-based templates varies, i.e., we do not perform any interpolation onto a homogenized wavelength grid. In cases where emission lines are added but the continuum template resolution is coarse, the resolution is increased in the vicinity of the emission line(s) to adequately sample the line profiles. Each galaxy's HDU is indexed by its Farmer ID (for COSMOS) or alternatively its uberID (GAMA). The COSMOS SEDs are split into three files for relative ease of access, while the GAMA sources are all in one file.</p> <p><em><strong>Emission line catalogs:</strong></em> The emission line catalogs are stored in .csv format with the following information:</p> <p>Tractor_ID (or uberID) [integer]: Unique identifier for each source</p> <p>RA/DEC [float, in degrees]: Celestial coordinates</p> <p>imag [AB]: i-band magnitude</p> <p>mass_best [float]: log-stellar masses estimated through SED fitting process</p> <p>ebv [float]: Galaxy intrinsic dust extinction (ranging from ebv=0 to 1)</p> <p>redshift [float]: Best-fit redshift from COSMOS2020 (GAMA) catalogs</p> <p>bfit_tid [integer]: Best fit template ID from library of templates</p> <p>dustlaw [integer]: Best fit dust law (1=Prevot, 2=Calzetti, 3=Seaton, 4=Allen, 5=Fitzpatrick)</p> <p>L_{line} [float, erg s-1]: Line luminosities from empirical model</p> <p>F_{line} [float, erg cm-2 s-1]: Line fluxes from empirical model</p> <p>ew_ha [float, Angstrom]: Equivalent width of H-alpha from empirical model</p> <p><strong><em>Fitting templates: </em></strong>We include the 31 model-based templates and 129 empirical templates from Brown+2014. In the Brown templates we have removed any relevant lines that were directly measured in the initial spectra, however we do not fit for/remove PAH features. These can serve as the basis for other empirically based galaxy simulations with different emission line prescriptions.</p> <p>Any questions regarding the use of these data products or related issues can be directed to Richard Feder (link to <a href="https://richardfeder.github.io/">personal website</a>). </p> <p> </p>
Data Release Scrutinising evidence for the triggering of Active Galactic Nuclei in the outskirts of massive galaxy clusters at z~1
<p>Dataset of the paper "Scrutinising evidence for the triggering of Active Galactic Nuclei in the outskirts of massive galaxy clusters at z~1".</p> <p> </p> <p>All the necessary code to deal with these data can be found at: https://github.com/IvanMuro/agn_frac_data_release</p>
Catalog-level blinding on the bispectrum for DESI-like galaxy surveys
<p>Points used in the Figures in the paper "Catalog-level blinding on the bispectrum for DESI-like galaxy surveys".</p>
WiFeS observation of southern nearby Type Ia supernova host galaxies
<p>This repository contains all the reduced and spatially-binned spectral data used in this work. Refer to the README for more detailed information. </p>
Galaxy job runtime measurements with Encrypted and plain storage volumes on Cloud deployments.
<p>Storage volume performance measurement on Cloud environment using Galaxy and Mapping tools: Bowtie2, STAR and Salmon. Galaxy job runtime for encrypted and not ecrypted storage volumes are reported.</p> <p>Scripts and Documentation on GitHub.</p>
Test data for Galaxy IUC `muon` tool
<p> Test data for Galaxy IUC `muon` tool. The data is based on published 10x human PBMC 3k multiomics data. The data was filtered for chromosome 21 only.</p>
MUSE Analysis of Gas around Galaxies (MAGG) -- VI. The cool and enriched gas environment of zβ³3 LyΞ± emitters
<p>Full sample of MgII absorption-line systems identified at z>3 in the MUSE Analysis of Gas around Galaxies (MAGG) survey presented in Galbiati et al. 2024.</p> <p>Each absorber has been modeled by a combination of Voigt profiles which are shown on top of the NIR quasar spectra obtained with X-shooter. </p>
Galaxy populations in the Hydra I cluster from the VEGAS survey III. The realm of low-surface brightness features and intra-cluster light
<p><span>This appendix provides the azimuthally-averaged surface bright</span><span>ness and colour profiles of the sample galaxies listed in Table 1 of the paper "Galaxy populations in the Hydra I cluster from the VEGAS survey III. The realm of low-surface brightness features and intra-cluster light", by <span>Marilena Spavone</span><span>,</span><span> Enrichetta Iodice</span><span>,</span><span> Felipe S. Lohmann</span><span>, Magda Arnaboldi</span><span>, Michael Hilker</span><span>, Antonio La </span><span>Marca</span><span>, Rosa Calvi</span><span>, Michele Cantiello</span><span>, Enrico M. Corsini</span><span>, Giuseppe D’Ago</span><span>, Duncan A. Forbes</span><span>, Marco </span><span>Mirabile</span><span>, and Marina Rejkuba.</span><br></span></p>
Galaxy Hi-C Training material dm3
<p>Hi-C data for Galaxy training, dm3 cells.</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.