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443 results for “galaxy”
Restart dataset for a single location in Norway ALP1 (61.0243N,8.12343E) for CTSM/FATES EMERALD Galaxy tutorial
<p>Restart files for CLM-FATES version 2.0.1 for <a href="https://github.com/NordicESMhub/ctsm/releases/tag/release-emerald-platform2.0.1">CLM-FATES EMERALD version 2.0.1</a>.</p> <p>CTSM_FATES-EMERALD_on_inputdata_version2.0.0_ALP1.tar_(restart_info):<br> - ALP1_refcase.datm.r.2300-01-01-00000.nc <br> - ALP1_refcase.datm.rs1.2300-01-01-00000.bin<br> - ALP1_refcase.cpl.r.2300-01-01-00000.nc <br> - ALP1_refcase.clm2.r.2300-01-01-00000.nc </p> <p>This dataset is being used in the <a href="https://training.galaxyproject.org/training-material/topics/climate/tutorials/fates/tutorial.html">Galaxy Training tutorial on CLM-FATES</a>.</p> <p> </p> <p>This work has been done in in collaboration with <a href="https://usegalaxy.eu/">Galaxy Europe</a> and <a href="https://www.eosc-life.eu/">EOSC-Life</a>:<br> - Within the 1st EOSC-Life Training Open Call, <a href="https://galaxyproject.eu/posts/2020/09/08/training-wp9-eosc-life/">two out of four proposals</a> have been awarded to the European Galaxy team to develop climate science e-learning material and mentoring and training opportunities for our communities.</p> <p>CLM-FATES documentation can be found <a href="https://fates-docs.readthedocs.io/en/latest/">here</a>.</p>
CO excitation, molecular gas density and interstellar radiation field in local and high-redshift galaxies
<p>This dataset includes the SED fitting figures and full sample table produced in the study of Liu et al. (2020, ApJ). Two example figures are shown in the Figure 1 of the paper. And selected columns of the full sample table is shown in the Table 1 of the paper. </p> <p> </p>
Sentiment analysis in Galaxy with IMDB movie review dataset
<p>IMDB movie review sentiment classification dataset (Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011)). For more information please refer to: https://ai.stanford.edu/~amaas/data/sentiment/<br> <br> The IMDB dataset was modified as follows to prepare it for use in a Galaxy Training Tutorial (https://training.galaxyproject.org/):<br> <br> The top 50 words are excluded (mostly stop words). Included the next 10,000 top words. Reviews are limited to 500 words max (Longer reviews trimmed and shorter reviews are padded). 25,000 reviews are used for training and testing each. Files are in tsv (tab separated value) format to be consumed by Galaxy (www.usegalaxy.org). </p>
Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
<p>This repository contains the data released in the paper "Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies" <em>(DOI to follow on publication).</em></p> <p>We release detailed morphology catalogues, both volunteer and automated, for Galaxy Zoo DECaLS.</p> <p>- gz_decals_volunteers_1_and_2 contains volunteer classifications for galaxies classified during the GZD-1 and GZD-2 campaigns.</p> <p>- gz_decals_volunteers_5 similarly contains classifications from the GZD-5 campaign. Note that GZD-5 used a modified schema designed to better detect mergers and weak bars, and includes many galaxies with only approx. five volunteer responses.</p> <p>- gz_decals_auto_posteriors contains the predicted posteriors for volunteer responses to all galaxies used in any campaign. The full posteriors are recorded as Dirichlet distribution concentrations. gz_decals_auto_posteriors also summarises these posteriors as the automated equivalent of previous Galaxy Zoo data releases;<strong> the expected vote fractions (mean posteriors)</strong>. Note that not all posteriors/vote fractions are relevant for every galaxy; we suggest assessing relevance using the estimated fraction of volunteers that would have been asked each question.</p> <p>We include a schema document, schema.md, to define the column names in each catalogue.</p> <p>We also release the galaxy images shown to volunteers on www.galaxyzoo.org during GZD-5. The images on which the automated classifier was trained may be derived from these volunteer-facing images. These images are split into four zip files, each of which contains images named by iauname inside a subfolder named by the first four characters in their iauname. Not all images were labelled during GZD-5 - refer to the catalog for training labels. We are working with the Zenodo team to add these large files to this repository - meanwhile, you can download them from The University of Manchester <a href="https://docs.google.com/document/d/1YgpnxiSJ7ffOW6FY8pX0pw93LTu8rLIdPL2PYhxW1fo/edit?usp=sharing">here</a>.</p> <p>The .csv and .parquet files contain identical data. Parquet is a fast column-oriented binary format which can be read with pd.read_parquet(loc, columns=[some columns]).</p> <p>You may also be interested in the <a href="https://github.com/mwalmsley/zoobot">github repository</a> which contains code to reproduce the model and to fine-tune it for new tasks (including pretrained weights).</p> <p>We will release updates if needed via Zenodo versioning. We recommend using the latest version of this repository. You can check the version you are currently viewing on the right-hand sidebar.</p> <p>Please cite the paper (DOI to follow on publication) when using the data in this repository.</p> <p>---</p> <p>History</p> <p>v0.0.1 (submission) provides the catalog files.</p> <p>v0.0.2 (first revision) renames the catalog files, adds flags for poorly sized galaxies, and includes the galaxy images via the University of Manchester</p>
Quenching of star formation from a lack of inflowing gas to galaxies
<p>This dataset provides HST and ALMA mosaics of the REQUIEM-ALMA survey of six strong gravitationally lensed quiescent galaxies at z=1.6 to z=3.2 (MRG-M1341, MRG-M0138, MRG-M2129, MRG-M0150, MRG-M0454, MRG-M1423) from Whitaker et al. (2021). The HST mosaics were produced with the <a href="https://github.com/gbrammer/grizli">grizli</a> software module. The ALMA data products include the full spectrally-averaged continuum data weighted for optimum sensitivity (with MRG-M2129 and MRG-M0138 including a correction for the primary beam response).</p>
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>
The data behind the ApJ article "Environmental Dependence of Type Ia Supernovae in Low-Redshift Galaxy Clusters"
<p>Data from "Environmental Dependence of Type Ia Supernovae in Low-Redshift Galaxy Clusters", <a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230601088L/abstract">NASA ADS</a></p><p>inner_cluster_data.csv and outer_cluster_data.csv include the SALT3 mB, x1, and c parameter values, distance moduli and Hubble residuals (with _1 referring to Figure 9 and _2 referring to Figure 10), outlier designation from MCMC procedure, host cluster, host cluster redshift (with Hubble diagram version converted to frame of CMB), host cluster r500, projected separation from cluster center, NED Host galaxy name, photometrically-derived estimate for host mass, host or SN redshift used in analysis, and the Host SFR category (Q: quiescent, SF: star-forming, GV: green valley) for our cluster SNe Ia.</p><p>sf_field.csv and quiescent_field.csv contain SALT parameter values, distance moduli and Hubble residuals (from Figure 10), host galaxy sSFR and mass measurements, and host redshifts (all spectroscopic, also with Hubble diagram converted values) for SNe Ia in our field samples.</p><p>full_cluster.csv contains the data from the table in the appendix of the paper.</p><p>The inner_cluster_/outer_cluster_mcmc_samples.csv files contain the samples needed to reproduce the corner plot for Figure 10.</p><p>The Python scripts recreate the figures from the paper given the above data. The details for which columns and constraints needed to reproduce the figures are included in these files.</p>
Data Tables for the CARMA Extragalactic Database for Galaxy Evolution (CARMA EDGE)
<p>Data tables accompanying the Python package <a href="https://github.com/tonywong94/edge_pydb">edge_pydb</a> which provides access to spatially resolved measurements of CO and optical emission from the CARMA EDGE sample of 125 nearby galaxies. A full description can be found in the article by <a href="https://doi.org/10.3847/1538-4365/ad20c9" target="_blank" rel="noopener">Wong et al. (2024)</a>.</p>
Selected properties of galaxy, MBHs and MBHBs populations (Izquierdo-Villalba et al. 2022)
<pre>This is a catalogue of galaxies, massive black holes (MBHs) and massive black hole binaries (MBHBs) <br>generated with L-Galaxies semi-analytical model in the version of Izquierdo-Villalba et al. 2022<br>and the dark matter merger trees extracted from the Millennium simulation (Springel et al. 2005). <br>The catalogue is created by making use of only 9 sub-volumes of the Millennium box (~2% of the whole<br>simulation, Volume = 5562144.30 Mpc3) and it contains galaxies, MBHs and MBHBs at<br>51 different redshifts (0 < z < 12.5). This catalogue is suited for studying the population<br>of MBHBs and their hosts. The properties stored in this catalogue are the following:<br> Redshift: Redshift of the galaxy/MBH/MBHB SnapNum: Snapnum of the simulation Pos: Position of the galaxy/MBH/MBHB inside the comoving box. It is an array of dimension 3. [Mpc/h] Vel: Velocity of the galaxy/MBH/MBHB inside the comoving box. It is an array of dimension 3. [km/s] Mvir: Virial mass of the dark matter sub-halo [1e10 Msun/h] Rvir: Virial radius of the dark matter sub-halo [Mpc/h] Vvir: Virial velocity of the dark matter sub-halo [km/s] Vmax: Maximum circular velocity of the dark matter halo [km/s] HotRadius: Radius of the hot gas atmosphere that surrounds the galaxy [1e10 Msun/h] ColdGas: Cold gas component of the galaxy [1e10 Msun/h] BulgeMass: Stellar mass of the bulge component [1e10 Msun/h] DiskMass: Stellar mass of the disc component [1e10 Msun/h]. The total stellar mass of the galaxy should be BulgeMass+DiskMass HotGas: Hot gas component of the galaxy [1e10 Msun/h] BlackHoleMass: Mass of the primary MBH of the galaxy [1e10 Msun/h] Lbol: Bolometric luminosity of the primary MBH of the galaxy [1e40 erg/s] fEDD: Ratio between the Lbol of the primary and the Eddington luminosity (<=1) [No dimensions] spin: Spin of the primary MBH [0,1] M_dot_acc: Accretion rate of the primary MBH of the galaxy [Msun/yr] BlackHoleMassSec: Mass of the secondary MBH (if exists) of the galaxy [1e10 Msun/h] LbolSec: Bolometric luminosity of the secondary MBH (if exists) of the galaxy [1e40 erg/s] fEDDSec: Ratio between the Lbol of the secondary MBH (if exists) and the Eddington luminosity (<=1) [No dimensions] spinSec: Spin of the primary MBH (if exists) [0,1] M_dot_acc_sec: Accretion rate of the secondary MBH (if exists) of the galaxy [Msun/yr] BinarySemiMajorAxis: Semi-major axis of the MBHB [Mpc/h] BinaryEccentricity: Eccentricity of the MBHB Sfr: Star formation rate of the galaxy [Msun/yr] BulgeSize: Size of the bulge stellar component [Mpc/h] StellarDiskRadius: Scale length of the disc stellar component [Mpc/h] GasDiskRadius: Scale length of the disc gas component [Mpc/h] The file can be read as follows: import h5py hf = h5py.File('LGal_IzquierdoVillalba2022_SubVol_0_9.h5', 'r')</pre>
The data for "Reconnaissance with JWST of the J-region Asymptotic Giant Branch in Distance Ladder Galaxies: From Irregular Luminosity Functions to Approximation of the Hubble Constant"
<p>Data used for "Reconnaissance with JWST of the J-region Asymptotic Giant Branch in Distance Ladder Galaxies: From Irregular Luminosity Functions to Approximation of the Hubble Constant" by Siyang Li, Adam G. Riess, Stefano Casertano, Gagandeep S. Anand, Daniel M. Scolnic, Wenlong Yuan, Louise Breuval, and Caroline D. Huang. Magnitudes provided are after correcting for foreground extinction and crowding bias.</p>
Data for "Identification of 4876 Bent-Tail Radio Galaxies in the FIRST Survey using Deep Learning Combined with Visual Inspection"
<p>The data are the full versions of tables that will be published in the manuscript titled "Identification of 4876 Bent-Tail Radio Galaxies in the FIRST Survey using Deep Learning Combined with Visual Inspection" by The Astrophysical Journal Supplement Series.</p> <p>The table file named "FIRST_bt_table1.csv" is the full table for "A catalog of 4876 BTRGs identified from VLA FIRST survey". </p> <p>The table file named "FIRST_bt_table2.csv" is the full table for "Cluster details for BTRGs". </p>
SARS-CoV-2 genomics resources for Galaxy
<p>Reference and custom annotation data expected as input by Galaxy SARS-CoV-2 variation analysis workflows developed by covid19.galaxyproject.org</p>
Interactive Visualizations for: "Virgo Filaments II: Catalog and First Results on the Effect of Filaments on galaxy properties"
<p>This deposit includes 13 HTML 3D interactive visualizations of filaments and galaxies investigated in the accepted article, "<em>Virgo Filaments II: Catalog and First Results on the Effect of Filaments on galaxy properties</em>" by Castignani et al. (accepted, 20-Oct-2021).</p> <p>The specific files correspond to the filaments listed in Table 2 of the accepted manuscript:</p> <table align="left"> <caption>Tabulated HTML files and filaments</caption> <thead> <tr> <th scope="col">HTML File</th> <th scope="col">Filament (Table 2)</th> </tr> </thead> <tbody> <tr> <td> <p>SG_cube_Virgo_Serpens_Filament.html</p> </td> <td> <p>Serpens F.</p> </td> </tr> <tr> <td> <p>SG_cube_Virgo_Coma_Berenices_Filament.html</p> </td> <td> <p>Coma Berenices F.</p> </td> </tr> <tr> <td> <p>SG_cube_VirgoIII_Filament.html</p> </td> <td> <p>VirgoIII F.</p> </td> </tr> <tr> <td> <p>SG_cube_Ursa_Major_Cloud.html</p> </td> <td> <p>Ursa Major Cloud</p> </td> </tr> <tr> <td> <p>SG_cube_NGC5353_4_Filament.html</p> </td> <td> <p>NGC5353/4 F.</p> </td> </tr> <tr> <td> <p>SG_cube_Leo_Minor_Filament.html</p> </td> <td> <p>Leo Minor F.</p> </td> </tr> <tr> <td> <p>SG_cube_LeoII_B_Filament.html</p> </td> <td> <p>LeoII B F.</p> </td> </tr> <tr> <td> <p>SG_cube_Canes_Venatici_Filament.html</p> </td> <td> <p>Canes Venatici F</p> </td> </tr> <tr> <td> <p>SG_cube_W-M_Sheet.html</p> </td> <td> <p>W-M Sheet</p> </td> </tr> <tr> <td> <p>SG_cube_Virgo_Draco_Filament.html</p> </td> <td> <p>Draco F.</p> </td> </tr> <tr> <td> <p>SG_cube_Virgo_Bootes_Filament.html</p> </td> <td> <p>Bootes F.</p> </td> </tr> <tr> <td> <p>SG_cube_Leo_Minor_B_Filament.html</p> </td> <td> <p>Leo Minor B F.</p> </td> </tr> <tr> <td> <p>SG_cube_LeoII_A_Filament.html</p> </td> <td> <p>LeoII A F.</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>For each visualization galaxies within 2 Mpc are color-coded by the 3D local density, and galaxies with separations greater than 2 Mpc are shown with the grey points. The filament spine is shown with the black curve.</p> <p>The files were created with plotly.js v1.58.4.</p> <p> </p>
Training data for 'Preparing genomic data for phylogeny reconstruction' (Galaxy Training Material)
<p>This data is used for Galaxy Training Network training 'Preparing genomic data for phylogeny reconstruction'. There are four nucleotide sequences from chromosome 5 of four strains of S. cerevisiae. The GenBank annotated sequenced were produced using 'funannotate predict annotation' (Galaxy Version 1.8.9+galaxy2) on the nucleotide sequences sequences. References: DOI: 10.1126/science.274.5287.546; DOI: 10.1126/science.1189015; DOI: 10.1016/j.cell.2016.08.020</p>
Reproduction package for the publication 'Galaxy cluster photons alter the ionisation state of the nearby warm-hot intergalactic medium'
<p>The following files can be used to reproduce the figures and data from the paper <strong>Galaxy cluster photons alter the ionisation state of the nearby warm-hot intergalactic medium</strong><strong> </strong>by L. Štofanová, A. Simionescu, N. A. Wijers, J. Schaye, and J. Kaastra to be accepted in Monthly Notices of the Royal Astronomical Society (MNRAS).</p>
Merger simulation (elliptical & spiral galaxy)
<p><strong>Gadget-2 (Springel 2005) simulation of 3.56 Gyr evolution of a shell-creating merger with 1:10 stellar-mass ratio, elliptical primary galaxy and secondary with inclined disk and 6 kpc impact parameter. One second of the video corresponds to 60 Myr. For more details, see <a href="https://ui.adsabs.harvard.edu/abs/2020A%26A...634A..73E/abstract">Ebrová et al. (2020, A&A 634, 73)</a> </strong></p>
The data for SKYSURF-5: Probing the Integrated Galaxy Light with a SDSS-SKYSURF Cross-Matched Catalog
<p>The SKYSURF Project (Windhorst et al. 2022) analyzes the extragalactic background light (both directly using sky background measurements and indirectly using galaxy counts) using the HST Archive. While HST images probe faint galaxies unseen by ground-based imaging, its small field of view prevents it from probing the large-scale structure around its observations.</p> <p>To supplement SKYSURF analysis, we cross-match SKYSURF pointings with SDSS observations able to probe the surrounding large-scale environment (Bhatia et al. 2024). The tables in this database include galaxies brighter than r=22.5 AB mag photometrically identified in SDSS, within +/-5 arcmin around a SKYSURF pointing.</p> <p>The tables in the Object_AB directory include information on all SDSS objects, organized by the HST camera and filter of the central pointing.</p> <p>The tables in the IGL directory include the total galaxy counts and integrated galaxy light (down to AB mag=22.5) for all SDSS objects surrounding a given SKYSURF image.</p>
Run 0 video in "Ram pressure stripping in elliptical galaxies – I. The impact of the interstellar medium turbulence"
<p>Run 0 presented in the paper "Ram pressure stripping in elliptical galaxies – I. The impact of the interstellar medium turbulence" (http://adsabs.harvard.edu/abs/2013MNRAS.428..804S or https://doi.org/10.1093/mnras/sts071).</p>
Sample datasets for Galaxy RNA-seq tutorial
<p>This is downsampled dataset from http://dx.doi.org/10.1038/nprot.2016.095. It was prepared as follows:</p> <ol> <li>Mapping data from http://dx.doi.org/10.1038/nprot.2016.095 against hg38 using HISAT2</li> <li>Restricting resulting BAM datasets to chrX:70,000,000-80,000,000</li> <li>Extracting reads using picard SamToFastq tool</li> </ol> <p>File rnaseq_sex.tab contains mapping between accession numbers and sex of the sequences individuals. </p>
Herschel‐ATLAS/GAMA: a census of dust in optically selected galaxies from stacking at submillimetre wavelengths
<p> </p> <p>Stacked sub-millimetre fluxes, luminosities, and derived dust masses and temperatures for GAMA galaxies...</p> <ul> <li>as a function of stellar mass, optical colour and redshift: StackResults_g-r_Mstar</li> <li>as a function of r-band absolute magnitude, optical colour and redshift: StackResults_g-r_Mr</li> </ul> <p>See readme files for full details.</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.