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443 results for “galaxies”
Data for The UNCOVER Survey: A First-Look HST+JWST Catalog of Galaxy Redshifts and Stellar Populations Properties Spanning 0.2 ≲ z ≲ 15
<p>The recent UNCOVER survey with the James Webb Space Telescope (JWST) exploits the nearby cluster Abell 2744 to create the deepest view of our universe to date by leveraging strong gravitational lensing. In this work, we perform photometric fitting of more than 50,000 robustly detected sources out to z ~ 15. We show the redshift evolution of stellar ages, star formation rates, and rest-frame colors across the full range of 0.2 < z < 15. The galaxy properties are inferred using the Prospector Bayesian inference framework using informative Prospector-beta priors on masses and star formation histories to produce joint redshift and stellar populations posteriors, and additionally lensing magnification is performed on-the-fly to ensure consistency with the scale-dependent priors. We show that this approach produces excellent photometric redshifts with NMAD ~ 0.03, of a similar quality to the established photometric redshift code EAzY. In line with the open-source scientific objective of the Treasury survey, we publicly release the stellar populations catalog with this paper, derived from the photometric catalog adapting aperture sizes based on source profiles. This release includes posterior moments, maximum-likelihood spectra, star-formation histories, and full posterior distributions, offering a rich data set to explore the processes governing galaxy formation and evolution over a parameter space now accessible by JWST.</p>
Input data for MFAssignR Galaxy workflow tutorial
<p>This is the input dataset for the MFAssignR Galaxy training workflow. The input dataset corresponds to the model data of MFAssignR (<a title="Raw_Neg_ML" href="https://github.com/skschum/MFAssignR/tree/master/MFAssignR/data" target="_blank" rel="noopener">Raw_Neg_ML</a>), containing a raw mass list, measured in a negative ESI mode.</p>
Simulated galaxy cluster data at z=0 demonstrating the entropy core problem with the SWIFT-EAGLE galaxy formation model
<p>Cluster simulated with the SWIFT hydrodynamic code with the Ref SWIFT-EAGLE model. This dataset contains the redshift 0 snapshot and the VELOCIraptor halo catalogue.</p> <p>Paper reference: https://arxiv.org/abs/2210.09978</p>
SERVS lightcone of model galaxies.
<p>This SERVS lightcone of model galaxies has been constructed using the Lagos12 Galform model using the techniques described in Merson et al. 2013. The lightcone covers the redshift range z = 0.0 to z = 6.0 and has a sky coverage of 18.09 deg<sup>2</sup>, centred on a sky position of (RA,DEC) = (93.5◦, 7.5◦ ).</p> <p>The SERVS lightcone contains 1518854 model galaxies with apparent, dust attenuated magnitudes in the Spizter 3.6 microns bands down to 2micro Jy (AB=23.1).</p> <p>The lightcone was constructed on the Millennium dark matter only N-body simulation.</p> <p>For each model galaxy, model multi-wavelength coverage is provided together with a range of global properties. Further details are provided in the SERVS.Lagos12.DB.Mill1.lightcone.readme.pdf document, within this dataset.</p>
Galaxy Zoo 2: Images from Original Sample
<p>The Galaxy Zoo team regularly receives requests for subject images for various versions of Galaxy Zoo, in order to facilitate other investigations, e.g. machine learning projects. This repository is an updated attempt to provide those in a way that is useful to the wider community.</p> <p>The images here are meant to be used with the data tables available at <a href="http://data.galaxyzoo.org">data.galaxyzoo.org</a>. They are the "original" sample of subject images in Galaxy Zoo 2 (Willett et al. 2013, MNRAS, 435, 2835, DOI: <a href="https://doi.org/10.1093/mnras/stt1458">10.1093/mnras/stt1458</a>) as identified in Table 1 of Willett et al. and also in Hart et al. (2016, MNRAS, 461, 3663, DOI: <a href="https://doi.org/10.1093/mnras/stw1588">10.1093/mnras/stw1588</a>). The original GZ2 subjects also gave the option to view an inverted version of the subject image; these inverted images are not provided but are easily reproducible from the included subject images. </p> <p><strong>If you use this dataset, please cite</strong> Willett et al. (2013) as the general data release and <em>also</em> cite the DOI for this dataset; if you use the updated debiased tables from Hart et al. (2016) please cite that as well.</p> <p>There are 243,434 images in total. This is off by about 0.08% from the total count in the tables - it's not clear what the cause of the discrepancy is, but we don't think the missing images have any particular sampling bias, so this sample should be useful for research.</p> <p>The images are available in a single zip file (<strong>images_gz2.zip</strong>).</p> <p>The most recent and reliable source for morphology measurements is "GZ2 - Table 1 - Normal-depth sample with new debiasing method – CSV" (from Hart et al. 2016), which is available at <a href="https://data.galaxyzoo.org">data.galaxyzoo.org</a>. To cross-reference the images with Table 1, this sample includes another CSV table (<strong>gz2_filename_mapping.csv</strong>) which contains three columns and 355,990 rows. The columns are:</p> <ul> <li><strong>objid</strong>: the Data Release 7 (DR7) object ID for each galaxy. This should match the first column in Table 1.</li> <li><strong>sample</strong>: string indicating the subsampling of the galaxy. </li> <li><strong>asset_id</strong>: an integer that corresponds to the filename of the image in the zipped file linked above.</li> </ul> <p>As an example row:</p> <p>587722981742084144,original,16</p> <p>The galaxy is 587722981741363294, which is in Table 1 and was identified by GZ2 volunteers as a barred spiral galaxy with a mild bulge and two tightly-wound arms (morphology='Sc2t'). It is in the original GZ2 sample, and can be found in the zipped file as 16.jpg. </p> <p>The overlap between the set of images, the attached table, and Table 1 is not 100%; there are a few rows in the tables that don't have a corresponding image. Again, it's not clear what the exact reason is for this, but we suggest just dropping any missing rows/images from your analysis unless you have a need for analyzing specific subjects. If you do need a 100% complete sample, you can obtain the missing images directly from SDSS. </p> <p>Based on spot checks the mappings between asset ID and DR7 object ID appear correct, but we strongly suggest that you pick some random images and verify on your own that the image seems to match the label/classifications that are listed in Table 1. </p> <p>If you have any issues using this dataset, please contact the Galaxy Zoo team, in particular Brooke Simmons (b.simmons@lancaster.ac.uk). Should Dr Simmons be unavailable, try contacting Karen Masters or Chris Lintott.</p> <p>- the GZ team, 5 Dec 2019<br> </p>
Supplementary Material for A Global Analysis of Dark Matter Signals from 27 Dwarf Spheroidal Galaxies using 11 Years of Fermi-LAT Observations
<p><strong>Description of the Supplementary Data</strong></p> <p>This record contains tabulated Bayesian and frequentist exclusion limits, profile likelihood maps and posterior probability maps for the publication S. Hoof, A. Geringer-Sameth, and R. Trotta, “<i>A Global Analysis of Dark Matter Signals from 27 Dwarf Spheroidal Galaxies using 11 Years of Fermi-LAT Observations</i>,” <a href="https://doi.org/10.1088/1475-7516/2020/02/012">JCAP 02 (2020) 012</a> (also available on the <a href="https://arxiv.org/abs/1812.06986">arXiv</a>). The dwarf spheroidal galaxies considered in this work are (in alphabetical order): Aquarius II, Boötes I, Canes Venatici I, Canes Venatici II, Carina, Carina II, Coma Berenices, Draco, Draco II, Fornax, Grus I, Hercules, Horologium I, Leo I, Leo II, Leo IV, Leo V, Pegasus III, Pisces II, Reticulum II, Sculptor, Segue 1, Sextans, Tucana II, Ursa Major I, Ursa Major II, and Ursa Minor.</p> <p>This record consists of the following files, which correspond to the limits presented Figures 9 and 10 of the paper. The files can be downloaded individually or obtained by downloading and unpacking the <code>record_2612268.zip</code>. In what follows,<code><strong>[CHANNEL]</strong></code> refers to the annihilation channel used, i.e. <i>e<sup>+</sup> e<sup>-</sup></i>, <i>μ<sup>+</sup> μ<sup>-</sup></i>, <i>τ<sup>+</sup> τ<sup>-</sup></i>, <i>b b̄</i>, <i>c c̄</i>, <i>t t̄</i>, <i>g g</i>, <i>W<sup>+</sup> W<sup>-</sup></i>, and <i>Z Z</i>. We also provide a simple plotting script for <code>Python</code>, named <code>plotting_script.py</code>, which provides basic plotting routines for all files.</p> <ul> <li>One-dimensional limits on <i><σ v></i>. The files <code>oneD_frequentist_limits_<strong>[CHANNEL]</strong>_channel.txt</code> contain the frequentist limits (at 95% confidence level, 1 degree of freedom) given the value of the WIMP mass <i>m<sub>χ</sub></i> tabulated there. The files <code>oneD_Bayesian_limits_<strong>[CHANNEL]</strong>_channel.txt</code> contain the Bayesian limit (95% credibility conditioned on the mass <i>m<sub>χ</sub></i> tabulated there).</li> <li>Two-dimensional grid of profile likelihood values. The files <code>twoD_profile_likelihood_map_<strong>[CHANNEL]</strong>_channel.txt</code> contain the natural logarithm of the profile likelihood w.r.t. the global best-fit likelihood value for that channel together with the corresponding values of <i>m<sub>χ</sub></i> and <i><σ v></i>. Note that for obtaining the limits in Fig. 10, which are conditioned on the WIMP mass, one needs to rescale the profile likelihood values with the maximum profile likelihood for a given WIMP mass.</li> <li>Two-dimensional grid of posterior probabilities for each combination of <i>m<sub>χ</sub></i> and <i><σ v></i>. The files <code>twoD_posterior_probability_map_<strong>[CHANNEL]</strong>_channel.txt</code> contain probabilities (obtained using a log-uniform prior on <i><σ v></i>) together with the corresponding values of <i>m<sub>χ</sub></i> and <i><σ v></i>. The tabulated values of <i>m<sub>χ</sub></i> and <i><σ v></i> correspond to the centres of the respective bins in <i>m<sub>χ</sub></i> and <i><σ v></i> and the posterior probability contained in them (the total posterior probability sums to 1).</li> </ul> <p>Please contact the authors if you require different data or have any questions regarding this data set.</p>
Ansible Galaxy roles, versions, and metadata
<p>A dataset of Ansible roles accompanying the SCAM 2020 publication: R. Opdebeeck, A. Zerouali, C. Velázquez-Rodríguez, C. De Roover. “Does Infrastructure as Code Adhere to Semantic Versioning? An Analysis of Ansible Role Evolution”, In Proc. 20th Int. Working Conf. on Source Code Analysis and Manipulation, 2020.</p> <p><strong>Contents</strong></p> <p>- `repos.tar.gz`: Tar-ball of git repositories of all roles included in the dataset. The subdirectories in this archive are named using the role's Ansible Galaxy qualified name, i.e., `<namespace>.<role_name>`<br> - `roles.json`: A JSON file containing metadata extracted from Ansible Galaxy for each role.<br> - `repo_paths.json`: A mapping from Ansible Galaxy role IDs to their path in the `repos` directory.<br> - `tag_versions.json`: A mapping from Ansible Galaxy role IDs to the role's repository's git tags and metadata on these tags.<br> - `version_analysis.json`: Similar to `tag_versions.json`, but with additional filtering applied.<br> - `versiondiff_analysis.json`: Contains syntactical change statistics for each version bump in the role repositories.<br> - `structural_diff_analysis.json`: Contains structural change statistics for each version bump in the role repositories.<br> - `struct_diff_cache.zip`: Directory containing per-role diff statistics, primarily used for caching during the pipeline.<br> - `metrics_diffs_releases.csv`: CSV containing the structural diff statistics merged with the bump type of the version increments.<br> - `reports.zip`: Graphs and charts describing some of the output of the pipeline, as well as CSVs containing raw data.<br> - `version.json`: The version of the dataset structure.</p> <p><strong>Software and Tools</strong></p> <p>Tools to gather, extract, and process this data can be found separately at <a href="https://zenodo.org/record/4040647">https://zenodo.org/record/4040647</a>.</p>
Data for "On the Practice of Semantic Versioning for Ansible Galaxy Roles: An Empirical Study and a Change Classification Model"
<p>This dataset accompanies a replication package provided for a study on Semantic Versioning for Ansible Galaxy roles.</p> <p>The replication package is available at https://github.com/ROpdebee/ansible_semver_ext_replication</p>
Selected properties of galaxy and SMBH populations (Spinoso et al. 2023)
<p>This record presents the catalogs of galaxy and Black Holes properties associated to the two runs of the modified version of the L-Galaxies Semi-Analytic Model (SAM) presented in Spinoso et al. 2023. These catalogs are aimed at providing the basic properties to study the population of Black Holes (BHs) and their host galaxies across cosmic times, obtained by running the L-Galaxies SAM over the whole Millennium-II box (see Boylan-Kolchin et al. 2009). The L-Galaxies SAM outputs summarized in these catalogs were obtained at several redshifts/snapshots, for two different runs which differ for the initial occupation fraction of BHs at the time of their formation. This initial occupation fraction is parametrized by the Gp parameter(see Spinoso et al. 2023 for details), with the two runs being characterized by Gp=1 and Gp=0.01. The catalogs are organized in two group of files, one group for each run. Each of these groups is composed by 18 different files, one per each availablle redshift, roughly corresponding to: z = 0, 0.5, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15. The two group of files can be easily distinguished by their names: indeed, the strings "Gp1" and "Gp001" are referred to the runs corresponding to the Gp=1 and Gp=0.01 values, respectively. In addition, every file includes a string of the form: "z[x.yz]" which specifies its redshift. </p> <p>The content of these catalogs is as follows: each redshift-file contains the same collection of arrays, each array being a galaxy or BH property. At each redshift, L-Galaxies outputs properties for the number 'NGAL' of galaxies identified in the Millennium-II box, at that specific redshift/snapshot. Therefore, most of the arrays have length equal to 'NGAL' (i.e. one value per each galaxy). Few of the arrays have a length of N * NGAL (i.e. N values per each galaxy). The content, units and data type of these arrays are as follows: </p> <ul> <li>"StellarMass" - Total stellar mass of each galaxy - [10^10 Msun / h] - array[NGAL]</li> <li>"Sfr" - Star formation rate of each galaxy - [Msun / yr] - array[NGAL]</li> <li>"SeedMass" - BH-seed mass. 7 values per galaxy; one value for each of the 7 possible BH-seed "flavors" modeled - [10^10 Msun / h] - array[NGAL, 7]</li> <li>"Rvir" - Virial radius of the DM halo hosting each galaxy - [Mpc / h] - array[NGAL]</li> <li>"Pos" - X, Y and Z position of each galaxy - [Mpc / h] - array[NGAL, 3]</li> <li>"Mvir" - Virial mass of the DM halo hosting each galaxy - [10^10 Msun / h] - array[NGAL]</li> <li>"Lbol" - Bolometric luminosity associated to the central AGN (==0 if the BH is not active) - [10^40 erg / s] - array[NGAL]</li> <li>"HotGas" - Mass of the hot-phase of each galaxy's gas component - [10^10 Msun / h] - array[NGAL]</li> <li>"fEDD" - Eddington ration (defined as Lbol/L_Edd, with L_Edd being the Eddington luminosity) for each AGN (==0 if the BH is not active) - [adim] - array[NGAL]</li> <li>"ColdGas" - Mass of the cold-phase of each galaxy's gas component - [10^10 Msun / h] - array[NGAL]</li> <li>"BlackHoleMass" - Mass of the central massive BH hosted by each galaxy (==0 if the galaxy does not host a central BH) - [10^10 Msun / h] - array[NGAL]</li> <li>"SeedType" - Identifier of the type of BH-seed which originated each BH (see below for details) - array[NGAL]</li> <li>"Redshift" - Redshift of each galaxy (within a single file, this is an array of identical values) - array[NGAL]</li> </ul> <p>NOTE:<br>The model presented in Spinoso et al. 2023 follows 7 different types of BH-seeds. The "SeedMass" array contains 7 mass values (one for each of these types of BH-seeds) for each galaxy in the Millennium-II box.This is the reason why the data type of "SeedMass" is [NGAL, 7]. Each of these 7 values is the sum, across the whole evolution of each galaxy, of the contributions to the total BH mass coming from each BH-seed who merged to form the final BH. In the vast majority of cases, BHs are associated to only one type of BH-seed. In those cases, 6 out of the 7 "SeedMass" values would be zero. Each element of "SeedMass" corresponds to one type of BH seed according the following scheme:<br>SeedMass[0] : total seed mass of light-seeds inherited from the GQd model (see Spinoso et al. 2023 for details)<br>SeedMass[1] : total seed mass of heavy-seeds inherited from the GQd model (see Spinoso et al. 2023 for details)<br>SeedMass[2] : un-resolved mass-growth driven by gas-accretion before the halo hosting the BH was resolved<br>SeedMass[3] : total seed mass formed as light-seeds in L-Galaxies<br>SeedMass[4] : total seed mass formed as Direct-Collapse BHs (DCBHs)<br>SeedMass[5] : total seed mass formed as intermediate-mass BH originated via Runaway Stellar Mergers (RSM)<br>SeedMass[6] : total seed mass formed as Merger-Induced Direct-Collapse BH (miDCBH)</p> <p>NOTE:<br>Similarly to "SeedMass", also the "Pos" array has more than one element per galaxy. These are the three cartesian positions of each galaxy.</p> <p>NOTE:<br>The possible values of the "SeedType" array are as follows (see Spinoso et al. 2023 for details):<br>-1 - No BH seed (the galaxy never hosted a BH)<br>1 - light seed (PopIII remnant)<br>6 - Direct-Collapse BH (DCBH)<br>7 - intermediate-mass BH originated via Runaway Stellar Mergers (RSM)<br>8 - Merger-Induced Direct-Collapse BH (miDCBH)<br>9 - mixed type: light+DCBH (the BH is the result of hierarchical mergers between light and DCBH seeds)<br>10 - mixed type: light+RSM (the BH is the result of hierarchical mergers between light and RSM seeds)</p>
The effect of dynamical states on galaxy clusters populations. I. Classification of dynamical states
<p>This repository contains three figures mentioned in "The effect of dynamical states on galaxy clusters populations. I. Classification of dynamical states" <em>(DOI to follow on publication)</em>.</p> <p>We show the contours of the X-ray surface brightness distribution (solid green lines) and the distribution of galaxies belonging to the red sequence (solid gray lines). Black crosses symbolize the positions of the X-ray peaks, black "X" marks represent the positions of the X-ray centroids, and open red circles denote the positions of the BCGs. The blue circle corresponds to the R200 of each cluster.</p>
Training data for 'Upload data to ENA' (Galaxy Training Material)
<p>The data here is a subset of the data published in 10.5281/zenodo.3732359 to be used in GTN 'Upload data to ENA' tutorial.</p> <p>Human traces have been removed following <a href="https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/human-reads-removal/tutorial.html">https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/human-reads-removal/tutorial.html</a></p> <p>We produced consensus sequences (*.fasta) for the Illumina PE data following SARS-CoV-2-PE-Illumina-WGS-variant-calling (https://workflowhub.eu/workflows/113?version=4), SARS-CoV-2-variation-reporting (https://workflowhub.eu/workflows/109?version=5) and COVID-19-consensus-construction (https://workflowhub.eu/workflows/138?version=4) workflows.</p>
X-rays across the galaxy population: The distribution of AGN accretion rates as a function of stellar mass and redshift
<p>We provide measurements of the probability distribution function of specific black hole accretion rates within a sample of galaxies of a given stellar mass and redshift, <span class="math-tex">\(p(\log \lambda_{sBHAR} | M_*,z)\)</span>. Measurements are provided for all galaxies, star-forming galaxies and quiescent galaxies. We also provide estimates of the AGN duty cycle, <span class="math-tex">\(f(\lambda_{sBHAR} >0.01)\)</span> i.e. the fraction of galaxies with an AGN above a given limit in specific accretion rate, based on the probability distribution functions. Full details are provided in Aird et al. (2018, MNRAS, 474, 1225); please cite this publication if you use these measurements. </p>
Dataset for Training Material - Galaxy Workflow - Analyse unaligned ncRNAs
<p>Input dataset for Galaxy Training Material for the Analyze unaligned ncRNAs workflow.</p> <p>See https://github.com/galaxyproject/training-material for more information.</p>
The Outer Stellar Mass of Massive Galaxies: A SimpleTracer of Halo Mass with Scatter Comparable to Richness and Reduced Projection Effects
<p>These are the data for reproducing the results of the publication titled "The Outer Stellar Mass of Massive Galaxies: A Simple Tracer of Halo Mass with Scatter Comparable to Richness and Reduced Projection Effects" by Song Huang et al.</p> <p>Please see the Python scripts and Jupyter notebooks provided in the <a href="https://github.com/dr-guangtou/jianbing">jianbing</a> repo for examples about how to use these data files. And please contact dr.guangtou@gmail.com if you have any questions about these data.</p> <p>-------------------------------------------------------------------------------------------------</p> <p>Here is a brief description of all the files:</p> <p><strong>Data from N-body simulation:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/mdpl2_halos_0.7333_reduced_logmvir_13.npy?versionId=1648006b-a91a-4300-aadf-c4746d6f3ef2">mdpl2_halos_0.7333_reduced_logmvir_13.npy</a> <ul> <li>Basic information about the dark matter halos from MDPL2 simulation</li> <li>For scale factor = 0.7333 (or z~0.4).</li> <li>Only for halos with logMvir > 13.0.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/mdpl2_particles_0.7333_72m.npy?versionId=ff7d5847-df44-46f5-9bcc-d8a7f3cc040d">mdpl2_particles_0.7333_72m.npy</a> <ul> <li>Particle catalog of the a=0.7333 snapshot from MDPL2</li> <li>This is a down-sampled version with 72 million particles.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/topn_theory_demo.pkl?versionId=7ed87c28-7adc-4987-9e00-b6223c744d42">topn_theory_demo.pkl</a> <ul> <li>These are the data used to create the theoretical demo of the TopN test.</li> <li>It is used for making the figures in <a href="https://github.com/dr-guangtou/jianbing/blob/master/notebooks/figure/fig1.ipynb">this notebook</a>.</li> </ul> </li> </ul> <p><strong>Catalogs of Galaxies or Galaxy Clusters:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/camira_s16a_cluster_use_bsm.fits?versionId=ca274c83-4025-41c4-b993-3cc9074f08b2">camira_s16a_cluster_use_bsm.fits</a> <ul> <li>The HSC S16A CAMIRA cluster catalog.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/redmapper_hsc_s16a_cluster_bsm.fits?versionId=11608e41-2427-4808-9060-a06139de165c">redmapper_hsc_s16a_cluster_bsm.fits</a> <ul> <li>The HSC S16A redMaPPer cluster catalog.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/redmapper_sdss_cluster_bsm.fits?versionId=b977c4ed-11c9-4751-b32f-60883d2e81b0">redmapper_sdss_cluster_bsm.fits</a> <ul> <li>The SDSS DR8 redMaPPer clusters in the HSC S16A footprint.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/s16a_massive_logm_11.2.fits?versionId=603cb17c-bb64-4aa7-ae05-5ec61c7ee861">s16a_massive_logm_11.2.fits</a> <ul> <li>0.2 <z < 0.5 massive galaxies in the HSC S16A footprint.</li> </ul> </li> </ul> <p><strong>Galaxy-Galaxy Lensing Data:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/s16a_weak_lensing_medium.hdf5?versionId=593c4ba0-6d7d-4b83-b8d9-01740a351fcd">s16a_weak_lensing_medium.hdf5</a> <ul> <li>A compilation of the weak lensing data to calculate the DeltaSigma profiles.</li> <li>This includes the weak lensing source catalog, photometric redshift calibration file, and the random catalog.</li> <li>"medium" here means we applied the medium criteria for selecting source galaxies. Please refer to <a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.5658S/abstract">Speagle et al. (2019)</a> for the exact meaning of these criteria.</li> <li>We also have a "basic" and "strict" version. Please send your request if you need them.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/topn_public_s16a_medium_precompute.hdf5?versionId=2216ecf9-b836-4dd5-a9dd-7e070e4977bf">topn_public_s16a_medium_precompute.hdf5</a> <ul> <li>A compilation of pre-computed lensing profiles for each individual object in a different galaxy or cluster samples for the TopN test.</li> <li>These are the data used to create the stacked DeltaSigma profiles.</li> <li>We also provide the "strict" and the "basic" versions if you want to test the robustness of the TopN tests against the different selections of source galaxies in weak lensing measurements. You just need these files to generate the stacked DeltaSigma profiles.</li> </ul> </li> </ul>
pop-cosmos: Galaxy property and redshift catalog for COSMOS2020
<p>This record (v≥2.0.0) contains data products associated with the paper "<em>pop-cosmos: Insights from generative modeling of a deep, infrared-selected galaxy population</em>" by Thorp et al. (2025). Earlier versions of this record (v<2.0.0) contain data products associated with the paper "<em>pop-cosmos: Scaleable inference of galaxy properties and redshifts with a data-driven population model</em>" by Thorp et al. (2024), which are superseded by the contents of v2.0.0. In v≥2.0.0, we include results for all COSMOS2020 galaxies with $\textit{Ch.1}<26$ or $r<25$.</p> <p>The included products are derived from spectral energy distribution (SED) fits to 26-band COSMOS2020 photometry, using the 16-parameter SPS model described in Thorp et al. (2024, 2025), and the <code>pop-cosmos</code> prior from Thorp et al. (2025). All results are based on Markov Chain Monte Carlo (MCMC) runs using the configuration described in Thorp et al. (2024, 2025). Results correspond to v2.1 of the COSMOS2020 catalog.</p> <p>The current release includes the following files:</p> <ul> <li><strong>README_v2_2_0.txt</strong>: Detailed information about how to read the other files in the record.</li> <li><strong>mcmc_summaries.h5.gz</strong>: Zipped HDF5 file with summaries (percentiles) of the posteriors.</li> <li><strong>mcmc_samples_pop_cosmos.h5.gz</strong>: Zipped HDF5 file with posterior samples (using <code>pop-cosmos</code> prior).</li> <li><strong>mcmc_samples_Prospector.h5.gz</strong>: Zipped HDF5 file with posterior samples (using <code>Prospector</code>-$\alpha$ prior).</li> </ul> <p>If you make use of any of these products, please cite this repository and Thorp et al. (2024, 2025). Please also cite the <code>pop-cosmos</code> overview paper by Alsing et al. (2024), and the paper by Deger et al. (2025). If you make use of any COSMOS data products, please cite Weaver et al. (2022) and any other relevant publications. If you make use of COSMOS spectroscopic data, please cite Khostovan et al. (2025) and references therein.</p> <p>If you spot any issues or have any requests, please contact the corresponding author (Stephen Thorp) using the details in the README.</p> <p>If you want to access <code>pop-cosmos</code> mock galaxy catalogs, these can be found on <a href="https://doi.org/10.5281/zenodo.15622324">Zenodo</a>.</p> <p>Related software, including a demo notebook for working with the data in this record, can be found on <a href="https://github.com/Cosmo-Pop/pop-cosmos">GitHub</a>. </p> <p>References:</p> <ol> <li>Alsing et al. (2024). ApJS 274, 12. [<a href="https://arxiv.org/abs/2402.00935">arXiv:2402.00935</a>][<a href="https://doi.org/10.3847/1538-4365/ad5c69">doi</a>]</li> <li>Deger et al. (2025). MNRAS, submitted. [<a href="https://arxiv.org/abs/2509.20430">arXiv:2509.20430</a>]</li> <li>Khostovan et al. (2025). ApJ, submitted. [<a href="https://arxiv.org/abs/2503.00120">arXiv:2503.00120</a>]</li> <li>Thorp et al. (2024). ApJ 975, 145. [<a href="https://arxiv.org/abs/2406.19437">arXiv:2406.19437</a>][<a href="https://doi.org/10.3847/1538-4357/ad7736">doi</a>]</li> <li>Thorp et al. (2025). ApJ, accepted. [<a href="https://arxiv.org/abs/2506.12122">arXiv:2506.12122</a>]</li> <li>Weaver et al. (2022). ApJS 258, 11. [<a href="https://arxiv.org/abs/2110.13923">arXiv:2110.13923</a>][<a href="https://doi.org/10.3847/1538-4365/ac3078">doi</a>]</li> </ol>
Si data files for Galaxy materials science tutorials
<p>This is a training dataset for use in Galaxy materials science tutorials. These files can be used to demonstrate the AIRSS (Ab-Initio Random Structure Searching) method for finding muon stopping sites, using the UEP (Unperturbed Electrostatic Potential) technique for the optimisation stage of that method.</p> <p>The files included are:</p> <ul> <li><strong>Si.cell:</strong> structure file containing atom locations</li> <li><strong>Si.den_fmt:</strong> electron density data, generated with CASTEP</li> <li><strong>Si.castep:</strong> CASTEP log file for the electron density calculation</li> <li><strong>Si-muairss-uep.yaml:</strong> configuration file for the AIRSS / UEP workflow</li> </ul>
Transfer learning for galaxy feature detection: Finding Giant Star-forming Clumps in low redshift galaxies using Faster R-CNN
<p>This repository contains the data released in the paper 'Transfer learning for galaxy feature detection: Finding Giant Star-forming Clumps in low redshift galaxies using Faster R-CNN' <em>(DOI: <a href="https://doi.org/10.1093/rasti/rzae013">10.1093/rasti/rzae013</a>).</em></p> <p>We release a detailed catalogue of Giant Star-forming Clumps (GSFCs), detected for the full set of Galaxy Zoo: Clump Scout galaxies observed by SDSS using the Faster R-CNN architecture with the Zoobot classification-CNN as a feature extraction backbone.</p> <p>The final models and code are made publicly available via Github: <a href="https://github.com/ou-astrophysics/Faster-R-CNN-for-Galaxy-Zoo-Clump-Scout">https://github.com/ou-astrophysics/Faster-R-CNN-for-Galaxy-Zoo-Clump-Scout</a>.</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: <a href="https://doi.org/10.1093/rasti/rzae013">10.1093/rasti/rzae013</a>) when using the data in this repository.</p> <p>The csv-file <em>FRCNN_Zoobot_SDSS_GZCS_detections.csv</em> has the following columns. Alternatively, the file <em>FRCNN_Zoobot_SDSS_GZCS_detections.gzip</em> contains the same data but stored as a parquet-file.</p> <table> <tbody><tr> <th>Column name</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>specobjid</td> <td>SDSS spec object ID</td> </tr> <tr> <td>dr7objid</td> <td>SDSS DR7 object ID</td> </tr> <tr> <td>clump_id</td> <td>Clump index</td> </tr> <tr> <td>clump_label_id</td> <td>Clump label ID (1 or 2)</td> </tr> <tr> <td>clump_label_name</td> <td>Clump label name</td> </tr> <tr> <td>clump_score</td> <td>Detection score for the clump</td> </tr> <tr> <td>clump_centre_ra</td> <td>Clump centroid RA in degrees</td> </tr> <tr> <td>clump_centre_dec</td> <td>Clump centroid dec in degrees</td> </tr> <tr> <td>clump_flux_u</td> <td>Clump u-band flux in Jy</td> </tr> <tr> <td>clump_flux_g</td> <td>Clump g-band flux in Jy</td> </tr> <tr> <td>clump_flux_r</td> <td>Clump r-band flux in Jy</td> </tr> <tr> <td>clump_flux_i</td> <td>Clump i-band flux in Jy</td> </tr> <tr> <td>clump_flux_z</td> <td>Clump z-band flux in Jy</td> </tr> <tr> <td>clump_flux_err_u</td> <td>Clump u-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_g</td> <td>Clump g-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_r</td> <td>Clump r-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_i</td> <td>Clump i-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_z</td> <td>Clump z-band flux error in Jy</td> </tr> <tr> <td>clump_mag_u</td> <td>Clump u-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_g</td> <td>Clump g-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_r</td> <td>Clump r-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_i</td> <td>Clump i-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_z</td> <td>Clump z-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_ext_mag_u</td> <td>Clump u-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_g</td> <td>Clump g-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_r</td> <td>Clump r-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_i</td> <td>Clump i-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_z</td> <td>Clump z-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_mag_corr_u</td> <td>Clump corrected u-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_g</td> <td>Clump corrected g-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_r</td> <td>Clump corrected r-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_i</td> <td>Clump corrected i-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_z</td> <td>Clump corrected z-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_u_g</td> <td>Clump colour (u-g)</td> </tr> <tr> <td>clump_mag_corr_g_r</td> <td>Clump colour (g-r)</td> </tr> <tr> <td>clump_mag_corr_r_i</td> <td>Clump colour (r-i)</td> </tr> <tr> <td>clump_mag_corr_i_z</td> <td>Clump colour (i-z)</td> </tr> <tr> <td>clump_flux_ratio</td> <td>Est. clump/galaxy near-UV flux ratio (u-band)</td> </tr> <tr> <td>is_clump_3pct</td> <td>Flag (True/False) if clump/galaxy flux ratio is >3%</td> </tr> <tr> <td>is_clump_8pct</td> <td>Flag (True/False) if clump/galaxy flux ratio is >8%</td> </tr> <tr> <td>galaxy_ra</td> <td>Host galaxy RA in degrees</td> </tr> <tr> <td>galaxy_dec</td> <td>Host galaxy dec in degrees</td> </tr> <tr> <td>galaxy_z</td> <td>Host galaxy redshift</td> </tr> <tr> <td>galaxy_mag_u</td> <td>Host galaxy u-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_g</td> <td>Host galaxy g-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_r</td> <td>Host galaxy r-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_i</td> <td>Host galaxy i-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_z</td> <td>Host galaxy z-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_u</td> <td>Host galaxy u-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_g</td> <td>Host galaxy g-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_r</td> <td>Host galaxy r-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_i</td> <td>Host galaxy i-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_z</td> <td>Host galaxy z-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_flux_u</td> <td>Host galaxy u-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_g</td> <td>Host galaxy g-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_r</td> <td>Host galaxy r-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_i</td> <td>Host galaxy i-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_z</td> <td>Host galaxy z-band flux in Jy</td> </tr> <tr> <td>galaxy_expAB_r</td> <td>Host galaxy axis ratio from SDSS</td> </tr> <tr> <td>galaxy_expRad_r</td> <td>Host galaxy exponential fit scale radius from SDSS</td> </tr> <tr> <td>galaxy_lmass</td> <td>Host galaxy log mass in MSun</td> </tr> <tr> <td>galaxy_lssfr</td> <td>Host galaxy log specific SFR</td> </tr> <tr> <td>galaxy_mag_corr_u</td> <td>Host galaxy corrected u-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_g</td> <td>Host galaxy corrected g-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_r</td> <td>Host galaxy corrected r-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_i</td> <td>Host galaxy corrected i-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_z</td> <td>Host galaxy corrected z-band magnitude (AB-mag)</td> </tr> </tbody> </table> <p> </p>
Panoply with Galaxy
<p>Dataset has been retrieved on the Copernicus Climate data Store (<a href="https://cds.climate.copernicus.eu/#!/home">https://cds.climate.copernicus.eu/#!/home</a>) and is meant to be used for teaching purposes only. Data retrieved were split per year and concatenated to create two separate files. Then these two files were converted from GRIB format to netcdf using xarray (<a href="http://xarray.pydata.org/en/stable/">http://xarray.pydata.org/en/stable/</a>). This dataset is used in the Galaxy training on "Visualize Climate data with Panoply in Galaxy".</p> <p>See <a href="https://training.galaxyproject.org/">https://training.galaxyproject.org/</a> (topic: climate) for more information.</p> <p>The python code below show how it has been retrieved on CDS:</p> <p> </p> <p>import cdsapi</p> <p>c = cdsapi.Client()</p> <p>c.retrieve(<br> 'ecv-for-climate-change',<br> {<br> 'variable': [<br> 'precipitation', 'sea_ice_cover', 'surface_air_temperature',<br> ],<br> 'product_type': 'monthly_mean',<br> 'time_aggregation': '1_month',<br> 'year': [<br> '1979', '2018',<br> ],<br> 'month': [<br> '01', '02', '03',<br> '04', '05', '06',<br> '07', '08', '09',<br> '10', '11', '12',<br> ],<br> 'origin': 'era5',<br> 'format': 'zip',<br> },<br> 'download.zip')</p>
The [CII] 158 μm line emission in high-redshift galaxies: Data Set
<p>This data set contains all data tables associated to the publication: "The [CII] 158 μm line emission in high-redshift galaxies"; A&A Lagache, Cousin, Chatzikos 2018. Please cite it if you use those data.</p>
Particle Trace of a Milky Way Mass Galaxy from the EAGLE simulations
<p>This repository contains .npy files, loaded like:</p> <pre><code class="language-python">with open('EAGLE_MW_trace_coords.npy', 'rb') as f: coordinates = np.load(f) dmcoordinates = np.load(f) with open('EAGLE_MW_trace_redshifts.npy', 'rb') as f: redshifts = np.load(f)</code></pre> <p>which contain the locations of particles (gas, stars and dark matter) which are within 30pkpc of the centre of a Milky Way stellar mass galaxy from the EAGLE suite of simulations. This dataset was primarily produced to look at the accretion of matter onto galaxies like the Milky Way, studying how they assemble over time, which makes for some quite pretty visualisations <a href="https://github.com/jmackereth/galactic-assembly-art.git">(explored in this repository)</a>.</p> <p>the file 'EAGLE_MW_trace_coords_downsampled_10.npy' contains the same data but for a downsampled set of particles (by a factor of 10).</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.