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443 results for “galaxy”
Bakta database test for galaxy wrapper
<p>This data repository contains a test database for Bakta genomic annotation tool.</p> <p>This not contain the database files except a json file</p> <p>- This repository is used to test the Galaxy wrapper related to the Bakta data_manager</p>
Supplementary figures for "Dark matter distribution in Milky Way-analog galaxies"
<p>Among the attached files, you will find:</p> <p>- All figures from the article in high-quality PDF format, ordered by name as follows: 'Figure1.pdf' corresponds to Figure 1 of the article, and so on;</p> <p>- Moment 0 (intensity), 1 (velocity), and 2 (dispersion) maps of the atomic hydrogen gas (HI) distribution for each galaxy in our sample. All moment maps were generated from our three-dimensional modeling with 3D-Barolo;</p> <p>- For each galaxy, we show the extended version of Figure 1 from the paper, which includes the intensity, velocity, and dispersion maps for the model and residual of each galaxy. For instance, 'NGC3521_kinematics.pdf' corresponds to the kinematic maps of NGC 3521;</p> <p>- Stellar distribution maps at 3.6 and 4.5 μm provided by the S4G survey. For instance, 'NGC3521.phot.1.fits' corresponds to the 3.6 μm image, while 'NGC3521.phot.2.fits' corresponds to the 4.5 μm image.</p>
Advanced PySPAM: An Infrastructure to Constrain Underlying Interacting Galaxy Parameters Synthetic Results
<p>This database contains the results for the Chapter 3 of DOR's thesis. For a full description of these results and the way they were built, please see <em>Link to be added on publication</em>.</p> <p>The aim of this Chapter was to use MCMC methods with a fast, efficient simulation algorithm (APySPAM) to constrain the underyling parameters of observed interacting galaxy systems. This algorithm used a Chi-Squared distance minimisation between morphology distributions of observed and simulated images to constrain 13 underlying parameters of galaxy interaction. We applied our algorithm to to 50 of the 62 systems described in <a href="https://ui.adsabs.harvard.edu/abs/2016MNRAS.459..720H/abstract">Holincheck et al. (2016).</a></p> <p>We opted to use the Holincheck et al. sample as the underlying parameters of these systems had already been constrained using a Citizen Science project named <a href="https://mergers.galaxyzoo.org/">Galaxy Zoo: Mergers</a>. This gave us a ground truth to which compare our constraints to. We created synthetic observations of each image, and then ran our MCMC over them, achieving constraint across the sample and parameter space. However, when applied to observational data (we opted to use SDSS images of these systems) we are unable to constrain the full parameter space. This is particularily true of the orientations of the interacting system and their relative sizes.</p> <p>Exploring using velocity information in our constraints find that we improve almost all our constrains considerably. Therefore, adding in spectroscopic information to this method could drastically improve it. The main limitation of this approach, however, is computation time with each system taking approximately 20 hours on a well parallelised HPC to converge. Alternatives to improve performance lie in simulation based inference (SBI, an introduction can be found <a href="https://arxiv.org/pdf/2009.08459">here</a>) or including the use of GPUs (such as done by NVIDEA in fluid dynamics <a href="https://developer.nvidia.com/blog/ai-powered-simulation-tools-for-surrogate-modeling-engineering-workflows-with-siml-ai-and-nvidia-modulus/">here</a>)</p> <p>The results are portrayed as corner plots, with contour plots showing the distribution of likelihoods found in each MCMC run and the histograms on the side showing the marginalised posterior distributions.</p>
Results of a Galaxy metagenomic analysis of bee gut microbiome data from PRJNA977416
<p>This dataset contains the outputs of a metagenomic Galaxy workflow run on the raw data of the project PRJNA977416, including the CSV file of associated metadata and the workflow.ga used for the analysis.</p> <p>Firstly, it has information on taxonomic assignment with :</p> <ul> <li>the reports of all samples for Kraken2, Bracken, and MetaPhlan taxonomic profilers. </li> <li>two tabular files obtained with Taxpasta, which merge samples and standardize taxonomic abundances.</li> <li>for the Bracken standardised abundance, a file with the measures of alpha diversity calculated </li> <li>two HTML files giving access to the Krona diagram for this taxonomic composition.</li> </ul> <p>Secondly, it contains functional informations with :</p> <ul> <li>a tabular file with the relative abundance of all GO terms for all samples</li> <li>a directory detailing pathways and genes families detected.</li> </ul>
Host Data from: [O II] as an Effective Indicator of the Dependence Between the Standardised Luminosities of Type Ia Supernovae and the Properties of their Host Galaxies
<p>Spectral properties of the Foundation Host Galaxies presented in the paper: <span>[O</span> II<span>] as an Effective Indicator of the Dependence Between the </span><span>Standardised Luminosities of Type Ia Supernovae and the Properties of </span><span>their Host Galaxies.</span></p> <p><span>Spectra were taken using the WiFeS instrument on the ANU 2.3m Telescope.</span></p>
YOLO-CIANNA: Galaxy detection with deep learning, predicted SDC1 source catalogs
<p>Set of detected source catalogs from the SKAO SDC1 560MHz - 1000h challenge image using the YOLO-CIANNA method.<br><br>This upload is made to accompany the publication of <a title="Cornu et al. (2024)" href="https://ui.adsabs.harvard.edu/abs/2024arXiv240205925C/abstract" target="_blank" rel="noopener">Cornu et al. (2024)</a> and contains catalogs that were produced with the <a title="CIANNA release" href="https://doi.org/10.5281/zenodo.12806325" target="_blank" rel="noopener">CIANNA</a> V-1.0 framework using trained models available at <a title="Network models" href="https://doi.org/10.5281/zenodo.12801421" target="_blank" rel="noopener">10.5281/zenodo.12801421</a></p>
Run 5 video in "Ram pressure stripping in elliptical galaxies – I. The impact of the interstellar medium turbulence"
<p>Run 5 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>
Dataset for Galaxy ViennaRNA Introduction
<p>This dataset contains small files that are used for the https://rna.usegalaxy.eu/tours/rnateam.viennarna</p>
Data for Galaxy CLIP-Seq Training Material
<p>The eCLIP data provided here is a subset of the eCLIP data of RBFOX2 from a study published by Nostrand et al. (2016, http://dx.doi.org/10.1038/nmeth.3810). The dataset contains the first biological replicate of RBFOX2 CLIP-seq and the input control experiment (*fastq files). The data was changed and downsampled to reduce data processing time, thus the datasets does not correspond to the original data pulled from Nostrand et al. (2016, http://dx.doi.org/10.1038/nmeth.3810). Also included is a text file (.txt) encompassing the chromosome sizes of hg19 and hg38 obtained from UCSC (http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19.chrom.sizes, http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes) and a genome annotation for hg19 (.gtf) taken from Ensembl (http://ftp.ensemblorg.ebi.ac.uk/pub/release-74/gtf/homo_sapiens/) and for hg38 taken from the Galaxy libraries (https://usegalaxy.eu/library/list#folders/F30cab321d898d2fb/datasets/9ba790aa79c9cf23). The data is used for a galaxy training course about CLIP-Seq data analysis. </p>
Training data for 'Somatic variant calling' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial that demonstrates identification of somatic and germline variants from tumor and normal sample pairs.</p>
CANDELS isolated galaxy images
<p><strong>CANDELS galaxy blender dataset</strong></p> <p>Dataset to be used to create realistic galaxy blends with <a href="https://github.com/aboucaud/candels-blender">candels-blender</a></p> <p> </p> <p><strong>Content</strong></p> <p>This dataset is based on the CANDELS bulge/disk decomposition catalogue and images from Dimauro et al. (2018). Our main addition to this dataset, was to perform a visual inspection of all the 2 823 stamps and rejects all those for which</p> <ul> <li>the central galaxy is possibly blended</li> <li>the neighbouring sources are too close or too diffuse</li> <li>the segmentation map does not cover well the sources in the stamp</li> <li>weird artefacts are present in the stamp.</li> </ul> <p>This process removed around 800 stamps to leave 2 001 entries, available in this tarball as</p> <ul> <li><strong><em>candels_img.npy </em></strong>: binary numpy array of shape (2001, 128, 128) containing 2 001 stamps (128 x 128 pixels) extracted from CANDELS F160W images, centered around isolated galaxies with a well defined morphology.</li> <li><em><strong>candels_seg.npy</strong></em> : binary numpy array of shape (2001, 128, 128) containing 2 001 segmentation maps (128 x 128 pixels) associated with the above stamps and obtained via <em>SExtractor</em> (Bertin et al. 1996).</li> <li><em><strong>candels_cat.csv </strong></em>: the catalogue of corresponding central sources, based on the catalogue obtained via <em>SExtractor</em>, containing the CANDELS ID, FIELD and (RA, DEC) position, the F160W magnitude and its estimated error, the F160W estimated radius, the spectroscopic redshift of the galaxy, and augmented with the galaxy type and the segmentation value of the central galaxy.</li> </ul> <p> </p> <p><strong>Usage</strong></p> <p>Load the array files in Python using <em>numpy</em> as:</p> <pre><code class="language-python">import numpy as np stamps = np.load("candels_img.npy") segmaps = np.load("candels_seg.npy")</code></pre> <p>The catalogue is standard comma-separated CSV which can be conveniently parsed by tools like e.g. pandas</p> <pre><code class="language-python">import pandas as pd cat = pd.read_csv("candels_cat.csv")</code></pre> <p>or astropy</p> <pre><code class="language-python">from astropy.io import ascii table = ascii.read("candels_cat.csv")</code></pre> <p> </p> <p><strong>Reference</strong></p> <p>The original CANDELS bulge/disk decomposition catalogue can be obtained at <a href="https://lerma.obspm.fr/huertas/form_CANDELS">lerma.obspm.fr/huertas/form_CANDELS</a>.</p>
Cellulose test input dataset for simulations with Galaxy and BRIDGE
<p>This coordinate and protein structure file dataset is that of cellulase and octaose substrate <em>in vacuo</em>. It has been derived from the <a href="https://www.rcsb.org/structure/7cel">7CEL PDB</a> structure of a fungal cellobiohydrolase. </p> <p>The original enzyme has been modified to revert the mutation at position 217 and to include disulfide bonds. The octaose substrate is an oligosaccharide consisting of 8 beta 1-4 linked glucose monomers. </p> <p>The files includes are: </p> <ul> <li><strong>cbh1test.crd</strong>: the coordinates of the entire system (protein and substrate) in CHARMM coordinate format. </li> <li><strong>cbh1test.psf</strong>: the CHARMM protein structure file which contains lists of molecular information including atomic masses, all bond pairs, angle triples and so on.</li> </ul>
Training material for the course "Exome analysis with GALAXY"
<p>Galaxy is an open source, web-based platform for data intensive biomedical research. It makes accessible bioinformatics applications to users lacking programming skills, enabling them to easily build analysis workflows for NGS data.<br /> <br /> The course "<strong>Exome analysis using Galaxy</strong>" is aimed at PhD student, biologists, clinicians and researchers who are analysing, or need to analyse in the near future, high throughput exome sequencing data. The aim of the course is to make participants familiarise with the Galaxy platform and prepare them to work independently, using state-of-the art tools for the analysis of exome sequencing data.</p> <p>The course will be delivered using a mixture of lectures and computer based hands-on practical sessions. Lectures will provide an up-to-date overview of the strategies for the analysis of exome next-generation experiments, starting from the raw sequence data. Analyses include sequence quality control, alignment to a reference genome, refinement of aligned sequences, variant calling, annotation and interpretation, and tools for visual inspection of results. Participants will apply the knowledge gained during the course to the analysis of Illumina’s real exome datasets, and implement workflows to reproduce the complete analysis. After the course, participants will be able to create pipeline for their individual analyses.</p> <p>Those are the needed datasets for this course.</p>
Training data for 'Genome annotation with Apollo' tutorial (Galaxy Training Material)
<p>Published scaffolds from the Apis mellifera assembly Amel_4.5 and Official Gene Set 3.2.</p> <p>Source: <a href="http://hymenopteragenome.org/beebase/?q=download_sequences">http://hymenopteragenome.org/beebase/?q=download_sequences</a></p>
A Galaxy-based training resource for single-cell RNA-seq quality control and analyses
<p>This is the tutorial data for the 'Single-cell quality control with scater' tutorial on the Galaxy Training Network. The data is the same dataset that is used as the inbuilt example dataset within scater, but has been implemented as individual files.</p>
FRGADB - FIRST Radio Galaxy Anomaly Detection Benchmark
<p>This dataset is a combination of samples from the MiraBest, FRGMRC and LRG catalogues. It is intended to serve as a benchmark for models' performance with respect to anomalous source detection in radio astronomy.</p>
GalaxiesML: an imaging and photometric dataset of galaxies for machine learning
<div># GalaxiesML README</div> <p> </p> <div>Version 6.1</div> <p> </p> <div>## Overview</div> <p> </p> <div>GalaxiesML is a machine learning-ready dataset of galaxy images, photometry, redshifts, and structural parameters. It is designed for machine learning applications in astrophysics, particularly for tasks such as redshift estimation and galaxy morphology classification. The dataset comprises **286,401 galaxy images** from the Hyper-Suprime-Cam (HSC) Survey PDR2 in five filters: g, r, i, z, y, with spectroscopically confirmed redshifts as ground truth.</div> <p> </p> <div>This dataset is particularly useful for developing machine learning models for upcoming large-scale surveys like **LSST** and **Euclid**.</div> <p> </p> <div>## Features</div> <p> </p> <div>- **286,401 galaxy images** in five photometric bands (g, r, i, z, y).</div> <div>- Spectroscopic redshifts for each galaxy, with redshift values ranging from **0.01 to 4**.</div> <div>- Morphological parameters derived from galaxy images, including **Sérsic index**, **half-light radius**, and **ellipticity**.</div> <div>- **Machine learning-friendly formats**: images are provided in **HDF5** format, along with CSV metadata.</div> <p><br><br></p> <div>## Examples of Using GalaxiesML</div> <p> </p> <div>Examples of uses of GalaxiesML are outlined in Do et al. (2024). The repository for example code are here:</div> <p> </p> <div><a href="https://github.com/astrodatalab/galaxiesml_examples">https://github.com/astrodatalab/galaxiesml_examples</a></div> <p> </p> <div>## Citation</div> <p> </p> <div>Please cite the following papers if you use this dataset in your work:</div> <p> </p> <div>1. **GalaxiesML Dataset**:</div> <div> <div> <div>Do, T. et al., *GalaxiesML: A Dataset of Galaxy Images, Photometry, Redshifts, and Structural Parameters for Machine Learning*. arXiv:2410.00271, <a href="https://arxiv.org/abs/2410.00271">https://arxiv.org/abs/2410.00271</a> (2024)</div> </div> </div> <p> </p> <div>2. **Hyper Suprime-Cam Subaru Strategic Program (HSC PDR2)**:</div> <div>- Aihara, H., et al., *Second Data Release of the Hyper Suprime-Cam Subaru Strategic Program*. Publications of the Astronomical Society of Japan, 71(6), 114 (2019). DOI: [10.1093/pasj/psz103](https://doi.org/10.1093/pasj/psz103)</div> <p> </p> <div>3. **Spectroscopic Surveys**:</div> <div>- Several publicly available spectroscopic redshift catalogs were used in creating this dataset. Notable sources include:</div> <div>- **zCOSMOS Survey**: Lilly, S. J., et al., *The zCOSMOS 10k-Bright Spectroscopic Sample*. The Astrophysical Journal Supplement Series, 184(2), 218-229 (2009). DOI: [10.1088/0067-0049/184/2/218](https://doi.org/10.1088/0067-0049/184/2/218)</div> <div>- **VIMOS Public Extragalactic Survey (VIPERS)**: Garilli, B., et al., *The VIMOS Public Extragalactic Survey (VIPERS): First Data Release of 57,204 Spectroscopic Measurements*. Astronomy & Astrophysics, 562, A23 (2014). DOI: [10.1051/0004-6361/201322790](https://doi.org/10.1051/0004-6361/201322790)</div> <div>- **DEEP2 Survey**: Newman, J. A., et al., *The DEEP2 Galaxy Redshift Survey: Design, Observations, Data Reduction, and Redshifts*. The Astrophysical Journal Supplement Series, 208(1), 5 (2013). DOI: [10.1088/0067-0049/208/1/5](https://doi.org/10.1088/0067-0049/208/1/5)</div> <p><br><br></p> <div>## How to Access</div> <p> </p> <div>The dataset is publicly available on **Zenodo** with the DOI: **[10.5281/zenodo.11117528](https://doi.org/10.5281/zenodo.11117528)**.</div> <p> </p> <div>## License</div> <p> </p> <div>This dataset is licensed under a **Creative Commons Attribution 4.0 International License (CC BY 4.0)**. You are free to share and adapt the dataset as long as appropriate credit is given. For more details, visit: **[CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/)**.</div> <p> </p> <div>Please cite the references mentioned above if you use this dataset in your work.</div>
The Lyman Alpha Reference Sample. XVI. Global 21cm HI properties of Lyman-alpha emitting galaxies.
<p>21cm maps and spectra of galaxies in the Lyman Alpha Reference Samples (LARS and eLARS). The field of view has been tailored to show the full extent of the main SoFIA 2 detection. In each FIgure, the top left panel shows DECaLS optical composite image with HI column density contours at level $1.0\times2^n \times10^{19}\,\textrm{cm}^{-2}$, with $n=0,1,2,...,5$ overlaid. The blue solid line shows the regions with SNR>3. Contours fully included in the SNR>3 mask are shown in white, low signal-to-noise contours are shown in gray. A gray cross indicates the position of the galaxy according to optical coordinates, a synthesized beam shaped aperture centred on these coordinates was used to extract HI properties in the center. The top right panel shows column density maps with the same contours as on the previous panel overlaid. The middle left pannel shows the moment-1 map. The middle right panel shows the linewidth map. The bottom pannel shows the total 21cm spectrum (gray) and beam-extracted 21cm spectrum (black). Velocity centroids are indicated by a vertical solid line, and velocity at half width on either side of the peaks by dashed lines, in either gray or black for the total or beam extraction respectively. In the Figure for eLARS05, the object on the bottom left of the top left panel is a separate detection by SoFIA-2.</p>
Science ready spectra, their best-fitting models and results of Jeans axisymmetric modelling described in the research paper "Transforming gas-rich low-mass discy galaxies into ultra-diffuse galaxies by ram pressure" by Grishin, Chilingarian, Afanasiev et al.
<p>This package contains data presented in the paper "Transforming gas-rich low-mass discy galaxies into ultra-diffuse galaxies by ram pressure" by Grishin, Chilingarian, Afanasiev et al. (2021 Nature Astronomy in press). The dataset can be used to reproduce Figures 3 and 4 from the main manuscript and Extended Data Figures 1, 2, 4, 5 from the Supplementary Information.</p> <p>(1) Python scripts and data points required to reproduce Figure 4 in the manuscript and Extended Data Figure 5 from the Supplementary Information. The data and scripts are presented in a combined .zip archive for both figures.</p> <p>(2) One-dimensional spectra extracted within 1 half-light radius from long-slit Binospec spectra and multi-wavelength far-UV-to-near-IR broadband spectral energy distributions (SEDs) assembled from the photometric measurements extracted within the same aperture for 11 galaxies from the main sample (9 in the Coma cluster and 2 in the Abell 2147 cluster) and 5 galaxies from the supplementary (auxiliary) list. The spectra and SEDs are accompanied with their best-fitting stellar population models and parameters determined by the NBursts+phot algorithm: radial velocity, velocity dispersion, truncation age, final stellar metallicity. The templates are MILES-based models with self-consistent chemical evolution presented in Grishin et al. 2019 (https://ui.adsabs.harvard.edu/abs/2019arXiv190913460G/abstract). The filenames contain the coefficient for galactic winds and the mass fraction of stars in the final starburst, e.g. _l15_60 means lambda=1.5, SSP_frac=60 per cent. The files are presented as binary FITS tables with the fields annotated using unified content descriptors (UCDs) from the list established by the International Virtual Observatory Alliance and physical units where applicable.</p> <p>(3) Two-dimensional profiles of internal kinematics (radial velocity and velocity dispersion) and stellar population properties (truncation age and final stellar metallicity) derived from the analysis of long-slit Binospec spectra for 12 galaxies after adaptive binning, 11 from the main sample and GMP3016 in the Coma cluster from the supplementary sample; best-fitting Jeans axisymmetric models without adaptive binning, i.e. full profiles along the slit. The data are presented in binary FITS tables in the two FITS extensions, one for the profiles derived from the corresponding spectra and the second one for dynamical models.</p>
Data release for paper "The Araucaria Project: Deep near-infrared photometric maps of Local and Sculptor Group galaxies. I. Carina, Fornax, Sculptor"
<p>Deep near-infrared J- and K-band photometry of three Local Group dwarf spheroidal galaxies: Fornax, Carina, and Sculptor, is made available for the community. Until now, these data have only been used by the Araucaria Project to determine distances using the tip of the red giant and RR Lyrae stars. Now, we present the entire data collection in a form of a database, consisting of accurate J- and K-band magnitudes, sky coordinates, ellipticity measurements, and timestamps of observations, complemented by stars' loci in their reference images. Depth of our photometry reaches about 22 mag at 5 sigma level, and is comparable to NIR surveys, like the UKIRT Infrared Deep Sky Survey (UKIDSS) or the VISTA Hemisphere Survey (VHS), and small overlap with VHS and no overlap with UKIDSS makes our database a unique source of quality photometry.</p> <p>Data release consists of:</p> <ul> <li>databases in a form of text files for Carina, Fornax and Sculptor galaxies<br> (db_Car.txt, db_For.txt , db_Scu.txt)</li> <li>completeness tables and plots for every field in Carina, Fornax and Sculptor galaxies<br> (compl_Car.pdf, compl_Car.txt, compl_Scu.pdf, compl_Scu.txt, compl_For.pdf, compl_For.txt)</li> <li>explanatory file for each galaxy<br> (info_Car.txt, info_Scu.txt, info_For.txt)</li> <li>FITS images of scientific quality of all analyzed fields in Carina, Fornax and Sculptor galaxies, archived in tar.gz files</li> </ul>
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.