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443
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Dataset results
443 results for “galaxy”
Galaxy tutorial for reference-based RNA-seq analysis
<p>https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html#functional-enrichment-analysis-of-the-de-genes</p> <p>the `compute` tool in the tutorial doesn't work. I used R to process the dataset and provide it so the students can continue with the tutorial.</p>
Galaxy clustering in real space
<p>Galaxy clustering in real space from BAM galaxy catalogs. Marked and two point correlation function0</p>
Slices of dark matter, halo and galaxy density fields
<p>Slices of 25 Mpc h−1 thick though different density fields involved in the calibration of the BAM products and its products. The<br> bottom panel shows the reconstruction of the halo number density field using different models of halo-bias (see §2.6). The rightmost<br> column shows galaxy density field from the reference and from BAM, built by populating halo catalogs with a model of halo occupation<br> distribution</p>
MargNet: Photometric identification of compact galaxies, stars and quasars
<p>This page contains the accompanying deep learning models, dataset and code for the paper on MargNet, titled "Photometric identification of compact galaxies, stars and quasars using multiple neural networks".</p> <p><strong>Deep Learning Models</strong>:</p> <p>MargNet is a deep learning-based classifier for identifying stars, quasars and compact galaxies using photometric parameters and images from the Sloan Digital Sky Survey. MargNet consists of a combination of Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) architectures. The deep learning Keras model for each experiment was saved as an h5 file after training. All saved models (organised by different experiments, as described in the paper) are available in SavedModels.zip.</p> <p><strong>Dataset</strong>:</p> <p>Our dataset consists of 240,000 compact objects and an additional 150,000 faint objects consisting of an equal number of stars, galaxies and quasars. This data is available as NumPy arrays and CSV files, as described below:</p> <ul> <li>SDSS ObjID of each object (objlist.npy)</li> <li>SDSS 5-band images of each object cropped to 32*32 pixels (X.npy)</li> <li>The set of 24 photometric features for each object (dnnx.npy)</li> <li>The classification label for each object (y.npy)</li> <li>SDSS spreadsheet containing all the features from dnnx, labels from y, ObjIDs from objlist and a couple of more SDSS specific parameters (photofeatures.csv)</li> </ul> <p>The complete dataset (organised by different experiments, as described in the paper) is available in Dataset.zip.<br> (Note: objlist, X, dnnx and y are in the same order. So, objlist[0], X[0], dnnx[0] and y[0] correspond to the same object.)</p> <p><strong>Code</strong>:</p> <p>All our code was written in Python in the form of Jupyter Notebooks. A copy of our code has also been made available on <a href="https://github.com/sidchaini/MargNet">GitHub</a>, but not all files could be included on GitHub due to the storage limit. So a complete copy of the repository has also been mirrored here on Zenodo and is contained in MargNet_RepositoryMirror.tgz</p>
A Photometric Survey of Globular Cluster Systems in Brightest Cluster Galaxies
<p>Complete photometry and reference images from HST, for globular cluster systems around 26 giant early-type galaxies.</p>
Detection of SARS-CoV-2 variants by genomic analysis of wastewater ampliconic samples (Galaxy Training Material)
<p>The tutorial aims to train how to run workflows to analyze lineages abundances in SAR-CoV-2 wastewater ampliconic samples. (https://training.galaxyproject.org/training-material/)</p>
Detection of SARS-CoV-2 variants by genomic analysis of wastewater metatranscriptomic samples (Galaxy Training Material)
<p>The tutorial aims to train how to run workflows to analyze lineages abundances in SAR-CoV-2 wastewater metatranscriptomic samples. (https://training.galaxyproject.org/training-material/)</p>
The circulation of relativistic electrons injected by radio galaxies in galaxy groups
<p>Remote talk describing the latest simulations on the injection, propagation and energy evolution of relativistic electrons released into the intracluster/group medium by the activity of radio galaxies.</p> <p>Based on these publications:</p> <p>https://ui.adsabs.harvard.edu/abs/2023A%26A...669A..50V/abstract</p> <p>https://ui.adsabs.harvard.edu/abs/2021A%26A...653A..23V/abstract</p>
Regular rotation and low turbulence in a diverse sample of z~4.5 galaxies observed with ALMA
<p>Clean [CII] line-emission datacubes, parameter files and outputs of 3DBarolo.</p>
Galaxy Training Data for "End-to-End Tissue Microarray Image Analysis with Galaxy-ME"
<p>This dataset provides the inputs used in the Galaxy Training Network (GTN) training 'End-to-End Tissue Microarray Image Analysis with Galaxy-ME'. The tutorial demonstrates how to use the Galaxy-ME tool suite for primary image processing, data analysis, and interactive visualization of multiple tissue imaging datasets. Original data was published by <a href="https://pubmed.ncbi.nlm.nih.gov/34824477/">Schapiro <em>et al</em></a>.</p>
Checkpoints for Morphological Classification of Radio Galaxies with wGAN-supported Augmentation
<p>Checkpoint for the Generator Model described in https://github.com/floriangriese/wGAN-supported-augmentation</p>
Gaia, NED, and SIMBAD in nearby galaxies
<p>Data table associated with the paper "Gaia, NED, and SIMBAD in nearby galaxies" (Hales & Barmby, MNRAS submitted).</p>
Photometry and Spectroscopy of a z=9.51 galaxy in the RXJ2129 cluster field
<p>Reduced HST and JWST imaging, plus reduced JWST spectroscopy, of a redshift z=9.51 galaxy that is triply imaged and highly magnified by the RXJ2129 galaxy cluster. </p>
Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event TEST_EVENT Preliminary
Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event TEST_EVENT Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2020arXivNicePaper
Run of an example Galaxy collection workflow
<p>This dataset is an <a href="https://www.researchobject.org/ro-crate/">RO-Crate</a> representation of an execution of an example Galaxy workflow, making use of some of Galaxy's platform specific features. It follows the <a href="https://w3id.org/ro/wfrun/workflow/0.1">Workflow Run Crate</a> profile. The workflow has been run with <a href="https://docs.galaxyproject.org/en/latest/index.html">Galaxy version 23.0</a> and exported using the implemented <a href="https://galaxyproject.org/news/2023-02-23-structured-data-exports-ro-bco/">export invocation to RO-crate feature</a>.</p>
Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS230322s Preliminary
Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS230322s Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2020arXivNicePaper
Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS230322s Preliminary
Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS230322s Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2020arXivNicePaper
Datasets for Transformer-based tool recommeder in Galaxy
<p>Datasets for Transformer-based tool recommeder in Galaxy:</p> <p>1. Tool popularity - Contains last one year usage of all Galaxy tools per month (Extracted from Galaxy Europe using query https://github.com/galaxyproject/gxadmin/blob/main/docs/README.query.md#query-tool-popularity)</p> <p>2. Workflow connections - Contains workflows as tabular files as pairs of tools - IN and OUT (Extracted from Galaxy Europe using query https://github.com/galaxyproject/gxadmin/blob/main/docs/README.query.md#query-workflow-connections)</p> <p> </p> <p> </p>
Protein Structure Files and Galaxy Workflows for Conducting Molecular Dynamics Simulations of Flavivirus Helicases -- Output Files
<p>These are the output files generated using the input files and Galaxy workflows for flavivirus helicase simulations, from: </p> <pre>https://doi.org/10.5281/zenodo.7493015</pre>
Galaxies simulated with GALFORM
<p>The galaxies in this dataset were simulated with the semi-analytical galaxy formation code GALFORM for the study of Jahns et al. 2023, titled “How limiting is optical follow-up for fast radio burst applications? Forecasts for radio and optical surveys”. The galaxies are used as the host galaxies of simulated fast radio bursts to do forecasts of their applications.</p> <p>The data comprises the galaxies star formation rate, stellar mass, and the photometric magnitudes in the observer frame, including extinction, in all passbands of the Sloan Digital Sky Survey, the Euclid satellite, the Vera Rubin Observatory, and the DECam.</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.