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443 results for “galaxy”
Training data for 'Unicycler assembly of SARS-CoV-2 genome with preprocessing to remove human genome reads' tutorial (Galaxy Training Material)
<p>The data here is a copy of the corresponding SRR records in the NCBI SRA. The duplication serves a dual purpose:</p> <ol> <li>as a backup should there be problems connecting to NCBI servers, e.g., during Galaxy user trainings.</li> <li>to illustrate how to obtain raw sequencing data from alternative sources, and to organize the data into the same collection structure in a Galaxy history that is generated by specialized Galaxy SRA download tools.</li> </ol>
Data for Precursor intensity-based label-free quantification software tools for Galaxy Platform.
<p><strong>Precursor intensity-based label-free quantification software tools for proteomic and multi-omic analysis within the Galaxy Platform.</strong></p> <p> </p> <p><strong>ABRF:</strong> Data was generated through the collaborative work of the ABRF Proteomics Research Group (<a href="https://abrf.org/research-group/proteomics-research-group-prg">https://abrf.org/research-group/proteomics-research-group-prg</a>). See Reference for details: Van Riper, S. et al. ‘<a href="http://cbs.umn.edu/sites/cbs.umn.edu/files/public/downloads/2016_ABRF_PRG_Poster_for_ASMS_20160501.pdf">An ABRF-PRG study: Identification of low abundance proteins in a highly complex protein sample</a>’ at the 64th Annual Conference of American Society of Mass Spectrometry and Allied Topics" at San Antonio, TX."</p> <p><strong>UPS:</strong> <strong>MaxLFQ Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M.<a href="https://www.ncbi.nlm.nih.gov/pubmed/24942700/"> Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ.</a> Mol Cell Proteomics. 2014 Sep;13(9):2513-26. doi: 10.1074/mcp.M113.031591. Epub 2014 Jun 17. PubMed PMID: 24942700; PubMed Central PMCID: PMC4159666;</strong></p> <p><strong>PRIDE #5412; ProteomeXchange repository PXD000279: </strong>ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2014/09/PXD000279</p>
Inputs for Galaxy Trainig ATAC-seq
<p>The fastq.gz are a subset of SRR891268 but enriched into pairs which map to chr22.</p> <p>The bed is from ENCODE.</p>
Datasets for ``Application of a helicity proxy to edge-on galaxies''
<pre>The run directories contain unformatted data files either as data/proc0 (for 128x128x32_20kc_BDMSST93c_Oml) or as data/proc0-3 (for 128x64_20kc_BDMSST93_wind10a), or as bb.sav just for the magnetic field components in the other directories. They can be read directly with the corresponding idl routines that are in the directory (run_directories/run_idl) together with those delivered with the Pencil Code (https://github.com/pencil-code).</pre>
Reproduction package for "ClG 0217+70: A massive merging galaxy cluster with a large radio halo and relics"
<p>This is the reproduction package for "ClG 0217+70: A massive merging galaxy cluster with a large radio halo and relics", which has been accepted for publication in A&A.</p> <p>To use this package, please read the README.</p> <p>This package is tested in an environment that contains:</p> <ul> <li>SPEX v3.06</li> <li>CIAO v4.12</li> <li>python 3.6.5 <ul> <li>numpy 1.14.3</li> <li>astropy 3.0.2</li> <li>astroquery 0.4</li> <li>scipy 1.1.0</li> <li>matplotlib 2.2.2</li> </ul> </li> </ul>
HIGH-REDSHIFT NARROW LINE SEYFERT 1 GALAXIES: A CANDIDATE SAMPLE
<p>The study of Narrow line Seyfert 1 galaxies (NLS1s) is now mostly limited to low redshift (z < 0.8). This is because their definition requires the presence of the Hβ emission line, which is red-shifted out of the spectral coverage of major ground-based spectroscopic surveys at z > 0.8. We studied the correlation between the properties of Hβ and Mg II lines of a large sample of SDSS DR14 quasars to find high-z NLS1 candidates. Based on the strong correlation of FWHM(MgII)=(0.880±0.005)×FWHM(Hβ)+(0.438±0.018), we present a sample of high-z NLS1 candidates having FWHM of Mg II < 2000 km s−1. The high-z sample contains 2684 NLS1s with redshift z = 0.8 − 2.5 with a median logarithmic bolometric luminosity of 46.16 ± 0.42 erg s−1, logarithmic black hole mass of 8.01 ± 0.35M⊙, and logarithmic Eddington ratio of 0.02 ± 0.27. The fraction of radio-detected high-z NLS1s is similar to that of the low-z NLS1s and SDSS DR14 quasars at a similar redshift range and their radio luminosity is found to be strongly correlated with their black hole mass.</p> <p>This page contains the catalog of high-z NLS1 candidates. </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: nicepaper
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: nicepaper
Training data for "From small to large-scale genome comparison", a tutorial for the Galaxy Training Network
<p>This dataset comprises two sequence pairs in FASTA format, one including two mycoplasmas (<em>Hyopneumoniae</em> 232 and 7422) and the other including the first chromosome of two plant genomes (<em>Aegilops tauschii</em> and <em>Triticum aestivum</em>).</p>
IMPETUS: New Cloudy's radiative tables for accretion onto a galaxy black hole. Calculations II. Table 2.
<p>This is a .ZIP file, which contains the results of Calculations II. The main directory contains sub-directories with ascii files. Each column is properly described in the article.</p>
IMPETUS: New Cloudy's radiative tables for accretion onto a galaxy black hole. Calculations I. Table 2.
<p>This is a .ZIP file, which contains the results of Calculations I. The main directory contains sub-directories with ascii files. Each column is properly described in the article.</p>
IMPETUS: New Cloudy's radiative tables for accretion onto a galaxy black hole. Calculations I-VI. Table 2.
<p>These are gzipped/tar files, which contain the results of Calculations I-VI. The main directories contain sub-directories with ascii files. Each column is properly described in the article. The equilibrium temperature for any configuration can be calculated and plot with the new script at https://doi.org/10.5281/zenodo.4381019.</p>
Sample datasets for Galaxy NGS tutorial
<p>Datasets in fastqsanger.gz format representing re-sequencing of human mitochondria </p>
Mothur MiSeq SOP Galaxy Tutorial Data
<p>These are files for use with the Galaxy metagenomics tutorial "Mothur MiSeq SOP"</p>
Galaxy Training Date for "Analyses of metagenomic data - The global picture"
<p>These training datasets are part of a Galaxy Training Network tutorial that analyzes metagenomic (amplicon and WGS) data. These datasets are extracted of a project studying the Argentinean agricultural pampean soils (https://www.ebi.ac.uk/metagenomics/projects/SRP016633). </p>
The Data For Inside-Out versus Upside-Down: The Origin and Evolution of Metallicity Radial Gradients in FIRE Simulations of Milky Way-mass Galaxies and the Essential Role of Gas Mixing
<p>The files titled Graf et al. 2024b store the x-axis and y-axis values for each line in each figure. The files which end in .py are the scripts which produced the data in the figures.</p> <p>This data abides by CC-BY.</p>
Wikidata selection of Cultural Heritage, Stars, Galaxies and Random entities and claims
<p>This work presents an analysis of the use of different representation methods in Wikidata to encode information with weaker logical status (e.g. contradictory information, temporally evolving information). The study examines three main approaches: ranked statements, null-valued objects, and qualified statements with properties P5102 (reason of statement), P1480 (sourcing circumstances) and P2241 (reason for deprecated rank) by analysing their prevalence, success, and clarity in Wikidata. The analysis is performed over cultural heritage artefacts stored in Wikidata divided in three subsets (i.e. visual heritage, textual heritage and audio-visual heritage) and compared with astronomical data (stars and galaxies entities from Wikidata) and a Random dataset (entities chosen randomly from the most used 100 classes). The findings indicate that (1) the representation of weaker logical status information is limited, with only a small proportion of items reporting such information, (2) the representation of WLS varies significantly between the two datasets. Finally, we propose several representation alternatives to simplify and standardize the representation of this type of information in Wikidata, with the hope of increasing its accuracy and richness</p>
The Discovery of 63 Giant Radio Galaxies in FIRST using DRAGN-hunter
<p>A catalog of 63 giant radio galaxies (GRGs) identified in the FIRST survey using the DRAGN-hunter algorithm. Included here are the table of GRGs (in votable format; described below) and a tarball containing images for all 63 sources. These images consist of radio contours from FIRST overlaid on grz optical images from LS DR9, with the position of the identified host marked by a green circle.</p><p> </p><p><i><strong>Table description</strong></i><br><br><i>Columns [unit]:</i></p><ul><li><strong>Name,</strong> Name of the host galaxy (1)</li><li><strong>RAJ2000</strong> [deg], R.A. of the host galaxy</li><li><strong>DEJ2000</strong> [deg], Decl. of the host galaxy</li><li><strong>rmag</strong> [mag], r-band magnitude of the host galaxy</li><li><strong>z</strong>, Redshift of the host galaxy</li><li><strong>zType</strong>, Photometric or spectroscopic redshift (2)</li><li><strong>SFIRST</strong> [mJy], Flux density in FIRST</li><li><strong>logLFIRST</strong> [W/Hz], log10 of 1.4GHz luminosity</li><li><strong>LAS</strong> [arcsec], Largest Angular Size</li><li><strong>LLS</strong> [Mpc], Largest Linear Size (3)</li></ul><p><i>Notes:</i></p><ol><li>uncertain hosts are chosen to yield the lower of several possible redshifts; SDSS J010931.12-023723.8 is the central of three potential host galaxies at similar redshifts and blended into WISEA J010930.98-023722.0<br> </li><li>p = photometric redshift from 2022MNRAS.512.3662D<br>s = spectroscopic redshift from SDSS DR16 (2020ApJS..249....3A)<br> </li><li>based on H_0_=70 km/s/Mpc, Omega_m_=0.3, Omega_Lambda_=0.7<br><br> </li></ol>
Alevin in commandline - Galaxy Training Material
<p>Input datasets for Generating a single cell matrix using Alevin (bash + R) tutorial on Galaxy Training Network. </p>
Reproduction package for the paper "A LOFAR sample of luminous compact sources coincident with nearby dwarf galaxies"
<p>Scripts to reproduce analyses from Vohl et al. 2023, A&A.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.