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Dataset results
443 results for “galaxy”
Galaxy spins
<p>Data for "An observed correlation between galaxy spins and initial conditions", Nature Astronomy (2020), https://arxiv.org/abs/2003.04800</p> <p>Added version 2 - Corrected errors in MaNGA and SAMI measurements</p>
Seechange Host Galaxy Spectra
<p>Spectra leading to host galaxy redshifts for transients in the Seechange SN Survey. These spectra are in a format that will work with the Weighted Cross Correlation routine released in Seechange Tools, DOI 10.5281/zenodo.4064139</p>
Properties of flat-spectrum radio-loud Narrow-Line Seyfert 1 Galaxies
<p>Numerical tables of the Spectral Energy Distributions (SEDs) of Figs. 8-13 of the paper http://arxiv.org/abs/1409.3716v2</p>
Data and Code for "Metal-enriched, sub-kiloparsec gas clumps in the circumgalactic medium of a faint z = 2.5 galaxy"
<p>This repository has code and data used in the paper "Metal-enriched, sub-kiloparsec gas clumps in the circumgalactic medium of a faint z = 2.5 galaxy" (http://arxiv.org/abs/1406.4239). If you find any of the data or code useful for a publication, please consider citing that paper.</p>
MCMC chains for distance and structural parameters of Pisces A&B dwarf galaxies
<p>These are Markov Chain Monte Carlo chains for Hubble Space Telescope observations of the dwarf galaxies Pisces A&B. More details on how they are derived will be provided in a forthcoming ApJ paper.</p>
Bacterial training dataset for Galaxy training network tutorials on Genome assembly
<p>This training dataset is from an imaginary <em>Staphylococcus aureus</em> bacterium with a miniature genome. There is a reference genome in various formats as well as some fastq reads of a closely related but also imaginary mutant strain.</p> <p>It is a useful dataset for demonstrating:</p> <ul> <li>de novo genome assembly</li> <li>read mapping and variant calling</li> <li>genome annotation</li> </ul> <p>The files included are:</p> <ul> <li><strong>wildtype.fna</strong>: the reference genome sequence of the wildtype strain in fasta format (a header line, then the nucleotide sequence of the genome.)</li> <li><strong>wildtype.gff</strong>: the reference genome sequence of the wildtype strain in general feature format (a list of features - one feature per line, then the nucleotide sequence of the genome.)</li> <li><strong>wildtype.gbk</strong>: the reference genome sequence in genbank format.</li> <li><strong>mutant_R1.fastq</strong> and <strong>mutant_R2.fastq</strong>: Fastq sequence reads of a closely related mutant strain. <ul> <li>The reads are paired-end.</li> <li>Each read is 150 bases long.</li> <li>The number of bases sequenced is equivalent to 19x the genome sequence of the wildtype strain. (Read coverage 19x - rather low!).</li> </ul> </li> </ul>
Training material for small RNA-seq data analysis (Galaxy Training Network tutorial)
<p>The data provided here are part of a Galaxy Training Network tutorial that analyzes small RNA-seq (sRNA-seq) data from a study published by Harrington et al. (DOI:10.1186/s12864-017-3692-8) to detect differential abundance of various classes of endogenous short interfering RNAs (esiRNAs). The goal of this study was to investigate "connections between differential retroTn and hp-derived esiRNA processing and cellular location, and to investigate the potential link between mRNA 3’ end cleavage and esiRNA biogenesis." To this end, sRNA-seq libraries were constructed from triplicate <em>Drosophila</em> tissue culture samples under conditions of either control RNAi or RNAi knockdown of a factor involved in mRNA 3’ end processing, <em>Symplekin</em>. This dataset (GEO Accession: GSE82128) consists of single-end, size-selected, non-rRNA-depleted sRNA-seq libraries. Because of the long processing time for the large original files, we have downsampled the original raw data files to include only reads that align to a subset of interesting transcript features including: (1) transposable elements, (2) <em>Drosophila</em> piRNA clusters, (3) <em>Symplekin</em>, and (4) genes encoding mass spectrometry-defined protein binding partners of <em>Symplekin</em> from Additional File 2 in the indicated paper by Harrington et al. More details on features 1 and 2 can be found here: https://github.com/bowhan/piPipes/blob/master/common/dm3/genomic_features (piRNA_Cluster, Trn). All features are from the <em>Drosophila</em> genome Apr. 2006 (BDGP R5/<em>dm3</em>) release.</p>
Magellan/M2FS Spectroscopy of Galaxy Clusters: Stellar Population Model and Application to Abell 267
<p>supplementary data products, including all sky-subtracted spectra from individual galaxies, as well as random draws from posterior PDFs for model parameters (see included README file)</p>
Training data for MaxQuant and Msstats label-free analysis in Galaxy
<p>The files serve as input and intermediate results for a MaxQuant and Msstats training on skin cancer tissues (<a href="https://doi.org/10.1016/j.matbio.2017.11.004">https://doi.org/10.1016/j.matbio.2017.11.004</a>) in the Galaxy training network (https://training.galaxyproject.org).</p> <p>Input files: human FASTA database for Maxquant. Annotation file and comparison matrix file for Msstats.</p> <p>Intermediate result files: MaxQuant protein groups, evidence and PTXQC.</p>
JWST spectrum of galaxy COSMOS-11142 from the Blue Jay survey.
<p>Spectroscopic and photometric data for galaxy COSMOS-11142, studied in Belli et al. (2024).</p> <ul> <li>The JWST/NIRSpec spectroscopy is stored as a FITS table which includes wavelength (in angstrom), calibrated flux, uncertainty, and best-fit model (in erg/(s cm2 A)).</li> <li>The JWST and HST photometry is stored as a FITS table which includes the name of each filter, the effective wavelength (in angstrom), the observed flux and its uncertainty (in microJy).</li> </ul>
Searching for HI around MHONGOOSE Galaxies via Spectral Stacking
<p>Stacked spectra obtained from spectrally stack around 14 nearby galaxies observed at 1.4 GHz as part of the MHONGOOSE survey (<a href="https://ui.adsabs.harvard.edu/abs/2024A%26A...688A.109D/abstract" target="_blank" rel="noopener">de Blok et al., 2024</a>). The reference paper is available at <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv241111584V/abstract" target="_blank" rel="noopener">NASA-ADS</a>.</p> <p><em>First page: </em>detections in the stacked spectra of the shallow cubes. Each panel shows a single-track stacked spectrum (in blue) and its 9-channel boxcar smoothed version (in red). The detected source is highlighted. The horizontal grey dashed lines are the ±σ level for the unsmoothed spectrum, while the black dashed line is the 0-flux level. The vertical black dashed-dotted line is, instead, the 0-km/s reference velocity. The galaxy name and the cell size are provided in the bottom-left corner, while at the bottom-right is reported if the detection was also visually identified in its corresponding cube.<br><em>Second page</em>: detections in the stacked spectra of the full-depth cubes. Each panel shows a single-track stacked spectrum (in blue) and its 9-channel boxcar smoothed version (in red). The detected source is highlighted. The horizontal grey dashed lines are the ±σ level for the unsmoothed spectrum, while the black dashed line is the 0-flux level. The vertical black dashed-dotted line is, instead, the 0-km/s reference velocity. The galaxy name and the cell size are provided in the bottom-left corner, while at the bottom-right is reported if the detection was also visually identified in its corresponding cube.</p>
Galaxy Training Data for "Overview of the Galaxy OMERO-suite"
<div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <p><strong>Sub-sample images: </strong> Images of cytoplasm to nucleus translocation of the transcription factor NFκB in MCF7 (human breast adenocarcinoma cell line) and A549 (human alveolar basal epithelial) cells in response to TNFα concentration. Images are at 10x objective magnification. The plate was acquired at Vitra Bioscience on the CellCard reader. For each well there is one field with two images: a nuclear counterstain (DAPI) image and a signal stain (FITC) image. Image size is 1360 x 1024 pixels. Images are in 8-bit BMP format.</p> <p><strong>Source</strong>: https://bbbc.broadinstitute.org/BBBC014</p> <p><strong>Citation</strong>: Ljosa, Vebjorn, Katherine L. Sokolnicki, and Anne E. Carpenter. "Annotated high-throughput microscopy image sets for validation." Nature methods 9.7 (2012): 637-637. <span>https://doi.org/10.1038/nmeth.2083</span></p> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Dataset for "From Halos to Galaxies. X: Decoding Galaxy SEDs with Physical Priors and Accurate Star Formation History Reconstruction"
<p>This deposit contains the data related to the manuscript "<em>From Halos to Galaxies. X: Decoding Galaxy SEDs with Physical Priors and Accurate Star Formation History Reconstruction</em>" submitted to the Astrophysical Journal. It includes the basic SDSS identifier, stellar mass, star formation rate, fractional formation time, and their errors. A detailed description can be found in Table 1 of the manuscript.</p> <p>The data is stored in a CSV file. It can be read with standard data analysis packages like <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html">Pandas</a> in Python. </p> <p>This deposit has also be updated to include a machine-readable table that follows the standards of the AAS Journals and Vizier (<span>datafile1_ApJ57534.mrt). More information on this standard can be found in the <a href="https://journals.aas.org/mrt-overview/">AAS</a> or <a href="http://cds.u-strasbg.fr/doc/catstd.htx">CDS</a> documentation. This format can be read in Python with packages like <a href="https://docs.astropy.org/en/stable/api/astropy.io.ascii.Mrt.html">astropy</a>. </span></p>
Intra-cluster summed galaxy colors
<p>Inter-cluster summed galaxy colors and other cluster properties for TNG300-1, SDSS NYU VAGC, and Buzzard Flock low redshift galaxies. </p> <p> </p> <pre>'Hmass': log10(Cluster mass/M_sun) TNG: M_200c, BZZ: M_vir, SDSS: M_vir 'N_gal': Number of galaxies in Cluster 'Z': Redshift 'rich_cts','rich_dsc': Richness, continuous and discrete 'mag_gap': Magnitude gap 'g_cen','r_cen','i_cen','z_cen': Magnitudes for central galaxies 'sum_g','sum_r','sum_i','sum_z': Summed Magntiudes for total cluster population 'sum_g_sat', 'sum_r_sat', 'sum_i_sat', 'sum_z_sat': Summed Magntiudes for sattelite cluster population </pre>
Training material for flye genome assembly (Galaxy Training Network tutorial)
<p>Datasets are subsets of 3 public datasets (Mucor mucedo Fresen. NRRL 3635 Standard Draft genome sequencing with PacBio technology)</p> <p>https://www.ncbi.nlm.nih.gov/sra/SRX5336965[accn]</p> <p>https://www.ncbi.nlm.nih.gov/sra/SRX5336964[accn]</p> <p>https://www.ncbi.nlm.nih.gov/sra/SRX5336963[accn]</p> <p> </p> <p> </p>
Long reads training material for 'Quality Control' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for reads Quality Control.</p> <p>PacBio HiFi reads were provided by PacBio - GIAB sample HG002 (https://www.pacb.com/smrt-science/smrt-resources/datasets/) and was downsampled using seqtk (https://github.com/lh3/seqtk)</p> <p>Nanopore reads were provided by Tim Kahlke as part of "Long-Read, long reach Bioinformatics Tutorials" (https://timkahlke.github.io/LongRead_tutorials/) and was basecalled using Guppy v5.0.2 (dna_r9.4.1_450bps_sup.cfg).</p>
The impact of galaxy selection on the splashback boundaries of galaxy clusters (Data)
<p>Data from O'Neil et al. (2022), The impact of galaxy selection on the splashback boundaries of galaxy clusters.</p>
Training data for the "Biodiversity data exploration" Galaxy-E tutorial
<p>Dataset sample from Reef life survey initiative https://reeflifesurvey.com/ to serve as a training set for "Biodiversity data exploration" tutorial for Galaxy and notably Galaxy for ecology initiative</p>
Galaxy Training Data for "Designing plasmids encoding predicted pathways by using the BASIC assembly method"
<p>This dataset provides the data needed for the Galaxy BASIC assembly workflow training tutorial (<a href="https://galaxy-synbiocad.org">https://galaxy-synbiocad.org</a>). This workflow provides a pathway to design plasmids encoding predicted metabolic pathways using the BASIC assembly method (<a href="https://doi.org/10.1021/sb500356d">https://doi.org/10.1021/sb500356d</a>). It generates scripts allowing the automatic construction of these plasmids using an Opentrons liquid handling robot. After downloading these scripts on a computer connected to an Opentrons (<a href="https://opentrons.com">https://opentrons.com</a>), the user can perform the automatic construction of the plasmids on the bench.</p> <p>The content of the dataset is as follows:</p> <ul> <li> <p>an SBML file modeling a heterologous pathway producing lycopene such as those produced by the Pathway Analysis Workflow (<a href="https://galaxy-synbiocad.org">https://galaxy-synbiocad.org</a>).</p> </li> <li> <p>a CSV file listing the parts to be used (linkers, backbone and promoters) in the constructions.</p> </li> <li> <p>two YAML files providing two examples of settings, i.e. providing the identifiers of the laboratory equipment and the parameters of the DNA robot.</p> </li> </ul>
A Parameterized Model for Differential Galaxy Counts at Any Wavelength
<p>Collected literature values of Schechter functions, resulting parameter values of fits to those data as a function of wavelength, and Python scripts to compute simulated differential galaxy counts based on those parameterisations.</p> <p> </p> <p>Future revisions of galaxy_counts.py can be found at https://github.com/Onoddil/macauff/blob/main/macauff/ galaxy counts.py, and future revisions of literature and parameterisations can be found at https://onoddil.github.io/galaxy evolution/galaxy counts.html.</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.