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443 results for “galaxy”
SURVEY OF IONIZED GAS OF THE GALAXY, MADE WITH THE ARECIBO TELESCOPE (SIGGMA): INNER GALAXY DATA RELEASE
<p>The Survey of Ionized Gas of the Galaxy, Made with the Arecibo telescope (SIGGMA) provides a fully-sampled view of the radio recombination line (RRL) emission from the portion of the Galactic plane visible by Arecibo. Observations use the Arecibo L-band Feed Array (ALFA), which has a FWHM beam size of 3 0 .4. Twelve hydrogen RRLs from H163α to H174α are located within the<br> instantaneous bandpass from 1225 MHz to 1525 MHz. We provide here cubes of average (“stacked”) RRL emission for the inner Galaxy region 32 ◦ ≤ ` ≤ 70 ◦ , |b| ≤ 1.5 ◦ , with an angular resolution of 6 0 . The stacked RRL rms at 5.1 km s<sup>−1</sup> velocity resolution is ∼ 0.65 mJy beam<sup>−1</sup> , making this the most sensitive large-scale fully-sampled RRL survey extant. We use SIGGMA data to catalogue 319 RRL detections in the direction of 244 known H ii regions, and 108 new detections in the direction of 79 HII region candidates. We identify 11 Carbon RRL emission regions, all of which are spatially coincident with known H ii regions. We detect RRL emission in the direction of 14 of the 32 supernova remnants (SNRs) found in the survey area. </p>
Cellulose test MD dataset for analysis with Galaxy and BRIDGE
<p>This coordinate and trajectory dataset is that of cellulase and octaose substrate in water. 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. The enzyme and substrate have been placed in a cubic TIP3P water box containing 0.15 M ions (NaCl).</p> <p>The files includes are: </p> <ul> <li><strong>cbh1test.pdb</strong>: the coordinates of the entire system (water, protein and substrate) in PDB format. </li> <li><strong>cbh1test.dcd</strong>: a short MD trajectory (16 frames) in CHARMM DCD format.</li> </ul>
Science ready spectra and their best-fitting models described in the research paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al.
<p>Science ready spectra of nine ultra-diffuse galaxies in the Coma cluster collected with the Binospec multi-object spectrograph and their best-fitting PEGASE.HR templates obtained using the NBursts full spectrum fitting code. These spectra were presented in the paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al. accepted for publication in the Astrophysical Journal on Sep/3/2019 (arXiv:1901.05489).</p> <p>Each spectrum is presented as a binary FITS table, which contains a spectrum (wavelength, flux, uncertainties), best-fitting template, best-fitting parameters (radial velocity, age, metallicity), and a pixel mask used in the fitting procedure. For six galaxies there are two files provided: (i) one-dimensional optimally extracted integrated spectrum and (ii) two dimensional spectrum for spatially resolved radial velocity information. For the remaining three galaxies, only spatially resolved spectra are provided.</p>
Dataset: Diamonds from Hadley's ggplot2 for Galaxy training
<p>Sample dataset created from https://doi.org/10.5281/zenodo.3522106 by selecting carat,price,color,clarity and cut columns only. In addition color and clarity are factors with integer values so we can reuse the dataset directly with an existing workflow (taught in Galaxy 101 for everyone).</p>
[NGC5084] SAUNAS II: Discovery of Cross-shaped X-ray Emission and a Rotating Circumnuclear Disk in the Supermassive S0 Galaxy NGC 5084
<p>The contained FITS files represent the processed Chandra/ACIS X-ray surface brightness maps of the NGC5084 galaxy, observed with Chandra/ACIS and analyzed with the SAUNAS pipeline as described in Borlaff et al. 2024b (https://ui.adsabs.harvard.edu/abs/2024arXiv240810449B/abstract). All the images have the photometric calibrations (in units of photons cm-2 s-1 pixel-1) and have been astrometrically aligned. </p> <div>Each file contains four FITS extensions as detailed below: </div> <div>----</div> <div>EXTENSION NAME TYPE SIZE DETAILS </div> <div>----</div> <div>0 INFO no-data 0 BLANK EXTENSION. <br>1 SB_FLUX float64 512x512 X-RAY SURFACE BRIGHTNESS MAP. [photons cm-2 s-1 pixel-1] <br>2 STD_SB_FLUX float64 512x512 X-RAY SURFACE BRIGHTNESS NOISE MAP [photons cm-2 s-1 pixel-1]<br>3 SNR float64 512x512 SIGNAL-TO-NOISE RATIO [ - ]</div> <div>-----------</div> <div> </div> <div>Use the SNR extension (extension #3) to determine if your source of interest in the SB_FLUX map (extension #1) is statistically significant over the background limit.</div> <div> </div> <div> </div> <div> </div> <div> </div>
The metal content of the hot atmospheres of galaxy groups - supporting data
<p>Supporting data used to generate the figures included in the review chapter "The metal content of the hot atmospheres of galaxy groups", to appear in the MDPI journal "Universe". These values were collected and compiled from existing literature; each text file lists the relevant references to the original articles where various sets of results were initially published. </p>
MuSpinSim data files for Galaxy materials science tutorials
<p>This is a training dataset for use in Galaxy materials science tutorials. These files can be compared to the output of simulations by MuSpinSim for dissipation of muon spins.</p> <p>The files included are:</p> <ul> <li><strong>dissipation_theory.dat:</strong> theoretical values formatted as a MuSpinSim output</li> <li><strong>experiment.dat:</strong> mock experimental values formatted as a MuSpinSim output</li> </ul>
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
<p>We present the data used in "DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection". It was also used in the conference paper presented in Machine Learning and the Physical Sciences workshop at NeurIPS 2022: "Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection".</p> <p>A plethora of AI methods, has already shown huge promise in increasing quality and speed of work with astronomical datasets, but high complexity of AI methods leads to extraction of dataset-specific non-robust features, which leads to models that cannot work on multiple datasets at the same time. We develop a Universal Domain Adaptation method <em><strong>DeepAstroUDA</strong></em>, capable of performing <strong>semi-supervised domain adaptation, that can be applied to datasets with different data distributions and class overlap</strong>. Extra classes can be present in any of the two datasets, and the method can even be used in the presence of unknown classes. We apply our model to three examples of galaxy morphology classification tasks of different complexities (3-class and 10-class problems), with anomaly detection i.e. in all our experiments we have one extra class in the unlabeled target dataset, which represents our anomaly class.</p> <p> </p> <p><strong>DATA:</strong></p> <p><strong>1) DA across two different data releases of the same survey (LSST 1 and 10 years of observation):</strong> We use data from Ciprijanovic et al. 2022. which can also be found on Zenodoo: <a href="https://zenodo.org/record/5514180#.Y6SM7y-B2_w">https://zenodo.org/record/5514180</a> . Data contains three classes: spiral (0), elliptical (1) and merging galaxies (3, anomaly class).</p> <p><strong>2) DA across two surveys (SDSS and DeCALS): </strong>We create datasets using data and labels from the Galaxy Zoo project. Datasets contain 10 classes (9 known classes present in both SDSS and DeCALS data, and one unknown anomaly class present only in DeCALS data): disturbed (0), merging (1), round smooth (2), cigar shaped smooth (3), barred spiral (4), unbarred tight spiral (5), unbarred loose spiral (6), edge-on without bulge (7), edge-on with bulge (8), lenses (9, unknown anomaly class).</p> <p>SDSS (wide filed): datasets is split into two files - sdss_1.h5, sdss_2.h5</p> <p>DeCALS: decals.zip</p> <p><strong>3) DA between wide and deep observing fields of the same survey (SDSS):</strong> We create datasets using data and labels from the Galaxy Zoo project. Datasets contain same 10 classes as in 2), with the final lens anomaly class being only present in the SDSS deep field.</p> <p>SDSS (wide filed): the same data as in 2)</p> <p>SDSS (Strip 82 deep field): sdss_stripe82.zip</p> <p>All SDSS and DECaLS files contain full datasets (train, validation and test). Exact split that we performed (0.6 : 0.2 : 0.2) can be done using the code that accompanies this publication: <a href="https://github.com/deepskies/DeepAstroUDA">https://github.com/deepskies/DeepAstroUDA</a> .</p>
Galaxy Training Material for Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)
<p>This dataset contains the training data for the <strong>Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)</strong> GTN tutorial. It includes 3 GC-[EI+]-HRMS files from seminal plasma samples, the RECETOX Metabolome HR-[EI+]-MS library collected from mostly endogoenous compounds from MetaSci Human Metabolite Library, reference alkanes, sample metadata table, and preprocessed XCMS object.</p>
The datasets for "The Cosmos in its Infancy: JADES Galaxy Candidates at z > 8 in GOODS-S and GOODS-N"
<p>The JADES dataset of z > 8 galaxies and galaxy candidates accompanying the paper "The Cosmos in its Infancy: JADES Galaxy Candidates at z > 8 in GOODS-S and GOODS-N" (Hainline et al.). This paper was accepted in ApJ, January 2024. We also include the EAZY template set used for these fits. Please see the README files for descriptions of the datasets. </p>
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>
Digital Assets for "Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey"
<p>These are morphological catalogs and trained <a href="https://github.com/aritraghsh09/GaMPEN">GaMPEN</a> models for Hyper Suprime-Cam galaxies. Please refer to <a href="https://gampen.readthedocs.io/en/latest/Public_data.html">https://gampen.readthedocs.io/en/latest/Public_data.html</a> and <a href="https://arxiv.org/abs/2212.00051">https://arxiv.org/abs/2212.00051</a> for more details about this data release. </p> <p> </p> <p><strong>Catalog Files</strong></p> <ol> <li>g_0_025_preds_summary.csv --> Structural parameter catalog for z < 0.25 HSC g-band galaxies </li> <li>r_025_050_preds_summary.csv --> Structural parameter catalog for 0.25 < z < 0.50 HSC r-band galaxies </li> <li>i_050_075_preds_summary.csv --> Structural parameter catalog for 0.50 < z < 0.75 HSC i-band galaxies </li> </ol> <p> </p> <p><strong>Trained PyTorch Model Files</strong></p> <ol> <li>g_0_025_real_data.pt --> Trained Model for z < 0.25 HSC g-band galaxies </li> <li>r_025_050_real_data.pt --> Trained Model for 0.25 < z < 0.50 HSC r-band galaxies </li> <li>i_050_075_real_data.pt --> Trained Model for 0.50 < z < 0.75 HSC i-band galaxies </li> <li>sim_g_0_025.pt --> Trained Model for Simulated z < 0.25 HSC g-band galaxies </li> <li>sim_r_025_050.pt --> Trained Model for Simulated 0.25 < z < 0.50 HSC r-band galaxies </li> <li>sim_i_050_075.pt --> Trained Model for Simulated 0.50 < z < 0.75 HSC i-band galaxies </li> </ol>
Magellan/M2FS and MMT/Hectochelle Spectroscopy of Dwarf Galaxies and Faint Star Clusters within the Galactic Halo
<p>m2fs_HiRes_catalog_public.fits: public catalog of measurements derived from spectroscopic observations of individual targets with the Magellan/M2FS spectrograph in HiRes configuration</p> <p>m2fs_MedRes_catalog_public.fits: public catalog of measurements derived from spectroscopic observations of individual targets with the Magellen/M2FS spectrograph in MedRes configuration</p> <p>hecto_catalog_public.fits: public catalog of measurements derived from spectroscopic observations of individual targets with the MMT/Hectochelle spectrograph</p> <p>fits_files.tar.gz: Supplementary data products, including all sky-subtracted spectra from individual targets and best-fitting model spectra.</p> <p>template_spectra.tar.gz: synthetic template spectra (columns are wavelength in air (Angstroms), normalized flux)</p>
Galaxy Zoo DESI: Detailed Morphology Classifications for 8.7M Galaxies in the DESI Legacy Imaging Surveys
<p>This repository contains the data released in the paper "Galaxy Zoo DESI: Detailed Morphology Classifications for 8.7M Galaxies in the DESI Legacy Imaging Surveys" <em>(DOI to follow on publication).</em></p> <p>We release detailed morphology measurements for bright (<em>r </em>< 19) galaxies in the DESI Legacy Imaging Surveys footprint. These measurements estimate the presence of bars, spirals arms, ongoing mergers, and more.</p> <p>---</p> <p><strong>GZ DESI Detailed Morphology Catalogs</strong></p> <p>These catalogs are created by training deep learning models on Galaxy Zoo volunteer responses, to predict what volunteers might say for new galaxies. The models are available at [www.github.com/mwalmsley/zoobot](www.github.com/mwalmsley/zoobot). Our measurements are predicted vote fractions i.e. the fraction of volunteers expected to select a given answer for a given question.</p> <p>We share two catalog versions containing the same morphology measurements but presented in different ways.</p> <p>gz_desi_deep_learning_catalog_friendly.parquet contains the morphology measurements</p> <p>gz_desi_deep_learning_catalog_advanced.parquet contains the same measurements, and additional information:</p> <p>- _friendly includes only relevant vote fractions, defined as vote fractions to answers of questions that a majority of volunteers would have been asked. This removes predicted vote fractions for e.g. the fraction of volunteers answering "2 spiral arms" to a galaxy with no spiral arms. _advanced includes all vote fractions and instead reports the (column "proportion_asked"). The user must select which vote fractions they consider relevant (we suggest proportion_asked > 0.5, which recovers the _friendly fractions).</p> <p>- _advanced includes columns with estimated credible intervals (error bars) around each vote fraction. These are calculated from the vote fraction posterior predicted by our models.</p> <p>Finally, we separately present volunteer votes collected for 96k galaxies during the GZD-8 campaign, i.e. after the release of GZ DECaLS but before this (GZ DESI) release. These are split into the _core and _extended catalogs, where _extended includes galaxies which received five or more votes for "artifact". The models above were trained on these votes as well as votes from GZ DECaLS.</p> <p>---</p> <p><strong>External Catalog</strong></p> <p>For convenience, we also include an additional catalog of non-morphology measurements created by other authors (external_catalog.parquet) cross-matched to our morphology catalogs. Please credit those authors if you use this catalog (references are in the GZ DESI paper).</p> <p>A particularly important external measurement is redshift. Morphology is increasingly hard to resolve at higher redshift and so <strong>distant galaxies appear less featured</strong>. external_catalog.parquet includes the column "redshift", which is the SDSS spectroscopic redshift where available and a photometric redshift estimate otherwise (again, see the GZ DESI paper for references and credit). You may want to select only galaxies at lower redshifts.</p> <p>---</p> <p><strong>Data Notes</strong></p> <p>Parquet is a fast csv-like format which can be read with pd.read_parquet(loc, columns=[some columns]). Parquet files are read column-by-column (rather than row-by-row) and so you can chose which columns to load. You can easily check which columns are available using columns=['foo'] and reading the error message. We suggest loading only the columns you need when working with the larger catalogs. This will require much less memory than loading every column.</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 to follow on publication) when using the data in this repository.</p> <p>---</p> <p><strong>History</strong></p> <p>v0.0.1 - closed pre-release for internal review</p> <p>v1.0.0 - draft public release. Removed low-z pre-filtered catalogs.</p> <p>v1.0.1 - first public release. Added .csv version of _friendly catalog. Tweaked catalog formatting for clarity and consistency.</p>
COSMOS deep field DECam + VIRCAM galaxies
<p>Galaxies from COSMOS field; observations merged from DECam and VIRCAM (see Hartley+22, <a href="https://doi.org/10.1093/mnras/stab3055">https://doi.org/10.1093/mnras/stab3055</a>). Formatted for Red Dragon galaxy characterization algorithm (see Black+20, <a href="https://doi.org/10.1093/mnras/stac2052">https://doi.org/10.1093/mnras/stac2052</a>). </p> <p>COSMOS_*.h5 give large datasets without redshift nor stellar mass cuts</p> <p>DES_DEEP_smass*to*_Z3p5.h5 vet above <em>griz</em> dataset to a single mass decade, extending only out to redshift z=3.5</p> <p> </p> <table align="left"> <caption>Summary of dataset parameters</caption> <thead> <tr> <th scope="col">Field</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>AGE</td> <td>Gyr; age of galaxy</td> </tr> <tr> <td>DEC</td> <td>degrees; declination</td> </tr> <tr> <td>RA</td> <td>degrees; right ascension</td> </tr> <tr> <td>SSFR</td> <td>1/yr; specific star formation rate</td> </tr> <tr> <td>Z</td> <td>redshift</td> </tr> <tr> <td>Z_err</td> <td>uncertainty estimated for redshift</td> </tr> <tr> <td>bands</td> <td>mag; photometric bands: [g, r, i, z] or [u, g, r, i, z, J, H, Ks] for ALL</td> </tr> <tr> <td>bands_err</td> <td>mag; uncertainty estimate for photometry</td> </tr> <tr> <td>smass</td> <td>dex; log stellar mass: log10(galactic stellar mass / 1 solar mass)</td> </tr> <tr> <td>smass_err</td> <td>dex; uncertainty estimate for lg mass</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
The COSMOS2020 Galaxy Stellar Mass Function -- Key Measurements
<p>Here we describe the release of the measurements of the galaxy Stellar Mass Function and quiescent mass fractions based on the COSMOS2020 Farmer Catalogue and LePhare estimates of redshifts, masses, and rest-frame colours as described in Weaver et al. 2023 (ApJS, in press).</p> <p>Measurements can be found in "SMF_Farmer_Weaver+23.tar.gz"<br> Markov chains are contained in the remaining '.dat.gz' files</p> <p>When using these data products please cite both this SMF paper (Weaver et al. 2023) and the COSMOS2020 Catalogue (Weaver et al. 2022). Links to ADS export citations:</p> <p> SMF | https://ui.adsabs.harvard.edu/abs/2022arXiv221202512W<br> COSMOS2020 | https://ui.adsabs.harvard.edu/abs/2022ApJS..258...11W</p> <p>Please reach out if you have any questions or concerns.</p>
Training data for 'Exome sequencing data analysis' tutorial (Galaxy Training Material)
<p>The data used in this tutorial are a subset of the data published previously in <a href="https://zenodo.org/record/3243160">Training material for the course "Exome analysis with GALAXY"</a>. Credit for uploading the original data goes to Paolo Uva and Gianmauro Cuccuru!</p> <p>Specifically, you may need the following datasets for following the tutorial:</p> <p><strong>Raw sequencing reads</strong></p> <ul> <li><a href="https://zenodo.org/record/3243160/files/father_R1.fq.gz?download=1">https://zenodo.org/record/3243160/files/father_R1.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/father_R2.fq.gz?download=1">https://zenodo.org/record/3243160/files/father_R2.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/mother_R1.fq.gz?download=1">https://zenodo.org/record/3243160/files/mother_R1.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/mother_R2.fq.gz?download=1">https://zenodo.org/record/3243160/files/mother_R2.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/proband_R1.fq.gz?download=1">https://zenodo.org/record/3243160/files/proband_R1.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/proband_R2.fq.gz?download=1">https://zenodo.org/record/3243160/files/proband_R2.fq.gz</a></li> </ul> <p><strong>Premapped sequencing reads</strong></p> <ul> <li><a href="https://zenodo.org/record/3243160/files/mapped_reads_father.bam?download=1">https://zenodo.org/record/3243160/files/mapped_reads_father.bam</a></li> <li><a href="https://zenodo.org/record/3243160/files/mapped_reads_mother.bam?download=1">https://zenodo.org/record/3243160/files/mapped_reads_mother.bam</a></li> <li><a href="https://zenodo.org/record/3243160/files/mapped_reads_proband.bam?download=1">https://zenodo.org/record/3243160/files/mapped_reads_proband.bam</a></li> </ul> <p><strong>Reference sequence (human chromosome 8)</strong></p> <ul> <li><a href="https://zenodo.org/record/3243160/files/hg19_chr8.fa.gz?download=1">https://zenodo.org/record/3243160/files/hg19_chr8.fa.gz</a></li> </ul> <p> </p> <p>If you would just like to play with GEMINI rather than work through the full tutorial, you'll find below a prebuilt GEMINI database (for GEMINI version 0.20.1) for the family trio. You can start exploring this database without having to run GEMINI load and, in fact, without having to install GEMINI's bundled annotation data.</p>
Stellar mass function for galaxies in A133
<p>This table provides the stellar mass functions (dN/dlg(M*)) of A133,<br> as shown in Fig 10 of Starikova et al. 2020, normalized to 1 square<br> degree on the sky.</p> <p> </p>
COSMOS isolated galaxy images (parametric models generated with GalSim)
<p>Isolated galaxy images generated with GalSim from parametric models extracted from the Hubble COSMOS catalog.</p> <p>These files contain images and data for 10 000 images of isolated galaxy:</p> <ul> <li><strong>galaxies_isolated_10000_images.npy: </strong>numpy array of shape (10 000, 10, 64, 64), 10 000 images of size 64x64 pixels, in 10 filters (4 Euclid filters and 6 <em>ugrizy</em> LSST filters, in that order). Images contain Poissonian noise.</li> <li><strong>galaxies_isolated_10000_data.csv: </strong>corresponding parameters: <ul> <li>SNR: signal-to-noise ratio</li> <li>redshift: redshift of the galaxy</li> <li>e1: e1 parameter of ellipticity (e = e1 + i.e2)</li> <li>e2: e2 parameter of ellipticity (e = e1 + i.e2)</li> <li>mag: magnitude</li> </ul> </li> </ul>
Training data for 'Genome annotation with Maker' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for genome annotation with Maker.</p> <p>It is based on data used in <a href="http://weatherby.genetics.utah.edu/MAKER/wiki/index.php/MAKER_Tutorial_for_WGS_Assembly_and_Annotation_Winter_School_2018">another Maker tutorial</a>.</p> <p>The full genome was <a href="https://www.ncbi.nlm.nih.gov/genome/?term=Schizosaccharomyces%20pombe[Organism]&cmd=DetailsSearch">downloaded from NCBI</a>, and mitochondria sequence removed from it for simplicity.</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.