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73 results for “Data Catalog”
Data for The UNCOVER Survey: A First-Look HST+JWST Catalog of Galaxy Redshifts and Stellar Populations Properties Spanning 0.2 ≲ z ≲ 15
<p>The recent UNCOVER survey with the James Webb Space Telescope (JWST) exploits the nearby cluster Abell 2744 to create the deepest view of our universe to date by leveraging strong gravitational lensing. In this work, we perform photometric fitting of more than 50,000 robustly detected sources out to z ~ 15. We show the redshift evolution of stellar ages, star formation rates, and rest-frame colors across the full range of 0.2 < z < 15. The galaxy properties are inferred using the Prospector Bayesian inference framework using informative Prospector-beta priors on masses and star formation histories to produce joint redshift and stellar populations posteriors, and additionally lensing magnification is performed on-the-fly to ensure consistency with the scale-dependent priors. We show that this approach produces excellent photometric redshifts with NMAD ~ 0.03, of a similar quality to the established photometric redshift code EAzY. In line with the open-source scientific objective of the Treasury survey, we publicly release the stellar populations catalog with this paper, derived from the photometric catalog adapting aperture sizes based on source profiles. This release includes posterior moments, maximum-likelihood spectra, star-formation histories, and full posterior distributions, offering a rich data set to explore the processes governing galaxy formation and evolution over a parameter space now accessible by JWST.</p>
The Spitzer Spectroscopic Data Fusion - Merged Spectroscopic Redshift Catalogs in Spitzer Fields
<p>The Spitzer Spectroscopic Data Fusion merges miscellaneous spectroscopic information available within "popular" extragalactic survey fields.</p> <p>Last Updated on 20 March 2025 - <a href="https://zenodo.org/record/6368347">https://zenodo.org/record/6368347</a> - <a href="https://www.mattiavaccari.net/df/specz">https://www.mattiavaccari.net/df/specz</a></p> <p>Based on the Spitzer Data Fusion Project - <a href="https://doi.org/10.5281/zenodo.7850783">https://doi.org/10.5281/zenodo.7850783</a> - <a href="https://mattiavaccari.net/df">https://mattiavaccari.net/df</a></p> <p>Merged Spec-Z ("specz-merged") catalogs merge miscellaneous spec-z information available within a given field. Different spec-z catalogs available within a given field are merged (using a search radius of 1.0 arcsec), and for sources with multiple spec-z measurements the most reliable one is chosen following the (largely arbitrarily) assumed order of decreasing reliability indicated below for each field. If CAT1,...,CATN spec-z catalogs are available in a given field, Z_1 from CAT1 (i.e. NED) is adopted as "best" redshift (i.e. ZBEST), if available, otherwise Z_2 from CAT_2 is adopted if available, and so on up to Z_N and CAT_N. In using ZBEST it's thus important to bear in mind that this is not necessarily actually the "best" redshift for science purposes, and in particular that the choice of NED as CAT1 is often not ideal. However, Z_1,...,Z_N are included to allow users to define the "best" redshift based on their science needs when multiple redshift estimates are available for a given source. ZFLAG specifies which catalog is providing the ZBEST value according to the CATN number below. ZCLASS is meant to provide further info about the class/quality of the spectroscopic redshift measurement, but for the time being is not populated and is simply a copy of ZFLAG. ZWHERE is an additional binary/bit flag indicating in which of the N catalogs each source was given a spec-z estimate in. ZWHERE will e.g. be 2^0=1 if a redshift if available <em>only</em> from CAT_1, whereas it will be 2^0+2^1=3 if a redshift is available *only* from CAT1 and CAT2,<br>so that a source with a redshift available from all catalogues will have ZWHERE=2^0+...+2^n.</p> <p>See AAAREADME.SPECZ-MERGED within the ZIP archive for further information.</p>
Planet Four Data Catalog
<p>This is the data catalog for the paper:</p> <p><a href="https://www.sciencedirect.com/science/article/abs/pii/S0019103518301039">Planet Four: Probing springtime winds on Mars by mapping the southern polar CO2 jet deposits</a></p> <p>The catalog can be automatically retrieved from here using the Python package <a href="https://pypi.org/project/p4tools/">p4tools</a></p>
A comprehensive evaluation of binning methods to recover human gut microbial species from a non-redundant reference gene catalog - Supporting Data
<p><strong>Description </strong></p> <p>The following files are available : </p> <ul> <li>Simulated non-redundant Gene Catalog (SGC) composed of 128267 genes;</li> <li>Gene abundance profiles across 40 samples: raw read counts, gene length normalized base counts, depth file computed by the jgi_summarize_bam_contig_depth script provided by MetaBAT;</li> <li>Gold Standard (GS) and Gold Standard Single Assignment (GS_SA) binning results;</li> <li>Binning results obtained on the SGC with nine binning methods: MSPminer, MGS-canopy, DAS Tool, MaxBin2, MetaBAT2, SolidBin, CONCOCT, COCACOLA and MyCC.</li> </ul> <p><strong>License</strong></p> <p>These files are licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>
Supplementary material 3: World Spider Catalog Bibliographic Data: Treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
List of journal/publisher by ranked by treatment count exported from the World Spider Catalog 14 October 2014 with total treatments by source, cumulative treatments, and cumulative proportion of treatments.
Supplementary material 2: World Spider Catalog Bibliographic Data: Publications from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Ranked list of journal/publisher exported from the World Spider Catalog 14 October 2014 with total articles by source, cumulative articles, and cucmulative proportion of articles.
Tabular Data for "A Comprehensive Catalog of UVIT Observations I: Catalog Description and First Release of Source Catalog (UVIT DR1)"
<p>The first comprehensive catalog of UVIT includes the sources from the observations between 2016 and 2017. </p> <p>The catalog is formatted according to the machine-readable format used by the AAS Journals and CDS/VizieR. Specific information on the structure of MRT files can be found at:</p> <p><span> </span>AAS: <a href="https://journals.aas.org/mrt-overview/"><span>https://journals.aas.org/mrt-overview/</span></a></p> <p><span> </span>CDS: <a href="http://cds.u-strasbg.fr/doc/catstd.htx"><span>http://cds.u-strasbg.fr/doc/catstd.htx</span></a></p> <p><span> </span>These files can be read in python using the astropy package<span> </span>or with the most recent version of TOPCAT (> Version 4.8)</p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Data Quality Products for GW Searches
<p>This material is part of several data products associated with GWTC-2.1, the deep extended catalog of compact binary coalescences observed by the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration and the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration during the first half of the third observing run. For further information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2100063/public">dcc.ligo.org/LIGO-P2100063/public</a>), the related material linked from this page, and the GWTC-2.1 data release documentation (<a href="https://www.gw-openscience.org/GWTC-2.1/">www.gw-openscience.org/GWTC-2.1/</a>).</p> <p>This release contains data quality products that are used by search analyses to help mitigate non-Gaussian noise in the detector data.</p> <p><strong>Renormalized iDQ timeseries</strong></p> <p>This release contains the renormalized iDQ timeseries data quality product used within the GstLAL search to generate results for GWTC-2.1 as described in <a href="https://arxiv.org/abs/2010.15282">Goodwin <em>et al</em>. 2020</a>. This data product was found to be statistically helpful in improving data quality within the GstLAL search. For further information about iDQ see <a href="https://iopscience.iop.org/article/10.1088/2632-2153/abab5f">Essick <em>et al</em>. 2020</a>.</p> <p>The file </p> <ul> <li>H1L1-IDQ_TIMESERIES-1238166018-15843600.h5</li> </ul> <p>contains a time series for each LIGO detector related to the probability of a glitch in the strain data given the behavior in the analyzed auxiliary channels which monitor the behavior of the detectors and their environment.</p> <p>The HDF5-formatted file contains two groups, H1 and L1, corresponding to LIGO Hanford and LIGO Livingston, respectively. Each group contains several datasets; the data dataset corresponds to the renormalized iDQ log-likelihoods, as described in Godwin <em>et al</em>. 2020, and the time dataset corresponds to the times associated with the renormalized iDQ log-likelihoods in the data dataset.</p> <p> </p> <p>For more general background on gravitational-wave data quality, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the guide to <a href="https://doi.org/10.1088/1361-6382/ab685e">LIGO-Virgo data analysis</a>.</p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Parameter Estimation Data Release
<p>This material is part of several data products associated with GWTC-2.1, an update to the second Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2100063/public">https://dcc.ligo.org/LIGO-P2100063/public</a>), the related material linked from this page, and the GWTC-2.1 data release documentation (<a href="https://www.gw-openscience.org/GWTC-2.1/">https://www.gw-openscience.org/GWTC-2.1/</a>).</p> <p><strong>Parameter estimation data release</strong></p> <p>This data release contains posterior samples (*.h5) for gravitational-wave candidates through the first part of the third observing run (O3a). We provide results for the 44 candidates that have a probability of astrophysical origin of over 0.5 from O3 as well as the 10 previously-reported binary-black-hole candidates from GWTC-1 (this excludes GW170817). There are two .h5 files per event</p> <ul> <li> <p>Cosmologically reweighted (*cosmo.h5)</p> </li> <li> <p>Not cosmologically reweighted (*nocosmo.h5)</p> </li> </ul> <p>The cosmologically reweighted posteriors are reweighted to have a luminosity-distance prior that has a uniform merger rate in the source's comoving frame. Each .h5 file contains samples for multiple runs with keys C01:RUN_NAME, where RUN_NAME is the waveform used for the run (and additional prior-choice information if necessary) or Mixed, indicating an equal mixture of samples from runs with similar physics if they exist. In cases where only one waveform was used, the Mixed dataset is simply a resampling of those results . GW190425 does not have Mixed samples. See the <a href="https://dcc.ligo.org/LIGO-P2100063/public">paper</a> appendices for further information. In addition to containing the posterior samples, the .h5 files also contain metadata about the analyses including the configuration files (which specify details such as the detector data analyzed), noise power spectral densities (potentially for a superset of the detectors used in the analysis) and calibration uncertainty envelopes.</p> <p>The python notebook explains how to use the posterior samples. This data release also contains .FITS skymap files, which can be read with <a href="https://lscsoft.docs.ligo.org/ligo.skymap/#">ligo.skymap</a>, and skymap statistics in *.txt files.</p> <p>The inference of the source parameters were performed with <a href="https://lscsoft.docs.ligo.org/bilby/">Bilby</a>, <a href="https://lscsoft.docs.ligo.org/parallel_bilby/">Parallel Bilby</a> and <a href="https://git.ligo.org/richard-oshaughnessy/research-projects-RIT/tree/temp-RIT-Tides">RIFT</a>. The results are formatted using <a href="https://lscsoft.docs.ligo.org/pesummary/">PESummary</a>.</p> <p><a href="https://zenodo.org/record/5546663#.YnAAcvPMKqC">A similar release has been made to accompany GWTC-3</a> for results from the second part of the third observing run.</p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5117702 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p>For more general background on gravitational-wave parameter estimation, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p>
The data for SKYSURF-5: Probing the Integrated Galaxy Light with a SDSS-SKYSURF Cross-Matched Catalog
<p>The SKYSURF Project (Windhorst et al. 2022) analyzes the extragalactic background light (both directly using sky background measurements and indirectly using galaxy counts) using the HST Archive. While HST images probe faint galaxies unseen by ground-based imaging, its small field of view prevents it from probing the large-scale structure around its observations.</p> <p>To supplement SKYSURF analysis, we cross-match SKYSURF pointings with SDSS observations able to probe the surrounding large-scale environment (Bhatia et al. 2024). The tables in this database include galaxies brighter than r=22.5 AB mag photometrically identified in SDSS, within +/-5 arcmin around a SKYSURF pointing.</p> <p>The tables in the Object_AB directory include information on all SDSS objects, organized by the HST camera and filter of the central pointing.</p> <p>The tables in the IGL directory include the total galaxy counts and integrated galaxy light (down to AB mag=22.5) for all SDSS objects surrounding a given SKYSURF image.</p>
Catalog Data for Prior-Informed AGN-Host Spectral Decomposition Using PyQSOFit
<p>This catalog contains 76,565 AGN-host decomposed spectral measurements for all quasars with z<0.8 in SDSS DR16Q. Our prior-informed decomposition method significantly improved the decomposition success rate from less than 60% to 94%. For the first time, we perform the AGN-host spectral decomposition on survey scale catalog.</p> <p>Our spectral decomposition results are highly consistent to those of HSC image decomposition. Our catalog suggests that an average host galaxy contribution at 5100A is 38.8%, which would lead to an overestimation of 0.215 dex in L5100 and 0.219 dex in black hole mass if the host is not removed. The Dn4000 and stellar velocity dispersion measurements from the decomposed host galaxy spectra are also provided.</p> <p>Please read this paper for more techinique details: <a href="https://arxiv.org/abs/2406.17598">arXiv: 2406.17598</a></p>
HELP Study Data Dictionary / Catalog of Items
<p>The <a href="https://doi.org/10.1136/bmjopen-2019-033391">HELP study</a> was a clinical trial in the form of a multicenter interventional randomized controlled trial (RCT) at five German university hospitals, conducted from 2020 to 2022. It aimed to enhance the clinical management of Staphylococcus bacteremia and used data both from Electronic Health Records (EHR), provided by German university hospital's Data Integration Centers in the HL7 FHIR format (German profiles of the Medical Informatics Initiative Core Data Set – <a href="https://www.medizininformatik-initiative.de/en/medical-informatics-initiatives-core-data-set">MII CDS</a>), as well as data from Electronic Case Report Forms (eCRF) used in the study for data which was too unstructered or not availabe in the EHR (hybrid data collection approach).<br>This dataset is a tabular listing and description of the data items used in the study and <em>serves (primarily) as a template for information and data modeling.</em></p>
Catalog for The MDW Hα Sky Survey: Data Release 0
<p>Here, we upload the source catalog for Data Release 0 (DR0) of the MDW Hα Sky Survey. This catalog of ~1.9 million sources is catalog-matched to the Pan-STARRS DR1 catalog, with a subset of 160k sources matched to the IGAPS survey of the Galactic plane. </p> <p>This catalog is related to the AJ manuscript titled "The MDW Hα Sky Survey: Data Release 0", currently undergoing review (submission ID: AAS55752R1).</p> <p>More data from DR0 (e.g. images, QA) can be accessed at <a href="https://mdw.astro.columbia.edu" target="_blank" rel="noopener">https://mdw.astro.columbia.edu</a>. Any use of DR0 data (including the source catalog) should include the following acknowledgement:</p> <blockquote> <p>Funding for the MDW Survey Project has been provided by the Michele and David Mittelman Family Foundation. David R. Mittelman, Dennis di Cicco, and Sean Walker are founding members of the survey and made possible the acquisition and reduction of the data. Columbia University Astronomy Department is responsible for the final data reduction, calibration, and dissemination of the survey data. All commercial rights for the use of the data are reserved.</p> </blockquote>
A catalog of associated, machine-learning-derived phase arrival times for ten days of seismic data in the Yellowstone region
<p>This dataset contains the associated phase picks and event information from applying a deep learning phase picker to continuous data recorded over March 25 – April 3, 2014, on 20 three-component stations and 14 vertical-component stations in the Yellowstone region. This 10-day period contains an M<sub>w</sub> 4.8 event, the largest earthquake in the Yellowstone region since 1980. The catalog and deep learning phase picker are described in Armstrong et al. (submitted).</p> <p>The arrivals were associated using the method described by Baker et al. (2021) and located using HypoInverse2000 (Klein, 2002). There are 1,053 events in this catalog, including 855 that were previously unidentified. Events that also appear in the University of Utah Seismograph Stations catalog have an event identifier (evid) beginning with “6”, while new events begin with “9”. </p> <p>Columns include:</p> <ul> <li>A simple event number</li> <li>the network, station, channel, and location code for the arrival time</li> <li>the arrival time in UTC (arrival_time) and Unix (arrival_time_epoch) format</li> <li>any static correction applied to the arrival time</li> <li>the P-pick first motion polarity as determined by a machine learning model - up (1), down (-1), or unknown (0)</li> <li>the arrival time residual </li> <li>the take off angle in degrees </li> <li>the event latitude and longitude in degrees</li> <li>the event depth in km</li> <li>the event origin time in UTC (origin_time) and Unix (origin_time_epoch) format</li> <li>the azimuthal gap of the event in degrees</li> <li>the root mean square error (RMS) of the event location</li> <li>the event identifier (evid) - begins with a “6” for events in the UUSS catalog and a “9” for new events</li> </ul> <p> </p>
The NGC Catalog, an exercise of data collection for UOC
<p>This project aims to download the NGC object catalog using webcrapping techniques and save it as a CSV file. The accessed URL is</p> <p><a href="https://in-the-sky.org/data/catalogue.php?cat=NGC&const=1&obj1Type=0&sort=0&view=1&page=x">https://in-the-sky.org/data/catalogue.php?cat=NGC&const=1&obj1Type=0&sort=0&view=1&page=x</a></p> <p>with 1 to 79 pages. The downloaded contents for each item compund the catalog are: Name = Name in the catalog type = Object kind mag = Visual magnitude of the object dist = Distance from the earth (when available) constellation = Constellation where to find the object RA = RA coordinates of the object. DEC = DEC Coordinates of the object str_name = Other name from the object. object_image = The image of the object.</p>
Orphanet catalog FAIR Data Point
<pre>This is POC FAIR Data Point to describe biobank and patient registry. The content of this FAIR Data Point is extracted via EJPRD LDP</pre>
Catalog of NCBI sequence read archive (SRA) data for salamanders at the Hubbard Brook Experimental Forest 2012-2021
This project was designed to describe fine-scale population genetic differentiation of the stream salamander Gryinophilus porphyriticus among five study streams in the Hubbard Brook Experimental Forest. The data are paired with intensive capture-recapture data to assess direct fitness effects of individual genetic diversity, including effects of individual multilocus heterozygosity on stage-specific survival probabilities. This dataset publishes a manifest of the genomic sequence reads submitted to the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). These samples are published at NCBI under the BioProject ID 1090913 (https://www.ncbi.nlm.nih.gov/bioproject/1090913). The tables here include sample metadata and the NCBI URLs to each sample. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
EcoTrends-Socioeconomic Catalog data for the VCR/LTER Airshed 1820-2000
The "data" sheet contains the county x variable matrix, while the "metadata" sheet contains the variable definitions and sources. For a complete description of sources and the content of variables you should consult the Data Dictionary on the EcoTrends-Socioeconomic Catalog (http://coweeta.ecology.uga.edu/trends/catalog_trends_base2.php) since the data collection procedure and sources used where the same. Nichole Rosamilia compiled all the data for the airshed as well as EcoTrends in case you have any particular questions that are not addressed in the data catalog or below. A few points to note on the attached data: 1) '-9999' indicates a null value - prior to a given county coming into existence there is no data so it is recorded as null; periodically, and typically for no recorded reason, no data will be recorded or available for a given county so again it is recorded as null. 2) the units of measure on certain variables, e.g., CWAGE (commercial establishments), changes over time from dollars, to thousands of dollars, to millions of dollars. Nichole left the data as it appears in the original rather than modify it on entry. For the on-line version we run the data through a script to standardize the units of measure, but that happens later in our data processing. The approximate airshed for the VCR/LTER was based on translation (of the oxidized nitrogen airshed for Shenandoah National Park created by R. Dennis of the Atmospheric Sciences Modeling Division of ARL, NOAA and NERL USEPA). The original Shenandoah National Park airshed was digitized from a printed copy, then the centroid of the airshed polygon was moved to from the park to the Virginia Coast Reserve. This was then intersected with ESRI counties data to generate the list of needed counties. A copy of the shapefile and a PDF is included in the "vcrairshed.zip" file in the data directory.
Station Data and Earthquake Catalogs - Distinct yet adjacent earthquake sequences near the Mendocino Triple Junction: 20 December 2021 Mw 6.1 and 6.0 Petrolia, and 20 December 2022 Mw 6.4 Ferndale
<p>Supplemental Material for publication from The Seismic Record (TSR):</p> <div> <div> <div> <p>Yoon, C. E. and D. R. Shelly (2024). Distinct Yet Adjacent Earthquake Sequences near the Mendocino Triple Junction: 20 December 2021 Mw 6.1 and 6.0 Petrolia, and 20 December 2022 Mw 6.4 Ferndale, The Seismic Record. 4(1), 81–92, doi: 10.1785/0320230053.</p> <p>Data Sets S0-S4 with station data and earthquake catalogs in text format</p> <p>See README_Data_Supplement.pdf for more details about contents of each data file. Please refer to the accompanying publication and its supplement for figures, tables, and equations.</p> </div> </div> </div> <p> </p>
The data catalog for Metallicity and alpha-abundance for 48 million stars in low-extinction regions in the Milky Way
<p>Stellar chemistry contains information on the environment in which the star was born. Therefore, measuring the chemical abundances of stars in the Milky Way, such as the overall metallicity [M/H] and the alpha-abundance [alpha/M], is essential in Galactic astronomy.</p> <p>We estimate ([M/H], [alpha/M]) for giants and dwarfs in low dust extinction region from the Gaia DR3 XP spectra by using tree-based machine-learning models trained on APOGEE DR17 (Abdurro’uf et al. 2022) and the metal-poor star sample of Li et al. (2022).</p> <p>Here, we upload the catalogues of ([M/H], [alpha/M]) for 182 million stars. The data are divided into 10 fits files. The i-th file (i=1,2,...,10) contains stars with E(B-V) value between 0.1*(i-1) and 0.1*i. Because our machine-learning models are trained on stars with low dust extinction (E(B-V)<0.1), we recommend using 48 million stars with low-dust extinction region with 0<E(B-V)<0.1 (table_light_mh_am_0p0ebv0p1.fits). The description for each column of the data is shown in column_description.png. </p> <p>The source paper of this catalog:</p> <ul> <li>Kohei Hatori "Metallicity and alpha-abundance for 48 million stars in low-extinction regions in the Milky Way" <br>https://iopscience.iop.org/article/10.3847/1538-4357/ad9686</li> </ul> <p>References:</p> <div> <div> <div> <ul> <li>Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 1026 35, doi: 10.3847/1538-4365/ac4414</li> </ul> </div> </div> </div> <ul> <li>Li, H., Aoki, W., Matsuno, T., et al. 2022, ApJ, 931, 147, doi: 10.3847/1538-4357/ac6514</li> </ul> <p> </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.