Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,394
datasets available to search
ShareScore release 0.9.0
Dataset results
2,394 results for “MS”
Chemicals associated with plastics packaging (CPPdb) for MS-FIDNER
<p>The original chemicals associated with plastic packaging database (CPPdb) was compiled by Ksenia J. Groh, etc., and is downloadable in https://zenodo.org/record/1287773. To make the use of this database for structural elucidation in MS-FINDER, the CPPdb is re-organized to the form that is compatible with MS-FINDER. Vital information, e.g., smiles and InChIKey, which are vital for in-silico fragmentation, are added to the CPPdb. Hope this re-formed database would be helpful for anyone who wants to use it for MS-FINDER.</p>
IAGOS-CARIBIC MS files collection (v2025.07.11)
<h2><strong>Content</strong></h2> <p><em><strong>IAGOS-CARIBIC_MS_files_collection_20250711</strong></em> contains merged IAGOS-CARIBIC data, on a 10s grid (CARIBIC-1 and CARIBIC-2; <https://www.caribic-atmospheric.com/>). There is one netCDF (version 4) file per IAGOS-CARIBIC flight. Files were generated from NASA Ames 1001 source files. For detailed content information, see global and variable attributes. Global attribute `na_file_header` contains the original NASA Ames file header as an array of strings.</p> <h2><strong>Data Coverage</strong></h2> <p>The data set covers 22 years of CARIBIC data from 1997 to 2020, flight numbers 1 to 591. There is no data available after 2020. Also, there is no data available for the following flight numbers within the [1..591] range:</p> <ul> <li>CARIBIC-1, 1997-2002, 1-97: 4, 5, 6, 21, 28, 30, 31, 32, 38, 39, 73, 81, 83, 91, 95</li> <li>98 to 109 do not exist</li> <li>CARIBIC-2, 2005-2020, 110-591: 217, 276, 277, 318, 320, 410, 411, 412, 425, 426, 427, 428, 434, 435 436</li> </ul> <h3>Special note on CARIBIC-1 data</h3> <p>CARIBIC-1 data only contains a subset of the variables found in CARIBIC-2 data files. To distinguish those two campaigns, use the global attribute 'mission'.</p> <h2>File format</h2> <p>netCDF v4, created with xarray, <https://docs.xarray.dev/en/stable/>. Compression: zlib, level 5. Metadata conventions: CF-1.10, ACDD-1.3 (see also 'comment' global attribute).</p> <h2><strong>Authors and Parameters Info</strong></h2> <p>See `CARIBIC-MS_files_species-and-contributors.csv` in the zip archive.</p> <h3><strong>Primary Contact</strong></h3> <ul> <li>Andreas Zahn, IAGOS-CARIBIC Coordinator , <andreas.zahn@kit.edu></li> <li>Florian Obersteiner, data management, <florian.obersteiner@kit.edu></li> </ul> <h2><strong>Data Availability<br></strong></h2> <p>This dataset is also available via the KIT-IMKASF THREDDS server, <https://thredds.atmohub.kit.edu/thredds/catalog/iagos-caribic/catalog.html>.</p> <h2><strong>Changelog</strong></h2> <ul> <li>`2025.07.11`: extend netCDF metadata (CF-1.10, ACDD-1.3), revise standard names and units. Data unchanged.</li> <li>`2025.06.02`: introduce netCDF file compression (zlib, level 5). Data unchanged.</li> <li>`2025.03.25`: variable name change (PosLat => lat, PosLong => lon, pstatic => p, Altitude => alt), add acetonitrile and acetone measurement precision columns</li> <li>`2024.10.28`: revise CARIBIC-1 data, all flights (note on lat/lon inaccuracy, range checks for static pressure and temperature). CARIBIC-2 data unchanged.</li> <li>`2024.07.17`: revise ozone data for flights 294 to 591</li> <li>`2024.01.12`: revise naming convention of (nc attributes), add POF IV funding reference (zenodo)</li> <li>`2023.11.15`: add CARIBIC-1 data, revise variable long names</li> <li>`2023.10.17`: remove duplicate flights ("MSA" is "MAA"), add previously missing flights (200, 201)</li> <li>`2023.09.26`: extend data; include soot photometer measurements</li> <li>`2023.07.26`: initial upload</li> </ul>
The Heber-Serrure codex (Ghent, University Library, Ms. 1374)
<p><strong>The Heber-Serrure codex (Ghent, University Library, Ms. 1374)</strong></p> <p>This repository holds the raw XML data underlying the diplomatic edition of the Heber-Serrure codex (Ghent, University Library, Ms. 1374), a Middle Dutch miscellany, dating to the late fourteenth century. The edition was published in the series "Middelnederlandse verzamelhandschriften", under the auspices of the series' editorial panel. The present, digital edition follows the (TEI-inspired) MVN-guidelines developed by Peter Boot and Herman Brinkman, supported by a publicly available Oxygen framework (<a href="https://github.com/HuygensING/mvn-xml">Github</a>). The edition and the (Dutch-language) introduction can be consulted <a href="https://hbsr.mvn.huygens.knaw.nl/">online</a> (additionally archived through the <a href="https://web.archive.org/web/20230928080836/https://hbsr.mvn.huygens.knaw.nl/">Wayback Machine</a>). A IIIF-compliant, open-access facsimile of the manuscript can be consulted through the <a href="https://lib.ugent.be/catalog/rug01:000763342">website</a> of Ghent University Library. The material in this repository is shared under an open access-license (Creative Commons; CC-BY-SA 4.0) that encourages re-use but requires an explicit attribution. If you use this edition, please provide an appropriate scholarly citation, e.g.:</p> <blockquote> <p>Renée Gabriël & Mike Kestemont (eds). De Heber-Serrurecodex: Gent, Universiteitsbibliotheek, Hs. 1374. Diplomatische editie bezorgd door Renée Gabriël en Mike Kestemont, met een dialectologische analyse door Amand Berteloot. Middeleeuwse Verzamelhandschriften uit de Nederlanden XVII. Amsterdam, Huygens Instituut voor Nederlandse Geschiedenis en Cultuur van de Koninklijke Nederlandse Akademie van Wetenschappen, 2023. URL: hbsr.mvn.huygens.knaw.nl. DOI: 10.5281/zenodo.8385501.</p> </blockquote> <p><strong>English summary</strong><br> The Heber-Serrure manuscript (Ghent, University Library, Ms. 1374) is a miscellany containing Middle Dutch rhyming texts, mostly ethical and didactic in content. Although the manuscript is not explicitly dated or localized, there is ample reason to assume that the codex was compiled near the end of the fourteenth century in the Carthusian monastery of Herne (about 18 miles southwest of Brussels). For a variety of reasons, this codex deserves our attention (and a new, modern edition), as it continues to fascinate both philologists and book historians.</p> <p>Until now, the Heber-Serrure manuscript has been primarily valued because of the many unique texts which it contains, including sizable excerpts from the <em>Spiegel historiael</em> (the Middle Dutch adaption of Vincent of Beauvais’ <em>Speculum historiale</em>) as well as a number of rare strophic poems by Jacob van Maerlant, but also the <em>Rinclus</em>. All of these works have already been edited in the past, based on the Heber-Serrure codex. These historic editions, however, were often heavily critical in orientation and appeared in isolation from one another, thus hindering our view on the joint survival of these works, as well as the original context in which this book was produced and meant to function. The present diplomatic edition aims to correct this situation.</p> <p>From the point of book history too, the Heber-Serrure manuscript present us with a remarkable object for scholarly study: the manuscript only contains rhyming texts, but these have been copied as continuous prose, most likely to save space (and time). Moreover, the available evidence suggests that the text collection wasn’t copied from a prior witness: in this manuscript, we can almost literally peak over the scribe’s shoulder, because we are dealing with a ‘growth miscellany’ that was composed in distinct phases, even though these phases were not meticulously planned beforehand. The single scribe of the book also acted as the book’s compiler, thus enabling privileged insights into the dynamic process that led to the gradual expansion of the codex’s content.</p> <p>That we can place the composition of the Heber-Serrure manuscript relatively precisely (in Herne) is unusual for a vernacular medieval codex in the medieval Low Countries. A such, we are able to study the codex in relation to a large number of contemporary sources that were produced in the same monastic environment. The manuscript’s main and only scribe is currently known under the pen name ‘Speculum scribe’, named so after his most famous copy, the second part of the Middle Dutch <em>Speculum historiale</em> adaptation (<em>Spiegel historiael</em>) in Vienna, Ö.N.B. Cod. 13.708; the scribe’s historic identity has not been established (yet), although a large number of manuscripts survive in his handwriting.</p>
Lipidomics LC-MS analysis support tools for outlier detection
<p>Identification of features with high levels of confidence in liquid chromatography-mass spectrometry (LC MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in bioinformatics work, and highlights the importance of data-driven outlier detection in assessing spectral outputs – here demonstrated using a machine learning approach based on support vector machine regression combined with leave-one-out cross validation – as well as manual curation, in order to identify software-driven errors driven by closely related lipids and by co-elution issues.</p> <p>The lipidomics case study dataset used in this work analysed a lipid extraction of a human pancreatic adenocarcinoma cell line (PANC-1, Merck, UK, cat no. 87092802) analysed using an Acquity M-Class UPLC system (Waters, UK) coupled to a ZenoToF 7600 mass spectrometer (Sciex, UK). Raw output files are included alongside processed data using MS DIAL (v4.9.221218) and Lipostar (v2.1.4) and a Jupyter notebook with Python code to analyse the outputs for outlier detection.</p>
Wikipedia: wikipedia-ms (Malay)
Wikipedia is a multilingual, web-based, free-content encyclopedia project supported by the Wikimedia Foundation and based on a model of openly editable content. EOL harvests articles from wikipedia that are indexed as species or higher taxa.<p></p><p></p>https://ms.wikipedia.org/
PTR-ToF-MS data from cooking experiments in Healthy Energy-efficient Urban Home Ventilation
<pre>The dataset contains high-resolution PTR-Tof MS data from preparing meals consisting of fried salmon and vegetables in SINTEFs ventilation laboratory. <br>The data are organized in csv files containing concatenated results of ppb-values. PTR-ToF-MS grouped by month, m/z-valuens in column names. Relatable to the list of experiments. See readme file for details and 10.1016/j.buildenv.2024.111743 for description</pre>
CE-MS data for Autonomous CE Mass-Spectra Examination (ACME) for the Ocean Worlds Life Surveyor (OWLS)
<p>These folders contain the original data used to develop the ACME software [1].</p> <p>The Golden and Silver dataset come from simulations. They underrepresent the complexity in the CE-MS observations but provide additional data with known peak locations and peak properties. For more information see [1]</p> <p>The Dev-, Train-, and Test-set contain CE-MS [2] observations of Mix25 (a standard set of 25 organic compounds relevant to astrobiology) and labels for peak locations from subject matter experts. </p> <p>The ACME software is available at: <br> https://github.com/JPLMLIA/OWLS-Autonomy </p> <p> </p> <p>When using the data please cite this dataset [3] and the two papers below. </p> <p>For further questions please reach out to:<br> Steffen Mauceri, Steffen.Mauceri@jpl.nasa.gov</p> <p> </p> <p>References:<br> [1] Mauceri, S., Lee, J., Wronkiewicz, M., et.al. (2022). Autonomous CE Mass-Spectra Examination (ACME) for the Ocean Worlds Life Surveyor (OWLS). (submitted) Earth and Space Science</p> <p>[2] Mora et al., F.(2021). Detection of biosignatures by capillary electrophoresis and mass spectrometry in the presence of salts relevant to missions to ocean worlds (submitted). Astrobiology.</p> <p>[3] 10.5281/zenodo.5849873</p> <p><br> © 2022. California Institute of Technology. Government sponsorship acknowledged</p>
Webis-MS-MARCO-Anchor-Texts-22
<p>The Webis MS MARCO Anchor Text 2022 dataset enriches Version 1 and 2 of the document collection of <a href="https://microsoft.github.io/msmarco/">MS MARCO</a> with anchor text extracted from six <a href="https://commoncrawl.org/">Common Crawl</a> snapshots. The six Common Crawl snapshots cover the years 2016 to 2021 (between 1.7-3.4 billion documents each). We sampled 1,000 anchor texts for documents with more than 1,000 anchor texts at random and all anchor texts for documents with less than 1,000 anchor texts (this sampling yields that all anchor text is included for 94% of the documents in Version 1 and 97% of documents for Version 2). Overall, the MS MARCO Anchor Text 2022 dataset enriches 1,703,834 documents for Version 1 and 4,821,244 documents for Version 2 with anchor text.</p> <p>Cleaned versions of the MS MARCO Anchor Text 2022 dataset are available in <a href="https://github.com/allenai/ir_datasets/issues/154">ir_datasets</a>, <a href="https://zenodo.org/record/5883456">Zenodo</a> and <a href="https://huggingface.co/datasets/webis/ms-marco-anchor-text">Hugging Face</a>. The raw dataset with additional information and all metadata for the extracted anchor texts (roughly 100GB) is available on <a href="https://huggingface.co/datasets/webis/ms-marco-anchor-text/tree/main/ms-marco-v1/anchor-text">Hugging Face</a> and <a href="https://files.webis.de/data-in-progress/ecir22-anchor-text/anchor-text-samples/">files.webis.de</a>.</p> <p>The details of the construction of the Webis MS MARCO Anchor Text 2022 dataset are described in the <a href="https://webis.de/publications.html#froebe_2022a">associated paper</a>. If you use this dataset, please cite<br> <code>@InProceedings{froebe:2022a,<br> address = {Berlin Heidelberg New York},<br> author = {Maik Fr{\"o}be and Sebastian G{\"u}nther and Maximilian Probst and Martin Potthast and Matthias Hagen},<br> booktitle = {Advances in Information Retrieval. 44th European Conference on IR Research (ECIR 2022)},<br> editor = {Matthias Hagen and Suzan Verberne and Craig Macdonald and Christin Seifert and Krisztian Balog and Kjetil N{\o}rv\r{a}g and Vinay Setty},<br> month = apr,<br> publisher = {Springer},<br> series = {Lecture Notes in Computer Science},<br> site = {Stavanger, Norway},<br> title = {{The Power of Anchor Text in the Neural Retrieval Era}},<br> year = 2022<br> }</code></p>
MALDI-TOF-MS reference spectra and sequence data for domesticated equids (horse and donkey) collagen for Zooarchaeology by Mass Spectrometry (ZooMS)
<p>MALDI-TOF-MS spectra of extracted collagen from modern reference and archaeological bone samples to develop markers for Zooarchaeology by Mass Spectrometry (ZooMS) to distinguish between Equus species. For each sample digestions were done in both trypsin and chymotrypsin separately. Information about the species of the samples can be found in 'sample metadata.csv' file. Information on the extraction and digestion protocol can be found in the associated manuscript. The sequence data contains alignments of the proteins COL1A1 and COL1A2 for available Equus collagen protein sequences. More information on these files can be found in the corresponding manuscript to this dataset.<br> </p>
Amoxicillin degradation pathways and mass spectra raw data (using LC-MS orbitrap)
<p>The link provides five documents namely:</p> <p>File No.1 (Proposed Chemical Structures-tabulated)</p> <p>File No.2 (MS and MS2 images) support for File no.1</p> <p>File No.3 Transformation Products Pathway</p> <p>File No.4 Explanation + Justification of proposed chemical structures</p> <p>Raw Data obtained from compound discoverer</p>
MS-based orthophotos (50 cm): the dynamics of the prehistoric communities located in the Mostiștea Valley and Danube Plain (between Oltenița and Călărași)
<p>This dataset is part of a larger project on the dynamics of the prehistoric communities located in the Mostiștea Valley and Danube Plain (between Oltenița and Călărași), supervised by the ArchaeoSciences Division of the Research Institute of the University of Bucharest (ICUB) and Kiel University (Germany), in partnership with HOGENT, University of Applied Sciences and Arts (Belgium), Museum of Bucharest, Museum of the Lower Danube Călărași, Museum of Gumelnița Civilization Oltenița, and "Vasile Pârvan" Institute of Archaeology (Romania), under the "Sultana School of Archaeology" initiative.</p> <p>Spatial data play a crucial role in archaeological research, and orthophotos, digital elevation models, and 3D models are frequently used for the mapping, documentation, and monitoring of archaeological sites. Thanks to the availability of compact and low-cost uncrewed airborne vehicles, the use of UAV-based photogrammetry is well matured in this field over the last two decades. More recently, compact airborne systems are also available that allow the recording of thermal data, multispectral data, and airborne laser scanning. For this project, various platforms and sensors are applied at the Chalcolithic archaeological sites in the Mostiștea Basin and Danube Valley (Southern Romania). By analyzing the performance of the systems and the resulting data, insight is given into the selection of the appropriate system for the right application. This analysis requires thorough knowledge of data acquisition and data processing as well. As both laser scanning and photogrammetry typically result in very large amounts of data, a special focus is also required on the storage and publication of the data. Hence, the objective of this project is to provide a full overview of various aspects of 3D data acquisition for UAV-based mapping. Based on the conclusions drawn in our related publications, it is stated that photogrammetry and laser scanning can result in data with similar geometrical properties when acquisition parameters are appropriately set. On the one hand, however, the used ALS-based system outperforms the photogrammetric platforms in terms of operational time and the area covered. On the other hand, conventional photogrammetry provides flexibility that might be required for very low-altitude flights, or emergency mapping. Furthermore, as the used ALS sensor only provides a geometrical representation of the topography, photogrammetric sensors are still required to obtain true color- or false color composites of the surface. Lastly, the variety of data, like pre- and post-rendered raster data, 3D models, and point clouds, requires the implementation of multiple methods for the online publication of data. Various client-side and server-side solutions are presented to make the data available for other researchers.</p>
MS data linked to manuscript https://doi.org/10.3390/ijms22169055
<p>Dataset of MS raw data linked to the publication https://doi.org/10.3390/ijms22169055.</p> <p>Data contains:</p> <p>1. 90% MeOH fraction_Rhodococcus_neg. raw file corresponding to the UPLC-ESI-HRMS/MS spectra of the enriched fraction containing threlolipids.</p> <p>2. Mgf file generated through Mzmine. The file contains 22 aligned MS/MS spectra corresponding to the 22 growth condition of the bacteria.</p>
MICCAI 2016 MS lesion segmentation challenge: supplementary results
<p>This package contains supplementary material for our article prepared for publication and under revision. It contains omitted results due to space limits of the article as well as detailed, patient per patient and team per team results for all metrics. Additional figures redundant with those of the article are also provided. </p> <p>The readme file Readme_SupplementalMaterial.txt provides details about each individual file content.</p>
RDF Linked Data representation of GC-MS data from the 'Rose Genome' article published in Nature genetics, June, 2018
<p>This dataset corresponds to the RDF Linked Data representation of the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable <a href="https://github.com/ISA-tools/stato">STATO</a> terms. Most of the semantics resources belong to the <a href="http://obofoundry.org">OBO foundry</a>.</p> <p>The transformation to RDF was performed on a Frictionless Tabular Data Package (<a href="https://frictionlessdata.io/specs/tabular-data-package/">https://frictionlessdata.io/specs/tabular-data-package/)</a>, holding the data extracted from a supplementary material table, available from <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip">https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip</a> and published alongside the Nature Genetics manuscript identified by the following doi: <a href="https://doi.org/10.1038/s41588-018-0110-3">https://doi.org/10.1038/s41588-018-0110-3</a>, published in June 2018. This supplementary material table was deposited to Zenodo and is identified by the following doi: <a href="https://doi.org/10.5281/zenodo.2598799">https://doi.org/10.5281/zenodo.2598799</a></p> <p>This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR) and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.</p> <p>It is associated to the following project: <a href="https://github.com/proccaserra/rose2018ng-notebook">https://github.com/proccaserra/rose2018ng-notebook</a> with all the necessary information, executable code and tutorials in the form of Jupyter notebooks.</p>
Wikipedia: wikipedia-ms (Malay)
Wikipedia is a multilingual, web-based, free-content encyclopedia project supported by the Wikimedia Foundation and based on a model of openly editable content. EOL harvests articles from wikipedia that are indexed as species or higher taxa.<p></p>
vPro-MS peptide spectral library for the identification of human-pathogenic viruses by untargeted proteomics
<p>The viral proteomics workflow (vPro-MS) enables identification of human-pathogenic viruses from patient samples by untargeted proteomics. vPro-MS is based on an in-silico derived peptide library covering the human virome in <a href="https://www.uniprot.org/" rel="nofollow">UniProtKB</a> (331 viruses, 20,386 genomes, 121,977 peptides). vPro-MS is intended to identify human-pathogenic viruses from DiaNN (<a href="https://github.com/vdemichev/DiaNN">https://github.com/vdemichev/DiaNN</a>) outputs of either DIA or diaPASEF data. A scoring algorithm (vProID) assesses the confidence of virus identification and the results are finally summarized in a report table. </p> <p>The vPro Peptide Library folder contains 3 peptide FASTA files (Contaminants.fasta, Human.fasta, vPro.Virus.fasta), which were used to predict the spectral library (vPro-lib.predicted.speclib). Please note, that the additional commands “--cut” and “--duplicate-proteins” are needed to reprocess the prediction in DiaNN. This spectral library should be used to identify peptide sequences from samples of human origin using DiaNN. Furthermore, the folder contains the metadata file of the viral peptide sequences (vPro.Peptide.Library.txt) and a summary file of the virus taxonomy covered by the library (Taxonomy.Summary.txt). The metadata file is used by the vPro script to identify viruses from the DiaNN main report.</p>
[MetFrag] MoNA Export LC-MS-MS Spectra for MetFrag
<p>This is an updated version of the LC-MS/MS MoNA library for use in <a href="https://ipb-halle.github.io/MetFrag/">MetFrag</a>. </p> <p>Once you download this file, you can use it in <a href="https://github.com/ipb-halle/MetFragRelaunched/releases/latest">MetFrag Command Line</a> with the following command: </p> <pre><code>OfflineSpectralDatabaseFile = ~/MoNA-export-LC-MS-MS_Spectra-20241014-0.005.mb</code></pre> <p>Thanks to Bego for thoroughly testing this file and to Christoph for his tips throughout the years!</p>
UHPLC-MS and MS/MS spectra
<p>The sets consist of raw data files generated during investigations into the racemization mechanism of the signaling molecule valdiazen and the stereoselective enzyme responsible for producing fragin.</p>
MALDI-TOF-MS spectra of archaeological bone fragments from Bandicoot Bay, Barrow Island (Australia) for ZooMS (Zooarchaeology by Mass Spectrometry)
<p>MALDI-TOF-MS spectra for archaeological bone fragments from Bandicoot Bay, Barrow Island, Western Australia. All spectra are uploaded in .mzml format. </p>
MALDI MS Data and Metadata from "A biological reading of a palimpsest"
<p>Spectra in mzML format along with the metadata associated with it:</p> <ul> <li>The mzML file names follow the following format UoCXX_Y.mzML, where UoCXX is the sample name and Y is the replicate number (1, 2 or 3)</li> <li>uoc_metadata.csv file contains species, book and quire number associated with each spectra file. It is used in the data analysis in <a href="https://doi.org/10.5281/zenodo.7406297">doi.org/10.5281/zenodo.7406297</a></li> <li>Dataset S1.xlsx contains extended metadata with the results of the visual analysis of the parchment.</li> </ul>
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.