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867 results for “repositories”
Data repository for "Spatio-temporal trends of Holocene peat carbon accumulation in China: climatic and human drivers"
<p>Dating results collected from peatlands in China are used to calculate the spatiotemporal trends of the Holocene peat accumulation rate (PAR) and net carbon balance (NCB), including all original dating, calculated intermediate results, and final composite results. This file includes a total of 14 tables (Supplementary Tables S1-S14).</p>
urbisphere-Berlin campaign BAMS data repository
<p>This data set accompanies Fenner et al. (2024) and contains the data (and references to data sources) of the plots and tables therein.</p> <p>Data are organized by Figure and Table in the article, each located in a separate (zip-)folder.</p> <p>See README.pdf for additional information and data descriptions.</p> <p>Detailed data processing details are given in the Appendices of the article.</p> <p>RAW measurement data are accessible via the <a title="Zenodo &ldquo;urbisphere&rdquo; community" href="../communities/urbisphere/" target="_blank" rel="noopener">Zenodo “urbisphere” community</a>.</p> <p> </p> <p>Fenner, D., Christen, A., Grimmond, S., Meier, F., Morrison, W., Zeeman, M., Barlow, J., Birkmann, J., Blunn, L., Chrysoulakis, N., Clements, M., Glazer, R., Hertwig, D., Kotthaus, S., König, K., Looschelders, D., Mitraka, Z., Poursanidis, D., Tsirantonakis, D., Bechtel, B., Benjamin, K., Beyrich, F., Briegel, F., Feigel, G., Gertsen, C., Iqbal, N., Kittner, J., Lean, H., Liu, Y., Luo, Z., McGrory, M., Metzger, S., Paskin, M., Ravan, M., Ruhtz, T., Saunders, B., Scherer, D., Smith, S. T., Stretton, M., Trachte, K. and Van Hove, M., 2024: urbisphere-Berlin campaign: Investigating multi-scale urban impacts on the atmospheric boundary layer. <em>Bull. Am. Meteorol. Soc. </em>DOI: <a href="https://doi.org/10.1175/BAMS-D-23-0030.1">10.1175/BAMS-D-23-0030.1</a><em><br></em></p> <p> </p>
Digital Repository of Ireland Member Digitisation Workflows for 2D Image Files: Survey Questions and Dataset
<p>The Digital Repository of Ireland (DRI) issued a survey to its membership, <strong>DRI Member Digitisation Workflows for 2D Images</strong>, which ran from December 7, 2023–January 31, 2024. The survey was conducted to improve the DRI’s understanding of the technical processes and metadata workflows that our members use to digitise and share images in the Repository, in order to better tailor our support for this work and deliver the most complete information about digital images files available to our users. </p> <p>The survey informed the actions taken in WorldFAIR Project WP13 deliverable <a href="https://doi.org/10.5281/zenodo.10850009" target="_blank" rel="noopener">13.3 Implementing and Testing the Cultural Heritage Image Sharing Recommendations: DRI Case Study Report</a>. The data will inform ongoing work at DRI aimed at improving the transparency of technical information associated with digital assets accessed through the Repository.</p> <p>Read more about the Cultural Heritage Image Sharing Case Study DRI on our website: <a href="https://dri.ie/the-worldfair-project/">https://dri.ie/the-worldfair-project/</a>. </p> <p>Summary: DRI is Ireland's national repository for the arts, humanities, and social sciences data, and operates on a membership scheme. There were 20 respondents to the survey, giving us a response rate of about 35% of DRI's membership. Representation from professional fields of work across the cultural heritage sector was captured in the results (note that some institutions gave multiple responses): 17 Archives, 12 Libraries, 5 Museums and 11 Higher Education Institutions. </p>
Supporting dataset for: Repository optimisation & techniques to improve discoverability and web impact : an evaluation
<p>This dataset supports the working paper, "Repository optimisation & techniques to improve discoverability and web impact : an evaluation", currently under review for publication and available as a preprint at: <a href="https://doi.org/10.17868/65389/">https://doi.org/10.17868/65389/</a>. </p> <ul> <li>Macgregor, G. (2018). <em>Repository optimisation techniques to improve discoverability and web impact: an evaluation</em>. (pp. 1-13). Glasgow: University of Strathclyde [Strathprints repository]. Available: <a href="https://doi.org/10.17868/65389/">https://doi.org/10.17868/65389/</a></li> </ul> <p>The dataset comprises a single OpenDocument Spreadsheet (.ods) format file containing seven data sheets of data pertaining to COUNTER compliant usage statistics, search query traffic from Google Search Console, web traffic data for Google Analytics and Google Scholar, and usage statistics from IRStats2. All data relate to the EPrints repository, Strathprints, based at the University of Strathclyde.</p>
Ranking web of Repositories
<p>TRANSPARENT RANKING: All Repositories by Google Scholar.</p> <p>Incluye Top 10 repositorios de Colombia 2018 y 2019</p> <p>Fuente: http://repositories.webometrics.info/en/node/30</p>
Repository of NbS guidance material
<p><strong>NbS Guidance Repository </strong></p> <p>The NbS Guidance Repository is a collection of guides, handbooks, manuals, toolboxes, and other guidance materials. These resources are specifically designed to facilitate knowledge brokering and capacity-building on Nature-based Solutions (NbS) for a diverse group of non-academic actors. The guidance materials contain information on specific actions supporting the governance, planning, implementation, management, and monitoring of NbS or related concepts that work with nature, such as Green Infrastructure, Urban Forestry, and Ecosystem-based Adaptation. Additionally, they address challenges related to planning and implementing NbS, including issues with climate change adaptation, biodiversity enhancement, and environmental justice.</p> <p>The repository is established as part of the European Union Horizon 2020 project, CONEXUS, to catalogue the current status of guidance materials. Many European-funded research and innovation projects similarly transferred their gained knowledge and perspectives on NbS through guidance materials. Different platforms, like OPPLA, bring these materials together, yet the focus is Europe-centric. However, as several projects have involved cases outside Europe, such as CONEXUS’ cooperation with Latin American cities, this repository specifically included guidance materials from or relevant to Latin America. Thus, the collected guidance material focuses on urban contexts in Latin America and Europe, but materials with a global emphasis are also included. The materials are available in digital format, as PDFs (portable digital documents) or websites, to ensure easy accessibility.</p> <p>The guidance materials were collected from different avenues, such as published literature like the review of European NbS projects by <a href="https://research-and-innovation.ec.europa.eu/knowledge-publications-tools-and-data/publications/all-publications/nature-based-solutions-state-art-eu-funded-projects_en" target="_blank" rel="noopener">Wild et al. (2020)</a> and resource databases like NetworkNature. To supplement the collected guidance materials with more examples relevant to Latin America, CONEXUS partners and practitioners from the region who were engaged were asked to mention the NbS guidance materials they know about. Additional materials were collected through an internet search using the Google search engine in Spanish and Portuguese and through snowballing. As the focus is on materials targeted at non-academic users, scientific databases were not consulted.</p>
Characterizing cell-type spatial relationships across length scales in spatially resolved omics data: data repository
<h1>CRAWDAD</h1> <p>Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we develop CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package with source code and additional documentation at https://jef.works/CRAWDAD/.</p> <p>During CRAWDAD's development, we generated simulated datasets and new cell-type annotations for human spleen data, provided here. The external datasets such as the mouse cerebellum, mouse embryo, mouse brain, and human breast cancer data used in the paper can be found in their original publication. See more information in CRAWDAD's data availability statement.</p> <h2>Simulated Datasets</h2> <ul> <li>sim.csv: the simulated data. Used in Figure 1 b-g, Supplementary Figure 1 a-c, and Supplementary Figure 9 a-b.</li> <li>ext_sim.csv: the extended simulated data. Used in Supplementary Figure 1 d-f.</li> <li>null_sim_visualization.csv: the null simulated data. Used to generate the plots Supplementary Figure 2 a-d.</li> <li>null_sim_1.csv - null_sim_10.csv: the 10 null simulated datasets. Used to quantitatively compare CRAWDAD, Squidpy’s co-occurrence implementation, and Ripley’s K Cross.</li> </ul> <h2>HuBMAP Datasets</h2> <ul> <li>pkhl.csv: annotated cell types and positions of sample HBM389.PKHL.936 from donor HBM966.VNKN.965. Used in Figure 5 a-h, Supplementary Figure 5 a, Supplementary Figure 7 a-c, and Supplementary Figure 8 c. doi:10.35079/HBM389.PKHL.936</li> <li>xxcd.csv: annotated cell types and positions of sample HBM772.XXCD.697 from donor HBM966.VNKN.965. Used in Figure 5 d-h, Supplementary Figure 5 a-c, and Supplementary Figure 7 a-c. doi:10.35079/HBM772.XXCD.697</li> <li>fsld.csv: annotated cell types and positions of sample HBM342.FSLD.938 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM342.FSLD.938</li> <li>pbvn.csv: annotated cell types and positions of sample HBM825.PBVN.284 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM825.PBVN.284</li> <li>ksfb.csv: annotated cell types and positions of sample HBM556.KSFB.592 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM556.KSFB.592</li> <li>ngpl.csv: annotated cell types and positions of sample HBM568.NGPL.345 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM568.NGPL.345</li> </ul> <h2>External Datasets</h2> <ul> <li>Mouse cerebellum: Used in Figure 2 a-e, Supplementary Figure 3 a-b, Supplementary Figure 4 a-d, and Supplementary Figure 8 a.</li> <li>Mouse embryo: Used in Figure 2 f-j, Supplementary Figure 3 c-d, Supplementary Figure 4 e-h, and Supplementary Figure 8 b.</li> <li>Human breast cancer: Used in Figure 3 a-c.</li> <li>Mouse brains: Used in Figure 4 a-e.</li> </ul>
Metal Additive Manufacturing Open Repository
<p><strong>Metal Additive Manufacturing Open Repository</strong></p> <p>This dataset gathers data from different parts of Additive manufacturing processes (Laser metal deposition - LMD, and Wire-arc additive manufacturing - WAAM). The dataset covers not only the process data, but also the design, NDT (Non-Destructive Testing) and dimensional inspection.</p> <p><br> <strong>Motivation</strong></p> <p>The industrialisation of Additive Manufacturing (AM) requires a holistic data management and integrated automation. The presented dataset is part of an end-to-end Digital Manufacturing solution, enabling a cybersecured bidirectional dataflow for a seamless integration across the entire AM chain.</p> <p>The goal is to develop a new manufacturing methodology capable of ensuring the manufacturability, reliability and quality of a target metal component from initial product design via Direct Energy Deposition (DED) technologies, implementing a zero-defect manufacturing approach ensuring robustness, stability and repeatibility of the process.</p> <p>To that end, we present the Metal Additive Manufacturing Open Dataset, the first holistic dataset for AM manufacturing, covering all engineering stages from desing to validation. We hope that this dataset will be the first step for the development of new data pipelines aimed to optimize and improve the AM processes and to speed up their digital transformation.</p> <p><br> <strong>Authors</strong></p> <ul> <li>Carlos Gonzalez-Val: Main contact (carlos.gonzalez@aimen.es)</li> <li>Baltasar Lodeiro</li> <li>Marcos Diez</li> </ul> <p> </p> <p><strong>Entities</strong></p> <p>This dataset was collected under the INTEGRADDE project. Attributions:</p> <ul> <li>AIMEN: Process data collection and manufacturing of T-Coupons, CC-Coupons-AIMEN and Jet Engine.</li> <li>MX3D: Process data collection and manufacturing of CC-Coupons-MX3D and Plates.</li> <li>University of West: Process data collection and manufacturing of CC-Coupons-WEST.</li> <li>IREPA: Process data collection and manufacturing of CC-Coupons-IREPA.</li> <li>CEA: Tomography analysis.</li> <li>DATAPIXEL: Dimensional inspection.</li> </ul> <p><br> <strong>Structure</strong></p> <p>The dataset follows this structure:</p> <ul> <li>Dataset <ul> <li>[SAMPLE 1 NAME] <ul> <li>README: metadata and information about the sample. Format: txt.</li> <li>Photo: a photo of the manufactured sample. Format: jpg.</li> <li>Design: a 3D design file of the piece before manufacturing (original design). Format: stl.</li> <li>Trajectories: the trajectories followed for the manufacturing. Format: gcode.</li> <li>Process data: data recorded from the process. Format hdf5.</li> <li>Tomography: data from a 3D tomographic reconstruction. Format: raw.</li> <li>Dimensional inspection: A comparison</li> </ul> </li> <li>[SAMPLE 2 NAME] <ul> <li>...</li> </ul> </li> </ul> </li> </ul> <p>Further information and metadata is contained in each stage's subdirectory.</p> <p>Note that not all the samples contain all the stages.</p> <p><br> <strong>Software</strong></p> <p>To open the different files that conform the dataset, we recommend the following Open softwares:</p> <ul> <li> hdf5 -> HDF5 Viewer: https://www.hdfgroup.org/downloads/hdfview/</li> <li> stl/amf -> Slic3r: https://slic3r.org / OpenJScad: https://openjscad.org/</li> <li> stp -> ShareCad: https://beta.sharecad.org/</li> <li> gcode -> Text editor / Slic3r: https://slic3r.org/</li> <li> raw -> ImageJ: https://imagej.net/</li> </ul> <p>More information on how to open the files of the dataset can be found in the README.</p>
SAST database of repository Luca App Android
<p>SAST database as sqlite database containing the<a href="https://gitlab.com/lucaapp/android"> LucaApp Android Gitlab repository</a>.</p> <p>Retrieved on June 21<sup>nd</sup>, 2021 using our SAST analysis pipeline.</p>
Knowledge repository for Non-Wood Forest Products - Dataset of factsheets from INCREDIBLE
<p>The<strong><em> Knowledge Repository for Non-Wood Forest Products</em></strong> is a collection of information on innovation about non-wood forest products gathered from experts and practitioners during the <a href="https://incredibleforest.net/">INCREDIBLE project</a>. It brings together, in a single platform, knowledge about cork, pine oleoresin, wild mushrooms & truffles, aromatic & medicinal plants and wild nuts & berries, around various themes, from Portugal, Spain, France, Italy, Croatia, Greece and Tunisia.</p> <p>Each piece of knowledge is summarised in a factsheet, that can concern one or several non-wood forest products, as some issues or solutions are transversal. The factsheets can either contain practical knowledge (success stories, good practices, technical reports) or more theoretical or academic results (research results, databases, policies). The factsheets can also be identified by the position in the value chain to which the knowledge applies, from forestry to end-consumers.</p>
Upper Penticton Creek Watershed Experiment -- Data Repository
<p>Data sets collected as part of the Upper Penticton Creek Watershed Experiment. Additional data sets are available, which will be included in future updates of the repository.</p> <p>Please see <strong>upc_data_description_2021Sept22.html</strong> for descriptions of the data sets currently included in the repository, including metadata and photographs of instrumentation and study sites. You cannot directly open this file in a browser by clicking on the link on this page. You will need to download the file to your local hard drive, then open the file within a browser.</p> <p>This version differs from version 1.1 in that the catchment boundaries for all three catchments are now based on the 1-m Lidar DEM. In the earlier versions, the catchments for 240 and 241 Creeks were based on the Canadian DEM.</p>
ERICA PROMs Repository
<p>The ERICA Patient Reported Outcome Measures (PROMs) Repository is the first attempt to identify and centralize Clinical Assessment Outcomes questionnaires of relevance for rare diseases and constitutes a milestone in the Europe-wide standardization of Patient-Centered Outcome Measures (PCOMs) and PROMs for rare diseases. It has been made possible through the joint collaboration between <a href="https://www.orpha.net/">Orphanet</a>, <a href="https://eprovide.mapi-trust.org/">Mapi Research Trust/ICON</a> and <a href="https://eurobloodnet.eu/">ERN EuroBloodNet</a> (VHIR, APHP), and the active contribution of ERNs and ePAGs. Available at ERICA website: <a href="https://erica-rd.eu/work-packages/patient-centred-research/proms-repository/">ERICA PROMs Repository</a></p>
Software repository for data-driven reconstruction of doping profiles in semiconductors
<p>Datasets and code used described in paper: "Data-driven solutions of ill-posed inverse problems arising from doping reconstruction in semiconductors" [arXiv:2208.00742]</p>
Research Data Repository Landscape infographic (European Research Data Landscape study)
<p>Infographic of the findings on research data repository landscape in the European Research Data Landscape study.</p>
The Unofficial Guide on applying NCN Open Access rules to GitHub repositories.
<p><b>The Unofficial Guide on applying NCN Open Access rules to GitHub repositories.</b> <i>Some</i> HTML code can be used here.</p>
European Earthquake Scenario Loss Repository
<p>This repository provides a set of OpenQuake-engine input files required to run past earthquake scenarios for the testing of risk models. These scenarios can be run using either earthquake rupture models or ShakeMaps.</p>
A census of research software in 171 academic institutional repositories.
<p> A dataset of metadata for 171 UK academic institutional repositories, including a census of research software contained.</p> <table> <tbody> <tr> <td><strong>URL</strong></td> <td>The OAI url</td> </tr> <tr> <td><strong>id</strong></td> <td>CORE Identifier</td> </tr> <tr> <td><strong>openDoarId</strong></td> <td>Open DOAR identifier</td> </tr> <tr> <td><strong>name</strong></td> <td>Name of repository</td> </tr> <tr> <td><strong>Russell_member</strong></td> <td>If the university is a member of the Russell Group of research intensive universities</td> </tr> <tr> <td><strong>RSE_group</strong></td> <td>If an RSE group is present (based on Soc of RSE data)</td> </tr> <tr> <td><strong>email</strong></td> <td>Redacted</td> </tr> <tr> <td><strong>uri</strong></td> <td>Not used</td> </tr> <tr> <td><strong>uni_sld</strong></td> <td>Second level domain (the part of the url between . And .ac.uk</td> </tr> <tr> <td><strong>homepageUrl</strong></td> <td>University website</td> </tr> <tr> <td><strong>source</strong></td> <td>Not used</td> </tr> <tr> <td><strong>ris_software</strong></td> <td>the Research Information System software used</td> </tr> <tr> <td><strong>ris_software_enum</strong></td> <td>Resolve ris_software into similar types (e.g. Eprints 3, EPrints3.3.16 both equal eprints)</td> </tr> <tr> <td><strong>metadataFormat</strong></td> <td>the protocol used for metadata</td> </tr> <tr> <td><strong>createdDate</strong></td> <td>Repository creation date</td> </tr> <tr> <td><strong>location</strong></td> <td>location of university</td> </tr> <tr> <td><strong>logo</strong></td> <td>University logo (resolves in error)</td> </tr> <tr> <td><strong>type</strong></td> <td>Only = Repository for this dataset. Can be = journal etc.</td> </tr> <tr> <td><strong>stats</strong></td> <td>Not used</td> </tr> <tr> <td><strong>contains_software_set</strong></td> <td>Whether the OAI-PMH software set is present in the repository.</td> </tr> <tr> <td><strong>Num_sw_records</strong></td> <td>The response of the OAI-PMH query for software (erroneous as discussed in paper)</td> </tr> <tr> <td><strong>Error</strong></td> <td>The category of error returned by the experiment’s OAI-PMH queries (see paper)</td> </tr> <tr> <td><strong>Manual_Num_sw_records</strong></td> <td>The true amount of software contained in the repository as found by a manual exhaustive search of each university website</td> </tr> <tr> <td><strong>Category</strong></td> <td>Whether the repository (a) contains software; (b) can contain software, but doesn’t yet; (c) has no separate type of research output called software or similar</td> </tr> </tbody> </table>
Does size matter? Quality assessment of the size property in research data repositories
<p>Code and data for master's thesis on quality assessment of the size property in research data repositories. Research questions:</p> <ul> <li> <p> For what semantic concepts is the size property of repositories being used?</p> </li> <li> <p>What kind of quality factors can be detected when assessing the size property in a registry for research data repositories?</p> </li> <li> <p>Which automated and intellectual measures can improve the quality of the size property?</p> </li> </ul> <p>Method 1: Data analysis of size and related properties over all re3data records</p> <ul> <li> <p>Property selection</p> </li> <li> <p>Data extraction from API</p> </li> <li> <p>Data normalization</p> </li> <li> <p>Typing of patterns: mainly units of size</p> </li> <li> <p>Analysis: ~quantitative, mainly univariate, but also some multivariate / time</p> </li> </ul> <p>[Included in the publication:</p> <p>Method 2: Case Study of size in individual repositories</p> <ul> <li> <p>Repository selection: purposive sampling</p> </li> <li> <p>Data capture from GUI / API</p> </li> <li> <p>Analysis: ~qualitative]</p> </li> </ul>
Snapshot Testing Dataset - Repositories using Jest
<p>This is a dataset of GitHub repositories that were tagged with Jest, for JavaScript and TypeScript languages, that used Snapshot Testing. Information on all repositories is available in the file "<a href="https://zenodo.org/api/files/68da2ad8-feaa-4a41-af84-278194516f0a/0_Snapshot%20Testing%20Dataset.xlsx?versionId=a2423c05-1628-4661-85cc-96d615f4ddf2">0_Snapshot Testing Dataset.xlsx</a>" (named to be the very first file). Most files represent the repository packed in targz format as "<user>_<repository_name>.tar.gz". We split large repositories (>50MB) using the "split" command on Unix (use cat to rejoin them). </p> <p>In total there are 686 repositories. We collected only public repositories that were tagged with the Jest keyword, for JavaScript and TypeScript, had at least 1 star, and at least 1 snapshot file. The spreadsheet data was collected on July 13, 2022.</p> <p>We also have all scripts used to gather this data. Here, "<a href="https://zenodo.org/api/files/00168583-5071-4ce2-8dc6-2d44a5f1bf77/python_scripts.zip">python_scripts.zip</a>" has all python scripts to find repositories based on queries and save their attributes, and "<a href="https://zenodo.org/api/files/00168583-5071-4ce2-8dc6-2d44a5f1bf77/node_and_shell_scripts.zip">node_and_shell_scripts.zip</a>" contain the node and shell scripts to download a tarball of the repository. Therefore you should first use the python scripts to collect repositories & their attribute, and later use the node & shell to download a copy of the repositories. Moreover, inside each script folder/zip there is a Readme file with instructions and examples.</p> <p>Our GitHub repository is an exact copy of this dataset <<a href="https://github.com/hscrocha/SnapshotTestingDataset">https://github.com/hscrocha/SnapshotTestingDataset></a>, but it is much better organized into folders and the README files for the scripts will be nicely displayed on it.</p>
Repository of Raw Datasets for the Study of Anticoagulation and the Incidence of Stroke and Other Outcomes in Patients with Left Ventricular Thrombus
<p>The optimal duration of anticoagulation in patients with left ventricular thrombus (LVT) is unknown. The data package herein presented contains the data used to assess the effect of duration of anticoagulation in the incidence of stroke in patients with left ventricular thrombus (LVT) in a tertiary hospital. These data includes clinical and demographic information, treatment choices (vitamin K antagonists [VKA] versus direct oral anticoagulants [DOAC]), duration of treatment, reason for interruption of treatment, occurrence of stroke, acute myocardial infarction, bleeding events, thrombus resolution and recurrence, and death.<br> The raw dataset is available upon request to the corresponding author.</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.