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110 results for “informatics”
BVNA Community Informatics Project Dataset I
<p>This collection comprises geospatial datasets used to create the Beaverdam Valley Neighborhood Association community map and the resulting map in pdf and jpeg formats. This scope of the map covers the borders of Buncombe County, North Carolina, the city limits of Asheville, NC, and the three registered neighborhoods of the Beaverdam Valley (Beaverdam Valley, Hills of Beaverdam, and Beaverdam Run). The geospatial data includes the following layers and associated files: </p> <ul> <li>"AVL City Limits.geojson": City of Asheville GIS municipal boundary data</li> <li>"AVL City Limits.qmd": QGIS metadata file for the above </li> <li>"AVL Neighborhoods.geojson": City of Asheville GIS registered neighborhood data</li> <li>"AVL Neighborhoods.qmd": QGIS metadata file for the above</li> <li>"Buncombe_County_Parcels.geojson": Buncombe County GIS parcel data. </li> <li>"Buncombe_County_Parcels.qmd": QGIS metadata file for the above</li> <li>"BV Boundaries.geojson": Beaverdam Valley Neighborhood boundaries. </li> <li>"BV Boundaries.qmd": QGIS metadata file for the above</li> <li>"BV Parcel Intersection.geojson": Intersection of the Beverdam Valley Neighborhood boundaries with the Buncombe County Parcel data.</li> <li>"BV Parcel Intersection.qmd": QGIS metadata file for the above</li> <li>"BVNA_Map_2022_v2.pdf": BVNA CIP Community Map</li> <li>"BVNA_Map_2022_v2_825.jpg": BVNA CIP Community Map</li> <li>"City Limits.geojson": Buncombe county boundaries and city limits boundaries witin the county. </li> <li>"QGIS BVNA CIP.zip": Zip file containing the above layers in a QGIS project folder and file.</li> </ul> <p>About the Project: The Beaverdam Valley Neighborhood Association (BVNA) Community Informatics Project aims to gain deeper understanding of the Beaverdam Valley community and to work towards gathering and sharing information about the community and its history. This collection represents a deliverable produced under the 2022-2023 City of Asheville Neighborhood Matching Grant program. </p>
BVNA Community Informatics Project StoryMap
<p>This collection represents the Annexation History StoryMap created for the Beaverdam Valley Neighborhood Association Community Informatics Project, containing the associated geojson, pdf, and tiff files. The StoryMap describes the annexation history of Beaverdam Valley (including the registered neighborhoods of Beaverdam Valley, Hills of Beaverdam, and Beaverdam Run) in Asheville, North Carolina from 1929 until August 2023 with the most recent annexation being in 2005. The collection includes the following associated files: </p> <ul> <li>"<a href="https://zenodo.org/api/files/53a59d4e-6f52-4b69-a634-f468c4fcce7c/Annex%201929-1960.tiff?versionId=4f2e551a-975a-461b-9c7d-9632ab598fa1">Annex 1929-1960.tiff</a>": image file showing Annexations in Beaverdam Valley through 1960</li> <li>"<a href="https://zenodo.org/api/files/53a59d4e-6f52-4b69-a634-f468c4fcce7c/Annex%201929-1964.tiff?versionId=063b677a-45a1-4b14-8586-884f91be865a">Annex 1929-1964.tiff</a>": image file showing Annexations in Beaverdam Valley through 1964</li> <li>"<a href="https://zenodo.org/api/files/53a59d4e-6f52-4b69-a634-f468c4fcce7c/Annex%201929-1980.tiff?versionId=9388d5e7-75e4-4025-8e9c-e661a7cf46f1">Annex 1929-1980.tiff</a>": image file showing Annexations in Beaverdam Valley through 1980</li> <li>"<a href="https://zenodo.org/api/files/53a59d4e-6f52-4b69-a634-f468c4fcce7c/Annex%201929-1991.tiff?versionId=e0f49d03-59b9-4ba3-95e4-a31ce5991603">Annex 1929-1991.tiff</a>" image file showing Annexations in Beaverdam Valley through 1991</li> <li>"<a href="https://zenodo.org/api/files/53a59d4e-6f52-4b69-a634-f468c4fcce7c/Annex%201929-1995.tiff?versionId=4412d044-aae1-4cc3-8826-7429519c9358">Annex 1929-1995.tiff</a>": image file showing Annexations in Beaverdam Valley through 1995</li> <li>"<a href="https://zenodo.org/api/files/53a59d4e-6f52-4b69-a634-f468c4fcce7c/Annex%201929-2005.tiff?versionId=0469a2e7-7132-46cd-b5df-5769be0082f2">Annex 1929-2005.tiff</a>": image file showing Annexations in Beaverdam Valley through 2005</li> <li>"<a href="https://zenodo.org/api/files/53a59d4e-6f52-4b69-a634-f468c4fcce7c/Annex%201929.tiff?versionId=d0666b27-80b8-48bd-b4cf-a43cedde1f32">Annex 1929.tiff</a>": image file showing Annexation in Beaverdam Valley in 1929 </li> <li>"Asheville_Annexation_History.geojson": City of Asheville GIS Annexation history</li> <li>"Asheville_Annexation_History.qmd": QGIS metadata for the above</li> <li>"Beaverdam_Annexation_Storymap_Final.pdf": pdf of StoryMap</li> </ul> <p>About the Project: The Beaverdam Valley Neighborhood Association (BVNA) Community Informatics Project aims to gain deeper understanding of the Beaverdam Valley community and to work towards gathering and sharing information about the community and its history. This collection represents a deliverable produced under the 2022-2023 City of Asheville Neighborhood Matching Grant program. </p>
Music Informatics for Radio Across the GlobE (MIRAGE) MetaCorpus (v0.2)
<h1>Overview</h1> <p>Welcome to the <strong><em>Music Informatics for Radio Across the GlobE</em></strong> (<em><strong>MIRAGE</strong></em>) <strong><em>MetaCorpus</em></strong>. The current (v0.2) development release consists of metadata (e.g., artist name, track title) and musicological features (e.g., instrument list, voice type, tempo) for 1 million events streaming on 10,000 internet radio stations across the globe, with 100 events from each station. </p> <p>Users who wish to access, interact with, and/or export metadata from the MIRAGE-MetaCorpus may also visit the MIRAGE online dashboard at the following url:</p> <ul> <li><a href="https://pearl-laboratory.github.io/mirage-mc/" target="_blank" rel="noopener">https://pearl-laboratory.github.io/mirage-mc/</a></li> </ul> <h1>Attribution</h1> <p>The current MIRAGE-MetaCorpus is available under a CC4 license. Users may cite the dataset here:</p> <blockquote> <p>Sears, David R.W. “Music Informatics for Radio Across the Globe (MIRAGE) Metacorpus -- 2024”. Zenodo, July 19, 2024. <a href="https://doi.org/10.5281/zenodo.12786202" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12786202</a>.</p> </blockquote> <p>Users accessing the MIRAGE-MetaCorpus using the online dashboard should also cite the following ISMIR paper:</p> <blockquote> <p>Ngan V.T. Nguyen, Elizabeth A.M. Acosta, Tommy Dang, and David R.W. Sears. "Exploring Internet Radio Across the Globe with the MIRAGE Online Dashboard," in <em>Proceedings of the 25th International Society for Music Information Retrieval Conference </em>(San Francisco, CA, 2024). </p> </blockquote> <h1>Data Sources</h1> <p>This repository of the MIRAGE-MetaCorpus contains 81 metadata variables from the following open-access sources:</p> <ul> <li>Radio Garden (RG) -- <a href="https://radio.garden" target="_blank" rel="noopener">https://radio.garden</a></li> <li>Natural Earth map data set (NE) -- <a href="https://www.naturalearthdata.com/" target="_blank" rel="noopener">https://www.naturalearthdata.com/</a></li> <li>Internet Radio Station Stream Encoder (SE)</li> <li>Annotator Review (AR)</li> <li>Monitoring/Matching Algorithm (MA)</li> <li>WikiData (WD) -- <a href="https://www.wikidata.org" target="_blank" rel="noopener">https://www.wikidata.org</a></li> <li>MusicBrainz (MB) -- <a href="https://musicbrainz.org/" target="_blank" rel="noopener">https://musicbrainz.org/</a></li> </ul> <p>Each event also includes attribution metadata from the following commercial sources:</p> <ul> <li>Spotify (SP) -- <a href="https://open.spotify.com/" target="_blank" rel="noopener">https://open.spotify.com/</a> <ul> <li>Note that users may examine an additional 19 metadata variables on the MIRAGE online dashboard that were obtained from the Spotify API.</li> </ul> </li> <li>Musixmatch (MX) -- <a href="https://www.musixmatch.com/" target="_blank" rel="noopener">https://www.musixmatch.com/</a></li> <li>YouTube (YT) -- <a href="https://www.youtube.com/" target="_blank" rel="noopener">https://www.youtube.com/</a></li> <li>Genius (GE) -- <a href="https://genius.com/" target="_blank" rel="noopener">https://genius.com/</a></li> <li>AZlyrics (AZ) -- <a href="https://www.azlyrics.com/" target="_blank" rel="noopener">https://www.azlyrics.com/</a></li> </ul> <h1>Data Sets</h1> <p>The metadata reflect information about each event's location (e.g., city, country), station (name, format, url), event (id, local time at station, etc.), artist (name, voice type, etc.), and track (e.g., title, year of release, etc.). For that reason, the MIRAGE-MetaCorpus includes the following datasets:</p> <ul> <li>MIRAGE.csv -- the complete metacorpus (1 million)</li> <li>events.csv -- all event-level metadata (1 million)</li> <li>tracks.csv -- all track-level metadata (414,886)</li> <li>artists.csv -- all artist-level metadata (259,783)</li> <li>stations.csv -- all station-level metadata (10,000)</li> <li>locations.csv -- all location-level metadata (4,324)</li> </ul> <p>A subset of the MIRAGE-MetaCorpus is also available for events with metadata from online music libraries that reliably matched the event's description in the radio station's stream encoder:</p> <ul> <li>MIRAGE_reliable.csv (473,850)</li> <li>events_reliable.csv (473,850)</li> <li>tracks_reliable.csv (204,969)</li> <li>artists_reliable.csv (80,005)</li> <li>stations_reliable.csv (9,284)</li> <li>locations_reliable.csv (4,142)</li> </ul> <h1>Contact</h1> <p>If you are a copyright owner for any of the metadata that appears in the MIRAGE-MetaCorpus and would like us to remove your metadata, please contact the developer team at the following email address: <a href="mailto:miragedashboard@gmail.com" target="_blank" rel="noopener">miragedashboard@gmail.com</a> </p>
Disseminating metaproteomic informatics capabilities and knowledge using the Galaxy-P framework
<p>Data for the "<strong>Disseminating metaproteomic informatics capabilities and knowledge using the Galaxy-P framework</strong>" paper and training.</p>
Predicting particle quality attributes of organic crystalline materials using Particle Informatics
<p>Dataset related to the publication: "Predicting particle quality attributes of organic crystalline materials using Particle Informatics" published in Powder Technology (<a title="Go to table of contents for this volume/issue" href="https://www.sciencedirect.com/journal/powder-technology/vol/443/suppl/C"><span>Volume 443</span></a>, 1 July 2024, 119927Volume 443, 1 July 2024, 119927). In this work, a novel quercetin solvate of dimethylformamide (QDMF) was studied. The crystal structure was solved using single crystal X-ray diffraction and analysed using synthon analysis and other particle informatics tools (<em>e.g.</em>, solvate analyser). The thermal behaviour and thermodynamic stability of QDMF were studied experimentally using Raman spectroscopy, ATR-FTIR spectroscopy, differential scanning calorimetry, and thermogravimetric analysis. A clear relationship between the two-step desolvation behaviour of QDMF and the type, strength, and directionality of the main bulk synthons characterizing the QDMF structure was observed. Additionally, the attachment energy model was used to predict the QDMF morphology, together with facet-specific topology and chemical nature of each of the dominant {001}, {110}, and {200} facets. The {200} facet was found to be significantly rougher than the other two; whereas, the {110} was characterized by a higher percentage of exposed DMF molecules compared to the other two facets. Specific scanning electron microscopy and contact angle measurements were used to experimentally detect differences among the three facets and validate the modelling results.</p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 5. Hierarchical clustering by scores across the EPQ–R scales for data about all the participants
<p>The clusters were generated using an implementation of a hierarchical clustering algorithm available in the R environment (R, n.d.). The top three clusters were extracted from a hierarchical cluster tree shown in Figure 5, while the color of data points in the visualization shown in figure 4 was determined based on cluster labels. Hierarchical clusters could be used when investigating which students in the analyzed sample share similar personality traits. This could be especially useful for smaller student groups as the teacher may manually inspect the cluster tree and its leaves, which designate individual students. For instance, there are three students in cluster 3, who are represented within the tree in Figure 5 by identifiers 14, 22, and 24. The students with identifiers 14 and 22 are more closely linked and more similar to each other than to the student with identifier 24. </p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 2. Comparison of mean scores on the EPQ–R scales
<p>The data for the workshop participants were loaded from the data warehouse, while the summary data from the original EPQ–R study were loaded from a CSV file. The bar chart featured in Figure 2 shows mean scores on the EPQ–R scales for the selected workshop participants (denoted by blue bars) and the selected participants of the original EPQ–R study (denoted by yellow bars). The mean scores on the P scale agree between the two samples, but the overall scores for the other scales vary. </p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 4. Radial visualization of scores across the EPQ–R scales for clustered data about all the participants
<p>On the other hand, the division of data points by gender might not be the only useful strategy when visually inspecting the analyzed sample in a coordinate system. Numerous clustering algorithms may be used to determine which data points share similar scores across the EPQ–R scales, i.e., which data points belong to the same cluster of similar entities based on their corresponding EPQ–R scores. A radial visualization in which data points were organized into three clusters is given in Figure 4. Each cluster is marked by a different color: cluster 1 by red, cluster 2 by green, and cluster 3 by blue. </p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 3. Radial visualization of scores across the EPQ–R scales for the male and female participants
<p>The radial visualization in Figure 3 depicts each participating student as a dot whose color indicates the gender of the student, blue for male students (M) and red for female students (F). The position of a dot in the visualization is determined by the scores of the associated student on the four EPQ–R scales. The radial overview may provide a much clearer outline of clustering within the analyzed group. Although there are only five female students, they are concentrated in a relatively narrow area within the radial coordinate system</p>
Short sentences on R analyses in a health informatics subject
<p>The dataset contains the list of sentences written by students, with a unique ID, its type (if either given for the hypothesis or normality test), its degree in a range from 0 to 1, and its fail/pass result, flanked with (i) the gold standard, (ii) an alternative gold standard, and (iii) the negated gold standard.</p>
SUPPLIMENTARY Dataset - A STUDY ON STATISTICAL MODELLING ON RECOVERY INFORMATICS OF COVID19
<p>Collection of data – a set of data of number of days with seven days interval vs number of recovered cases with effect from 7th September – 25th October ,2020 .Active Covid cases with respect to Sept,7 to Oct ,25 -2020 in India . Retrieved from -Sharpest weekly fall as cases down 16%, toll 19% | Man hits docs over Covid death, -Oct 26, 2020, ethealthworld.com. ( data report ) .The above dataset is converted in terms of number of Recovered Covid-cases with respect .Data Informatics Tool : Standard Statistical Software Curve Expert v.1.4 .</p>
globalbioticinteractions/AEC-DBCNet: Collaborative databasing of North American bee collections within a global informatics network project archive
<p>Data in this archive are from the <em>Collaborative databasing of North American bee collections within a global informatics network project</em>. Data was originally captured using Arthropod Easy Capture software developed at the American Museum of Natural History (AMNH), New York. Project lead investigators are John Ascher (Principal Investigator) and Jerome Rozen (Co-Principal Investigator) at the AMNH, and Douglas Yanega (Principal Investigator), University of California Riverside.</p> <p><strong>Please use this citation for this archive: </strong>John Ascher, Digital Bee Collections Network data archive from the C<em>ollaborative databasing of North American bee collections within a global informatics network project</em>. Version: 08 Mar 2016. https://doi.org/10.5281/zenodo.1436853</p> <p>This project was supported by the National Science Foundation grant <a href="https://nsf.gov/awardsearch/showAward?AWD_ID=0956388">DBI 0956388</a> and <a href="https://nsf.gov/awardsearch/showAward?AWD_ID=0956340">DBI 0956340</a></p> <p><strong>ABSTRACT</strong> Natural history collections contain millions of bee specimens documenting the geographic ranges, temporal occurrence patterns, and floral associations of the 20,000 described bee species. This project will digitize and consolidate specimen records from 10 bee collections across the United States. The investigators will make or verify species identifications, capture full label data, georeference and error-check localities, and upload this information to publicly accessible databases. Web-based tools will be used to capture data across collections efficiently, validate bee and plant names through automated comparison with taxonomic authority files, and synthesize data on species pages with images, digitized literature records, and other information about bees and their host plants. Data will be uploaded to the Global Biodiversity Information Facility and to Discover Life (www.discoverlife.org), a website that features customizable global maps for all global bee species and dynamic identification keys for North American species. To obtain information needed to conserve and manage pollinators, the investigators will work with ecologists to model geographic and temporal trends in bee populations in relation to environmental variables. Bees are the most important pollinators of the approximately 1/3 of crops that require animal pollination. Recent declines in honey bee populations highlight the need to understand better the roles of native bees in agricultural and natural systems. This project will help predict risks to bees and their pollination services from climate change, habitat loss, and other factors. The outreach program Bee Hunt (www.discoverlife.org/bee) will educate the public, including students in underserved communities, about bee diversity and the importance of pollination services. Using digital photography and rigorous research protocols, Bee Hunt will empower people at biological field stations, nature centers, parks, schools, and other sites to collect high-quality data to augment information from specimen records.</p>
Enhancing research informatics core user satisfaction through agile practices
<p><strong>Objective:</strong> The Huntsman Cancer Institute (HCI) Research Informatics Shared Resource (RISR), a software and database development core facility, sought to address a lack of published operational best practices for research informatics cores. It aimed to use those insights to enhance effectiveness after an increase in team size from 20 to 31 full-time equivalents coincided with a reduction in user satisfaction.</p> <p><strong>Materials and Methods:</strong> RISR migrated from a water-scrum-fall model of software development to agile software development practices, which emphasize iteration and collaboration. RISR's agile implementation emphasizes the product owner role, which is responsible for user engagement and may be particularly valuable in software development that requires close engagement with users like in science.</p> <p><strong>Results: </strong>All RISR's software development teams implemented agile practices in early 2020. All project teams are led by a product owner who serves as the voice of the user on the development team. Annual user survey scores for service quality and turnaround time recorded nine months after implementation increased by 17% and 11%, respectively.</p> <p><strong>Discussion:</strong> RISR is illustrative of the increasing size of research informatics cores and the need to identify best practices for maintaining high effectiveness. Agile practices may address concerns about the fit of software engineering practices in science. The study had one time point after implementing agile practices and one site, limiting its generalizability.</p> <p><strong>Conclusion:</strong> Agile software development may substantially increase a research informatics core facility's effectiveness and should be studied further as a potential best practice for how such cores are operated.</p>
Overview of social informatics scientific activities (1985–2019): Three datasets
<p>The <strong>dataset</strong> was created as part of the overview of social informatics scientific activities published from 1985 to 2019. The results of the overview are presented in the article <strong>Evolution of social informatics: Publications, research, and educational activities</strong>.</p> <p>Three files are included:</p> <ol> <li>The file <em>SI-dataset-SI-N.xslx</em> lists all peer-reviewed scientific publications containing the term <em>social informatics</em> in the title, abstract or keywords. Each listed publication is coded according to ten variables.</li> <li>The file <em>SI-dataset-SocInfo.xslx</em> lists all papers published in the Proceedings of the international conference on Social Informatics (SocInfo conference). Each listed publication is coded according to seven variables.</li> <li>The file <em>SI-dataset-Activities.xslx </em>lists all research and educational activities as well as conferences, journals and blogs that contain the term <em>social informatics</em> in their title or name.</li> </ol> <p>The methodology is detailed in the article.</p> <p>Vehovar, V., Smutny, Z., and Bartol, J. (2022). Evolution of social informatics: Publications, research, and educational activities. <em>The Information Society</em>, <em>38</em>(5), 307–333. <a href="https://doi.org/10.1080/01972243.2022.2092570">https://doi.org/10.1080/01972243.2022.2092570</a></p>
Scientific production of the University of Informatics Sciences in the period 2004-2020 in Scopus
<p><span>The data were part of the research to analyze the behavior of the scientific production of the University of Informatics Sciences of Cuba indexed in the Scopus database in the period 2004 to 2020, through a descriptive, longitudinal bibliometric study, the scientific production was analyzed. , research areas, and scientific collaboration in 366 records found.</span></p>
Section 5.4 "Task Area 4: Bioimage informatics and analysis" Figure 11
<p>Figure 11. <em><span>FAIR pipelines for image analysis to balance the current status quo of hardly recordable manual processing (point-and-click) with the goal of increasing reproducibility of workflows</span></em>.</p> <p>from NFDI Grant Application, "<strong>National Research Data Infrastructure for Microscopy and Bioimage Analysis</strong>" (NFDI4BIOIMAGE)</p>
COLLABORATIVE RESEARCH: Cyberinfrastructure for a Virtual Observatory and Ecological Informatics System (VOEIS)
<p>Internally logging temperature sensor (HOBO Water Temp Pro v2) tethered to a buoy at 5 m fixed depth from 2011 to 2014. Buoy located in Flathead Lake, Montana, USA, at the sampling location termed 'Midlake Deep' (-114.0663, 47.8631).</p> <p>Data presented in: Evans, K. A., L. M. Peoples, J. R. Ranieri, E. K. Wear, and M. J. Church. 2023. Mixing-driven changes in distributions and abundances of planktonic microorganisms in Flathead Lake.</p>
Insights to Inspire - Informatics: The Journey to Interoperability
<p>To accelerate translation, researchers need access to a broad range of data from a variety of sources (electronic health records, imaging, genetics, behavioral, etc.). These sources manage and store data differently, which creates the need for standardization. The Informatics Common Metric, which is in its second year, addresses the need to harmonize data across the CTSA Program. This will enhance our ability to collaborate on initiatives both within and outside the consortium. The metric also supports the following NCATS strategic objective: “Develop interoperable and integrative biomedical informatics resources to facilitate translational innovation in disease prevention, diagnosis and treatment.”<br> <br> The Common Metrics Initiative team is disseminating a series of brief webcasts from CTSA Program experts with topics ranging from foundational information to understand the field of informatics to how to get to interoperability.</p> <p>The goal for I2I 2021 is to build a community of expertise among CTSA Programs to create true data interoperability across the consortium. To meet this goal, the focus of these webcasts is to:</p> <ul> <li>Encourage hubs to assess their current status with regard to data quality and completeness and standardization to advance clinical and translational science</li> <li>Assist hubs in improving their processes, such as implementing protocols and developing new tools</li> <li>Identify the needs of personnel and interdisciplinary teams, and finally, to</li> <li>Determine key national data networks and local partnerships</li> </ul> <p><em>The first five webcasts are an introduction to informatics, created to build foundational knowledge and to define key terms. We encourage you to watch them in order. </em></p>
Early Severe Illness TrAnslational BioLogy InformaticS in Humans
ClinicalTrials.gov study NCT05591924. IPD Sharing: YES. Countries: 1. Publications: 8.
Enhancing research informatics core user satisfaction through agile practices
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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.