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55 results for “Researcher profiles”
GitHub Profiles (users/organisations) and Repositories (research/non-research) of Potsdam Researchers and Research Organisations: An annotated dataset of with howfairis and software quality variables.
<p>This dataset accompanies the paper <em>"Software FAIRness, Documentation and Development Practices in Potsdam Researchers' GitHub Repositories"</em> It includes 3 CSV files that contain data related to github profiles of users/organisations, their repositories annotated as research/non-research repositories and followed by FAIRness and other software qualtiy variables. The data were collected using <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP">SWORDS-template-UP</a> (v1.0.0) methods (collect_users, collect_repositories, collect_variables) which is extended version of <a href="https://github.com/UtrechtUniversity/SWORDS-template">SWORS-template</a> adopted according our needs and detailed in the paper.</p> <p><strong>GitHub (research) user/organisation profiles. ( <em>github_profiles.csv )</em></strong></p> <table> <tbody> <tr> <td><strong>Column name</strong></td> <td><strong>Description </strong></td> </tr> <tr> <td>user_id</td> <td>GitHub username </td> </tr> <tr> <td>html_url </td> <td>URL of the GitHub profile </td> </tr> <tr> <td>type </td> <td>Type of profile (user or organization)</td> </tr> <tr> <td>organisation</td> <td>Acronym or name of the organization </td> </tr> </tbody> </table> <p><strong>GitHub repositories <em>(github_repositories.csv)</em></strong></p> <p>This file contains the repositories scraped from the GitHub profiles of research users and organizations.</p> <table> <tbody> <tr> <td><strong>Column name </strong></td> <td><strong>Description </strong></td> </tr> <tr> <td>html_url </td> <td>URL link to the repository </td> </tr> <tr> <td>description</td> <td>GitHub project description </td> </tr> <tr> <td>project</td> <td>Specifies if the project is research or non-research</td> </tr> <tr> <td>language</td> <td>Programming language used in the project </td> </tr> <tr> <td>organisation</td> <td>Acronym or name of the university, institution, or research organization</td> </tr> <tr> <td>research_group</td> <td>Acronym or name of the research group the repository belongs to</td> </tr> </tbody> </table> <p><strong>Research repositories filtered and annotated <em>(github_research_repositories_filtered_annotated.csv)</em></strong></p> <p>This file contains filtered and annotated information about research repositories.</p> <table> <tbody> <tr> <td><strong>Column Name </strong></td> <td><strong>Description </strong></td> <td><strong>Collection Method </strong></td> </tr> <tr> <td>html_url </td> <td>Repository URL </td> <td> </td> </tr> <tr> <td>howfairis_repository</td> <td>Indicates if the repository is public or private (True/False) </td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/tree/main/collect_variables#usage">howfairis_variable.py</a>) is a wrapper for <a href="https://pypi.org/project/howfairis/">howfairis</a> pypi library that checks the 5 recommendations of <a href="https://fair-software.nl">FAIR</a></td> </tr> <tr> <td>howfairis_license </td> <td>Indicates if the repository has a license (True/False)</td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/tree/main/collect_variables#usage">howfairis_variable.py</a>) is a wrapper for <a href="https://pypi.org/project/howfairis/">howfairis</a> pypi library that checks the 5 recommendations of <a href="https://fair-software.nl">FAIR</a></td> </tr> <tr> <td>howfairis_registry</td> <td>Indicates if the repository has implemented community registry (True/False)</td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/tree/main/collect_variables#usage">howfairis_variable.py</a>) is a wrapper for <a href="https://pypi.org/project/howfairis/">howfairis</a> pypi library that checks the 5 recommendations of <a href="https://fair-software.nl">FAIR</a></td> </tr> <tr> <td>howfairis_citation</td> <td>Indicates if the repository has a .cff file (True/False) </td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/tree/main/collect_variables#usage">howfairis_variable.py</a>) is a wrapper for <a href="https://pypi.org/project/howfairis/">howfairis</a> pypi library that checks the 5 recommendations of <a href="https://fair-software.nl">FAIR</a></td> </tr> <tr> <td>howfairis_checklist</td> <td>Indicates if the repository has implemented OpenSSF best practices badge (True/False)</td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/tree/main/collect_variables#usage">howfairis_variable.py</a>) is a wrapper for <a href="https://pypi.org/project/howfairis/">howfairis</a> pypi library that checks the 5 recommendations of <a href="https://fair-software.nl">FAIR</a></td> </tr> <tr> <td>fair_score</td> <td>Score based on howfairis variables (0-5) </td> <td> </td> </tr> <tr> <td>dlr_soft_class</td> <td>Name of the university, company, research institute, or research organization</td> <td>(Manual) Annotated the repository based on <a href="https://core.ac.uk/reader/211557820">DLR software engineering guideline.</a> There are no specific definitions on metrics how to categorise them (github repositories) into application classes. Which were needed to do a comparitive analysis. </td> </tr> <tr> <td>installation_instruction</td> <td>Presence of installation instruction (True/False) </td> <td>(Manual) Checked the presense of Installation Instruction in the readme or in the project wiki pages. </td> </tr> <tr> <td>project_information </td> <td>Presence of basic project information in README (True/False) </td> <td>(Manual) Checked if the readme have basic information about the project. </td> </tr> <tr> <td>usage_guide</td> <td>Presence of folder named test/tests in the root directory (True/False)</td> <td>(Manual) Checked the presense of Usage Guide in the readme or in the project wiki pages. For command line tools checked if they have help command which guides how to use the tool. </td> </tr> <tr> <td>test_folder</td> <td>Presence of folder named test/tests in the root directory (True/False)</td> <td> <p>(Script - <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/docs/collect_variables/scripts/soft_dev_pract/test_folder.py">test_folder.py</a>) Checks the folder names test/tests in the root directory of the repository.</p> </td> </tr> <tr> <td>requirements_explicit </td> <td>Explicit requirements for Python, R, C++ repositories (True/False)</td> <td>(Script - <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/requirement_explicit.py">requirement_explicit.py</a>) Checks the files (requirements.txt, DESCRIPTION, CMakeLists.txt) in the root directory. </td> </tr> <tr> <td>continuous_integration</td> <td>Indicates if the repository uses continuous integration (True/False)</td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/continious_integration.py">continious_integration.py</a>) Checks the presence of folder .github (github actions) same for other continious integration (travisCI, CircleCI, Jekins, azure pipeline)</td> </tr> <tr> <td>ci_tool </td> <td>Name of the continuous integration tool used</td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/continious_integration.py">continious_integration.py</a>) Checks the presence of folder .github (github actions) same for other continious integration (travisCI, CircleCI, Jekins, azure pipeline)</td> </tr> <tr> <td>add_lint_rule </td> <td>Indicates if additional linting rules are present (True/False)</td> <td>(Script - <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/add_ci_rules.py">add_ci_rules.py</a>) - it scans the YAML files in the <br>.github/workflows directory to detect the presence of (linters) Python, R, and C++.</td> </tr> <tr> <td>add_test_rule</td> <td>Indicates if additional testing rules are present (True/False) </td> <td>(Script - <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/add_ci_rules.py">add_ci_rules.py</a>) - it scans the YAML files in the <br>.github/workflows directory to detect the presence of (testing libraries) Python, R, and C++.</td> </tr> <tr> <td>comment_at_start</td> <td>Indicates the level of comments at the start of the program (most, more, some, less)</td> <td>(Script - <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/comment_at_start.py">comment_at_start.py</a>) Checks the presence of brief comments at the start at source code files in GitHub repositories.</td> </tr> <tr> <td>language </td> <td>Programming language used in the repository </td> <td> </td> </tr> <tr> <td>type </td> <td>Specifies if the profile is a user or organization </td> <td>Github organisation or user profiles.</td> </tr> <tr> <td>organisation </td> <td>Name of the university, company, research institute, or research organization</td> <td>Oraganisation name (from where the user was found)</td> </tr> <tr> <td>research_group</td> <td>Name or acronym of the research group </td> <td> </td> </tr> </tbody> </table> <p> </p> <p>Data for publication - https://github.com/Software-Engineering-Group-UP/potsdam-research-repos</p>
A large EEG database with users' profile information for motor imagery Brain-Computer Interface research
<p><em><strong>Context </strong></em>: <br> We share a large database containing electroencephalographic signals from 87 human participants, with more than 20,800 trials in total representing about 70 hours of recording. It was collected during brain-computer interface (BCI) experiments and organized into 3 datasets (A, B, and C) that were all recorded following the same protocol: right and left hand motor imagery (MI) tasks during one single day session.<br> It includes the performance of the associated BCI users, detailed information about the demographics, personality and cognitive user’s profile, and the experimental instructions and codes (executed in the open-source platform OpenViBE).<br> Such database could prove useful for various studies, including but not limited to: 1) studying the relationships between BCI users' profiles and their BCI performances, 2) studying how EEG signals properties varies for different users' profiles and MI tasks, 3) using the large number of participants to design cross-user BCI machine learning algorithms or 4) incorporating users' profile information into the design of EEG signal classification algorithms.<br> <br> Sixty participants (Dataset A) performed the first experiment, designed in order to investigated the impact of experimenters' and users' gender on MI-BCI user training outcomes, i.e., users performance and experience, (Pillette & al). Twenty one participants (Dataset B) performed the second one, designed to examined the relationship between users' online performance (i.e., classification accuracy) and the characteristics of the chosen user-specific Most Discriminant Frequency Band (MDFB) (Benaroch & al). The only difference between the two experiments lies in the algorithm used to select the MDFB. Dataset C contains 6 additional participants who completed one of the two experiments described above. Physiological signals were measured using a g.USBAmp (g.tec, Austria), sampled at 512 Hz, and processed online using OpenViBE 2.1.0 (Dataset A) & OpenVIBE 2.2.0 (Dataset B). For Dataset C, participants C83 and C85 were collected with OpenViBE 2.1.0 and the remaining 4 participants with OpenViBE 2.2.0. Experiments were recorded at Inria Bordeaux sud-ouest, France.</p> <p><em><strong>Duration</strong> </em>: Each participant's folder is composed of approximately 48 minutes EEG recording. Meaning six 7-minutes runs and a 6-minutes baseline.</p> <p><br> <strong><em>Documents</em></strong><em> </em><br> <em>Instructions</em>: checklist read by experimenters during the experiments.<br> <em>Questionnaires</em>: the Mental Rotation test used, the translation of 4 questionnaires, notably the Demographic and Social information, the Pre and Post-session questionnaires, and the Index of Learning style. English and french version<br> <em>Performance</em>: The online OpenViBE BCI classification performances obtained by each participant are provided for each run, as well as answers to all questionnaires<br> <em>Scenarios/scripts</em> : set of OpenViBE scenarios used to perform each of the steps of the MI-BCI protocol, e.g., acquire training data, calibrate the classifier or run the online MI-BCI</p> <p><strong><em>Database </em></strong>: raw signals<br> Dataset A : N=60 participants<br> Dataset B : N=21 participants<br> Dataset C : N=6 participants<br> <br> The article that expained the database is available here:<br> Dreyer, P., Roc, A., Pillette, L. <em>et al.</em> A large EEG database with users’ profile information for motor imagery brain-computer interface research. <em>Sci Data</em> <strong>10</strong>, 580 (2023).<br> https://doi.org/10.1038/s41597-023-02445-z<br> </p>
Terrestrial Ecosystem Research Network (TERN) Metadata Profile of ISO 19115-3:2016 and ISO 19157-2:2016
<p>This is the first release of Terrestrial Ecosystem Research Network (TERN) Metadata Profile of ISO 19115-3:2016 and ISO 19157-2:2016. The profile will be used by TERN data editorial system (SHaRED v4).</p>
Research data from the survey on Smart Cities professional profiles for the Article "Modelling and analyzing the availability of technical professional profiles for the success of Smart Cities projects in Europe"
<p>The file includes the complete version of data collected through the surrvey on recommended profile for two professional roles in the context of Smart Cities (SC) projects: SC engineer and SC technician. It complements the previous version focused on IoT implementation stired at <a href="../doi/10.5281/zenodo.7492254">https://zenodo.org/doi/10.5281/zenodo.7492254</a></p>
Eco-hydrology Cikapundung Project: citation connections and research profile building
<p>This image is uploaded as an integrated part of Eco-Hydrology Cikapundung project. This image will be cited across all future publications related to this project as CC-BY image. Therefore it should not be treated as prior publication of any kind.</p> <p>We used www.Draw.io and the source code is available on GIthub (https://github.com/dasaptaerwin/CikapundungProject/blob/master/citationConnection.xml).</p> <p>---<br> Dokumen ini disusun sebagai pelengkap riset untuk menggambarkan kaitan sitasi antar dokumen dari hulu ke hilir. Setiap dokumen diupayakan ber-DOI agar dapat <em>autosync</em> dengan profil riset yang tersedia: Google Scholar, Sinta, ORCID. Dengan dibuatnya dokumen hubungan sitasi ini, maka diharapkan dapat menjelaskan bahwa tidak terjadi duplikasi dalam publikasi.</p>
Vertical profiles of stable water isotopes and thermodynamic properties from research flights during the L-WAIVE field campaign in June 2019
<p>This datasets contains the measurements of stable water isotopes conducted during the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021). The measurements were conducted using a Picarro laser spectrometer L2130-i that was installed on an ultralight aircraft. The Picarro measurements of atmospheric humidity are merged measurements of thermodynamic properties by a fast-response temperature and humidity probe (iMet XQ-2; see also Chazette et al. 2021) interpolated on 10s temporal resolution.</p> <p>The data is provided on a one file per flight. All variables are described in README.</p> <p>This dataset has been used in Thurnherr et al. (submitted) for a comparison study of stable water isotopes measurements from various platforms and COSMOiso model simulations.</p>
Research data supporting "Correlated heterospectral lipidomics for biomolecular profiling of remyelination in multiple sclerosis"
<p>Research data supporting the paper:</p> <p>Bergholt, M.S. et al., "Correlated heterospectral lipidomics for biomolecular profiling of remyelination in multiple sclerosis", ACS Central Science, 2017, DOI: 10.1021/acscentsci.7b00367.</p>
TROPOMI research ozone profile dataset in August 2022
<p>This dataset contains ozone profile retrievals from TROPOMI (TROPOspheric Monitoring Instrument) backscattered ultraviolet measurements during the Asian Summer Monsoon Chemical and Climate Impacts Project (ACCLIP) conducted over the Western Pacific in summer 2022. The data is stored in individual files for each date, with all variables described in the attribute field. This dataset has been used in Bak et al. (submitted) for an integrated analysis of ozone and carbon monoxide alongside ACCLIP field datasets and other satellite products.</p> <p><strong>Details on Retrieval Methodology:</strong></p> <ol> <li><strong>Inverse Method:</strong> Optimal estimation</li> <li><strong>Radiative Transfer Model:</strong> PCA-VLIDORT v2.8</li> <li><strong>A Priori Ozone:</strong> Tropopause-based ozone profile climatology</li> <li><strong>Fitting Window:</strong> 310-335 nm in the TROPOMI UV3 channel</li> <li><strong>Spatial Resolution:</strong> Cross-track pixels are binned to match the spatial resolution of the TROPOMI UV1 channel, with every 5 scan pixels coadded.</li> <li><strong>Spatial coveragne</strong> : 50E-180E, 10S-70S</li> <li><strong>Heritage:</strong> OMI PROFOZ ozone profile algorithm (Liu et al., 2010; Bak et al., 2024)</li> </ol>
Eight Mile Lake Research Watershed, Thaw Gradient: Growing season soil profile CO2 production at 10, 20, 30, and 40 cm, 2005-2007.
This dataset contains CO2 concentrations, diffusion coefficient, and soil CO2 fluxes at each depth interval and soil CO2 production at each replicate. The data were collected during the growing season as well as shoulder seasons.
Research on Universities Profile upon Entrepreneurship and Innovation Orientation: Case of Developing Countries
<p>This Data set is a part of a research project of "Research on Universities Profile upon Entrepreneurship and Innovation Orientation: Case of Developing Countries" </p>
Research Compendium for Himes et al. (2024): "Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements"
<p>This archive is the Reproducible Research Compendium for</p> <p>Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements</p> <p>by Himes et al. (2024), submitted to Atmospheric Measurement Techniques.</p> <p>This compendium includes all files related to MARGE associated with the manuscript.</p> <p>NN model files are split into smaller files for convenience, given their sizes. To recombine the files, do, e.g., <br> cat cnn_weights_NH-LW.h5* > cnn_weights_NH-LW.h5</p> <p>User interested in running MARGE will need to clone the GitHub repo (https://github.com/exosports/MARGE), apply the patch file to checksum fc95b3c, organize the relevant files into directories as listed in the configuration files (Zenodo does not support organizing files into directory structures) and calculate the number of training, validation, and test cases to be stored in the relevant input file specified in the configuration file. MARGE is under the Reproducible Research Software License (https://planets.ucf.edu/resources/reproducible-research/software-license/). For more details on MARGE, see the User Manual on GitHub.</p>
Learn2Link: Linking the Social and Academic Profiles of Researchers
<p>Data and code to reproduce work in Learn2Link: Linking the Social and Academic Profiles of Researchers.</p>
The clinical impact of high-profile animal-based research reported in the UK national press: a detailed discussion of articles from 1995, and full search results from the Nexis database
<p><span><span><span><span><span><span><span><span><span><span><span><b>Objectives</b>: We evaluated animal-based biomedical 'breakthroughs' reported in the UK national press in 1995 (25 years prior to the conclusion of this study). Based on evidence of over-speculative reporting of biomedical research in other areas (e.g. press releases and scientific papers), we specifically examined animal research in the media, asking, "In a given year, what proportion of animal research 'breakthroughs' published in the UK national press had translated, more than 20 years later, to approved interventions?"</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Methods</b>: We searched the Nexis media database (LexisNexis.com) for animal-based biomedical reports in the UK national press. The only restrictions were that the intervention should be specific, such as a named drug, gene, biomedical pathway, to facilitate follow-up, and that there should be claims of some clinical promise. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Main Outcome Measures</b>: Were any interventions approved for human use? If so, when and by which agency? If not, why, and how far did development proceed? Were any other, directly related interventions approved? Did any of the reports over-state human relevance?</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results</b>: Over-speculation and exaggeration of human relevance was evident in all the articles examined. Of 27 unique published 'breakthroughs', only one had clearly resulted in human benefit. Twenty were classified as failures, three were inconclusive, and three were partially successful.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusions</b>: The results of animal-based pre-clinical research studies are commonly over-stated in media reports, to prematurely imply often-imminent 'breakthroughs' relevant to human medicine.</span></span></span></span></span></span></span></span></span></span></span></p>
Tumor multi-omics profiles and clinical information employed in TMO-Net research
<p>This dataset includes tumor multi-omics profiles used in TMO-Net research. The preprocessed TCGA pan-cancer multi-omics were employed during pre-training stage of TMO-Net. The metabric multi-omics dataset, metastatistic tumor multi-omics dataset, PDX cell line multi-omics dataset, GDSC cell line multi-omice dataset, CPTAC cancer multi-omics dataset and metastatic dataset were used for downstream tasks and analysis.</p>
Data products associated with: Tamburo, Withers, Dalba, Moore, and Koskinen (2023) Cassini radio occultation observations of Saturn's ionosphere: Electron density profiles from 2005 to 2013, Journal of Geophysical Research, doi:10.1029/2023JA031310
<p>These data products are associated with Tamburo, Withers, Dalba, Moore, and Koskinen (2023) Cassini radio occultation observations of Saturn’s ionosphere: Electron density profiles from 2005 to 2013, Journal of Geophysical Research, doi:10.1029/2023JA031310. At the time of writing, this manuscript is under review. In the future, these data products will be submitted for archiving at the NASA Planetary Data System (PDS).</p> <p> </p>
Data containing alitude profiles of Joule heating and Pedersen conductivity from EISCAT and TIE-GCM used for the research work "A comparative assessment of the distribution of Joule heating in altitude as estimated in TIE-GCM and EISCAT over one solar cycle"
<p>This archive contains the data used for the research work with title "A comparative assessment of the distribution of Joule heating in altitude as estimated in TIE-GCM and EISCAT over one solar cycle". The data represent Joule heating and Pedersen conductivity altitude profiles for the duration of the solar cycle 24 between the years 2009 and 2019. The information is separated by Magnetic Local Time, Kp index and altitude. There are data from the EISCAT incoherent scatter radar in Matlab format and from a TIE-GCM simulation executed at the Department of Electrical and Computer Engineering, Democritus University of Thrace in NetCDF format. </p>
The clinical impact of high-profile animal-based research reported in the UK national press: a detailed discussion of articles from 1995, and full search results from the Nexis database
Open the record for dataset details and reuse information.
Anonymized Researcher Interview Data - from the Raising the Profile of the NCSU Libraries Research Support Strategies & Engagement project
Open the record for dataset details and reuse information.
Fig 4 from: Riede K (2018) Acoustic profiling of Orthoptera: present state and future needs. Journal of Orthoptera Research 27(2): 203-215. https://doi.org/10.3897/jor.27.23700
Fig 4 A data warehouse for sound management. The scheme illustrates elements and workflow for acoustic profiling of Orthoptera. Songs are sampled either by recording individual songsters (Targeted Recordings), or entire acoustic scenes, each of which could contain several Orthoptera songs. Targeted recordings are treated like specimens, with time and locality stamps and, preferably, a voucher specimen. All databases listed in Table 1 are designed to store individual recordings. These distributed databases could be federated via ABCD- or Darwin-protocol. Soundscapes require distinct data management of large multimedia files. Orthoptera songs could be extracted manually or semi-automatically as sound snippets, and eventually be identified (ID) manually, or using automatic sound recognition algorithms (ASR). Many snippets can be extracted from each scene, resulting in a one-to-many relationship between scenes and snippets.
Fig 2 from: Riede K (2018) Acoustic profiling of Orthoptera: present state and future needs. Journal of Orthoptera Research 27(2): 203-215. https://doi.org/10.3897/jor.27.23700
Fig 2 The SYSTAX database. Screenshot of the new SYSTAX user interface, to be released under www.systax.org. A search for the Neotropical tettigoniid genus Anaulacomera recovers several sound recordings from a voucher specimen of a hitherto undescribed species, documented by photographs. Faceting allows searching by images or sounds exclusively.
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.