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8,537 results for “OpenITI”
Towards an open-source landscape for 3D CSEM modelling
<p>Accompanying data to journal article</p> <blockquote> <p>Werthmüller, D., R. Rochlitz, O. Castillo-Reyes, and L. Heagy, 2021, Towards an open-source landscape for 3D CSEM modelling: Geophysical Journal International; ggab238, DOI: <a href="https://doi.org/10.1093/gji/ggab238">10.1093/gji/ggab238</a>.</p> </blockquote> <ul> <li>Official article: <a href="https://doi.org/10.1093/gji/ggab238">https://doi.org/10.1093/gji/ggab238</a></li> <li>GitHub repo: <a href="https://github.com/swung-research/3d-csem-open-source-landscape">https://github.com/swung-research/3d-csem-open-source-landscape</a></li> <li>arXiv.org: <a href="https://arxiv.org/abs/2010.12926">https://arxiv.org/abs/2010.12926</a></li> </ul> <p>The Marlim R3D model can be found at:</p> <ul> <li>Original, fine resistivity model: <a href="https://doi.org/10.5281/zenodo.400233">https://doi.org/10.5281/zenodo.400233</a></li> <li>Upscaled computational model: <a href="https://doi.org/10.5281/zenodo.3748491">https://doi.org/10.5281/zenodo.3748491</a></li> <li>CSEM data set: <a href="https://doi.org/10.5281/zenodo.1256786">https://doi.org/10.5281/zenodo.1256786</a></li> <li>Noise-free CSEM data set: <a href="https://doi.org/10.5281/zenodo.1807134">https://doi.org/10.5281/zenodo.1807134</a></li> </ul>
Survey of open data and information seeking in Kenya's Urban Slums and Rural Settlements
<p>This dataset provides survey responses from 240 people surveyed as part of the "Investigating the Impact of Kenya’s Open Data Initiative on Marginalized Communities: Case Study of Urban Slums and Rural Settlements" project.</p> <p>The data, collected in mid-2013 looks at issues of where citizens look for data, and how successful they have been in getting government information from different sources, as well as their awareness of the Kenya open data portal, and their interest in getting information through different digital channels in future.</p> <p>Descriptive statistics have been analysed in the publication "Open Government Data for Effective Public Participation: Findings of a Case Study Research Investigating The Kenya's Open Data Initiative in Urban Slums and Rural Settlements", but no further analysis has yet been carried out.</p> <p><strong>Data descriptions</strong></p> <p>The Codebook.csv file lists variable names and the questions asked to elicit each response.</p> <p>JHC-Data.csv contains the results from the questionnaires collected through structured in-person interview in the three locations. The questionnaires were administered at chiefs centres, community resource centres, constituency development fund office and religious centres). The questionnaires were filled in by every 2nd these centres.</p> <p><strong>More information</strong></p> <p>More information on the research project can be found at http://opendataresearch.org/project/2013/jhc</p>
Prevalence of Creative Commons licenses in the Directory of Open Access Journals by discipline, author fees, number of journals per country and publisher
<p>An analysis on the prevalence of Creative Commons licenses in the Directory of Open Access Journals by discipline, author fees, country and publisher according to the number of journals.</p>
Research Data of the 2014 Census of Open Access Repositories in Germany, Austria and Switzerland
<p>The "2014 Census of Open Access Repositories in Germany, Austria and Switzerland” (2014 Census) is a study on the green open access landscape conducted in the course of a project seminar at the Berlin School of Library and Information Science (BSLIS) at Humboldt-Universität zu Berlin. The 2014 Census not only succeeds the "2012 Census of Open Access Repositories in Germany"[1] but enhances it by adding an online survey to the qualitative analysis of the open access repository websites and the automatic validation of its metadata. Like in 2012 the 2014 Census gives insights into the development of open access repositories and current trends in repository design being of substantial use to open access repository operators.</p> <p>This 2014 Census data set represents the data collected in three different ways:</p> <ul> <li>qualitative analysis of the open access repository websites</li> <li>automatic validation of the metadata via OAI-PMH using the DINI-Validator [2] </li> <li>online survey of repository operators</li> </ul> <p>As in 2012 [3] the data set is provided in XLSX as well as in CSV format. The columns represent the criteria and the rows represent the analyzed open access repositories. In the XLSX file the header row gives the definition of each criterion in English and German. In the CSV "content" file the header row is in English short terms. The respective English and German definition can be found in the CSV "readme" file.</p> <p> </p> <p>[1] Vierkant, P. (2013). 2012 Census of Open Access Repositories in Germany: Turning Perceived Knowledge Into Sound Understanding. <em>D-Lib Magazine</em>, 19. http://dx.doi.org/10.1045/november2013-vierkant </p> <p>[2] http://oanet.cms.hu-berlin.de/validator/pages/validation_dini.xhtml</p> <p>[3] Vierkant, Paul; Voigt, Michaela; Dupski, Jens; David, Sammy; Lösch, Mathias (2013): 2012 Census of Open Access Repositories in Germany. fig<strong>share</strong>. <br /> http://dx.doi.org/10.6084/m9.figshare.677099</p>
ROARMAP Open Access Policy data - Country list
<p>This data is a sub set of a dump from ROARMAP [http://roarmap.eprints.org/] taken on 24th August 2014.</p> <p>ROARMAP is the Registry of Open Access Repository Mandates and Policies, a searchable international registry charting the growth of open access mandates and policies adopted by universities, research institutions and research funders that require or request their researchers to provide open access to their peer-reviewed research article output by depositing it in an open access repository.</p> <p>Number of Open Access policies and a ranking is shown for each country.</p> <p>The data is being used for a series of data visualisations [http://pasteur4oa-dataviz.okfn.org/] for the PATEUR4OA Project [http://pasteur4oa.eu/].</p> <p> </p> <p>PASTEUR4OA (Open Access Policy Alignment Strategies for European Union Research) aims to support the European Commission’s Recommendation to Member States of July 2012 that they develop and implement policies to ensure Open Access to all outputs from publicly-funded research. </p> <p>PASTEUR4OA will help develop and/or reinforce open access strategies and policies at the national level and facilitate their coordination among all Member States. It will build a network of centres of expertise in Member States that will develop a coordinated and collaborative programme of activities in support of policymaking at the national level under the direction of project partners.</p>
Numbers and shares of Open Access Journals in Sociology using Creative Commons Licenses, June 2014
<p>a) The data for the year 2014 was retrieved as a CSV-file (doaj_2014-05-07_1330_utf8.csv) from the Directory of Open Access Journals (DOAJ) homepage at 2014-06-08.<br> b) A subset of journals assigned to the subject category Sociology was generated (n=109).<br> c) I manually checked the information on CC-licenses for each of the 109 journals<br> d) Where necessary I added correct information on licenses, see column k in the CSV-file for the updated information. Column l marks entries that were updated.</p> <p> </p>
Open Access journals charging publication fees for a) the ten countries with the highest output of Open Access journals and b) sociological journals (2012, 2014)
<p>The files represent data on Open Access journals charging publication fees for a) the ten countries with the highest output of Open Access journals and b) all sociological journals indexed in the Directory of Open Access Journals DOAJ. The data covers a time span of about 18 months, it was collected in November 2012 and June 2014.</p>
Numbers and shares of Open Access Journals from all disciplines and from the discipline Sociology using Creative Commons Licenses as listed by the Directory of Open Access Journals (2014-06-08)
<p>These files contains the data on frequencies and shares of Open Access journals using Creative Commons Licenses (comparing journals from all disciplines and sociologial journals). Date of data collection: 2014-06-08.</p>
Numbers and shares of Open Access Journals in Sociology charging publication fees (article processing charges APCs)
<p>On the eleventh of June 2014 the Directory of Open Access Journals DOAJ listed 109 sociology journals. For these journals the information on publication charges was wrong in more than 10 % of the cases. Focusing on the eleven journals that - according to the DOAJ - used (always or conditionally) APCs the situation is even worse: 10 of these journals (90,9 %) were categorized wrongly.</p> <p>In fact only three out of these 109 journals (2,75 %) are charging their authors. The files contain corrected information for the 109 journals including the sums charged by the journals.</p>
Artikel- und Buchpublikationen aus der Soziologie: Open Science, Open Access zu Texten, Open Access zu Forschungsdaten, Open Access zu Forschungssoftware, Altmetrics [open version]
<p>Die Daten umfassen Stichproben an je 100 Journalartikeln und Buchpublikationen aus der Soziologie. Ausgwählt wurden Veröffentlichungen aus deutschsprachigen und nicht-deutschsprachigen Ländern.</p> <p>Für alle Artikel der Stichprobe wurde geprüft,</p> <p>1) ob sie im Gold Open Access verfügbar waren<br> 2) ob sie im Green Open Access verfügbar waren<br> 3) auf welchem Repository-Typ sie (im Falle einer Green Open Access Publikation) verfügbar waren<br> 4) seit wann sie im Green Open Access verfügbar waren<br> 5) ob Forschungsdaten zum Artikel verfügbar waren<br> 6) ob Forschungssoftware zum Artikel verfügbar war<br> 7) wie häufig der Artikel in Google Scholar zitiert wurde<br> 8) wie häufig der Artikel im Web of Science zitiert wurde<br> 9) wie häufig der Artikel in Scopus zitiert wurde<br> 10) wie häufig der Artikel in den Sociological Abstracts zitiert wurde<br> 11) wie viele Mendeley User Counts der Artikel aufwies<br> 12) wie häufig der Artikel getwittert wurde (Datenquelle: Topsy)</p> <p> </p> <p>Für alle Bücher der Stichprobe wurde geprüft,</p> <p>1) ob sie im Gold Open Access verfügbar waren<br> 2) ob sie im Green Open Access verfügbar waren<br> 3) auf welchem Repository-Typ sie (im Falle einer Green Open Access Publikation) verfügbar waren<br> 4) seit wann sie im Green Open Access verfügbar waren<br> 5) wie häufig das Buch in Google Scholar zitiert wurde<br> 6) wie häufig das Buch im Web of Science zitiert wurde (als Cited-Reference-Analyse)<br> 7) wie häufig das Buch in Scopus zitiert wurde<br> 8) wie häufig das Buch in der International Bibliography of the Social Sciences zitiert wurde<br> 9) wie viele Mendeley User Counts das Buch aufwies<br> 10) wie häufig das Buch getwittert wurde (Datenquelle: Topsy)</p> <p><br> Da die Impact-Informationen unter Copyright-Schutz der Datenbank-Anbieter stehen, können diese nicht frei zugänglich gemacht werden. Es existiert jedoch eine nicht offen verfügbare Version dieser Datensammlung, die Impact-Informationen enthält und die auf Anfrage bereitgestellt werden kann.</p>
Open-data release of aggregated Australian school-level information. Edition 2016.1
<p>The file set is a freely downloadable aggregation of information about Australian schools. The individual files represent a series of tables which, when considered together, form a relational database. The records cover the years 2008-2014 and include information on approximately 9500 primary and secondary school main-campuses and around 500 subcampuses. The records all relate to school-level data; no data about individuals is included. All the information has previously been published and is publicly available but it has not previously been released as a documented, useful aggregation. The information includes:<br /> (a) the names of schools<br /> (b) staffing levels, including full-time and part-time teaching and non-teaching staff<br /> (c) student enrolments, including the number of boys and girls<br /> (d) school financial information, including Commonwealth government, state government, and private funding<br /> (e) test data, potentially for school years 3, 5, 7 and 9, relating to an Australian national testing programme know by the trademark 'NAPLAN'<br /> <br /> Documentation of this Edition 2016.1 is incomplete but the organization of the data should be readily understandable to most people. If you are a researcher, the simplest way to study the data is to make use of the SQLite3 database called 'school-data-2016-1.db'. If you are unsure how to use an SQLite database, ask a guru.<br /> <br /> The database was constructed directly from the other included files by running the following command at a command-line prompt:<br /> <em>sqlite3 school-data-2016-1.db < school-data-2016-1.sql</em><br /> Note that a few, non-consequential, errors will be reported if you run this command yourself. The reason for the errors is that the SQLite database is created by importing a series of '.csv' files. Each of the .csv files contains a header line with the names of the variable relevant to each column. The information is useful for many statistical packages but it is not what SQLite expects, so it complains about the header. Despite the complaint, the database will be created correctly.<br /> <br /> Briefly, the data are organized as follows.<br /> (a) The .csv files ('comma separated values') do not actually use a comma as the field delimiter. Instead, the vertical bar character '|' (ASCII Octal 174 Decimal 124 Hex 7C) is used. If you read the .csv files using Microsoft Excel, Open Office, or Libre Office, you will need to set the field-separator to be '|'. Check your software documentation to understand how to do this.<br /> (b) Each school-related record is indexed by an identifer called 'ageid'. The ageid uniquely identifies each school and consequently serves as the appropriate variable for JOIN-ing records in different data files. For example, the first school-related record after the header line in file 'students-headed-bar.csv' shows the ageid of the school as 40000. The relevant school name can be found by looking in the file 'ageidtoname-headed-bar.csv' to discover that the the ageid of 40000 corresponds to a school called 'Corpus Christi Catholic School'.<br /> (3) In addition to the variable 'ageid' each record is also identified by one or two 'year' variables. The most important purpose of a year identifier will be to indicate the year that is relevant to the record. For example, if one turn again to file 'students-headed-bar.csv', one sees that the first seven school-related records after the header line all relate to the school Corpus Christi Catholic School with ageid of 40000. The variable that identifies the important differences between these seven records is the variable 'studentyear'. 'studentyear' shows the year to which the student data refer. One can see, for example, that in 2008, there were a total of 410 students enrolled, of whom 185 were girls and 225 were boys (look at the variable names in the header line).<br /> (4) The variables relating to years are given different names in each of the different files ('studentsyear' in the file 'students-headed-bar.csv', 'financesummaryyear' in the file 'financesummary-headed-bar.csv'). Despite the different names, the year variables provide the second-level means for joining information acrosss files. For example, if you wanted to relate the enrolments at a school in each year to its financial state, you might wish to JOIN records using 'ageid' in the two files and, secondarily, matching 'studentsyear' with 'financialsummaryyear'.<br /> (5) The manipulation of the data is most readily done using the SQL language with the SQLite database but it can also be done in a variety of statistical packages.<br /> (6) It is our intention for Edition 2016-2 to create large 'flat' files suitable for use by non-researchers who want to view the data with spreadsheet software. The disadvantage of such 'flat' files is that they contain vast amounts of redundant information and might not display the data in the form that the user most wants it.<br /> (7) Geocoding of the schools is not available in this edition.<br /> (8) Some files, such as 'sector-headed-bar.csv' are not used in the creation of the database but are provided as a convenience for researchers who might wish to recode some of the data to remove redundancy.<br /> (9) A detailed example of a suitable SQLite query can be found in the file 'school-data-sqlite-example.sql'. The same query, used in the context of analyses done with the excellent, freely available R statistical package (http://www.r-project.org) can be seen in the file 'school-data-with-sqlite.R'.</p>
The Debsources Dataset: Two Decades of Free and Open Source Software
<p>This is the Debsources Dataset: source code and related metadata spanning two decades of Free and Open Source Software (FOSS) history, seen through the lens of the Debian distribution.</p> <p>The dataset spans more than 3 billion lines of source code as well as metadata about them such as: size metrics (lines of code, disk usage), developer-defined symbols (ctags), file-level checksums (SHA1, SHA256, TLSH), file media types (MIME), release information (which version of which package containing which source code files has been released when), and license informa-<br> tion (GPL, BSD, etc).</p> <p>The Debsources Dataset comes as a set of tarballs containing deduplicated unique source code files organized by their SHA1 checksums (the source code), plus a portable PostgreSQL database dump (the metadata).</p> <p>The Debsources Dataset is described in full in the paper The Debsources Dataset: Two Decades of Free and Open Source Software, published on the Empirical Software Engineering journal with DOI 10.1007/s10664-016-9461-5 . A preprint of the paper is available at https://upsilon.cc/~zack/research/publications/debsources-ese-2016.pdf .</p>
JDC2015 - A multimodal dataset from facilitating multi-tabletop lessons in an open-doors day
<p><strong>IMPORTANT NOTE: Two of the files in this dataset are incorrect, see this dataset's errata at https://zenodo.org/record/204063 and https://zenodo.org/record/204819</strong></p> <p>This dataset contains eye-tracking, EEG, accelerometer, indoor location and video coding data from a single subject (a researcher with limited teacher experience), facilitating four maths lessons in a simulated multi-tabletop classroom, with four cohorts of 10-12 year old students, using tangible paper tabletops and a projector. These sessions were recorded in the frame of the MIOCTI project (http://chili.epfl.ch/miocti).</p> <p>This dataset has been used in several scientific works, such a submitted journal paper "Orchestration Load Indicators and Patterns: In-the-wild Studies Using Mobile Eye-tracking", by Luis P. Prieto, Kshitij Sharma, Lukasz Kidzinski & Pierre Dillenbourg (the analysis and usage of this dataset is available publicly at https://github.com/chili-epfl/paper-IEEETLT-orchestrationload)</p>
Data of the Open Access Repository Ranking 2015
<p>The Open Access Repository Ranking 2015 ranks open access repositories from Germany, Austria and Switzerland. Data for the 2015 ranking was partly submitted by the respective repository managers, and partly automatically validated via the OAI interfaces. The OARR team reviewed all submissions assuring the quality and validity.<br> The ranking is based on an open and transparent metric that was developed in accordance with the open access community. This metric is a synthesis of different schemes and studies that surveyed and describe open access repositories.<br> Data includes the 2015 scores and descriptive information on the repositories as well as the 2015 metric.</p>
Review of definitions of Open Peer Review in the scholarly literature 2016
<p>This data set contains:</p> <ul> <li>Full data files of a 2016 review of definitions of Open Peer Review in the scholarly literature in xls and csv formats</li> <li>Description of data collection methodology (txt)</li> <li>Readme file (txt)</li> </ul> <p>The term “open peer review” has neither a standardized definition nor an agreed schema of its features and implementations. Recognising the absence of a consensus view on what OPR is, OpenAIRE has undertaken a systematic review of definitions of “open peer review” or “open review”, to create a corpus of 122 definitions. These definitions have been systematically analysed to build a coherent typology of the many different innovations in peer review signified by the term and hence provide the precise technical definition currently lacking. This quantifiable data offers rich information on the range and extent of differing definitions over time and by broad subject area.</p> <p>Contact:</p> <p>Tony Ross-Hellauer: http://orcid.org/0000-0003-4470-7027 / ross-hellauer@sub.uni-goettingen.de</p>
OpenAIRE Open Peer Review Survey 2016
<p>This data set contains:</p> <p>- Full data files of a 2016 survey of attitudes to Open Peer Review in xls and csv formats<br> - Survey questions (pdf)<br> - Readme file (txt)</p> <p>Between 8 September and 7 October 2016, OpenAIRE held a survey designed to aid the development of appropriate OPR approaches by providing evidence about the attitudes of authors, editors and reviewers towards OPR, their reservations and needs, as well as to gauging current levels of experience and reservation with different types of OPR. A supplementary aim was to collect feedback on a provisional definition of OPR as created during another strand of work. The survey aimed to aid the development of appropriate OPR approaches by providing evidence about the attitudes of authors, editors and reviewers towards OPR, their reservations and needs, as well as to gauge current levels of experience and reservations with different types of OPR. The survey was conducted via an openly accessible online questionnaire (using the scientific survey platform SoSci, www.soscisurvey.de). It received a total of 3062 complete responses (a further 635 responses were discarded as incomplete). The survey was open to all wishing to take part and distributed via social media, scholarly communications mailing lists, publisher newsletters and, in one case, a publisher internal mailing list (Copernicus Publications). </p> <p>Acknowledgement: This work is funded by the European Commission H2020 project OpenAIRE2020 (Grant agreement: 643410, Call: H2020-EINFRA-2014-1)</p> <p>Contact: Dr Tony Ross-Hellauer, University of Göttingen, State and University Library, ross-hellauer@sub.uni-goettingen.de<br> </p>
Utrecht University Open Access Monitor: Methodology
<p>Methodology used for the Utrecht University Open Access Monitor, which attributes OA labels to peer reviewed journal articles registered by researchers in the UU CRIS and UMCU CRIS (PURE).</p>
National Open Access Monitor, Ireland - Research product metadata
<p>This dataset contains the foundational data for the National Open Access Monitor under an open license. OpenAIRE will routinely provide monthly data dumps to Zenodo, encompassing a comprehensive set of data and indicators related to the Open Access Monitor. This collaborative effort ensures accessibility and openness in sharing the data, promoting transparency and facilitating its use for research and analysis purposes.<br>The dataset comprises of the metadata of the research products metadata stored in the parquet format. The file contains two columns ("id", "xml"), the first of which is the OpenAIRE identifier of the research product and the the second the xml representation of the metadata. The schema of the metadata can be found in https://www.openaire.eu/schema/1.0/oaf-1.0.xsd and a detailed description of the contents can be found in https://graph.openaire.eu/docs/ and https://zenodo.org/records/2643199</p>
Weather Data Cutouts for PyPSA-Eur: An Open Optimisation Model of the European Transmission System
<p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the entire ENTSO-E area. It can be built using the code provided at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a>.</p> <p><strong>It contains</strong> alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.</p> <p><strong>Not all data dependencies</strong> are shipped with the <a href="https://github.com/PyPSA/PyPSA-eur">code repository</a> since git is not suited for handling large changing files. Instead, we provide separate data bundles and cutouts to be downloaded and extracted, as noted in the documentation.</p> <p>The provided <strong>cutouts </strong>are merged spatiotemporal subsets of the European weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V003">CMSAF SARAH-3</a> solar surface radiation dataset for the years 1996, 2010, 2012, 2013, 2019, 2020 and 2023. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>).</p> <p>Solar irradiation data is taken from SARAH-3 while all other weather data is from ERA5.</p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul> <p><strong>CMSAF SARAH-3</strong></p> <ul> <li>Pfeifroth, Uwe; Kothe, Steffen; Drücke, Jaqueline; Trentmann, Jörg; Schröder, Marc; Selbach, Nathalie; Hollmann, Rainer (2023): Surface Radiation Data Set - Heliosat (SARAH) - Edition 3, Satellite Application Facility on Climate Monitoring, DOI:10.5676/EUM_SAF_CM/SARAH/V003, <a href="https://doi.org/10.5676/EUM_SAF_CM/SARAH/V003" target="_blank" rel="noopener">https://doi.org/10.5676/EUM_SAF_CM/SARAH/V003</a>.</li> <li><strong>Terms of Use:</strong> All intellectual property rights of the CM SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "copyright (year) EUMETSAT" on each of the products used.</li> </ul>
Open ruimte voor de monitoring van patrijs in Vlaanderen
<p>Deze kaartlaag omvat de relevante open ruimte voor patrijs (<em>Perdix perdix</em>) in Vlaanderen. <a href="https://doi.org/10.5281/zenodo.5814813">https://doi.org/10.5281/zenodo.5814813</a> bevat de broncode en basisdata die aan de oorsprong liggen van deze kaartlaag.</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.