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641 results for “Guideline”
IPBES Data Management Tutorials - Session 4.5: Guidelines for the use of external data
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>Data management of active research data </em>chapter provides an introduction for IPBES experts on how to manage data while actively being used, analyzed, and produced to fulfill the criteria of the IPBES data management policy.</p> <p>This session,<em> Guidelines for the use of external data</em>, introduces the guiding principles for using external data, data discovery platforms, and correct citation practices.</p>
Data and analysis for Association of meeting 24-hour movement guidelines with low back pain among adults
<p>Introduction</p> <p>This data and code forms the analytical process of a study examining associations between meeting different combinations of 24-h movement guidelines (that integrates a recommendations on physical activity, sedentary behaviour, and sleep) with prevalence, frequency and intensity of low back pain in a sample of adults aged 18 years and over. </p> <p>Notes: </p> <p>* the raw data is provided alongside this upload, but the processing is not addressed here. <br> * the authors of this document are a subset of the authors of the related paper.<br> * this document and the related data files were uploaded at the time of submission for review. An update providing the doi of the related paper will be provided when it is available.</p>
IPCC Climate Zones (from the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories)
<p><strong>Description</strong></p> <p>These data (re)create spatial data for the 2019 IPCC Climate Zones, shown in <em>Figure 3A.5.1</em> of <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/4_Volume4/19R_V4_Ch03_Land%20Representation.pdf">Chapter 3: Consistent Representation of Lands</a> in <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/vol4.html">Volume 4: Agriculture, Forestry and Other Land Use</a> of the <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html">2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories</a>. I recreated these data because I could not readily identify the data in a spatial format online, a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of <em>Figure 3A.5.1</em> from the original 2006 guidelines</a>.</p> <p>Resolution: 0.5 arc degree</p> <p>CRS: lon/lat WGS 84</p> <p><strong>If you use these data please ensure you also cite the IPCC</strong> - Calvo Buendia, E et al. (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. IPCC, Switzerland.</p> <p> </p> <p><strong>Methods</strong></p> <p>The data were derived using the classification scheme shown in <em>Figure 3A.5.2</em> based on the gridded Climate Research Unit (CRU) Time Series (TS) monthly climate data (<a href="https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.3711">Harris et al., 2014</a>) for the period from 1985 to 2015 following the methods described in <em>Annex 3A.5 Default climate and soil classifications </em>of the above Chapter. All data were processed in <em>R</em> version 4.2.1, with the packages <a href="https://cran.r-project.org/web/packages/elevatr/index.html"><em>elevatr</em></a> (v0.4.2), <a href="https://cran.r-project.org/web/packages/lubridate/index.html"><em>lubridate</em></a> (v1.8.0), <a href="https://cran.r-project.org/web/packages/magrittr/index.html"><em>magrittr</em></a> (v2.0.3), and <a href="https://cran.r-project.org/web/packages/terra/index.html"><em>terra</em></a> (v1.6-7)<em> </em>attached. The full session info is included as a <em>.txt</em> file. As these methods are not exhaustively described in the Annex, the following assumptions were made:</p> <ul> <li><a href="http://http://dx.doi.org/10.5285/c311c7948e8a47b299f8f9c7ae6cb9af">CRU TS3.25</a> was used as the most recently published data (published on 2017-09-22) that could have been incorporated into the Refinement. Other possibilities include CRU TS3.24 (which are the first data to include 2015), or CRU TS4.00 or CRU TS4.01 (both of which were published in parallel to 3.24 and 3.25). These data were all investigated, and CRU TS3.25 produced results that were the most visually similar to the published <em>Figure 3A.5.1</em> (though non-identical).</li> <li>As the methods did not mention a preferred elevation data source, the <a href="https://cran.r-project.org/web/packages/elevatr/index.html"><em>elevatr</em></a> R package was used to obtain data at zoom level 2 (approx resolution of 0.15 arc degree), that was then resampled to match the 0.5-degree resolution of the CRU data. These data originally come from the <a href="https://www.ngdc.noaa.gov/mgg/global/global.html">ETOPO1 global relief model</a>.</li> </ul> <p> </p> <p><strong>Known discrepancies</strong></p> <ul> <li>The distribution of Tropical Wet and Tropical Moist in South America does not exactly match the original data.</li> <li>There are small discrepancies in Tropical Montane classifications (likely arising from the use of a different elevation layer). These are most noticeable in, but not restricted to, Africa.</li> <li>The classification of Boreal Dry, Polar Dry, and Polar Moist in northern Russia and (to a lesser extent) in northern Canada does not exactly match the original data.</li> <li>There are a small number of Cool Temperate Dry pixels in the UK, and Warm Temperate Dry pixels around Brittany which do not occur in the original data.</li> </ul> <p> </p> <p><strong>Disclaimer</strong></p> <p><strong>I am not affiliated with the IPCC in any way</strong>, I just needed spatial data of the Climate Zones, and could not readily identify any online. This is a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of <em>Figure 3A.5.1</em> from the original 2006 guidelines</a>.</p> <p> </p> <p><strong>File description</strong></p> <ul> <li><em>README.html</em> - ~this description file.</li> <li><em>IPCC_Climate_Zones_ts_3.25.tif</em> - the output Climate Zones map at 0.5-arc degree resolution based on the CRU TS3.25 data.</li> <li><em>IPCC_Climate_Zones_colour_map.clr </em>- a colour map file to render the output map with the same colours as in the IPCC 2019 Refinement figure.</li> <li><em>IPCC_Climate_Zones_ts_3.25.png</em> - an image file of the output Climate Zones map.</li> <li><em>ipcc_climate_zones_2019.R</em> - the script used to produce these data.</li> <li><em>session_info.txt</em> - the R session info.</li> </ul>
OpenAIRE Guidelines for CRIS Managers 1.0
<p>The Guidelines specify the interoperability layer between Current Research Information Systems (CRIS) and the OpenAIRE infrastructure. The information interchange is based on the Common European Research Information Format (CERIF) data model, the CERIF XML exchange format, and the OAI-PMH protocol. The Guidelines are intended mainly for implementers and administrators of CRIS who plan to communicate research information to OpenAIRE. OpenAIRE (openaire.eu) is the European infrastructure enabling researchers to comply with the European Union requirements for Open Access to research results. OpenAIRE collects metadata from a variety of data sources: publication repositories, data archives and CRIS across Europe and beyond. Interoperability guidelines are defined for each type of source. CERIF is a standard data model for research information and a recommendation by the European Union to its Member States. The custody of CERIF has been entrusted by the European Union to euroCRIS (eurocris.org), an international not-for-profit organisation dedicated to the interoperability of CRIS.</p> <p> </p>
GAPs Data Repository on Return: Guideline, Data Samples and Codebook
<p><span>The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at <a href="https://data.returnmigration.eu/" target="_new"><span>https://data.returnmigration.eu/</span></a><span>. </span></span></p> <p><span>This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels.</span></p> <p><span>The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts. </span></p> <p><span>The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap </span><span>(Research Electronic Data Capture). It <span> </span>is a secure web application for building and managing online surveys and databases.</span><span>The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data.<span> </span></span></p> <p><span>This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data.</span></p> <p><span>The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field.</span></p> <p><span>Explore the GAPs Data Repository at <a href="https://data.returnmigration.eu/" target="_new">https://data.returnmigration.eu/</a>.</span></p>
Kobalt: Extension Corpus and Annotation Guidelines for Verb Classification and Dependency Adjustments
<p>Kobalt (Zinsmeister et al. 2012) is a task-based corpus of essays written by learners and native speakers of German. This repository contains data that was not included in the original corpus and new layers of annotation to the original and the extended corpus, specifically morphological and syntactic classification of verbs and corrections and changes to dependency parses. Please refer to the annotation guidelines included in this repository for further information.<br> </p>
A Survey on Adoption Guidelines for the FAIR4RS Principles: Dataset
<p>A list of 30+ online resources have been identified and curated by the FAIR4RS Subgroup 5: Adoption Guidelines. These resources are available as the supplementary materials of the report (<a href="https://doi.org/10.5281/zenodo.6374598">Martinez et al., 2022</a>) and can be downloaded and cited from this landing page.</p> <p><strong>The list is open for additions by the community via comments directly to this <a href="https://docs.google.com/spreadsheets/d/1pMWEyadkGW22zYaBl3LsNYoQVk9AW711-2Ogyi8UZZo/edit#gid=0">link</a>. We particularly encourgae authors of new and exisiting resources to add as much detail as possible to describe their resource and its relevance to the <a href="http://doi.org/10.15497/RDA00068">FAIR4RS Principles</a>. Each of the columns has a description and whether the information is optional or not. Whe plan to add tags to the added resources for each semester and when there is another set of 30 resources we can resealease a new version. </strong></p> <p>This list reflects the wide spectrum of global contributions supporting the implementation of the FAIR Principles, particularly regarding research software. It is a snapshot of currently available resources, although we expect that new resources will become available in the future and that the contents of the current list will evolve. It is important to note that most of these resources precede the definition of the <a href="https://doi.org/10.15497/RDA00068">FAIR4RS Principles</a>; however, these still support their implementation.</p> <p>The resources were manually collected, analyzed, and categorized according to their type: guidelines, tools, metadata schemas and registries/repositories. For each resource detail is also provided on which of the FAIR4RS Principles that the resource supports.</p> <p> </p> <p><strong>Data collection</strong></p> <p>This subgroup initiated a crowdsourcing effort to identify relevant resources. All members had the opportunity to provide and describe existing FAIR research software guidelines and tools. During the first two months of the subgroup operation in 2021, subgroup participants (referred to as data providers) added resources to an online spreadsheet. Data providers were encouraged to list resources that they were aware of, authored, or were supported by their institutions. Subsequently, the subgroup organized virtual calls to discuss the resources, their descriptions and the categorization. Over the next two months each data provider added descriptions to resources they were familiar with. This meant that some resources gained descriptions from different data providers. Before the completion of the list, the subgroup leads checked the list and cleaned it (removing items that lacked information or providing complementary information). The resulting list is the first crowdsourced list of its type and it welcomes your contributions!</p>
RADAR – Guideline on Personal Data
<p><strong>The HTML publication is available at <a href="https://nfdi4culture.de/go/E5380" target="_blank" rel="noopener">https://nfdi4culture.de/go/E5380</a>.</strong></p> <p>This guideline is intended to help you understand what information falls under the term “personenbezogene Daten” and which of these can be published on RADAR4Culture.</p>
Supplementary Document to "Design Guidelines and Applications for Dual-Band Rat-Race Couplers and Gysel Power Dividers with Unequal Amplitude Imbalances"
<p>This document presented additional results generated using a CAD application [1] developed for the submitted paper [2]. The CAD application is freely available under Creative Commons Attribution 4.0 International. The application can be downloaded from https://zenodo.org/records/11199141.</p> <p>REFERENCES<br>[1] R. Sinha, “Single/ Dual band Rat-race Coupler and Gysel Power Divider with unequal power division ratio,” May 2024. [Online]. Available:<br>https://doi.org/10.5281/zenodo.11199141<br>[2] ——, “Design guidelines and application of dual-band rat-race couplers and Gysel power dividers with unequal amplitude imbalances [application notes],”<br>IEEE Microwave Magazine, vol. vv, no. nn, p. pp, 2024.</p>
Extended data of the project "A survey exploring biomedical editors' perceptions of editorial interventions to improve adherence to reporting guidelines"
<p>Figure S1: Survey questionnaire</p> <p>Table S2: Barriers, facilitators and possible improvements of the interventions included in the survey</p>
Evolution of FDA Guidelines on Control of Nitrosamine Impurities in Human Drugs – A Comparative Analysis of September 2024 Revisions
<p>Nitrosamine impurities have become a significant concern in the pharmaceutical industry due to their carcinogenic potential. In response, the U.S. Food and Drug Administration (FDA) has continuously updated its guidelines to ensure the safety and efficacy of drug products. This review article provides a comprehensive analysis of the evolution of FDA guidelines on the control of nitrosamine impurities, with a particular focus on the September 2024 revisions. By comparing the latest guidance with previous versions, this article highlights key changes, including the expanded focus on Nitrosamine Drug Substance-Related Impurities (NDSRIs), updated risk assessment strategies, and the introduction of new Acceptable Intake (AI) limits. The analysis underscores the FDA's commitment to enhancing drug safety through rigorous control measures and global harmonization efforts.</p>
A Comprehensive Review of ANDA Submissions and Amendments Under GDUFA: FDA Guidelines for the Generic Drug Industry
<p><span>This review provides an in-depth analysis of the Food and Drug Administration's (FDA) guidance document titled <em>ANDA Submissions — Amendments to Abbreviated New Drug Applications (ANDAs) Under the Generic Drug User Fee Amendments (GDUFA)</em>, released in September 2024. The document serves as a comprehensive guide for the pharmaceutical industry, detailing the FDA's expectations regarding the classification, submission, and assessment of amendments to ANDAs and Prior Approval Supplements (PASs). The review discusses key elements of the guidance, including amendment categories (major, minor, and unsolicited), assessment timelines, the process for reclassification of amendments, and potential deficiencies in submissions. The guidance also addresses changes in classifications and assessment goals, deferred amendments, and best practices for ensuring timely FDA approval. This review aims to clarify the FDA’s current thinking on ANDA submissions under GDUFA and the practical implications for generic drug manufacturers seeking to comply with the established regulations.</span></p>
MULTIPLIERS_WP2_Needs analysis in open schooling and interview guidelines_UCY_20231006_v2
<p>This dataset contains data on the review of needs analysis in open schooling. The dataset is composed mainly by the following items:</p> <ul> <li>description of surveyed persons (interviews transcripts; focus groups and written survey with open ending questions)</li> <li>contents of the semi‐structured interview protocol used for conducting both interviews and focus groups. The interview protocols were produced in local languages by partners who did not conduct the interviews in English.</li> <li>summary of the transcripts of interviews with experts in the selected case‐study regions (in English). Interviewees were asked to provide their views and perceptions on OSC network building and open schooling learning projects.</li> <li>summary of the transcripts of focus groups (in English). Each focus group was held in the local language of the beneficiary organising it, recorded with audio equipment and then transcribed. Focus groups were run in Germany, Cyprus, Spain, Slovenia and Sweden.</li> </ul>
Named-Entity Recognition for Modern Tibetan Newspapers: Tagset, Guidelines and Training Data
<p>This dataset, tagset and guidelines were the output of a six-month incubator project on the feasibility of developing Named-Entity Recognition (NER) for modern Tibetan, primarily for use with contemporary Tibetan-language newspapers and media published inside the PRC. The project was carried out by the Mongolian and Inner Asian Studies Unit at Cambridge University’s Department of Social Anthropology. It was funded by an incubator grant from Cambridge Language Sciences. The project title was “Named-Entity Recognition in Tibetan and Mongolian Newspapers.” The Project PI was Dr Hildegard Diemberger (Cambridge), the Coordinator and Lead Author was Dr Robert Barnett (SOAS), and Senior Advisers were Dr Nathan Hill (SOAS), Dr Marieke Meelen (Cambridge), and Dr Thomas White (Cambridge). <br> <br> Although some forms of NER and other NLP procedures have been developed within China for modern Tibetan (see Liu, Nuo <em>et al</em>, 2011), the data underlying those initiatives have not been made publicly available and their findings cannot be tested or reproduced. Significant work on developing NLP for Tibetan has been carried out outside China, but has focused largely on classical Tibetan and religious texts (see Hill & Garrett, Edward, 2017). </p> <p>The Cambridge incubator project therefore produced a tagset, guidelines and training data for developing NER for modern Tibetan, with a focus on historical and political analysis of contemporary newspapers, media and other public documents in Tibetan. We compiled 3.11m syllables of data in Tibetan extracted from articles downloaded from Chinese-language news aggregator sites within China, primarily tibet.cpc.people.com.cn and tibet.people.com.cn. From this data, we selected texts containing 280,000 syllables in Tibetan, grouped in 26,000 utterances/sentences (available on request). Using Lighttag, an online annotation site, we developed a tagset for NER consisting of 17 tags (and one for wrong segmentation if using segmented data). We annotated approximately 186,000 syllables, leading to 9,884 annotations. Of these, after discounting flawed data, we produced training data containing c.6,700 annotations. We carried out the secondary, manual review offline (for our method of converting Lighttag data for offline review, see the attached report “Using Spreadsheets to Review Annotations Offline.pdf”), and found an error rate of 3.6%. The final total of reviewed annotations was 6,624. </p> <p>The dataset, tagset, guidelines and reports were developed and documented by Robert Barnett, with assistance from Tsering Samdrup, Dr Hill and Dr Meelen. Primary annotation was by Tsering Samdrup, assisted by Dr Barnett.<br> <br> The datasets published here include: </p> <ol> <li>The <strong>tagseet guidelines and annotation manual</strong>, including the 17-tag tagset, guidelines, and recommendations ("NER for Modern Tibetan-tagset and guidelines.pdf").</li> <li>The <strong>tagged training data </strong>in .csv format ("Tibetan NER Training Data-tagged, reviewed wth context-v10-UTF-8.csv") and .xls format ("Tibetan NER Training Data-tagged with context-v10-UTF-8.xlsx"). This includes 6,624 reveiwed annotations, arranged according to the Tibetan alphabet together with the tags and context (utterance) for each annotation.</li> <li>The <strong>raw annotation results </strong>downloaded from Lighttag as .json files ("Raw Training Data for NER in Modern Tibetan -Jobs2-11-JSON.zip") and as .xls files ("Training Data for NER in Modern Tibetan -Jobs2-11-XLS.zip"). These include 10 "tasks" or datasets of articles scraped from Tibetan-language websites within Tibet. </li> <li>A <strong>guide to preparing Lighttag annotation results for manual review offline </strong>(“Using Spreadsheets to Review Annotations Offline.pdf”).</li> </ol> <p>The project's findings regarding the status of NER and NLP for vertical Mongolian are available at DOI: 10.5281/zenodo.5103499.</p>
Dataset: Health worker compliance with severe malaria treatment guidelines in the context of implementing pre-referral rectal artesunate in the Democratic Republic of the Congo, Nigeria and Uganda: an operational study
<p>Dataset underlying the publication "<strong>Health worker compliance with severe malaria treatment guidelines in the context of implementing pre-referral rectal artesunate in the Democratic Republic of the Congo, Nigeria and Uganda: an operational study</strong>" (Plos Medicine)</p> <p>Data originating from the Community Access to Rectal Artesunate for Malaria (CARAMAL) Project, 2018-2021.</p> <p>Analysis of health workers' compliance with the treatment guidelines for severe malaria in the context of rolling out pre-referral rectal artesunate (RAS) in the Democratic Republic of the Congo, Nigeria and Uganda. Details provided in the publication.</p>
Ayres 2019: Quantitative Guidelines for Establishing and Operating Soil Archives (repackaging of occurrences published by the NEON Biorepository Data Portal)
Ayres, E. 2019. Quantitative Guidelines for Establishing and Operating Soil Archives. Soil Science Society of America Journal, 83(4): 973-981. https://doi.org/10.2136/sssaj2019.02.0050
Crosswalk of most used metadata schemes and guidelines for metadata interoperability
<p>This resource provides crosswalks among the most commonly used metadata schemes and guidelines to describe digital objects in Open Science, including:</p> <ul> <li>RDA metadata IG recommendation of the metadata element set,</li> <li>EOSC Pilot - EDMI metadata set,</li> <li>Dublin CORE Metadata Terms,</li> <li>Datacite 4.3 metadata schema,</li> <li>DCAT 2.0 metadata schema and DCAT 2.0 application profile,</li> <li>EUDAT B2Find metadata recommendation,</li> <li>OpenAIRE Guidelines for Data Archives,</li> <li>OpenAire Guidelines for literature repositories 4.0,</li> <li>OpenAIRE Guidelines for Other Research Products,</li> <li>OpenAIRE Guidelines for Software Repository Managers,</li> <li>OpenAIRE Guidelines for CRIS Managers,</li> <li>Crossref 4.4.2 metadata XML schema,</li> <li>Harvard Dataverse metadata schema,</li> <li>DDI Codebook 2.5 metadata XML schema,</li> <li>Europeana EDM metadata schema, </li> <li>Schema.org, </li> <li>Bioschemas,</li> <li>The PROV Ontology.</li> </ul>
An Educational Video Showing How to Use the CoBRA Guideline
<p>Standardized and retrievable citation of bioresources is paramount for the recognition of the work needed for setting and maintaining them. Here we present an educational video to help researchers and biobankers to correctly use the CoBRA guideline, when writing a scientific paper in which the bioresources used in the study have to be cited.</p> <p>As in the acronym, CoBRA is a guideline for the Citation of BioResources in journal Articles and it sets a standard for citing bioresources (including biobanks) in scientific articles, whenever a study based on the use of a bioresource is published.</p>
Guidelines for the rational design and engineering of 3D manufactured solid oxide fuel cell composite electrodes
<p>This file contains the data reported in the paper:</p> <p>A Bertei, F Tariq, V Yufit, E Ruiz-Trejo, N P Brandon, <em>Guidelines for the rational design and engineering of 3D manufactured solid oxide fuel cell composite electrodes</em>, <strong>Journal of the Electrochemical Society</strong> (2016)</p> <p>All the data here reported can be reproduced by solving the equations reported in the manuscript with the corresponding parameters.</p>
Interview guideline for maker
<p>In the framework of the EU funded project Make-IT, 10 case studies of maker spaces in different European countries have been compiled based on four interviews each (amongst other data). One interview was conducted with the manager of the maker space and three interviews with makers who regularly made use of the maker space. The results are publised in aggregated form in D3.1 and D3.2 on the project's website: http://make-it.io/</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.