The FAIR Principles
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How to cite this record FAIRsharing.org: FAIR; The FAIR Principles; DOI: https://doi.org/10.25504/FAIRsharing.WWI10U; Last edited: Oct. 24, 2019, 2:56 p.m.; Last accessed: Jul 10 2020 8:23 p.m.
Record added: March 16, 2018, 10:45 a.m.
Record updated: Oct. 24, 2019, 1:08 p.m. by The FAIRsharing Team.
Edits to 'https://fairsharing.org/FAIRsharing.WWI10U' by 'The FAIRsharing Team' at 13:08, 24 Oct 2019 (approved): 'user_defined_tags' has been modified: Before: Data sharing Data standards Metadata standardization After: Data sharing Data standards General purpose Metadata standardization Added: General purpose Removed:
Edits to 'https://fairsharing.org/FAIRsharing.WWI10U' by 'The FAIRsharing Team' at 13:08, 24 Oct 2019 (approved): 'onto_domains' has been modified: Before: After: FAIR Added: FAIR Removed:
Edits to 'https://fairsharing.org/FAIRsharing.WWI10U' by 'MarkWilkinson' at 11:29, 24 Apr 2018 (approved): 'description' has been modified: Before: One of the grand challenges of data-intensive science is to facilitate knowledge discovery by assisting humans and machines in their discovery of, access to, integration and analysis of, task-appropriate scientific data and their associated algorithms and workflows. Here, we describe FAIR - a set of guiding principles to make data Findable, Accessible, Interoperable, and Re-usable. These guidelines may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. After: One of the grand challenges of data-intensive science is to facilitate knowledge discovery by assisting humans and machines in their discovery of, access to, integration and analysis of, task-appropriate scientific data and their associated algorithms and workflows. The term "FAIR" was launched at a Lorentz workshop in 2014, attended by a wide range of academic, corporate, and governmental stakeholders. The resulting draft FAIR Principles were initially made available for public comment via the websites of peer-initiatives such as, for example, Force11. Based on this feedback, the final Principles were published in 2016 (https://www.nature.com/articles/sdata201618). FAIR is a set of guiding principles to make data Findable, Accessible, Interoperable, and Re-usable. These guidelines provide advice for those wishing to enhance the (re)usability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.
Edits to 'https://fairsharing.org/FAIRsharing.WWI10U' by 'The FAIRsharing Team' at 10:50, 16 Mar 2018 (approved): 'homepage' has been modified: Before: https://www.force11.org/group/fairgroup/fairprinciples After: https://www.go-fair.org/fair-principles/
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The FAIR Guiding Principles for scientific data management and stewardship.
Wilkinson MD,Dumontier M,Aalbersberg IJ,Appleton G,Axton M,Baak A,Blomberg N,Boiten JW,da Silva Santos LB,Bourne PE,Bouwman J,Brookes AJ,Clark T,Crosas M,Dillo I,Dumon O,Edmunds S,Evelo CT,Finkers R,Gonzalez-Beltran A,Gray AJ,Groth P,Goble C,Grethe JS,Heringa J,'t Hoen PA,Hooft R,Kuhn T,Kok R,Kok J,Lusher SJ,Martone ME,Mons A,Packer AL,Persson B,Rocca-Serra P,Roos M,van Schaik R,Sansone SA,Schultes E,Sengstag T,Slater T,Strawn G,Swertz MA,Thompson M,van der Lei J,van Mulligen E,Velterop J,Waagmeester A,Wittenburg P,Wolstencroft K,Zhao J,Mons B
Sci Data 2016
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FAIRsharing is a FAIR-supporting resource that provides an informative and educational registry on data standards, databases, repositories and policy, alongside search and visualization tools and services that interoperate with other FAIR-enabling resources. FAIRsharing guides consumers to discover, select and use standards, databases, repositories and policy with confidence, and producers to make their resources more discoverable, more widely adopted and cited. Each record in FAIRsharing is curated in collaboration with the maintainers of the resource themselves, ensuring that the metadata in the FAIRsharing registry is accurate and timely. Every record is manually reviewed at least once a year. Records can be collated into collections, based on a project, society or organisation, or Recommendations, where they are collated around a policy, such as a journal or funder data policy.
4TU.Centre for Research Data
4TU.Centre for Research Data (short: 4TU.ResearchData) was started in 2008 as an initiative of the three technical universities in the Netherlands – Delft University of Technology, Eindhoven University of Technology, and the University of Twente. The ambition was, and still is, to create and maintain a national state-of-the-art facility for storing and preserving science and engineering research data and for making those data openly accessible. The data archive has been fully operational since 2010 and it has evolved to become a trusted and certified repository for science and engineering. By publishing data-sets via 4TU.ResearchData you will make your data FAIR. Every single data-set is assigned a DOI and metadata (F), the archive is accessible 24/7 online worldwide via https protocol (A), the data-files adhere to community and preservation standards (I), and a readme-file and usage license is provided for every data-set (R). This archive is accessible and usable for any researcher from the science and engineering disciplines. Please visit our website for more details.
The Tromsø Repository of Language and Linguistics
The Tromsø Repository of Language and Linguistics (TROLLing) is a repository of data, code, and other related materials used in linguistic research. The repository is open access, which means that all information is available to everyone. All postings are accompanied by searchable metadata that identify the researchers, the languages and linguistic phenomena involved, the statistical methods applied, and scholarly publications based on the data (where relevant). DataverseNO is aligned with the FAIR Guiding Principles for scientific data management and stewardship. Being part of DataverseNO, TROLLing is CoreTrustSeal certified.
DataverseNO (https://dataverse.no/) is a national, generic repository for open research data, owned and operated by UiT The Arctic University of Norway. DataverseNO is aligned with the FAIR Guiding Principles for scientific data management and stewardship. The technical infrastructure of the repository is based on the open source application Dataverse, which is developed by an international developer and user community led by Harvard University. DataverseNO is CoreTrustSeal certified.
Metabolic Atlas integrates open source genome-scale metabolic models (GEMs) of human and yeast for easy browsing and analysis. It also contains many more GEMs constructed by our organization. Detailed biochemical information is provided for individual model components, such as reactions, metabolites, and genes. These components are also associated with standard identifiers, facilitating integration with external databases, such as the Human Protein Atlas.