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 27 2021 2:48 a.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.
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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.
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CORA. Repositori de dades de Recerca
The CORA. Repositori de dades de Recerca is a repository of open, curated and FAIR data that covers all academic disciplines. CORA. Repositori de dades de Recerca is a shared service provided by participating Catalan institutions (Universities and CERCA Research Centers). The repository is managed by the CSUC and technical infrastructure is based on the Dataverse application, developed by international developers and users led by Harvard University (https://dataverse.org).
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This site contains data generated by (Re)Building a Kidney (RBK), an NIDDK-funded consortium of research projects working to optimize approaches for the isolation, expansion, and differentiation of appropriate kidney cell types and their integration into complex structures that replicate human kidney function. RBK's goal is to coordinate and support studies that will result in the ability to generate or repair nephrons that can function within the kidney. This resource includes data from both the RBK project and the GenitoUrinary Development Molecular Anatomy Project (GUDMAP). Data submission is restricted to members of the Consortium only.
GenitoUrinary Development Molecular Anatomy Project
The GenitoUrinary Development Molecular Anatomy Project (GUDMAP) provides data and tools to facilitate research on the GenitoUrinary (GU) tract for the scientific and medical community. In addition to current GUDMAP data, this resource incorporates data generated by previous phases of GUDMAP and by the (Re)Building a Kidney (RBK) consortium.
Data Archive for Social Sciences & The Humanities in Bosnia & Herzegovina
DASS-BiH (Data Archive for Social Sciences in Bosnia and Herzegovina) is a national service whose role is to ensure long-term preservation and dissemination of social science research data. The purpose of the data archive is to provide a research data resource for researchers, teachers, students, and all other interested users. economy, education, employment and labour, political science, psychology, sociology, society and culture, social welfare policy and systems.
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