Cost-benefit analysis for FAIR research data - Publications
All members . Turning FAIR into reality . research,, , report ., . FAIR data is essential for us to bring global solutions to this public health crisis, as well as the others that are sure to come in the future. Emerging tech has revolutionised how we use data. In recent years, the life sciences industry has suffered an unignorable decline in innovation efficiency, but AI … New author guidelines supporting open and FAIR data in scholarly publishing are being adopted throughout the Earth, space, and environmental sciences community.
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Finding a black cab in London la This collection aims to aggregate scholarly literature a well as grey literature on the principles of FAIR (findable, accessible, interoperable, reusable) data and its principles that we refer to as the FAIR Data. Principles.” Wilkinson et al. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Or find out more about making your research FAIR – Findable, Accessible, Interoperable Management of data, models, and operating procedures is constantly The ALLEA report “Sustainable and FAIR Data Sharing in the Humanities” provides Accessible, Interoperable and Reusable”, in line with the FAIR principles.
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SND, Fair Data och vägen framåt - PDF Gratis nedladdning
According to the FAIR principles, the data should be Findable, Accessible, Interoperable and Re-usable. The Ministry of Education and Culture is committed to these principles.
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Accessible: hur får man tillgång till data? Interoperable: är data och metadata interoperabla? Reusable: kan andra använda Ett exempel på detta är EU:s program för forskningsfinansiering, där FAIR hade en framträdande roll i mallen för datahanteringsplan för Horizon 2020.
De beskriver några av de mest centrala riktlinjerna för god datahantering och öppen tillgång till forskningsdata. FAIR innebär att forskningsdata ska vara Findable (sökbara), Accessible (tillgängliga), Interoperable (interoperabla) och Reusable (återanvändbara). FAIR data are data which meet principles of findability, accessibility, interoperability, and reusability. A March 2016 publication by a consortium of scientists and organizations specified the "FAIR Guiding Principles for scientific data management and stewardship" in Scientific Data, using FAIR as an acronym and making the concept easier to discuss. FAIR research data shall be Findable, Accessible, Interoperable, and Reusable. There are a total of 15 FAIR principles that can be applied to research in all scientific disciplines. The FAIR principles are mainly focused on machine readability, but also target human understanding of research data, in order to enable the reuse of data.
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While the DSA focuses primarily on the responsibilities and conduct of data producers and repositories, FAIR focuses primarily on the data itself.
This implies (in fact, requires) that resources that wish to maximally fulfil the FAIR guidelines must utilise a widely-accepted machine-readable framework for data and knowledge representation and exchange.
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Sometimes, it is difficult to differentiate between them, especially when the concepts are very similar as is the case between Open Data and FAIR Data, which we will be explain below. This output supersedes the FAIR Data Maturity Model: specification and guidelines DOI: 10.15497/rda00045 Context. Findability, Accessibility, Interoperability and Reusability – the FAIR principles – intend to define a minimal set of related but independent and separable guiding principles and practices that enable both machines and humans to find, access, interoperate and re-use data and One of the key goals of the FAIR guiding principles is defined by its final principle – to optimize data sets for reuse by both humans and machines.
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The FAIR principles, first published in 2016, contain guidelines for good data management practice that aim at making data FAIR: findable, accessible, interoperable, and reusable. "Data" refers in this context to all kinds of digital objects that are produced in research: research data in the strictest sense, code, software, presentations, etc. It will propose measures for increasing FAIR maturity to maximise data sharing and re-use.
FAIR research data shall be Findable, Accessible, Interoperable, and Reusable. There are a total of 15 FAIR principles that can be applied to research in all scientific disciplines. The FAIR principles are mainly focused on machine readability, but also target human understanding of research data, in order to enable the reuse of data.