Everybody wants to play FAIR, but how do we put the principles into practice?
There is a growing demand for quality criteria for research datasets. In this webinar, we will argue that the DSA (Data Seal of Approval for data repositories) and FAIR principles get as close as possible to giving quality criteria for research data. They do not do this by trying to make value judgements about the content of datasets, but rather by qualifying the fitness for data reuse in an impartial and measurable way. By bringing the ideas of the DSA and FAIR together, we will be able to offer an operationalization that can be implemented in any certified Trustworthy Digital Repository.
In 2014 the FAIR Guiding Principles (Findable, Accessible, Interoperable and Reusable) were formulated. The well-chosen FAIR acronym is highly attractive: it is one of these ideas that almost automatically get stuck in your mind once you have heard it. In a relatively short term, the FAIR data principles have been adopted by many stakeholder groups, including research funders.
The FAIR principles are remarkably similar to the underlying principles of DSA (2005): the data can be found on the Internet, are accessible (clear rights and licenses), in a usable format, reliable and are identified in a unique and persistent way so that they can be referred to. Essentially, the DSA presents quality criteria for digital repositories, whereas the FAIR principles target individual datasets.
Who should attend this webinar?
Data experts, data repository managers, policy makers, research administrators, project coordinators and researchers will all benefit from participating.
Date 12th December 2016 at 3pm CET or on 13th December 2016 at 10am CET.
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