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Research Data Management (RDM): FAIR Data Principles

UCT Libraries Research Data Services provide guidance and support for all aspects of the data lifecycle, from planning your data management strategy during the proposal phase through preserving your data at the conclusion of your project.

FAIR Data Principles

The FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) were drafted at a Lorentz Center workshop in Leiden in the Netherlands in 2015, and have since received worldwide recognition by various organisations including FORCE11, National Institutes of Health (NIH) and the European Commission as a useful framework for thinking about sharing data in a way that will enable maximum use and reuse. They are a way of thinking about getting the most out of your research data, and its place in the wider researcher community. 

 

Findable

Can your data be found if someone is looking for it? Does it have a DOI or a Handle? Does it have rich metadata? Is it discoverable through a research portal, or a repository? 

Accessible

Does your data utliise a standardised protocol? Your data does not necessarily have to be "open" - there are sometimes good reasons why data cannot be made open, i.e. privacy concerns, national security or commercial interests - but if it is not there should be clarity and transparency around the conditions governing access and reuse.

Interoperable

To be interoperable the data will need to use community agreed formats, language and vocabularies. Will someone who finds your data be able to meaningfully reuse it, and build or reproduce your work? The metadata you use will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.

Reusable

Reusable data should maintain its initial richness. For example, it should not be diminished for the purpose of explaining the findings in one particular publication. It needs a clear machine readable licence and provenance information on how the data was formed. It should also have discipline-specific data and metadata standards to give it rich contextual information that will allow for reuse.

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