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Introduction
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The FAIR principles state that data should be Findable, Accessible, Interoperable, and Reusable.
FAIR data enhance impact, reuse, and transparancy of research.
FAIRification is an ongoing effort accross many different fields.
FAIR principles are a set of guiding principles, not rules or standards.
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Documentation
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Metadata
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From a FAIR perspective, metadata are more important than your data.
Metadata are preferably created according to a disciplinary standard.
To be FAIR, metadata must have a findable persistent identifier.
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File format
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Choose formats that are common to your field/community to ensure the interoperability and reusability of your data.
Make sure that the file formats you choose can hold the necessary data elements and information.
Decide on how long do you intend to preserve your data.
Make sure to check requirements of the repository where data is stored.
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Access to the data
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Accessible does not mean open without constraint.
Metadata can still be accessible, even if the data itself is not (anymore).
EU-funded projects are expected to make generated data accessible to the public.
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Persistent identifiers
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Promote reproducibility by assigning persistent identifiers to your processed data.
Promote reuse by adding a persistent identifier to your raw data.
If your data do not have a PID, they will not be FAIR!
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Data licenses
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A permissive license ensures the re-usability of your data.
Many big inter-comparison projects already have suitable licenses.
For derived work, existing licenses may restrict your choice of license.
Ownership of data (FIXME)
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Conclusion
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FAIRification practices are about documentation, metadata, file formats, access to data, persistent identifiers, and data licenses.
The FAIR principles provide clear handles on data management in the movement towards open and sustainable science.
The FAIR principles promote maximum use of research data, involving all the stakeholders from data producers to funding agencies.
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