Are you at the start of your project and planning to create research data? Read on to find out how to make it more findable, accessible, interoperable and reusable via the FAIR principles.
Why are the FAIR principles needed? The increasing availability of online resources means that data need to be created with longevity in mind. Providing other researchers with access to your data facilitates knowledge discovery and improves research transparency.
In this context, during the Lorentz Workshop "Jointly Designing a Data FAIRport" (2014), participants formulated the FAIR data vision to optimise data sharing and reuse by humans and machines, which resulted in the publication of The FAIR Guiding Principles for scientific data management and stewardship, published in "Scientific Data".
The FAIR principles describe how research outputs should be organised so they can be more easily accessed, understood, exchanged and reused. Major funding bodies, including the European Commission, promote FAIR data to maximise the integrity and impact of their research investment.
The EC supports FAIR data not as a standard but as a framework to follow when designing a Data Management Plan. It has produced a set of Guidelines for FAIR data management.
|'Findable'||i.e. discoverable with metadata, identifiable and locatable by means of a standard identification mechanism|
|'Accessible'||i.e. always available and obtainable; even if the data is restricted, the metadata is open|
|'Interoperable'||i.e. both syntactically parseable and semantically understandable, allowing data exchange and reuse between researchers, institutions, organisations or countries; and|
|'Reusable'||i.e. sufficiently described and shared with the least restrictive licences, allowing the widest reuse possible and the least cumbersome integration with other data sources.|
Use this checklist to evaluate your data against the FAIR principles