Why use the FAIR principles for your research data?
Reusing existing data sets for new research purposes is becoming more common across all research disciplines.
Research funders and publishers are asking researchers to make data sets produced in their projects available to others. And research institutes are promoting measures to secure the transparency and accessibility of locally produced data sets. To facilitate this, datasets need to be Findable, Accessible, Interoperable and Reusable.
This is what the FAIR principles are all about.
Watch two of the authors of the FAIR principles share their thoughts on the advantages of making research data FAIR.
What’s in it for you to make your data FAIR?
Susanna-Assunta Sansone: ”The FAIRer the data, the higher the return will be in terms of credit, in terms of citation, in terms of discovering your data.”
Why do you need FAIR data?
Barend Mons: “We need FAIR machine-readable data to cross disciplines, because there is a big problem to cross disciplines.”
FAIR sounds like a lot of work… What can you do to get started?
Susanna-Assunta Sansone: “Be aware and be engaged, that’s what you can do now.”
How will FAIR change the way we do science?
Barend Mons: “Now we have papers – meant for people - and behind the paper wall there is supplementary data. We have to turn that around and publish the research objects in their own right”.
Why FAIR?
As Susanna-Assunta Sansone and Barend Mons have said, making your research data Findable, Accessible, Interoperable and Reusable could:
- Help peers and your future-self understand the research project and data
- Facilitate data sharing and collaborations
- Increase the visibility of research and can lead to more citations
- Improve the transparency, reliability and reproducibility of research
- Prevent data loss
And thereby:
- Maximise potential from data assets
- Maximise research impact