Research data is an important and valuable resource. When data is not correctly preserved and managed, it can be lost or altered.
High-quality research data management should follow the FAIR principles. These pricniples were introduced in 2016 for achieving Findability, Accessibility, Interoperability and Reuse of research data. Navigate in the sections below to learn more about each letter of the FAIR acronym:
USEFUL LINKS:
- Explanation of the FAIR principles
- How to go FAIR
- Take the FAIR Quiz
- How FAIR is your data?
- Automated FAIR data assessment tool
- How to find a FAIR Data repository
- The FAIR data management checklist
- Tools for FAIR research data management
- The CESSDA data management expert guide
- Applying the FAIR principles and practices in a data management plan.
Findable
Your data can be found by others if you have:
- associated it a globally unique persistent identifier (PIDs, such as a DOI)
- used rich and detailed metadata to describe your data
- deposited it in repositories, catalogs and databases which enable automatic harvesting of metadata (indeed, if you choose the right one half of the work is done!)
- sponsored it
Interoperable
Other researchers should be able to use your data with their own data if your data is clearly comprehensible. It can be achieved if you:
- describe your data in a detailed and comprehensible way in the metadata fields
- add helpful and detailed documentation on data creation and processing: compile a readme file
- use the right language: well-known terminology, domain standards, English language
- use readable, non-proprietary and open formats
Accessible
Data and metadata should be preserved in the long term such that they can be easily accessed and downloaded. It can be achieved if you:
- have shared your data in a dedicated repository
- openly published your metadata with a public domain license
- used clear statements and instructions for closed data: remember that access to your data should be easy, but it does not mean that everything must be shared without limitations. The moto is: as open as possible, as closed as necessary.
Reusable
Other researchers should be allowed to (confidentially) reuse your data if:
- data and its provenance is trustworthy: describe it (and all the processes on data) in detail in the metadata and in the readme file
- open licenses have been used for both metadata and data to make clear what it is allowed to do with the data
Image adapted from: Illustration by Scriberia with The Turing Way community, used under a CC-BY 4.0 licence. DOI: 10.5281/zenodo.3332807