FAIR data principles aim to make data available to be harvested by automated systems and human scholars. They also provide a set of measurable practices to make sure data comply with funder and institutional policies.
These principles set out a framework that allows research and research data to meet minimum accessibility levels while protecting the interests of researchers, funders and third parties.
This means other scholars can find your data due to strong metadata and persistent identifiers (DOIs).
- Use consistent file naming conventions and logical folder structures when organising your data.
- Use robust file management when you archive your data.
- Use a repository that exposes your metadata and manages your files according to your wishes. Trusted data repositories will also allocate a digital object identifier (DOI) to your data.
This means your data exist in file formats that are not dependent on proprietary or obsolete software.
- Use a language and standard vocabularies that can be understood beyond your discipline. Avoid jargon.
- Use universal best practice and standard data handling procedures.
- Use the appropriate open and standard data format for each data type, including data that is originally non-digital.
This means your data are available under open licences and their provenance are fully documented, to show they are valid and collected legally and ethically.
- In your README file and metadata include detailed information and accurately cite the provenance of data you're reusing.
- Know from the start which data you can't share and what restrictions you may need to account for. Consider the implications for personal data, intellectual property or commercial potential.
- Make sure your intentions for data reuse are unambiguous by applying a Creative Commons or similar standard licence.
Need help with research data management? Email email@example.com
Find out all the ways you can get in touch: