The FAIR principles are the standard for responsible data management and practicing open science. They focus on ensuring that research data are reusable, will actually be reused and will become as valuable as possible. FAIR is not only aimed at human beings but puts emphasis on enhancing the ability of machines to automatically find and use the data. FAIR stands for:
- Findable - easy discovery by both fellow researchers and computers
- Accessible - availability to others, under well-defined conditions
- Interoperable - easy integration and sharing across systems and platforms
- Reusable - others can reuse your data for new research and reproduce your results
Benefits of making data FAIR
- Better science - others can reproduce your findings, leading to more reliable, transparent, and impactful research
- Increased visibility and citations for your published articles and datasets
- Enabling new research questions to be answered
- Alignment with international standards and requirements of TU/e and funding agencies
- Increased opportunities for new partnerships with fellow researchers, as well as business, policy partners, and broader communities
FAIR data does not necessarily mean open data
FAIR does not imply that your data must be openly accessible to everyone. There could be valid reasons for restricting access to your data, such as safeguarding the privacy of participants, protecting intellectual property rights, or preserving commercial interests. The ultimate goal is to keep a balance between openness and restrictions. TU/e encourages you to make your research data ‘as open as possible and as closed as necessary’.
How to make data FAIR?