The continuous growth of the Open Science movement raises some challenges. One of them is to enable knowledge discovery especially by machines and a more satisfactory reuse of research data and methods. In this context, various stakeholders (researchers, scholarly publishers, funding agencies and industry representatives) jointly designed a concise and community-agreed set of foundational principles: Findability, Accessibility, Interoperability, and Reusability (Martone, 2015).
• Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services |
• It is important to be able to access data, whether it is openly available or requires authorization or application. |
• Interoperable data can be integrated with other data and is compatible with applications or workflows for analysis, storage and processing. |
• Metadata and data should be well-described so that they can be easily replicated and combined. |
Reference: Wilkinson et al. (2016) formally published the FAIR principles for the first time as “The FAIR Guiding Principles for scientific data management and stewardship”, in the journal Scientific Data.
To make sure that your data can be made open or FAIR, at the beginning of your research it is important to consider:
Open data can be freely used, modified, and shared by anyone for any purpose. It is made available under an open licence like Creative Commons.
To be fully open, data must also be FAIR.
However, data can be FAIR without being open: restricted-access data could be FAIR if the descriptive metadata is openly accessible.
