FAIR data guidelines and approaches
FAIR
Data and resources are FAIR when they are findable, accessible, interoperable and reusable.
Importance of FAIR data
Findable data help overcoming barriers to data sharing within and among research communities, facilitating data access and data re-use. Indeed, data providers have to give information on the provenance of their FAIR data (metadata). FAIR data are sustainable as they facilitate the transparency, reliability and reproducibility of studies,data sharing and collaboration, thereby avoiding unnecessary duplication of efforts and resources (both financial and energy costs). By making data more findable and reusable, the work of quality data providers becomes more visible. Moreover, by generating machine-readable data, FAIR is a fundamental enabler for digital transformation e.g. to enable powerful AI analytics.
FAIR data object
- “F1. (meta)data are assigned a globally unique and persistent identifier”
- “F3. […] include the identifier of the data it describes”
- “F2. data are described with rich metadata […]”
- “F4. (meta)data are registered or indexed in a searchable resource”
FAIRness
The approaches on how to implement the FAIR principles as well as the degree to which they are implemented vary across communities. In this sense, FAIR implementation needs to be understood as a spectrum, and hence various degrees of FAIRness for different types of digital objects are possible.
Links
Discipline-specific FAIR initiatives
FAIR Principles in Solid Earth Research Infrastructures
Ensuring Access to Precise Positioning by Improving Geodetic Standards