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

A FAIR Data Object (FDO) follows the guidelines set out by Wilkinson et al. (2016) to increase the findability, accessibility, interoperability and reuse of data. In the context of the FAIR-GNSS project, the following FAIR principles on the use of Persistent Identifier (PID) and metadata are of high importance:

  1. “F1. (meta)data are assigned a globally unique and persistent identifier”
  2. “F3. […] include the identifier of the data it describes”
  3. “F2. data are described with rich metadata […]”
  4. “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.