The FAIRification Framework (Figure 1) provides a structured, step-by-step approach and supporting resources for transforming datasets, digital objects, or data management practices into FAIR-compliant assets. It guides users through the entire FAIRification journey — from assessing the initial state of their assets, to implementing targeted FAIRification goals, to re-evaluating the FAIR status of the assets after implementation.
The Framework embraces the FAIR principles by making optimal use of available resources and enhancing an organization’s capacity for FAIR data management. It does so by prioritizing needs based on a thorough analysis of the specific FAIR challenges unique to each project or use case. By following its structured phases, organizations can ensure their data becomes more discoverable, accessible, interoperable, and reusable in line with their specific FAIR needs – unlocking the data’s full potential for scientific research, innovation, and collaboration.
The Framework, illustrated in Figure 1, is comprised of three components:
- Process outlines the main phases any FAIRification activity with references to helpful types of tools
- Template(s) breaks down key elements of the process into a series of steps with links to supporting resources.
- Workplan structure porganises design decisions and tasks to guide iterative design, implementation and assessment towards achieving the activity’s objectives.
The FAIRification Template and the FAIRification Workplan are available to support users as needed. Unlike the FAIRification Process however, where every phase should be followed, the Template steps should be selected as appropriate on a case-by-case basis, and inserted into the Workplan in whichever way best addresses the users’ needs.
Figure 1. Overview of the FAIRification Framework.