Cost-Benefit Decision Making for Dataset FAIRification in Pharmaceutical R&D

The FAIR (findable, accessible, interoperable and reusable) principles of scientific data management and stewardship are aimed at facilitating data reuse at scale by both humans and machines. Research and development (R&D) in the pharmaceutical industry is becoming increasingly data driven, but managing its data assets according to FAIR principles remains a costly and challenging endeavour. But what are the associated costs and benefits, and how are decisions supported about the value of the FAIRification of existing datasets in pharmaceutical R&D? A decision-making framework called FAIR-Decide uses the application of business analysis techniques (cost-benefit and multi-criteria analyses) in assessing estimated costs and expected benefits. The framework and associated tool were developed through workshops and focus groups with a number of Pharma R&D stakeholders associated with the IMI FAIRplus project.