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. To date, little scientific evidence has been gathered about how FAIR is currently implemented in practice, what its associated costs and benefits are and how decisions are made about the FAIRification of existing datasets in pharmaceutical R&D. This thesis sets out to illuminate such issues, adding to the literature by documenting another critical aspect of FAIRâ€”the decision-making process. To this end, semi-structured interviews were conducted with pharmaceutical professionals to examine their current practices in- depth and establish a conceptual model for the FAIRification decisions. Pharmaceutical industrial requirements for the design of a framework that aids decision making regarding FAIRification were identified. On the basis of the results, a decision-making framework called FAIR-Decide was developed using a novel method that involved the application of business analysis techniques (costâ€“benefit and multi-criteria analyses) in assessing estimated costs and expected benefits. To validate the framework, a FAIR-Decide tool was created and evaluated through focus group discussions of two scenarios (industry and non-industry) as a means of ascertaining the suitability of the tool for its intended work environment. The findings have significant implications for pharmaceutical R&D professionals engaged in driving FAIR implementation and for external parties who seek to better understand existing practices and challenges.
|Date of Award||31 Dec 2022|
- The University of Manchester
|Supervisor||Carole Goble (Supervisor) & Caroline Jay (Supervisor)|
- decision-making process
- pharmaceutical R&D