The FAIR principles (findable, accessible, interoperable, and reusable) for scientific data management and stewardship aim to facilitate data reuse at scale by both humans and machines. The pharmaceutical industry's research and development (R&D) is becoming increasingly data-driven, yet managing its data assets according to these principles remains challenging. There is currently little empirical evidence concerning how FAIR is now used in reality, what its related costs and benefit are, and how decisions about retrospective FAIRification of datasets in pharmaceutical R&D are reached.
This talk aims to report the results of semi-structured interviews with pharmaceutical experts involved in various stages of drug R&D in seven pharmaceutical companies. The findings identified three main themes of the benefits and costs of FAIRification, as well as the factors that influence the decision to FAIRify historical datasets. The participants noted that the potential contribution of FAIRification to data reusability in several research disciplines, as well as the possibility for cost reductions. Participants, however, still saw implementation costs as a hurdle, citing the need for a significant investment in terms of resources and cultural change. legal and ethical reasons, management commitment, and data prioritization all influenced how decisions were made.
Main contributions of this talk are as follows:
- The findings have important implications for people in the pharmaceutical R&D business who are working to implement FAIR, as well as for outside parties who want to learn more about current practices and challenges.
The main motivation to look forward at the Open Science FAIR The feedback received on this talk at the Open Science FAIR will be extremely helpful for our project. This opportunity will support us to learn more about what others are developing, how our findings may be integrated and shared, and to discuss the most potential needs for the future development