Abstract
The findable, accessible, interoperable, reusable (FAIR) principles for scientific data management and stewardship aim to facilitate 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 costly and challenging. To date, little scientific evidence exists about how FAIR is currently implemented in practice, what its associated costs and benefits are, and how decisions are made about the retrospective FAIRification of data sets in pharmaceutical R&D. This paper reports the results of semi-structured interviews with 14 pharmaceutical professionals who participate in various stages of drug R&D in seven pharmaceutical businesses. Inductive thematic analysis identified three primary themes of the benefits and costs of FAIRification, and the elements that influence the decision-making process for FAIRifying legacy data sets. Participants collectively acknowledged the potential contribution of FAIRification to data reusability in diverse research domains and the subsequent potential for cost-savings. Implementation costs, however, were still considered a barrier by participants, with the need for considerable expenditure in terms of resources, and cultural change. How decisions were made about FAIRification was influenced by legal and ethical considerations, management commitment, and data prioritisation. The findings have significant implications for those in the pharmaceutical R&D industry who are engaged in driving FAIR implementation, and for external parties who seek to better understand existing practices and challenges.
Highlights
The FAIR principles articulate the importance of making scientific research data findable, accessible, interoperable, and reusable (FAIR) [1]
Little scientific evidence exists about how FAIR is currently implemented in practice, what its associated costs and benefits are, and how decisions are made about the retrospective FAIRification of data sets in pharmaceutical Research and development (R&D)
The interviews aimed to comprehensively explore the thoughts of the experts involved in FAIR implementation, and covered the associated costs and expected benefits, and how decisions were made about the retrospective FAIRification of data in pharmaceutical R&D
Summary
The FAIR principles articulate the importance of making scientific research data findable, accessible, interoperable, and reusable (FAIR) [1]. Seeing the potential of implementing FAIR principles, the pharmaceutical industry has responded quickly [12] and is to tackle the data challenges faced by these large, complex global enterprises [13]. Implementing these principles as effective data management strategies could amplify the value of data assets through higher data reusability [14]. The Innovative Medicines Initiative (IMI)c has sponsored data management projects that have dealt with developing data centres These projects have shown that proper data asset annotation and management is a complex, resource-intensive process that must be improved [17, 18, 19, 20]
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