TY - JOUR
T1 - Exploring the current practices, costs and benefits of FAIR Implementation in pharmaceutical Research and Development
T2 - A Qualitative Interview Study
AU - Skeva, Rigina
AU - Juty, Nick
AU - Jay, Caroline
AU - Goble, Carole
A2 - Alharbi, Ebtisam
N1 - Funding Information:
C. Goble and N. Juty acknowledge the FAIRplus project (IMI2 Joint Undertaking under grant agreement No. 802750). The EPSRC supported C. Jay in this work under EP/S021779/1. E. Alharbi’s scholarship is sponsored by Umm Al-Qura University, the Kingdom of Saudi Arabia (No. 1057493924).
Funding Information:
The findings reported here are supported by those of previous studies investigating the implementation of FAIR principles in the pharmaceutical industry [12, 13], which highlighted the expected benefits of the implementation of FAIR principles and the anticipated requirements for financial investment, cultural change, training and the technical infrastructure. Research has also highlighted the challenge of dealing with legacy data [30, 31]. FAIRifying data retrospectively remains challenging when data and metadata are curated and re-annotated retrospectively [29]. We extend the literature by documenting another critical aspect of FAIRification—the decision-making process.
Publisher Copyright:
© 2021 Chinese Academy of Sciences.
PY - 2021/10/25
Y1 - 2021/10/25
N2 - 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.
AB - 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.
KW - Cost-benefit
KW - Decision-making process
KW - FAIR
KW - FAIRification
KW - Pharmaceutical R&D
KW - Retrospective FAIRification
UR - http://www.scopus.com/inward/record.url?scp=85118201656&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/bb522618-4b9f-3b89-a4e9-faaad49fc6ef/
U2 - 10.1162/dint_a_00109
DO - 10.1162/dint_a_00109
M3 - Article
SN - 2641-435X
VL - 3
SP - 507
EP - 527
JO - Data Intelligence
JF - Data Intelligence
IS - 4
ER -