Implementation and relevance of FAIR data principles in biopharmaceutical R&D. Issue 4 (April 2019)

• Record Type: Journal Article
• Authors: Wise, John; de Barron, Alexandra Grebe; Splendiani, Andrea; Balali-Mood, Beeta; Vasant, Drashtti; Little, Eric; Mellino, Gaspare; Harrow, Ian; Smith, Ian; Taubert, Jan; van Bochove, Kees; Romacker, Martin; Walgemoed, Peter; Jimenez, Rafael C.; Winnenburg, Rainer; Plasterer, Tom; Gupta, Vibhor; Hedley, Victoria
• Is Part Of: Drug discovery today. Volume 24:Issue 4(2019)
• Page Start: 933
• Page End: 938
• Publication Date: 2019-04
• Subjects: Drugs -- Design -- Periodicals; Drugs -- Research -- Periodicals; 615.1
• DOI: 10.1016/j.drudis.2019.01.008
• Languages: English
• ISSNs: 1359-6446

Abstract

Highlights: Biopharma R&D productivity can be improved by implementing the FAIR Data Principles. FAIR enables powerful new AI analytics to access data for machine learning and prediction. FAIR is a fundamental enabler for digital transformation of biopharma R&D. The Pistoia Alliance supports sharing of best practices for FAIR implementation. Abstract : Biopharmaceutical industry R&D, and indeed other life sciences R&D such as biomedical, environmental, agricultural and food production, is becoming increasingly data-driven and can significantly improve its efficiency and effectiveness by implementing the FAIR (findable, accessible, interoperable, reusable) guiding principles for scientific data management and stewardship. By so doing, the plethora of new and powerful analytical tools such as artificial intelligence and machine learning will be able, automatically and at scale, to access the data from which they learn, and on which they thrive. FAIR is a fundamental enabler for digital transformation.