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Implementation and relevance of FAIR data principles in biopharmaceutical R&D
John Wise ,
Alexandra Grebe de Barron ,
Andrea Splendiani ,
Beeta Balali-Mood ,
Drashtti Vasant ,
Eric Little ,
Gaspare Mellino ,
Ian Harrow ,
Ian Smith ,
Jan Taubert ,
Kees van Bochove ,
Martin Romacker ,
Peter Walgemoed ,
Rafael C. Jimenez ,
Rainer Winnenburg ,
Tom Plasterer ,
Vibhor Gupta ,
Victoria Hedley
April 2019
April 2019
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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.