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Driving FAIR in Biopharma - Expert insider views on leading transformative FAIRification efforts within biopharma

Driving FAIR in Biopharma - Expert insider views on leading transformative FAIRification efforts within biopharma

The transformative shift envisioned by FAIR pioneers is still very much still in progress. The standard reference tools, documents such as the FAIR Cookbook from the FAIRplus project and the FAIR Toolkit by the Pistoia Alliance, are exemplary. This report is designed to complement those other resources by documenting additional direct thoughts and insights from people who are leading FAIRification programmes on the ground.

Who contributed to this report?

We’re extremely grateful to the following people for their insights and contributions to this report:

  • Lawrence Callahan, Chemist, Office of Health Informatics, Global Substance Registration System/Office of Health Informatics, Office of Chief Scientist, FDA
  • Isabella Feierberg, Associate Principal Scientist, AstraZeneca
  • Ben Gardener, Solution Architect – Knowledge Management, AstraZeneca
  • Tom Plasterer, Director of Bioinformatics, Data Science & AI, BioPharmaceuticals R&D, AstraZeneca
  • Ellen L. Berg, Chief Scientific Officer, Translational Biology, Eurofins Discovery
  • Philippe Rocca-Serra, Group Coordinator and Associate Member of Faculty, University of Oxford e-Research Centre
  • Martin Romacker, Senior Principal Scientist in Scientific Solution Delivery and Architecture, Roche
  • Andrea Splendiani, Director of Data Strategy, Novartis

Who is this report for?

Those in data architecture roles including, but not limited to, those in data analysis or data science focuses; as well as data repository managers, policymakers, strategic leaders, project coordinators and researchers who need to ensure that their data is reusable and publishable would benefit from reading this report.

Contents

  • Prologue: Advice for starting a FAIR journey
  • Chapter 1: Why does FAIR matter?
  • Chapter 2: People and processes
  • Chapter 3: Moving from an application centric perspective to a data-centric one
  • Chapter 4: The challenges of enterprise-level FAIR implementation
  • Chapter 5: Evaluating FAIRness at scale
  • Chapter 6: Can all graphs be FAIR?
  • Chapter 7: The impact of AI and ML on FAIR data use
  • Chapter 8: How far along are different sectors in adopting FAIR standards?
  • Epilogue: Future Needs

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