The document discusses how the biopharma industry has changed and how implementing FAIR principles can help improve research and development productivity. It provides three key points: 1) The biopharma industry has shifted from proprietary internally-driven research to more open collaborations across different organizations as part of a wider ecosystem. Data is now distributed across multiple sources rather than just internal. 2) Implementing FAIR (Findable, Accessible, Interoperable, Reusable) principles enables powerful new AI analytics by allowing data to be more accessible for machine learning and prediction. 3) The Pistoia Alliance supports sharing best practices for organizations to implement FAIR through public-private partnerships from 2019 to 2022.

1. The FAIR Cookbook
working with biopharmas
Susanna-Assunta Sansone, PhD
Associate Professor, Engineering Science
Associate Director, Oxford e-Research Centre
EOSC Symposium, Budapest, 15-29 November 2019
datareadiness.eng.ox.ac.uk

2. The way biopharma works has changed
Big
Life
Science
Company
Last century Yesterday Today & tomorrow
Last century Yesterday Today & tomorrow
Innovation Model Innovation inside Searching for Innovation Heterogeneity of collaborations; part of the
wider ecosystem
IT Internal apps & data Struggling with change
security and trust
Cloud services, AI
Data Mostly inside In and out Distributed
Portfolio Internally driven and owned Partially shared Shared portfolio
Modification of a slide credit to:
Big
Life
Science
Company
Proprietary
content
providers
Public
content
providers
Academic
groups
Software vendors
CROs
Service providers
Regulatory
authorities

3. • Biopharma R&D productivity can be improved
by implementing the FAIR Principles
• FAIR enables powerful new AI analytics to
access data for machine learning and prediction
• The Pistoia Alliance supports sharing of best
practices for FAIR implementation
Credit to:
Ian Harrow,
FAIR & OM projects
FAIR as enabler for the digital transformation

4. Define, document and implement
a data FAIRification process
PPPs, funded from 2019 to 2022

5. Example of a recipe: P. Rocca-Serra and S.A. Sansone https://doi.org/10.5281/zenodo.3274256 - Scientific Data (in press)
• Content and learning outcomes:
o recipes on how to FAIRify a number of
exemplar datasets, putting the FAIR principles
in practices;
o learn about levels and indicators of FAIRness;
o maturity model, technologies and tools
available to assess and improve FAIRness;
o learn about the skills required, as well as the
challenges
• Collaborative writing:
https://fairplus.github.io/the-fair-cookbook/intro
o CC-BY-4.0
o Markdown and jupyter-tool
o Leads: P. Rocca-Serra (Oxford);
A. Splendiani (Novartis)