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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
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
• 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
Define, document and implement
a data FAIRification process
PPPs, funded from 2019 to 2022
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)

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FAIRcookbook: working with biopharmas

  • 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)