FAIRcookbook: working with biopharmas
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.
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)