Live training program comprised of 15 sessions beginning November 4, 2025
Training summary
This comprehensive training program offers a deep dive into the principles and practical implementation of FAIR (Findable, Accessible, Interoperable, Reusable) data, tailored for life science researchers and technical professionals looking to implement FAIR principles, as well as heads of R&D and lab managers in the early stages of FAIR adoption. Participants will gain both foundational knowledge and hands-on insights across a wide range of relevant topics.
By the end of the program, participants will be equipped with the knowledge and tools to implement FAIR principles effectively, drive data governance, and contribute to organizational and scientific advancement.
Curriculum
Session 1: November 4
Introduction to FAIR Principles – Understand the history, context, and high-level overview of FAIR.
Break for the Pistoia Alliance USA Conference, November 11-12, 2025
Session 2: November 18
Making Data Findable – Explore persistent identifiers, rich metadata, registries, and indexing.
Session 3: November 25
Making Data Accessible – Review protocols, licensing, and access control for data sharing.
Session 4: December 2
Making Data Interoperable – Learn about vocabularies, ontologies, and semantic technologies.
Session 5: December 9
Making Data Reusable – Discuss data documentation, provenance, licenses, and community standards.
Session 6: January 13
FAIR Implementation Frameworks – Introduce tools and frameworks like FAIR Implementation Profiles
Session 7: January 20
FAIR Maturity indicators – FAIR Data Maturity and FAIR organizational maturity, Roadmapping
Session 8: January 27
Technical Infrastructure for FAIR – Dive into repositories, APIs, workflows, and metadata schemas.
Session 9: February 3
FAIR in Practice (Case Studies) – Review use cases in academia and industry. Resource: FAIR toolkit
Session 10: February 10
FAIR roles in cultural Change Management – Types of roles involved in FAIR and Change Management.
Session 11: February 17
FAIR in Life Science from Research to Clinical – Examples from Research.Resource: FAIR4Clin
Session 12: February 24
Business Value of FAIR – Why FAIR? Business drivers for FAIR implementation, ROI and related challenges
Session 13: March 3
FAIR for AI, ML & Digital Transformation – Links between FAIR Data and Good Practices for AI projects
Session 14: March 10
Governance of FAIR Data
Break for the Pistoia Alliance Spring Conference, March 17-18, 2026
Session 15: March 17
FAIR Data Strategy & Architecture
Speakers
- Giovanni Nisato, Project Manager, Pistoia Alliance
- Rama Balakrishnan, Senior Principal Data Sciences Product Leader, Genentech
- Joerg Werner, Associate Director Data Management, Curevac
- Birgit Meldal, Senior Manager, Global Data Standards and Ontologies, Pfizer
- Andrea Splendiani, Principal, Head of the Center for Health Data Semantics, IQVIA
- Baptiste Tauzin, Enabling R&D Technology Manager, CSL Behring
- Wouter Franke, Strategic Data Consultant, The Hyve
- Sujeegar Jeevanandam, Principal Consultant, Zifo
- Alexandra Grebe de Barron, Principal Product Manager, Bayer
- Ibrahim Emam, Early Science Lead Information Architect, AstraZeneca
- Matthias Jurisch, Data Architect, Bayer
- Ben Gardner, R&D Lead for Data Mesh and Semantic Infrastructure, AZ
- Martin Romacker, Product Manager, Roche
Prices
- Member price: $750 / $500 early bird
- Non-Member price: : $1000 / $750 early bird
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Members:
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No Refund Policy for Training
Please review our refund and exchange policy carefully:
No Refunds: All payments for training sessions are final. We do not offer refunds for any reason, including but not limited to scheduling conflicts, personal emergencies, or dissatisfaction with the content.
By registering for our training, you acknowledge and agree to this policy.