Join a 1-hour introductory webinar to learn about current challenges in AI approaches to biomedical research and a the ECRAID-ESCMID Data and Sample Governance for Biomedical Research course, organised in collaboration with CERCLE, CoMeCT, MORU, IDDO and The Global Health Network.

Biomedical AI experts will discuss current challenges and a way forward for ethical, equitable AI applications in health research. Webinar participants will have a chance to exchange with speakers and learn about the Data and Governace course structure and content, which cover a range of essential concepts for developing and scaling responsible, proportional governance for biomedical data and samples.

Join to engage with AI in biomedical research experts, clarify course expectations, and set the stage for advancing your data and sample governance skills to enusure ethical, equitable data and sample reuse in support of global health and infectious disease research.

Register for the self-paced Data and Sample Governance Course on this page.

This module covers the following topics on Data Governance:

1. What is health data governance
2. The aims of data governance frameworks
3. Why are health data frameworks important?
4. The data governance act
5. The European Health Data Space
6. F.A.I.R. data tools for global collaboration in outbreak research
7. Case study - researcher journey in the Swiss Personal Health Network (SPHN)

By the end of this module, learners would be able to:

• Understand the core principles and frameworks governing biomedical data management and reuse.
• Identify key ethical, legal, and practical considerations in health data governance.
• Review major international guidelines and policies shaping biomedical data sharing.
• Analyse case studies of successful data reuse in infectious diseases and other health research areas.

By the end of the module, learners would be able to:

• Define the goals and essential components of a robust data governance framework.
• Recognize best practices for Data Access Committees (DACs) and their role in governance.
• Understand different data access mechanisms (e.g., direct download, virtual research environments, federated reuse).
• Evaluate governance approaches suitable for epidemic response and large-scale data sharing.
• Apply practical tools and strategies to develop and assess data governance frameworks.

By the end of the module, learners would be able to:

• Identify key ethical issues including broad consent, benefit sharing, and engagement with data producers.
• Understand legal frameworks and agreements necessary for compliant data and sample sharing.
• Differentiate between various consent models and legal controls supporting data reuse.
• Integrate considerations of data protection, privacy, and AI ethics into governance.
• Understand intellectual property issues in academic-industry partnerships.
• Plan budgeting and cost recovery for FAIR data and data preservation.

By the end of the module, learners would be able to:

• Compare governance structures for biomedical data and sample reuse.
• Understand local and international legislation affecting sample sharing, including the Nagoya Protocol.
• Identify best practices for managing sample metadata and biobank governance.
• Recognize special ethical considerations for pediatric and living biobanks.
• Assess cost recovery and budgeting for sample storage and sharing.
• Make informed decisions on sample preservation and commercial use.