• Setting up standard for FAIR
• Outcomes of FAIR & overcoming implementation challenges
• Moving towards a more FAIR-data oriented environment
• Metadata – importance of generating metadata & applications
• Data standardization and harmonization
• Advancing the development of RWE/RWD generation

• Data integration to support infrastructure
• Implementation strategies
• Defining the workflows
• AI/ML applications, strategies & approaches

        » Advanced analytics  platforms
        » Advanced tools & techniques: Knowledge graphs, Deep & future learning
     » Challenges around interpretability of ML results
»Measuring the success of AI/ML technologies 

Part 1: Increasing Data Capture & Digitalisation In The Lab of the Future

• Virtual lab assistants and virtual notebooks
• How to deal with legacy equipment and move forward
• Going paperless: strategies & best practices
• Remote monitoring/operation tools
• Addressing cultural aspects –training & education

Part 2: Data Management & Data Automation in the Lab of the Future

• Making data FAIR in the lab
• Advanced automation & robotics tools & tech
• Data standardization and governance in lab informatics
• Advances in lab data use:
     » Tools for controlling equipment and collecting data
     » Tools for integrating, storing and communicating data

• Scaling up data infrastructure
• Virtualisation of existing systems
• Data compression & storage platforms
• Data Mesh
• Workflow automation
• Data sharing networks – challenges & opportunities
• Data Governance
• Data democratisation

• Bioinformatics in in silico R&D
• Integrating genomics with AI initiatives
• Linking omics data to population data, clinical data, personalized medicine
• Developing new therapeutics / diagnostics / prognostics – new digital therapeutics
• Case studies from therapeutic areas and uses of data