This workshop addresses data as a central asset of modern research systems, underpinning scientific discovery, innovation, and evidence-based decision-making. As research becomes increasingly data-intensive, questions surrounding how data are produced, curated, shared, reused, and protected have moved to the forefront of research governance and practice. Ensuring that research data are accessible, trustworthy, interoperable, and secure is essential for scientific integrity, reproducibility, and societal impact.

The workshop explores the full research data lifecycle, from data generation and quality assurance to storage, sharing, and long-term preservation. It examines the implementation of FAIR (Findable, Accessible, Interoperable, Reusable) data principles alongside legal, ethical, and security constraints, including data protection, intellectual property, and sensitive data handling. Particular attention is paid to the tensions between open science objectives and legitimate needs for data control, confidentiality, and strategic protection.

Academic contributions will address data standards, metadata, reproducibility, and emerging practices in open and computational science. Industry and policy speakers will highlight perspectives on data governance, trusted data spaces, cross-border data sharing, and the protection of commercially or strategically sensitive information. The workshop also considers the skills, infrastructure, and incentives required to make high-quality data stewardship a routine part of research culture.

The aim of the workshop is to develop a shared understanding of responsible data practices and to provide actionable guidance for researchers, institutions, funders, and policymakers navigating the evolving data landscape.

Extended Key Takeaways & Outputs

• Clear guidance on responsible research data lifecycle management
• Best practices for implementing FAIR data principles in diverse research contexts
• Frameworks for balancing open access with legal, ethical, and strategic constraints
• Recommendations for data governance models and trusted data infrastructures
• Identification of skills, incentives, and investments needed to professionalise data stewardship