FAIR Data Train - The FAIR Data Train approach was introduced as a domain-agnostic system designed to enhance automated interoperability while adhering to FAIR principles. Key aspects include privacy-focused "data visiting", foundational agreements for metadata handling, and core components like data stations and gateways. The environment promotes user-centric control, robust metadata, and simplified system integration, providing a scalable framework for FAIR data management. The separation of distributed, data holding stations and applications making (station controlled) use of the data is a key concept in the FAIR data train ecosystem. 

FAIR Data Train - Specification - A flexible methodology for designing the FAIR Data Train architecture was presented, emphasizing interoperability without replacing existing systems. The development process includes iterative agile cycles, rich documentation, and scalable designs. This approach ensures practical, evolving solutions aligned with the diverse needs of stakeholders and supports seamless updates across domains.

HINQ - HINQ focuses on repurposing primary healthcare data for primary use, but would also aim to make subsets of data available for secondary uses like research and training of machine learning models. Highlighted projects include resolving medication discrepancies and identifying risks in maternal health. The initiative aligns with national and European frameworks and leverages existing infrastructures to demonstrate FAIR principles, setting an example for future projects.

Visma Connect - Visma Connect shared their expertise in secure, scalable data exchange and federative data spaces, emphasizing system-to-system communication and minimal duplication. Their solutions facilitate FAIR-compliant data sharing across domains and support cross-domain collaboration, trust-building, and user-centric workflows for LIFES initiatives.

DZNE - Swarm Learning - Swarm Learning technology was showcased as a decentralized method for secure AI model training without transferring data. Real-world applications, like the Finnish leukemia project, demonstrated its scalability and effectiveness. The approach aligns with FAIR principles, enabling collaborative AI and advancing global healthcare data sharing.

Discussion on Specifications - The main theme of the discussion was a commitment to collaborate with the aim to show that with FAIR metadata and proper (explicitly published) machine actionable metadata and software components, all these existing systems can interoperate without replacement of legacy components or choices, unless these are not compatible with FAIR principles. The discussion focused on defining metadata templates, ensuring interoperability, and testing specifications iteratively. Agile development methods were recommended, emphasizing shared protocols, practical scalability, and leveraging existing systems to achieve FAIR compliance efficiently.

General Discussion - Key topics included collaboration challenges, scaling FAIR principles, and prioritizing practical use cases. Participants agreed on forming dedicated teams, aligning with European frameworks, and leveraging strategic partnerships. Upcoming funding and meetings aim to refine priorities and advance implementation. Practical tools and resources were emphasized to simplify FAIR compliance and data management for organizations and researchers.