What does this research mean for the field?

FAIR and federated Data Ecosystems can enhance interdisciplinary research by improving data management, promoting reproducibility, and facilitating cross-domain collaboration. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Examine the limitations of current data management approaches and propose a federated system to enhance collaboration and reproducibility in research.

February 16, 2026Open Access

Towards FAIR and federated data ecosystems for interdisciplinary research

Key Points

Examine the limitations of current data management approaches and propose a federated system to enhance collaboration and reproducibility in research.
Proposed a layered architecture for data ecosystems encompassing governance, data, service, and application layers.
Integrated concepts from Data Commons, Data Meshes, and Data Spaces into the design.
Emphasized adaptive metadata management and semantic enrichment for better data integration.
Identified key requirements for effective data ecosystems, including robust security and transparent transactions.
Presented a decentralized peer-to-peer network that supports both compute-to-data and data-to-compute paradigms.
Suggested a framework that upholds domain autonomy while promoting cross-domain collaboration.

Abstract

Scientific data management is at a critical juncture, driven by exponential data growth, increasing cross-domain dependencies, and a severe reproducibility crisis in modern research. Traditional centralized data management approaches are not only struggling with data volume but also fail to address the fragmentation of research results across domains. This hinders scientific reproducibility and cross-domain collaboration and increases concerns about data sovereignty and governance. This article proposes FAIR and federated Data Ecosystems as an improved architectural pattern for future research data ecosystems. It tries to incorporate the latest advancements in decentralized, distributed systems into existing research infrastructure to promote cross-domain collaboration. Based on established patterns from Data Commons, Data Meshes, and Data Spaces, our approach focuses on a layered architecture that consists of governance, data, service, and application layers. With this, it could be possible to preserve domain-specific expertise and control while facilitating data integration through standardized interfaces and semantic enrichment. Key requirements include adaptive metadata management, simplified user interaction, robust security, and transparent data transactions. Our architecture supports compute-to-data as well as data-to-compute paradigms, implementing a decentralized peer-to-peer network that scales horizontally. This article aims to provide both an impulse for the technical architecture as well as concepts for a governance framework so that FAIR and federated Data Ecosystems could enable researchers to build on existing work while maintaining control over their data and computing resources. This could provide a practical path towards an integrated research infrastructure that respects domain autonomy as well as interoperability requirements.

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