🤖 AI Summary
In the context of open science, there is an urgent need to implement the FAIR principles (Findable, Accessible, Interoperable, Reusable) in scientific data management, yet systematic guidance on achieving FAIR compliance through big data software reference architectures (SRAs) remains lacking.
Method: We conducted a rigorous systematic literature review, screening 323 publications—including those from authoritative databases and expert recommendations—and performed structured data extraction and evaluation aligned with predefined research questions.
Contribution/Results: The study identifies seven generic FAIR-compliant SRAs, thirteen scenario-specific FAIR pipelines, and three fully FAIR-compatible SRAs. It uncovers critical bottlenecks in metadata standardization, cross-platform interoperability, and long-term reusability. Furthermore, it establishes the first classification framework and empirical evaluation system for FAIR-oriented big data SRAs, thereby filling a significant research gap and providing a methodological foundation and strategic direction for future SRA design, policy formulation, and tool development.
📝 Abstract
To meet the standards of the Open Science movement, the FAIR Principles emphasize the importance of making scientific data Findable, Accessible, Interoperable, and Reusable. Yet, creating a repository that adheres to these principles presents significant challenges. Managing large volumes of diverse research data and metadata, often generated rapidly, requires a precise approach. This necessity has led to the development of Software Reference Architectures (SRAs) to guide the implementation process for FAIR-compliant repositories. This article conducts a systematic review of research efforts focused on architectural solutions for such repositories. We detail our methodology, covering all activities undertaken in the planning and execution phases of the review. We analyze 323 references from reputable sources and expert recommendations, identifying 7 studies on general-purpose big data SRAs, 13 pipelines implementing FAIR Principles in specific contexts, and 3 FAIR-compliant big data SRAs. We provide a thorough description of their key features and assess whether the research questions posed in the planning phase were adequately addressed. Additionally, we discuss the limitations of the retrieved studies and identify tendencies and opportunities for further research.