The FAIR Principles and Data Quality: Advancing Reproducibility and Reusability in Scientific Research

Abstract

The FAIR principles Findable, Accessible, Interoperable, and Reusable—have emerged as a foundational framework for scientific data management, providing guidance for ensuring that data remain valuable and usable over time. This review examines the relationship between the FAIR principles and data quality, analyzing how FAIR implementation supports and enhances data quality across the research lifecycle. Drawing on literature from data science, information management, and research policy, we examine the conceptual foundations of the FAIR principles and their relationship to established data quality frameworks. We analyze the practical implementation of FAIR across scientific domains, identifying successes, challenges, and lessons learned. The review addresses the extension of FAIR to research software and the emerging concept of AI-readiness as a complement to FAIR. We propose an integrated framework that links FAIR implementation to data quality assurance and scientific reproducibility, providing practical guidance for researchers, institutions, and policymakers seeking to advance open and reproducible science.