Awareness and implementation of FAIR data principles in research data management: A systematic literature review

Online research environments have transformed the way scholarly communication occurs radically; however, enduring gaps in the management of research data and disjointed infrastructural setups still persist in driving the reproducibility crisis and limiting the impact of research. The FAIR (Findable, Accessible, Interoperable, Reusable) principles of data, which was issued in 2016, offers broadly supported principles of improving data stewardship in open science environments. The proposed systematic review examines awareness level, practical adoption, key barriers and facilitating variables in the adoption of FAIR data principles in research data management at field and geographical levels. It attempts to determine the lapses in the current practice and make suggestions that would guide future policy and practice. The search took place in Scopus, Web of Science, and Google Scholar between 2016 to 2025. The sources that could qualify included peer-reviewed articles, case studies, and policy documents which specifically covered FAIR awareness or adoption. Out of 420 identified records, 40 studies met the inclusion criteria after undergoing a PRISMA-based screening procedure and were synthesised through thematic analysis.A review of peer-reviewed articles led to the selection of 40 articles that were included in a general thematic synthesis. Findings indicate the overall awareness of FAIR, but inconsistency in practice. The biomedical and natural sciences have the most consistent conformability to FAIR standards, and the literature and social sciences have comparatively poor compliance.The key obstacles are the absence of training, the inadequacy of institutional infrastructure and regulatory-ethical restrictions. On the other hand, institutional requirements, requirements to the funding agency, sustainable identifiers and the community based initiatives (e.g., FAIR Data Austria) are the important pre-requisites to the application of FAIR principles. The principles of the FAIR are slowly transforming to a key driver of open science and sustainable management of research data, yet there are still considerable gaps in expertise, infrastructure, and governance. The next decade will require the creation of well-developed institutional frameworks, the deepening of international cooperation, and the implementation of FAIRification processes with the help of AI/ML-based tools to make the implementation of the later more systematic and fair.