FAIR Data and the Future of Scholarly Communication
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Abstract
In the digital age, the way research data is managed, shared, and reused has become a critical factor in shaping the quality, transparency, and impact of scholarly communication. The FAIR data principles—Findability, Accessibility, Interoperability, and Reusability—provide a foundational framework for ensuring that scientific data can be effectively located, understood, and applied by both humans and machines. This mini-review explores how FAIR data enhances global collaboration, accelerates scientific discovery through AI and automation, and fosters open, inclusive research ecosystems. While significant barriers remain—including technical, cultural, and infrastructural challenges—widespread adoption of FAIR is essential for building a future-ready, responsible, and equitable scientific enterprise.
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