Nature, Published online: 01 July 2026; doi:10.1038/d41586-026-01982-y The FAIR guidelines laid a solid foundation for ensuring data accessibility and reproducibility. A decade on, researchers are thinking beyond them.
A decade after their inception, the foundational principles of FAIR data require re-evaluation to match the evolving demands of scientific research and technological capabilities.
Improving data accessibility and reproducibility is critical for accelerating scientific discovery, fostering public trust, and maximizing research investment returns across all sectors.
The focus is shifting from basic data FAIRness to more advanced, possibly AI-driven, methods of data management and interoperability, indicating a maturing data science landscape.
- · Open science initiatives
- · Data scientists
- · AI-driven research platforms
- · Research institutions
- · Proprietary data silos
- · Traditional publishing models
- · Researchers with poor data management skills
Further development of advanced data sharing and integration technologies for scientific research.
Increased efficiency and speed of scientific breakthroughs, particularly in data-intensive fields.
New standards and regulatory frameworks for managing and sharing scientific data, potentially influencing intellectual property and collaboration models.
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