The FAIR Guiding Principles for scientific data management and stewardship

Area of Science:

• Data Science
• Information Science
• Scholarly Communication

Background:

• Scholarly data reuse is critical but hindered by inadequate infrastructure.
• A diverse group of stakeholders recognized the need for standardized data principles.

Purpose of the Study:

• To formally introduce and endorse the FAIR Data Principles.
• To provide guidelines for enhancing the reusability of scholarly data.
• To emphasize machine-actionability for data discovery and use.

Main Methods:

• Development of a concise and measurable set of principles.
• Community-wide endorsement from academia, industry, publishers, and funding agencies.
• Documentation of the rationale and exemplar implementations.

Main Results:

• Formal publication of the FAIR Data Principles (Findable, Accessible, Interoperable, Reusable).
• Emphasis on machine-driven data access and reuse alongside human accessibility.
• Establishment of a guideline for improving data reusability.

Conclusions:

• The FAIR Data Principles offer a framework to enhance scholarly data infrastructure.
• Adoption of these principles can significantly improve data discoverability and usability.
• The principles support both individual researchers and automated systems in data reuse.