The FAIR Guiding Principles represent a concise and measurable set of guidelines designed to improve the Findability, Accessibility, Interoperability, and Reusability of digital research objects. Developed by a diverse group of stakeholders from academia, industry, funding agencies, and publishers, these principles aim to enhance the infrastructure supporting scholarly data management and stewardship.
In today's data-intensive research environment, there is an urgent need to improve how scholarly data is managed and shared. Good data management is not an end in itself but a fundamental component leading to knowledge discovery, innovation, and data integration. The FAIR Principles provide guidelines for those wishing to enhance the reusability of their data holdings.
Unlike initiatives that focus primarily on human scholars, the FAIR Principles emphasize the ability of machines to automatically find and use data, in addition to supporting its reuse by individuals. This machine-actionability is becoming increasingly important as the scale of data and the complexity of research questions continue to grow.
| Findable | Data and metadata should be easy to find for both humans and computers |
| Accessible | Once found, data should be retrievable through standardized protocols |
| Interoperable | Data should be able to be integrated with other data and work with applications for analysis |
| Reusable | Data should be well-described so it can be replicated and/or combined in different settings |
[adapted from CMU Libraries]