FAIR Data • Machine Actionability • Compliance

The FAIR movement in academia is an initiative aimed at making research data machine actionable through improving the Findable, Accessible, Interoperable, and Reusable in scientific information. These four principles provide a framework for ensuring that scientific data and related metadata are well-described, well-organized, and widely available for reuse. This movement was launched in response to the growing realization that a significant portion of scientific data is lost, forgotten, or simply inaccessible to other researchers due to poor data management practices. By following the FAIR principles, researchers can increase the value and impact of their research by making it more discoverable, accessible, and reusable by others in the scientific community.

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