The FAIR Guiding Principles for scientific data management and stewardship

TLDR

There is an urgent need to improve infrastructure for scholarly data reuse, prompting diverse stakeholders to endorse concise, measurable FAIR Data Principles that emphasize machine findability and use. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. This Comment is the first formal publication of the FAIR Principles, including their rationale and exemplar implementations in the community.

Abstract

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders-representing academia, industry, funding agencies, and scholarly publishers-have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.