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.
PubMed ID: 26978244
Projects: FAIRDOM training
Publication type: Not specified
Journal: Sci Data
Citation: Sci Data. 2016 Mar 15;3:160018. doi: 10.1038/sdata.2016.18.
Date Published: 16th Mar 2016
Registered Mode: Not specified
Created: 2nd Dec 2016 at 09:46
Last updated: 6th Dec 2022 at 11:21