Scientific Data· 2016Q1
The FAIR Guiding Principles for scientific data management and stewardship
- 17,987citations
- Q1SCImago
- 2016year
Short summary
The FAIR Data Principles have been jointly developed by diverse stakeholders to improve the infrastructure for scholarly data reuse, emphasizing machine-readability for automatic data discovery and use.
AI-generated from the title and abstract; the full text is not read.
Key points
- FAIR Data Principles aim to improve scholarly data reuse infrastructure.
- Principles emphasize machine-readability for automatic data discovery and use.
- Developed through collaboration among academia, industry, funders, and publishers.
- This publication is the first formal introduction of the FAIR Principles.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Scientific Data, 2016 · DOI ↗
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Field: Information Systems
Information SystemsComputer Science