FAIR Data Principles

FINDABLE

ACCESSIBLE

INTEROPERABLE

REUSABLE

FAIR – DATA QUALITY PRINCIPLES

 

With the transition to Open Science and all the changes and challenges that imposes to the research cycle and scholarly communication, there is a growing demand for quality criteria for research data by funders and providers and generally all the stakeholders of the research ecosystem. The FAIR (Findable Accessible Interoperable Reusable) data quality principles are community-agreed guiding principles and practices, created with the purpose to enhance the research ecosystem by assisting data providers and consumers, humans and machines, to easily find, access, interoperate and reuse with proper citation the vast amount of information that resulted from contemporary data-intensive science.

 

‘The FAIR Guiding Principles for scientific data management and stewardship’ (Wilkinson et al. 2016) are well described and supported by FORCE 11 (https://www.force11.org/fairprinciples), which actually supports also Data Citation principles (https://www.force11.org/datacitationprinciples) indicating articulation with data FAIRness. Data citation is the practice of providing a reference to data so it can be identified in a unique and persistent way, along with other evidence and sources, is part of the scholarly ecosystem and it is one of the key research practices for recognising data as a primary research output and for supporting data reuse. With the new web and linked data services being increasingly developed, globally unique and persistent identifiers are necessary for data citation, linking and so reuse, but also for discovery, integration and, generally, for making data FAIR.