What are the FAIR data principles?

FAIR data principles are guidelines to improve the findability, accessibility, interoperability, and reusability of digital assets. 

These guidelines aim to maximize the usefulness of digital assets by mandating usability requirements that go beyond permission alone. Unlike other types of data which follow different principles like open data, FAIR data prioritizes usability over maximizing openness. These usability requirements optimize the impact of scientific data and promote its preservation and utility.

What are the requirements for FAIR data?

There are well-defined requirements for data to be considered FAIR. The data must be:

• Defined and entered in online public records for the purpose of discovery and citation. 

• Made accessible such that it can be opened, read, and processed. 

• Recorded and presented in a way that can be understood and utilized.

Data management software can help keep your data FAIR compliant, allowing you to have access to all of your data, all of the time. That's the case with Dotmatics data management software. Our platform complies with all the FAIR principles, making it easy to uphold the integrity and utility of your hard-earned data. 

Why are the principles of FAIR important to data science?

Fair data principles aim to outline the ideal way that data should be stored and shared to optimize its utility to the entire research community. 

Whether it be raw data, workflows, analyses, or written publications, each phase of the research cycle produces information that can be invaluable to other researchers. However, until the development of FAIR data guidelines, only a few phases of the research cycle would regularly be made available publicly, and most often only as final publications. 

FAIR principles help to ensure that as much data as possible is made available to other researchers for its ongoing use and study. Data is the fuel for scientific research and development — and science is a collaborative process. As such, FAIR data principles empower scientists and researchers to benefit from each other's work.

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