FAIR Principles

FAIR stands for findable, accessible, interoperable, resuable. The aim is that open data 'can be freely used, modified, and shared by anyone for any purpose', The Open Definition.

Since they were first published in 2016, the FAIR Principles have achieved widespread acceptance, and have been adopted as standards for management of data, development of infrastructure and delivery of services.

Findable 

This means other scholars and computers can find your data due to strong metadata and persistent identifiers (DOIs).

What does this mean?

Your data are described with metadata; your data and metadata have a global unique persistent identifier; your data and metadata are registered or indexed in a searchable resource, i.e. a research data repository.

What to do:

• You need a unique persistent identifier and many data repositories assign a persistent identifier. 
• Use consistent file naming conventions and logical folder structures when organising data.
• Use robust file management when you archive your data.
• Use a repository that exposes your metadata and manages your files according to your wishes. Trusted data repositories will also allocate a digital object identifier (DOI) to your data. 

Accessible 

Your data and/or metadata also needs to be accessed by both humans and computers.

What does this mean?

Your data are available and downloadable from a reputable repository.

What to do:

• If you can share your data openly then deposit your data in the ORDO (The OU’s Research Data Repository), a discipline specific research data repository or well-known general data repository.
• If you cannot share openly, create a metadata record only, which includes under what conditions the data can be shared.

Interoperable 

As the aim is to speed up discover and uncover new insights, research data should be easily combined with other datasets, applications and workflows by humans and computer systems.

What does this mean?

This means your data exist in file formats that are not dependent on proprietary or obsolete software.

What to do:

• You should use whenever possible, well-known and preferably open formats and software.
• Use standard vocabulary and language that can be understood beyond your discipline. Avoid jargon.

Reusable 

Research data should be ready for future research and processing. It should be clear that findings can be replicated and the data/results can be used to build/develop new research questions.

What does this mean?

That there is proper documentation to support interpretation of the data. That there is a clear and accessible data usage licence. 

What to do:

• Include detailed information and accurately cite the provenance of data you're reusing in your metadata and README file.
• Know from the start which data you cannot share and what restrictions you may need to account for. Consider the implications for personal data, intellectual property or commercial potential.
• Make sure your intentions for data reuse are unambiguous by applying a Creative Commons license or similar.