Findable emphasizes the importance of making research data easy to find, both by humans and machines, so that it can be found when needed.

To be Findable

F1. (meta)data are assigned a globally unique and persistent identifier.

F2. data are described with rich metadata (defined by R1 below).

F3. metadata clearly and explicitly include the identifier of the data it describes.

F4. (meta)data are registered or indexed in a searchable resource.

F1-F4 taken from: The FAIR Guiding Principles for scientific data management and stewardship.

It should be possible for both humans and machines to access the data under the right conditions. FAIR data is not the same as open data. If there are conditions that prevent data from being openly available, you make your data FAIR by making your metadata available.

To be Accessible

A1. (meta)data are retrievable by their identifier using a standardized communications protocol.

A1.1. the protocol is open, free, and universally implementable.

A1.2. the protocol allows for an authentication and authorization procedure, where necessary.

A2. metadata are accessible, even when the data are no longer available.

A1-A2 taken from: The FAIR Guiding Principles for scientific data management and stewardship.

Interoperable means that your research data should be organized and formatted in a way that allows it to be combined and analyzed with other data, using standard formats, terminology, and information about the data (metadata).

With interoperable data, researchers can share and collaborate on data, find meaningful patterns and insights, and work towards new discoveries that would not be possible with fragmented, incomprehensible, or siloed data sets.

To be Interoperable

I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.

I2. (meta)data use vocabularies that follow FAIR principles.

I3. (meta)data include qualified references to other (meta)data.

I1-I3 taken from: The FAIR Guiding Principles for scientific data management and stewardship.

Reusable data is possible for others to understand and use. This means your data is accompanied by clear documentation such as sufficient metadata and preserved somewhere in line with the FAIR principles.

To be Reusable

R1. meta(data) are richly described with a plurality of accurate and relevant attributes.

R1.1. (meta)data are released with a clear and accessible data usage license.

R1.2. (meta)data are associated with detailed provenance.

R1.3. (meta)data meet domain-relevant community standards.

R1-R1.3 taken from: The FAIR Guiding Principles for scientific data management and stewardship.

Working to meet the FAIR principles is a way to achieve good data management. To achieve a high level of FAIR data you will have to organize and manage your research data in such a way, that it gets easier to find, understand and reuse. It is almost impossible to make your research data FAIR in the end of your research project. Therefore, start as early as possible working with the FAIR-principles in a data management plan.

By adopting the FAIR principles, you deliberately work with documentation and description of your data and how you handle it. This will enhance the transparency, reliability, and reusability of your research. Making your data and metadata more FAIR you improve the visibility of your research which can lead to more citations and possible new collaborations and funding opportunities.

Both nationally and internationally there is a big focus on open and FAIR data. So big, that the FAIR principles have found their way into The European Open Data Directive (PSI Directive), which include that national policies must be made for making research data accessible in accordance with the FAIR principles. As a result the FAIR principles are the basis of The Danish National strategy for Data Management. Also, at AAU all researchers should work with the FAIR principles and write a data management plan when working with research activities (AAU policy for research data management).

More and more funders and publishers require that research data are managed according to the FAIR principles.