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The FAIR principles

The FAIR data principles are a set of guiding principles for making data Findable, Accessible, Interoperable and Reusable.  

Why adhere to the FAIR principles?

There is a need to improve the infrastructure that supports the reuse of scientific data. Stakeholders from the academic world, industry, foundations and scientific publishers have signed up to a set of FAIR data principles, which should act as guidelines for those who want to improve the ability to reuse their data.

Advantages when using FAIR principles

  • Strategy: You have a strategy for good data management in your project 
  • Visibility: Your work becomes more transparent and credible, and visibility will give you recognition when it is used or cited, and create opportunities for collaboration  
  • Timesaving: You save time when you can find research data that has already been generated and it minimises the risk of wasting time and money on duplicating work. 
  • Spin offs: You make it possible to easily reuse research data, for example, for new analyses and possible spin offs. 
  • Society: You help society to gain access to publicly funded research data 
  • Compliance: You comply with international standards and institutional policies


Data Management plans and FAIR

When you draw up a data management plan, you comply with the principles of openness and transparency by documenting data provenance. By describing who has created data and metadata, as well as when and under what conditions the data has been generated, a data management plan helps to further an understanding of the data and make research results reproducible. You can read more about the benefits of preparing data management plans.     

Repositories and FAIR

Repositories can help make your data findable (the F in the FAIR principles).  

They can guide you a long way towards making your research compatible with the FAIR principles. For example, if you publish your data in an open repository such as Dataverse, Zenodo or Figshare, they will be able to help you to:

  • set up a DOI
  • have fields for metadata and descriptions that are relevant
  • assign a data license to your data.  

At AU, we don’t have an institutional repository, but there are many open repositories you can use. The choice will often depend on the subject area, but there are also several cross-disciplinary repositories.

Persistent Digital Identifiers (PID) and FAIR

A PID (persistent identifier) is a unique key used to permanently identify a given document, dataset or person. Some descriptive metadata is often linked to a PID. PIDs make it possible for others to find your data and refer them to it. See: The FAIR principles make PID data and suchlike findable and accessible (F and A in the FAIR principles).   

Examples of persistent identifiers:  

  • DOIs (Document Object Identifiers) are used for documents, data and datasets. Most repositories can generate and maintain DOIs or other PIDs.  
  • Researcher IDs are another example of PIDs. They are used to ensure that a given person is not mistaken for someone else, for example, with the same name, by assigning each individual a unique number. ORCID is an example of a researcher ID by which a person is given a unique number that can be used as an ID.

Licenses for datasets and FAIR

Data licenses are used to show whether your data may be used by others and how it may be reused (R in the FAIR principles).  

Research data is of great value to you, but also to researchers around the world. You can make it easier for others to refer to or use your research data by giving it a persistent identifier and by sharing your data with a clear license. Read more about licenses.

Metadata and FAIR

The use of metadata is fundamental when you want to make your research compatible with the FAIR principles. It is metadata that ensures that your research and research data comply with the FAIR principles, even when your data has to be concealed, due to confidentiality, sensitivity, etc. 

By adding metadata to your datasets, you ensure that data can be retrieved by both people and machines. Metadata supports the F, A, I and R in the FAIR principles.

Would you like to know more about the FAIR principles?

Here you will find a number of resources where you can read, see or hear more about the FAIR principles: 

Data Management and Open Science support

  • AU Library supports good data management practice at Aarhus University
  • The library offers support to researchers and students on the handling of research data, supervision, planning and sharing of data throughout the research process  

Need help?

If you have any questions or concerns, feel free to contact the liason librarian associated with your field, who will be happy to assist you with your questions.

Alternatively, you are always welcome to contact your local library.