The FAIR data principles are a set of guiding principles to make data Findable, Accessible, Interoperable and Reusable, so that data can be found, understood and reused by others.
There is a need to improve the infrastructure that supports the reuse of scientific data. Stakeholders from the academic world, industry, funding organisations and scientific publishers have agreed to a set of FAIR data principles, which serve as guidelines for those wanting to improve the ability of their data being reused.
Data Management plans and FAIR
When preparing a data management plan, you are complying with the principles of openness and transparency by documenting provenance of the data. By describing who has created the data and metadata, as well as when and under what conditions the data were generated, the data management plan contributes to understanding the data and making research results reproducible. Read more about the benefits of creating data management plans.
Persistent Identifiers and FAIR
A persistent identifier (PID) is a unique key which is used to permanently identify a given document, dataset or person. Often, there are a few descriptive metadata associated with a PID. PIDs allow other people to find and refer to your data. In line with the FAIR principles, PID make data findable and accessible (the "F" and the "A" in the FAIR principles).
Examples of persistent identifiers:
- A document object identifier (DOI) is 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 one particular person is not confused with another person, for example with the same name, by assigning a unique number to each person. ORCID is an example of a researcher ID where a person is assigned a unique number which can be used for identification
Licenses for datasets and FAIR
Data licenses are used to show whether your data can be used by others, and how the data can be reused (the "R" in the FAIR principles)
Research data holds considerable value for you, but also for researchers around the world. You can make it easier for others to refer to or use your research data by sharing your data with a clear license. Read more about licenses.
Metadata and FAIR
Working with metadata is fundamental when you want to make your research compatible with the FAIR principles. Metadata ensure that your research and research data follow the FAIR principles, even when your data are closed, because the data are confidential, or they contain sensitive personal information etc.
Adding metadata to your datasets ensures that data can be found by both people and machines. Metadata supports the "F", "A", "I" and "R" in the FAIR principles.
Repositories and FAIR
Repositories can help you make your data findable, accessible and reusable (the "F", "A" and "R" in the FAIR principles).
The repositories can guide you a long way in making your research compatible with the FAIR principles. If you publish your data in an open repository such as Dataverse, Zenodo, or Figshare, these will, for example, be able to help you:
- set up a DOI
- have relevant fields for metadata and descriptions
- assign a data license to your data
At AU, we do not have an institutional repository, but there are numerous open repositories which you can use. Often the choice will depend on the subject area, but there are also a number of inter-disciplinary repositories.
There are several resources where you can read, view or hear more about the FAIR principles:
