The FAIR data principles are a set of guiding principles for making data Findable, Accessible, Interoperable and Reusable.
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.
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 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:
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.
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:
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.
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.
Here you will find a number of resources where you can read, see or hear more about the FAIR principles: