One of the challenges of making data openly available is to make it easily discoverable by others, so that it can be accessed, analysed and integrated into research, tasks and projects. FAIR data principles act as guidelines to enhance the reusability of your data. They focus on enhancing the ability of machines to automatically find and use the data, as well as individuals, and aim to promote good data management.
FAIR data principles
FAIR stands for: findable, accessible, interoperable, and reusable.
Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services.
It is important to be able to access data, whether it is openly available or requires authorisation or application.
Interoperable data can be integrated with other data and is compatible with applications or workflows for analysis, storage and processing.
Metadata and data should be well-described so that they can be easily replicated and combined.
Make your data FAIR
To make sure that your data can be made open or FAIR, at the beginning of your research it is important to consider:
- how you collect and document the data
- the formats you use to store the data
- how you preserve and share the data
- how data are licensed for re-use.
Relationship between open and FAIR data
Open data can be freely used, modified, and shared by anyone for any purpose. It is made available under an open licence like Creative Commons.
To be fully open, data must also be FAIR.
However, data can be FAIR without being open: restricted-access data could be FAIR if the descriptive metadata is openly accessible.
We support both open and FAIR data in the data repositories we manage: the Research Data Leeds Repository and the Restricted Access Data Repository (RADAR).