Summary: FAIR data Management aims to ensure sustainable research data management. What exactly is meant by this acronym, and what is the difference between FAIR and open data?
Table of Contents
What is FAIR data?
The Principles define characteristics that contemporary data resources, tools, vocabularies and infrastructures should exhibit to assist discovery and reuse by third-parties.1
The principles aim to ensure sustainable research data management by preparing and storing data in ways that others can reuse. FAIR stands for Findable, Accessible, Interoperable, Reusable. The principles apply to data management itself as well as to infrastructures and services.
| Findable | The first step in (re)using data is to find them. 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, so this is an essential component of the FAIRification process. |
| Accessible | Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation. |
| Interoperable | The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing. |
| Reusable | The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings. |
Additional information
- How to FAIR
- TIB Blog: The FAIR Data Principles for Research Data
- GO FAIR Initiative
- Force11: The FAIR Data Principles
FAIR and open data
FAIR data management does not necessarily mean open data. For example, some data must not be published by legal reasons. Restrictions on access are consistent with the FAIR Principles as long as the conditions and ways of access are apparent.