In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data, an online Nature magazine journal. The authors intended to provide guidelines to improve the findability, accessibility, interoperability and reuse of digital assets.
The principles emphasise machine-actionability: the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention. This is because people increasingly rely on computational support to deal with data in the face of a rapid increase in volume, complexity, and creation speed of data.
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 a crucial component of the FAIRification process.
Once the user finds the required data, she/he needs to know how they can be accessed, possibly including authentication and authorisation.
The data usually need to be integrated with other data. Also, the data need to interoperate with applications or workflows for analysis, storage, and processing.
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.
The principles refer to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure.
You can find detailed information at go-fair.org/fair-principles
FAIR - What does it stand for? What does it mean? And even more so; why should we? And what it could mean for you(r research). Watch PhD candidate Chang Sun's video about what FAIR means to her.
PhD researcher Adam Jassem tells us about his experience with making research FAIR.