5  The FAIR principles

The FAIR principles of Findability, Accessibility, Interoperability and Reusability (Wilkinson et al. 2016), are the most widely adopted framework for guiding the management of digital resources, in particular, scientific data and metadata. They define guiding principles and practices intended to enable both machines and humans to reuse research data. These core precepts have led to a myriad of guidelines and initiatives to define, evaluate and implement them in practice. We endorse the flexible FAIRification framework (Welter et al. 2023) that emphasizes three key practical aspects: setting realistic, incremental goals; tailoring support to the individual needs; and working in multidisciplinary teams, involving all scientific and infrastructure stakeholders relevant to the individual project.

We recommend to follow a flexible approach aiming for a balanced “FAIR enough” status of the data, depending on the available resources and required capabilities.

5.1 Basic first steps

Caution

Many of the references and resources for data FAIRification are written for data engineers or data stewards and can be too technical for researchers from outside that domain. However, all researchers from any field have the responsibility of documenting their protocols and producing adequately formatted metadata. This is essential for transparency and scientific reproducible, and the starting point for generating reusable data.

Each FAIR principle has subprinciples with definitions that may slightly vary depending on the field. FAIRification is a process with the ultimate goal of making our data become machine actionable digital objects. That is not always achievable and requires many resources. However, at a minimum, researchers can aim at producing metadata that is human and machine-readable to a degree sufficient to enable experts to reuse their data without requiring unreasonable efforts to understand it. FAIR does not necessarily mean the data will be open and publicly available. Thus, as a starting point, we consider that a research must:

  1. provide sufficient machine-readable metadata or context to their data

  2. share or publish the data in repositories that are FAIR-compatible to a reasonable degree (e.g., provide persistent identifiers and require metadata).

5.2 Summary of FAIR principles

FAIR Principle Description
Findable It should be possible for others to discover your data. Rich metadata should be available online in a searchable resource, and the data should be assigned a persistent identifier.
F1 (Meta)data are assigned globally unique and persistent identifiers.
F2 Data are described with rich metadata.
F3 Metadata clearly and explicitly include the identifier of the data they describe.
F4 (Meta)data are registered or indexed in a searchable resource.
Accessible It should be possible for humans and machines to gain access to your data, under specific conditions or restrictions where appropriate. FAIR does not mean that data need to be open! There should be metadata, even if the data are not accessible.
A1 (Meta)data are retrievable by their identifier using a standardised communication protocol.
A1.1 The protocol is open, free and universally implementable.
A1.2 The protocol allows for an authentication and authorisation procedure where necessary.
A2 Metadata should be accessible even when the data is no longer available.
Interoperable Data and metadata should conform to recognized formats and standards to allow them to be combined and exchanged.
I1 (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
I2 (Meta)data use vocabularies that follow the FAIR principles.
I3 (Meta)data include qualified references to other (meta)data.
Reusable Lots of documentation is needed to support data interpretation and reuse. The data should conform to community norms and be clearly licensed so others know what kinds of reuse are permitted.
R1 (Meta)data are richly described with a plurality of accurate and relevant attributes.
R1.1 (Meta)data are released with a clear and accessible data usage license.
R1.2 (Meta)data are associated with detailed provenance.
R1.3 (Meta)data meet domain-relevant community standards.

5.3 Selected resources

  • FAIR data page from the library of the University of Zurich. It includes tutorial and checklists.
  • FAIR checker is a webapp to help you improve your metadata and inspect metadata of published data. Just add the URL of a dataset. It will give a ‘FAIRness’ score and describe how much each FAIR principle is fulfilled.