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The FAIR principles

This article presents the framework FAIR for datasharing
Binary code of many ones and zeros. In middle of image, some of the numbers are red and form the shape of a heart.

It is widely acknowledged that sharing research data in a timely and open manner is crucial to furthering progress in science and healthcare. However, due to the diverse nature of different research approaches, systems, national guidance and field-specific practice, merely making data available does not always maximise the utility to the global community.

With these issues in mind, a group of researchers hosted a stakeholder workshop with experts from academia and the private sector in 2014 to discuss how to address the challenges. The workshop generated a draft set of principles guiding the sharing of data. They agreed that all research output should be Findable, Accessible, Interoperable and Reusable (FAIR). These FAIR guiding principles were subsequently expanded upon as below.

To be Findable:
F1. (meta)data are assigned a globally unique and persistent identifier.

F2. data are described with rich metadata (defined by R1 below)

F3. metadata clearly and explicitly include the identifier of the data it describes.

F4. (meta)data are registered or indexed in a searchable resource.

To be Accessible:

A1. (meta)data are retrievable by their identifier using a standardized communications protocol.

A1.1 the protocol is open, free, and universally implementable.

A1.2 the protocol allows for an authentication and authorization procedure, where necessary.

A2. metadata are accessible, even when the data are no longer available.

To be Interoperable:

I1. (meta)data use a formal, accessible, shared and broadly applicable language for knowledge representation.

I2. (meta)data use vocabularies that follow FAIR principles.

I3. (meta)data include qualified references to other (meta)data.

To be Reusable:

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

These principles were published in 2016 and have been implemented widely across many fields since then. During the current pandemic, a number of groups have sought to consider how to implement these principles with the additional fast-paced pressure of an ongoing outbreak.

The Virus Outbreak Data Network (VODAN) established a project to develop an international data network infrastructure supporting evidence-based responses to the pandemic. One of the projects associated with VODAN has shown that this approach can be implemented in a hospital setting to allow rapid linking and sharing of relevant patient information in a machine-readable format. A further project highlighted the limited genomic data emerging from Africa underpinned by concerns around data ownership and availability of health data at the point of care. A system was developed to provide this data at the point of care as well as aggregated for global analysis and has been deployed in a number of African countries.

As well as the FAIR principles for data sharing, The Research Data Alliance International Indigenous Data Sovereignty Interest Group created a set of principles concerning indigenous data governance. These principles intend to maximise the benefits of data and research on individual communities, whilst promoting the outward sharing of data for global good in a manner which respects previous barriers to sharing and misuse of data. These principles state that there should be Collective benefit, Authority to control, Responsibility and Ethics (CARE**). There have been subsequent efforts to guide the use of both **FAIR and CARE guidance in data usage (Figure 1).

image showing how CARE principles can be applied across the data journey Click here to enlarge

Figure 1 – Implementation of the CARE Principles across the data lifecycle. Source: Scientific Data

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