Data standards in Scotland's public sector - framework for action: consultation analysis

Appendix A: FAIR Principles and FAIR Implementation Profiles

Appendix A presents information on the FAIR Principles and FAIR Implementation Profiles. This is informed by discussions with Erik Schultes, International Science Coordinator, GOFAIR Initiative, information from the GOFAIR website and papers provided by Erik Schultes.

FAIR Principles

The 15 FAIR Principles specify that, to truly benefit from existing data, data and metadata must be machine actionable, and for that the data must be Findable, Accessible, Interoperable and Reusable, i.e. FAIR.

The principles refer to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure. For instance, principle F4 defines that both metadata and data are registered or indexed in a searchable resource (the infrastructure component).

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 and the associated principles are:

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 they describe

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

Accessible

Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation. The accessible principles are that:

A1. (Meta)data are retrievable by their identifier using a standardised communications 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 are accessible, even when the data are no longer available

Interoperable

Data often needs to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing. The interoperable principles are that:

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

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. The reusable principles are that:

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

FAIR Implementation Profiles (FIPs)

However, the FAIR principles do not explicitly consider the implementation choices that have to be made in support of FAIR data and metadata. This has led to the development of FAIR Implementation Profiles (FIPs) and a FAIR Wizard that records and compares the FIPs created by different communities.

The development of a FIP is undertaken by a community of interest. The community might be complex or simple and could be single organisation or a collaboration of organisations in a specific field of interest. The community will work together to define the data standards they will use as a group.

When a community comes together there are two possible approaches. In many cases, the community may need to find an optimal balance between the two approaches:

• Community declares a commitment to defining the data standards they will work with and will all use; and/or
• Community commits to create a map (interoperation) between systems/standards they use. In this case the community is committing to understanding the map and to make sure that the different systems can interoperate.

The core requirements of a community are:

• It is a well-described community;
• It has a commitment to FAIR principles; and
• It develops a shared FIP.

A FIP is a framework within which the negotiations around a common approach to data can occur. A FIP is a list of implementation choices that have been made on behalf of well-described community. To complete a FIP, the FAIR convergence matrix platform (questions to answer) is one step, it has 15 questions, however they are complicated to answer. The answers are technical and describe the algorithms and platforms selected by the community.

The development of a FIP from scratch will require substantial investment. However, FIPS can be shared with others and reused. As more FIPs are created, it is increasingly likely that a community will be able to adopt the FIP, and the standards developed, by another community with potentially only a few modifications to make it fit their characteristics and requirements. The FAIR Wizard and FAIR Sharing tools mean that some elements of FIP can be auto completed and a FIP report produced, the FIP is also designed to form a substantial part of an organisations' data management plan. The degree to which common FIPS can be developed and reused will drive the speed of convergence around data standards. The reusability of a FIP is likely to offer an attractive proposition to those who find the challenge of developing common standards overwhelming.

FIPs have been developed, however, the FIP concept is still in an emergent phase. There is work underway internationally in the late-summer and Autumn of 2020 to develop a guide for potential users and developers of FAIR and FIPs.

Further information on how to Go Fair can be found at How to go Fair At the time of interview, it is understood that Denmark and Ireland were considering the development of FIPs in support of the response to Covid-19.