The FAIR (Findability, Accessibility, Interoperability, and Reusability) guiding principles originate from the need to make research data discoverable and reusable. Unlike other initiatives that prioritise human scholars, the main intent here is to improve data reusability by focusing on machine accessibility alongside human use (1). Beyond the principles themselves, some works have addressed implementation considerations (2), the process of making data FAIR (FAIRification) (3), and the evaluation of compliance with FAIR principles (4, 5). The principles also appear to be taken into account in various fields, such as medicine, information systems, agriculture, finance, etc. see the derived works and citations of the aforementioned works for more information.
This post aims to discuss each FAIR principle and explain why together they are relevant in a systems engineering context and in cross-functional settings more generally. The post also shows why the principles form a basis for clear and effective communication in such a heterogeneous context and how they can help to pre-empt silos.
In a previous article, I discussed what Systems Engineering (SE) entails – in particular how it differs from Project Management (PM) – and the ways in which SE can benefit you, regardless your business sector. For the sake of exposition, we assume that the SE function can span the four types of processes listed in ISO/IEC/IEEE 15288:2023, namely Agreement, Organizational Project-Enabling, Technical Management and Technical Processes. This function applies to different phases of the system’s life cycle. These phases go from idea to retirement.
Whatever form or structure it may take, the term ‘document’ in the following refers to any container or record of data.
I shall now proceed with each principle (available and taken here: https://www.go-fair.org/fair-principles/) in turn.
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…
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
This principle basically should ensure that any useful document is identifiable and can be efficiently located throughout the system’s life cycle. A comparison can be made here with an office where documents are scattered and untitled. Finding a specific document among them would take time, not only to search through them, but also to check the content of each one to see if it is the one one is looking for. An equivalent metaphor can be found when looking for a document within a tool or an information system.
Accessible : Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorisation.
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
This principle is particularly important in an SE or cross-functional setting, where users tend to use different tools with various access levels to support the different processes and phases of the system’s life cycle. Therefore, data indicating who can access a document and how they can access it should be readily available whenever necessary. For example, consider an important document that only one person can access at a given time, whereas several people should be able to access it. This would clearly hinder collaboration and communication.
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.
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
The principle of interoperability poses a number of challenges, prompting extensive research in various fields. Interoperability is often broken down into layers or levels, with definitions and interpretations varying across different domains. Put simply, data interoperability means that data can be clearly and unambiguously understood or interpreted. However, it is important to note that not everything that can be clearly understood can also be processed directly by a machine.
In the context of SE, it is crucial to produce clear and precise documents (work products or deliverables) when sharing them among multiple stakeholders. The clarity of these documents directly impacts the integrity of the system, as they are essential for managing it throughout its life cycle.
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.
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
This principle is closely related to the previous two: Accessible and Interoperable. It further emphasises the importance of the context in which data is used. Therefore, documents that are clear and precise may not be enough to make reuse effective or even optimised. In such cases, further data indicating how to reuse the documents effectively may be necessary, in addition to the core content itself. This again will facilitate collaboration and communication among stakeholders.
The foregoing shows that pre-empting silos and enabling clear and effective communication and collaboration between different stakeholders in an SE context, or in a cross-functional context more generally, can start at the level of the documents produced by those stakeholders. These documents should simply adhere to the FAIR guiding principles. These stakeholders, or their organisations, should do their utmost to adhere to these principles. Note that organisations implementing configuration management (CM), which is part of the SE function, would a priori cover FAIR principles. Unfortunately, CM is not usually applied to all relevant documents throughout the system’s life cycle. As for information systems and life cycle management tools, they generally do not cover the entire system’s life cycle and sometimes conflate data with tool logic.
In view of the above, what can Timeinx offer your business?
While an organisation can benefit from Timeinx, provided it adheres to the FAIR+V principles, Timeinx also facilitates adherence to these principles. We add ‘V’ (versioned) to emphasise that different versions of a given document should be considered and remain available throughout the system’s life cycle. Timeinx gives all stakeholders involved in their projects, programmes or daily operations, quick, clear, high-level visibility of all aspects of their endeavours throughout their life cycle. This visibility enables specific or internal details to be recovered from information systems or dedicated repositories when necessary. These aspects are based on documents produced during the system’s life cycle. Timeinx thus provides all stakeholders with a comprehensive, high-level view that fosters open communication, the valuation of contributions from all departments or domains, and genuine and trusted collaboration, which may ultimately result in timely alignments.
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- Wilkinson, M. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016).
- Jacobsen, A. et al. (2020). FAIR principles: interpretations and implementation considerations. Data intelligence, 2(1-2), 10-29.
- Jacobsen, A. et al. (2020). A generic workflow for the data FAIRification process. Data Intelligence, 2(1-2), 56-65.
- David, R. et al. (2020). FAIRness literacy: the Achilles’ heel of applying FAIR principles. CODATA Data Science Journal, 19(32), 1-11.
- de Miranda Azevedo, R., & Dumontier, M. (2020). Considerations for the conduction and interpretation of FAIRness evaluations. Data Intelligence, 2(1-2), 285-292.