Initiatives, Concepts, and Implementation Practices of the Findable, Accessible, Interoperable, and Reusable Data Principles in Health Data Stewardship: Scoping Review

BACKGROUND

Thorough data stewardship is a key enabler of comprehensive health research. Processes such as data collection, storage, access, sharing, and analytics require researchers to follow elaborate data management strategies properly and consistently. Studies have shown that findable, accessible, interoperable, and reusable (FAIR) data leads to improved data sharing in different scientific domains.

OBJECTIVE

This scoping review identifies and discusses concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in health research data.

METHODS

The Arksey and O'Malley stage-based methodological framework for scoping reviews was applied. PubMed, Web of Science, and Google Scholar were searched to access relevant publications. Articles written in English, published between 2014 and 2020, and addressing FAIR concepts or practices in the health domain were included. The 3 data sources were deduplicated using a reference management software. In total, 2 independent authors reviewed the eligibility of each article based on defined inclusion and exclusion criteria. A charting tool was used to extract information from the full-text papers. The results were reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.

RESULTS

A total of 2.18% (34/1561) of the screened articles were included in the final review. The authors reported FAIRification approaches, which include interpolation, inclusion of comprehensive data dictionaries, repository design, semantic interoperability, ontologies, data quality, linked data, and requirement gathering for FAIRification tools. Challenges and mitigation strategies associated with FAIRification, such as high setup costs, data politics, technical and administrative issues, privacy concerns, and difficulties encountered in sharing health data despite its sensitive nature were also reported. We found various workflows, tools, and infrastructures designed by different groups worldwide to facilitate the FAIRification of health research data. We also uncovered a wide range of problems and questions that researchers are trying to address by using the different workflows, tools, and infrastructures. Although the concept of FAIR data stewardship in the health research domain is relatively new, almost all continents have been reached by at least one network trying to achieve health data FAIRness. Documented outcomes of FAIRification efforts include peer-reviewed publications, improved data sharing, facilitated data reuse, return on investment, and new treatments. Successful FAIRification of data has informed the management and prognosis of various diseases such as cancer, cardiovascular diseases, and neurological diseases. Efforts to FAIRify data on a wider variety of diseases have been ongoing since the COVID-19 pandemic.

CONCLUSIONS

This work summarises projects, tools, and workflows for the FAIRification of health research data. The comprehensive review shows that implementing the FAIR concept in health data stewardship carries the promise of improved research data management and transparency in the era of big data and open research publishing.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/22505.

Design of a FAIR digital data health infrastructure in Africa for COVID-19 reporting and research

The limited volume of COVID-19 data from Africa raises concerns for global genome research, which requires a diversity of genotypes for accurate disease prediction, including on the provenance of the new SARS-CoV-2 mutations. The Virus Outbreak Data Network (VODAN)-Africa studied the possibility of increasing the production of clinical data, finding concerns about data ownership, and the limited use of health data for quality treatment at point of care. To address this, VODAN Africa developed an architecture to record clinical health data and research data collected on the incidence of COVID-19, producing these as human- and machine-readable data objects in a distributed architecture of locally governed, linked, human- and machine-readable data. This architecture supports analytics at the point of care and-through data visiting, across facilities-for generic analytics. An algorithm was run across FAIR Data Points to visit the distributed data and produce aggregate findings. The FAIR data architecture is deployed in Uganda, Ethiopia, Liberia, Nigeria, Kenya, Somalia, Tanzania, Zimbabwe, and Tunisia.

FAIR Principles: Interpretations and Implementation Considerations

The FAIR principles have been widely cited, endorsed and adopted by a broad range of stakeholders since their publication in 2016. By intention, the 15 FAIR guiding principles do not dictate specific technological implementations, but provide guidance for improving Findability, Accessibility, Interoperability and Reusability of digital resources. This has likely contributed to the broad adoption of the FAIR principles, because individual stakeholder communities can implement their own FAIR solutions. However, it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible implementations. Thus, while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways, for true interoperability we need to support convergence in implementation choices that are widely accessible and (re)-usable. We introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible, robust, widespread and consistent FAIR implementations. Any self-identified stakeholder community may either choose to reuse solutions from existing implementations, or when they spot a gap, accept the challenge to create the needed solution, which, ideally, can be used again by other communities in the future. Here, we provide interpretations and implementation considerations (choices and challenges) for each FAIR principle.

Towards the Tipping Point for FAIR Implementation

This article explores the global implementation of the FAIR Guiding Principles for scientific management and data stewardship, which provide that data should be findable, accessible, interoperable and reusable. The implementation of these principles is designed to lead to the stewardship of data as FAIR digital objects and the establishment of the Internet of FAIR Data and Services (IFDS). If implementation reaches a tipping point, IFDS has the potential to revolutionize how data is managed by making machine and human readable data discoverable for reuse. Accordingly, this article examines the expansion of the implementation of FAIR Guiding Principles, especially how and in which geographies (locations) and areas (topic domains) implementation is taking place. A literature review of academic articles published between 2016 and 2019 on the use of FAIR Guiding Principles is presented. The investigation also includes an analysis of the domains in the IFDS Implementation Networks (INs). Its uptake has been mainly in the Western hemisphere. The investigation found that implementation of FAIR Guiding Principles has taken firm hold in the domain of bio and natural sciences. To achieve a tipping point for FAIR implementation, it is now time to ensure the inclusion of non-European ascendants and of other scientific domains. Apart from equal opportunity and genuine global partnership issues, a permanent European bias poses challenges with regard to the representativeness and validity of data and could limit the potential of IFDS to reach across continental boundaries. The article concludes that, despite efforts to be inclusive, acceptance of the FAIR Guiding Principles and IFDS in different scientific communities is limited and there is a need to act now to prevent dampening of the momentum in the development and implementation of the IFDS. It is further concluded that policy entrepreneurs and the GO FAIR INs may contribute to making the FAIR Guiding Principles more flexible in including different research epistemologies, especially through its GO CHANGE pillar.