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.

FAIR Equivalency with Regulatory Framework for Digital Health in Uganda

This study explores the possibility of opening a policy window for the adoption of the FAIR Guidelines—that data be Findable, Accessible, Interoperable, and Reusable(FAIR)—in Uganda’s e Health sector. Although the FAIR Guidelines were not mentioned in any of the policy documents relevant to Uganda’s e Health sector, the study found that 83% of the documents mentioned FAIR Equivalent efforts, such as the adoption of the National Identification Number(NIN) as a unique identifier in Uganda’s national Electronic Health Management Information System(e HMIS)(findability), the planned/ongoing integration of various information systems(interoperability), and the alignment of various projects with international best practices/standards(reusability). A FAIR Equivalency Score(FE-Score), devised in this study as an aggregate score of the mention of the equivalent of FAIR facets in the policy documents, showed that the documents at the core of Uganda’s digital health/e Health policy have the highest score of all the documents analysed, indicating that there is a degree of alignment between Uganda’s National e Health Vision and the FAIR Guidelines. Therefore, it can be concluded that favourable conditions exist for the adoption and implementation of the FAIR Guidelines in Uganda’s e Health sector. Hence, it is recommended that the FAIR community adopt a capacity building strategy through organisations with a worldwide mandate, such as the World Health Organization, to promote the adoption of the FAIR Guidelines as part of international best practices.

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.

FAIR Practices in Africa

This article investigates expansion of the Internet of FAIR Data and Services(IFDS)to Africa,through the three GO FAIR pillars:GO CHANGE,GO BUILD and GO TRAIN.Introduction of the IFDS in Africa has a focus on digital health.Two examples of introducing FAIR are compared:a regional initiative for digital health by governments in the East Africa Community(EAC)and an initiative by a local health provider(Solidarmed)in collaboration with Great Zimbabwe University in Zimbabwe.The obstacles to introducing FAIR are identified as underrepresentation of data from Africa in IFDS at this moment,the lack of explicit recognition of situational context of research in FAIR at present and the lack of acceptability of FAIR as a foreign and European invention which affects acceptance.It is envisaged that FAIR has an important contribution to solve fragmentation in digital health in Africa,and that any obstacles concerning African participation,context relevance and acceptance of IFDS need to be removed.This will require involvement of African researchers and ICT-developers so that it is driven by local ownership.Assessment of ecological validity in FAIR principles would ensure that the context specificity of research is reflected in the FAIR principles.This will help enhance the acceptance of the FAIR Guidelines in Africa and will help strengthen digital health research and services.

FAIR Equivalency with Regulatory Framework for Digital Health in Ethiopia

This paper investigates whether or not there is a policy window for making health data ‘Findable’, ‘Accessible’(under well-defined conditions), ‘Interoperable’ and ‘Reusable’(FAIR) in Ethiopia. The question is answered by studying the alignment of policies for health data in Ethiopia with the FAIR Guidelines or their ‘FAIR Equivalency’. Policy documents relating to the digitalisation of health systems in Ethiopia were examined to determine their FAIR Equivalency. Although the documents are fragmented and have no overarching governing framework, it was found that they aim to make the disparate health data systems in Ethiopia interoperable and boost the discoverability and(re)usability of data for research and better decision making. Hence, the FAIR Guidelines appear to be aligned with the regulatory frameworks for ICT and digital health in Ethiopia and, under the right conditions, a policy window could open for their adoption and implementation.

Agenda Setting on FAIR Guidelines in the European Union and the Role of Expert Committees

The FAIR Guidelines were conceptualised and coined as guidelines for Findable, Accessible, Interoperable and Reusable(FAIR) data at a conference held at the Lorentz Centre in Leiden in 2014. A relatively short period of time after this conference, the FAIR Guidelines made it onto the public policy agenda of the European Union. Following the concept of Kingdon, policy entrepreneurs played a critical role in creating a policy window for this idea to reach the agenda by linking it to the policy of establishing a European Open Science Cloud(EOSC). Tracing the development from idea to policy, this study highlights the critical role that expert committees play in the European Union. The permeability of the complex governance structure is increased by these committees, which allow experts to link up with the institutions and use the committees to launch new ideas. The High Level Expert Groups on the EOSC provided the platform from which the FAIR Guidelines were launched, and this culminated in the adoption of the FAIR Guidelines as a requirement for all European-funded science. As a result, the FAIR Guidelines have become an obligatory part of data management in European-funded research in 2020 and are now followed by other funders worldwide.

FAIR Versus Open Data: A Comparison of Objectives and Principles

This article assesses the difference between the concepts of ‘open data’ and ‘FAIR data’ in data management. FAIR data is understood as data that complies with the FAIR Guidelines—data that is Findable, Accessible, Interoperable and Reusable—while open data was born out of awareness of the need to democratise data by improving its accessibility, based on the idea that data should not have limitations that prevent people from using it. This study compared FAIR data with open data by analysing relevant documents using a coding analysis with conceptual labels based on Kingdon’s theory of agenda setting. The study found that in relation to FAIR data the problem stream focuses on the complexity of data collected for research, while open data primarily emphasises giving the public access to non-confidential data. In the policy stream, the two concepts share common standpoints in terms of making data available and reusable, although different approaches are adopted in practice to accomplish these goals. In the politics stream, stakeholders with different objectives support FAIR data and from those who support open data.

Introduction to the Special Issue: Data Intelligence on Patient Health Records

Data Intelligence is the ultimate purpose of FAIR data management.FAIR as in data that is Findable,Accessible(under well defined conditions),Interoperable and Reusable.FAIR also as in ethical data;data that fulfils the requirements of Personal Data Protection,is collected for well defined purposes and is held and curated within ownership of the location where the data is produced.

Implementation of FAIR Guidelines in Selected Non-Western Geographies

This study provides an analysis of the implementation of FAIR Guidelines in selected non-Western geographies. The analysis was based on a systematic literature review to determine if the findability, accessibility, interoperability, and reusability of data is seen as an issue, if the adoption of the FAIR Guidelines is seen as a solution, and if the climate is conducive to the implementation of the FAIR Guidelines. The results show that the FAIR Guidelines have been discussed in most of the countries studied, which have identified data sharing and the reusability of research data as an issue(e.g., Kazakhstan, Russia, countries in the Middle East), and partially introduced in others(e.g., Indonesia). In Indonesia, a FAIR equivalent system has been introduced, although certain functions need to be added for data to be entirely FAIR. In Japan, both FAIR equivalent systems and FAIR-based systems have been adopted and created, and the acceptance of FAIRbased systems is recommended by the Government of Japan. In a number of African countries, the FAIR Guidelines are in the process of being implemented and the implementation of FAIR is well supported. In conclusion, a window of opportunity for implementing the FAIR Guidelines is open in most of the countries studied, however, more awareness needs to be raised about the benefits of FAIR in Russia and Kazakhstan to place it firmly on the policy agenda.

Expanding Non-Patient COVID-19 Data: Towards the FAIRification of Migrants' Data in Tunisia, Libya and Niger

This article describes the FAIRification process(which involves making data Findable, Accessible, Interoperable and Reusable—or FAIR—for both machines and humans) for data related to the impact of COVID-19 on migrants, refugees and asylum seekers in Tunisia, Libya and Niger, according to the scheme adopted by GO FAIR. This process was divided into three phases: pre-FAIRification, FAIRification and postFAIRification. Each phase consisted of seven steps. In the first phase, 118 in-depth interviews and 565 press articles and research reports were collected by students and researchers at the University of Sousse in Tunisia and researchers in Niger. These interviews, articles and reports constitute the dataset for this research. In the second phase, the data were sorted and converted into a machine actionable format and published on a FAIR Data Point hosted at the University of Sousse. In the third phase, an assessment of the implementation of the FAIR Guidelines was undertaken. Certain barriers and challenges were faced in this process and solutions were found. For FAIR data curation, certain changes need to be made to the technical process. People need to be convinced to make these changes and that the implementation of FAIR will generate a long-term return on investment. Although the implementation of FAIR Guidelines is not straightforward, making our resources FAIR is essential to achieving better science together.

Terminology for a FAIR Framework for the Virus Outbreak Data Network-Africa

The field of health data management poses unique challenges in relation to data ownership, the privacy of data subjects, and the reusability of data. The FAIR Guidelines have been developed to address these challenges. The Virus Outbreak Data Network(VODAN) architecture builds on these principles, using the European Union’s General Data Protection Regulation(GDPR) framework to ensure compliance with local data regulations, while using information knowledge management concepts to further improve data provenance and interoperability. In this article we provide an overview of the terminology used in the field of FAIR data management, with a specific focus on FAIR compliant health information management, as implemented in the VODAN architecture.

The Impact of COVID-19 and FAIR Data Innovation on Distance Education in Africa

Prior to the advent of the COVID-19 pandemic, distance education, a mode of education that allows teaching and learning to occur beyond the walls of traditional classrooms using electronic media and online delivery practices, was not widely embraced as a credible alternative mode of delivering education, especially in Africa. In education, the pandemic, and the measures to contain it, created a need for virtual learning/teaching and showcased the potential of distance education. This article explores the potential of distance education with an emphasis on the role played by COVID-19, the technologies employed, and the benefits, as well as how data stewardship can enhance distance education. It also describes how distance education can make learning opportunities available to the less privileged, geographically displaced, dropouts, housewives, and even workers, enabling them to partake in education while being engaged in other productive aspects of life. A case study is provided on the Dutch Organisation for Internationalisation in Education(NUFFIC) Digital Innovation Skills Hub(DISH) project, which is implemented via distance education and targeted towards marginalised individuals such as refugees and displaced persons in Ethiopia,Somalia, and other conflict zones, aiming to provide them with critical and soft skills for remote work for financial remuneration. This case study shows that distance education is the way forward in education today, as it has the capability to reach millions of learners simultaneously, educating, lifting people out of poverty, and increasing productivity and yields, while ensuring that the world is a better place for future generations.

Incomplete COVID-19 Data: The Curation of Medical Health Data by the Virus Outbreak Data Network-Africa

The incompleteness of patient health data is a threat to the management of COVID-19 in Africa and globally.This has become particularly clear with the recent emergence of new variants of concern.The Virus Outbreak Data Network(VODAN)-Africa has studied the curation of patient health data in selected African countries and identified that health information flows often do not involve the use of health data at the point of care,which renders data production largely meaningless to those producing it.This modus operandi leads to disfranchisement over the control of health data,which is extracted to be processed elsewhere.In response to this problem,VODAN-Africa studied whether or not a design that makes local ownership and repositing of data central to the data curation process,would have a greater chance of being adopted.The design team based their work on the legal requirements of the European Union’s General Data Protection Regulation(GDPR);the FAIR Guidelines on curating data as Findable,Accessible(under well-defined conditions),Interoperable and Reusable(FAIR);and national regulations applying in the context where the data is produced.The study concluded that the visiting of data curated as machine actionable and reposited in the locale where the data is produced and renders services has great potential for access to a wider variety of data.A condition of such innovation is that the innovation team is intradisciplinary,involving stakeholders and experts from all of the places where the innovation is designed,and employs a methodology of co-creation and capacity-building.

Data Access, Control, and Privacy Protection in the VODAN-Africa Architecture

The Virus Outbreak Data Network(VODAN)-Africa aims to contribute to the publication of Findable Accessible, Interoperable, and Reusable(FAIR) health data under well-defined access conditions. The next step in the VODAN-Africa architecture is to locally deploy the Center for Expanded Data Annotation and Retrieval(CEDAR) and arrange accessibility based on the ‘data visiting’ concept. Locally curated and reposited machine-actionable data can be visited by queries or algorithms, provided that the conditions of access are met. The goal is to enable the multiple(re)use of data with secure access functionality by clinicians(patient care), an idea aligned with the FAIR-based Personal Health Train(PHT) concept. The privacy and security requirements in relation to the FAIR Data Host and the FAIRification workspace(to produce metadata) or dashboard(for the patient) must be clear to design the IT architecture. This article describes a(first) practice, a reference implementation in development, within the VODAN-Africa and Leiden University Medical Center community.

Proof of Concept and Horizons on Deployment of FAIR Data Points in the COVID-19 Pandemic

Rapid and effective data sharing is necessary to control disease outbreaks,such as the current coronavirus pandemic.Despite the existence of data sharing agreements,data silos,lack of interoperable data infrastructures,and different institutional jurisdictions hinder data sharing and accessibility.To overcome these challenges,the Virus Outbreak Data Network(VODAN)-Africa initiative is championing an approach in which data never leaves the institution where it was generated,but,instead,algorithms can visit the data and query multiple datasets in an automated way.To make this possible,FAIR Data Points—distributed data repositories that host machine-actionable data and metadata that adhere to the FAIR Guidelines(that data should be Findable,Accessible,Interoperable and Reusable)—have been deployed in participating institutions using a dockerised bundle of tools called VODAN in a Box(Vi B).Vi B is a set of multiple FAIR-enabling and open-source services with a single goal:to support the gathering of World Health Organization(WHO)electronic case report forms(e CRFs)as FAIR data in a machine-actionable way,but without exposing or transferring the data outside the facility.Following the execution of a proof of concept,Vi B was deployed in Uganda and Leiden University.The proof of concept generated a first query which was implemented across two continents.A SWOT(strengths,weaknesses,opportunities and threats)analysis of the architecture was carried out and established the changes needed for specifications and requirements for the future development of the solution.