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.

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.