FAIR practices in Africa

Mirjam van Reisen, Mia Stokmans, Munyaradzi Mawere, Mariam Bassaja, A.O. Ong'ayo, P. Nakazibwe, Christine Kirkpatrick, Kudakwashe Chindoza

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)246–256
JournalData Intelligence
Volume2
Issue number1
DOIs
Publication statusPublished - 2020

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Keywords

  • FAIR data
  • GO FAIR
  • GO CHANGE
  • GO BUILD
  • GO TRAIN
  • digital health
  • Africa

Cite this

van Reisen, M., Stokmans, M., Mawere, M., Bassaja, M., Ong'ayo, A. O., Nakazibwe, P., Kirkpatrick, C., & Chindoza, K. (2020). FAIR practices in Africa. Data Intelligence, 2(1), 246–256. https://doi.org/10.1162/dint_a_00047