2022
DOI: 10.1162/dint_a_00181
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Expanding Non-Patient COVID-19 Data: Towards the FAIRification of Migrants’ Data in Tunisia, Libya and Niger

Abstract: 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 post-FAIRification. Each phase consisted of seven steps. In the first phase, 118 in-depth interviews and 565 … Show more

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Cited by 3 publications
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“…Hajjar and Alagoz developed a randomized modeling framework that provides an accurate solution algorithm and personalized disease screening decision for chronic disease patients or potential chronic disease patients [23]. In addition, multiple scientific studies have shown the significant role of information technology in promoting health and well-being during the COVID-19 pandemic [24][25][26][27].…”
Section: Introductionmentioning
confidence: 99%
“…Hajjar and Alagoz developed a randomized modeling framework that provides an accurate solution algorithm and personalized disease screening decision for chronic disease patients or potential chronic disease patients [23]. In addition, multiple scientific studies have shown the significant role of information technology in promoting health and well-being during the COVID-19 pandemic [24][25][26][27].…”
Section: Introductionmentioning
confidence: 99%