Designing and Developing a Data Science Programme in Bhutan

Namgay, Phub; Wangdi, Pema; Thinley, Sangay

doi:10.1109/fie56618.2022.9962738

Cited by 1 publication

(2 citation statements)

References 36 publications

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“…The principle has been adopted and is advocated as a benchmark for data sharing [25] by publishers and funders [22]. FAIR Data Principles is included in an undergraduate data science programme to familiarise students with the FAIR data concept and improve their skills to make data reusable for humans and machines in a datafied world [26]. Gesing et al [27] Nevertheless, FAIR Data Principles has not been used as an analytical framework to unpack and unravel data sharing and reuse practices among humans and technologies for teaching, learning, and research in the STEM community.…”

Section: B Fair Data Principlesmentioning

confidence: 99%

“…RUB is decentralised and comprises nine constituent and two affiliated colleges. Nine out of eleven colleges have some aspects of a STEM programme (see [26]). The emphasis to date on STEM research has been on pedagogy and the application of contemporary technological methods and tools to enrich teaching-learning processes among primary, middle, and higher secondary school students.…”

Section: A Study Settingmentioning

confidence: 99%

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FAIRifying STEM Data Ecosystem to Enhance Data Reuse

Namgay,

Wangdi,

Thinley

et al. 2023

2023 IEEE Frontiers in Education Conference (FIE)

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In the science, technology, engineering, and mathematics (STEM) community, academic and research workflows and work practices are increasingly mediated and informed by data. However, making digital materials and resources findable, accessible, interoperable, and reusable (FAIR) for teaching, learning, and research is an underresearched area. Thus, it is vital to examine the current data practices of STEM students and faculties and acquaint them with the FAIR data concept. FAIR Data Principles is a set of guidelines that underscore metadata, vocabularies, licences, and standards to enhance data reuse. A study was conducted among students and faculties in the STEM community of the Royal University of Bhutan (RUB) to unpack their current data practices and explore areas for improvement using the FAIR Data Principles. The STEM students and faculties of the RUB share and reuse digital materials and resources for teaching, learning, and research. Nevertheless, their data practice is not as widespread or desired in the literature on optimum data reuse. Moreover, the compliance of current data practices to the tenets of FAIR Data Principles is not satisfactory. A pragmatic solution is complementing data practices with policies and infrastructural systems that underscore FAIR Data Principles. A sensitisation programme such as seminars and hands-on exercises on data FAIRification is crucial to familiarise people with the essentialness of FAIR data, and doing so will provide a platform to develop their repertoire for FAIRifying data and encourage systematic sharing and reuse of data. An in-depth account of the FAIRifying STEM data ecosystem in the study contributes to the growing knowledge base on adopting FAIR Data Principles in other areas of data-informed work and life.

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