“Ethics When You Least Expect It”: A Modular Approach to Short Course Data Ethics Instruction

Louise Bezuidenhout, Rob Quick, Hugh Shanahan

Research output: Contribution to journalArticlepeer-review


Data science skills are rapidly becoming a necessity in modern science. In response to this need, institutions and organizations around the world are developing research data science curricula to teach the programming and computational skills that are needed to build and maintain data infrastructures and maximize the use of available data. To date, however, few of these courses have included an explicit ethics com-ponent, and developing such components can be challenging. This paper describes a novel approach to teaching data ethics on short courses developed for the CODATA-RDA Schools for Research Data Science. The ethics content of these schools is cen-tred on the concept of open and responsible (data) science citizenship that draws on virtue ethics to promote ethics of practice. Despite having little formal teach-ing time, this concept of citizenship is made central to the course by distributing ethics content across technical modules. Ethics instruction consists of a wide range of techniques, including stand-alone lectures, group discussions and mini-exercises linked to technical modules. This multi-level approach enables students to develop an understanding both of “responsible and open (data) science citizenship”, and of how such responsibilities are implemented in daily research practices within their home environment. This approach successfully locates ethics within daily data sci-ence practice, and allows students to see how small actions build into larger ethi-cal concerns. This emphasises that ethics are not something “removed from daily research” or the remit of data generators/end users, but rather are a vital concern for all data scientists.
Original languageEnglish
Pages (from-to)2189-2213
Number of pages25
JournalScience and Engineering Ethics
Early online date17 Feb 2020
Publication statusPublished - Aug 2020


  • Data Science
  • Data Ethics
  • Open Science

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