Data literacy in academia: Basics and pedagogical views

Tibor Koltay


This paper, based on a non-exhaustive review of the literature addresses selected issues of a relative new complex of abilities and skills, i.e. data literacy by providing insight into its nature and the approaches to teaching in higher education.

We live in a data-intensive era, because the capacity to store massive amounts of data and forward them on high bandwidth networks generated interest in research data in the natural sciences, social sciences as well as the arts and humanities, never seen before (boyd and Crawford, 2012). The recognition of this fact motivated varied researchers, universities and different funding bodies to make efforts to encourage the openness of research data. The stakeholders of Open Data are – among others governments, multilateral organisations, journalists and the media. They come from the civil society and the private sector. Last, but not least teaching staff members, and researchers, i.e. the academic community constitute a crucial group of its stakeholders (Corrall, 2019a).

On the one hand, there remain several technological, social, organizational, economical, and legal barriers to data sharing (Sayogo & Pardo, 2013). On the other hand, despite obstacles, we can see a shift away from a research culture, where data is viewed as a private preserve (Pryor, Jones, & Whyte, 2013). This drive toward openness is guided among others by the principle that scholarly research does not need more data, but requires having the right data (Borgman 2015). In other words, researchers require high quality, actively curated data to work with, because data is both the raw material and the output of research (Pryor, 2012).

Research data is the output from any systematic investigation that involves observation, experiment or the testing of a hypothesis (Pryor, 2012) and it consists of “heterogeneous objects and items used and contextualized, depending on the academic discipline of origin” (Semeler, Pinto, & Rozados 2017, p. 3). To serve as research data, little data can be just as valuable as big data, and – in many cases – there is no data, because relevant data cannot be found, is not available, or does not exist at all (Borgman, 2015).

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