Data in various forms permeates and centrally shapes the life of everyone all over the globe. With this datafication of society - including e.g. childhood (e.g. Mascheroni, 2020), health (e.g. Ruckenstein & Schüll, 2017), all levels of education (Jarke & Breiter, 2019) or journalism (e.g. Kouts-Klemm, 2019), to name just a few – knowledge, skills and reflexive capacities are needed to navigate this environment, this need exacerbated through the currently overly digitized and datafied living situation (Nguyen, 2021).

In that context, the concept of data literacy has received increased attention over the past years, exemplary defined “as the ability to understand and use data effectively to inform decisions. […] These skills include knowing how to identify, collect, organize, analyze, summarize, and prioritize data. They also include how to develop hypotheses, identify problems, interpret the data, and determine, plan, implement, and monitor courses of action” (Mandinach & Gummer, 2013, p. 30). “Information and data literacy” is presently considered as one of five core competence areas in the “European digital competence framework for citizens” (Carretero, Vuorikari & Punie, 2017), and found to have overlaps with other concepts e.g. statistical literacy (Wolff et al., 2016), is related to personal data literacies (Pangrazio & Selwyn, 2019) and algorithmic literacy (Cotter & Reisdorf, 2020). This makes it a complex concept and difficult to grasp.

The ERASMUS+ development project “Data literacy for citizenship” (DALI), focusses the active involvement of adults from Spain, the UK, Norway and Germany in order to foster their data literacy. The ambition is co-creating a suite of game-based and playful (Arnab, 2020) networked learning strategies that help people to improve their data literacy. Thus, when addressing and involving a heterogeneous population, the systematic familiarization with existing research delineating data literacy in regard to citizenship, citizenry and civic engagement was deemed appropriate as a first step. 

The following research questions are addressed, drawing in their scope closely on Bond et al. (2021), to further a first understanding of existing research on data literacy, pinpoint topics in need of further research and elicit implications and recommendations for practice.

1. Where, when and by whom has research on data literacy and adults been published?

2. How is data literacy and related skills and competences defined within empirical and theoretical research?

3. What are the characteristics of, methods used, and topics studied in research on data and adults?

4. What implications for research and practice are drawn from studies on data literacy?

In order to grasp existing research on data literacy, we conducted a mapping review (Grant & Booth, 2009) that seeks to “[m]ap out and categorize existing literature from which to commission further reviews and/or primary research by identifying gaps in research literature” (p. 94). Our search of SCOPUS, Web of Science, PsycInfo and ProQuest Education Database using the search string ("Data literac*" OR "data skill*" OR "data agency" OR "data competenc*" OR "Data sovereignty" OR "algorithmic literac*" OR "algorithmic skill*" OR "algorithmic agency" OR "algorithmic competenc*" OR "algorithmic sovereignty") yielded an initial 1659 sources. Following the screening of abstracts and full text using predefined inclusion and exclusion criteria (e.g. inclusion when published in 2015 or later, focus on adults and data literacy, journal article in English). Out of these, our final study corpus included 82 studies that were published between 2015 and 2021. The studies are currently being coded in regard to study-related meta-information (e.g. country of author affiliation, study population and empirical methods used), as well as their content focus across the three overarching dimensions of understanding data, acting on data and engaging with data, and the transversal dimension of ethics and privacy (own developed DALI framework).

Whilst the coding process within the review is still in progress, we can already reiterate previous findings that data literacy is a concept that is heavily entangled with or often synonymously used with e.g. digital literacy, algorithmic literacy, information literacy or media literacy – making it “messy” and open to interpretation (see also: Koltay, 2015). This is also evidenced in a wide range of synonyms of data literacy and related terms that are detected across the studies. Despite the three-dimension framework of understanding, acting on data and engaging with data that we apply as a heuristic, categories are not clear cut and show significant overlaps. Furthermore, data literacy is addressed from various disciplinary perspectives, including, for example, media studies, education, urban planning or journalism, among others, emphasizing its importance, but also showing different ways of addressing it.

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