Data journeys in popular science: Producing climate change and COVID-19 data visualizations at Scientific American

Abstract

Vast amounts of (open) data are increasingly used to make arguments about crisis topics such as climate change and global pandemics. Data visualizations are central to bringing these viewpoints to broader publics. However, visualizations often conceal the many contexts involved in their production, ranging from decisions made in research labs about collecting and sharing data to choices made in editorial rooms about which data stories to tell. In this paper, we examine how data visualizations about climate change and COVID-19 are produced in popular science magazines, using Scientific American, an established English-language popular science magazine, as a case study. To do this, we apply the analytical concept of "data journeys" (Leonelli, 2020) in a mixed methods study that centers on interviews with Scientific American staff and is supplemented by a visualization analysis of selected charts. In particular, we discuss the affordances of working with open data, the role of collaborative data practices, and how the magazine works to counter misinformation and increase transparency. This work provides a theoretical contribution by testing and expanding the concept of data journeys as an analytical framework, as well as practical contributions by providing insight into the data (visualization) practices of science communicators.