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Vast amounts of (open) data are increasingly used to make arguments aboutcrisis topics such as climate change and global pandemics. Data visualizationsare 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 datato choices made in editorial rooms about which data stories to tell. In thispaper, we examine how data visualizations about climate change and COVID-19 areproduced in popular science magazines, using Scientific American, anestablished English-language popular science magazine, as a case study. To dothis, we apply the analytical concept of "data journeys" (Leonelli, 2020) in amixed methods study that centers on interviews with Scientific American staffand is supplemented by a visualization analysis of selected charts. Inparticular, we discuss the affordances of working with open data, the role ofcollaborative data practices, and how the magazine works to countermisinformation and increase transparency. This work provides a theoreticalcontribution by testing and expanding the concept of data journeys as ananalytical framework, as well as practical contributions by providing insightinto the data (visualization) practices of science communicators.

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