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

TL;DR

This paper investigates how Scientific American produces climate change and COVID-19 data visualizations, revealing the complex data practices, open data use, and efforts to combat misinformation through a mixed methods case study.

Contribution

It applies the data journeys framework to analyze science communication practices in popular science magazines, highlighting transparency and collaborative data practices.

Findings

Open data facilitates transparency in visualizations.

Collaborative practices enhance data storytelling.

Counter-misinformation efforts are integral to visualization production.

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 an empirical contribution by providing insight into the data (visualization) practices of science communicators and demonstrating how the concept of data journeys can be used as an analytical framework.