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Data Journeys in Popular Science: Producing Climate Change and COVID-19 Data Visualizations at Scientific American

Forthcoming. Now Available: Just Accepted Version.
Published onMar 25, 2024
Data Journeys in Popular Science: Producing Climate Change and COVID-19 Data Visualizations at Scientific American


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.

Keywords: data visualization, science communication, data practices, open data, data reuse, climate change, COVID-19

03/25/2024: To preview this content, click below for the Just Accepted version of the article. This peer-reviewed version has been accepted for its content and is currently being copyedited to conform with HDSR’s style and formatting requirements.

©2024 Kathleen Gregory, Laura Koesten, Regina Schuster, Torsten Möller, and Sarah Davies. This article is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the article.

Preview Image graphic reproduced with permission. Copyright © (2022) SCIENTIFIC AMERICAN, a Division of Springer Nature, America, Inc. All rights reserved.

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