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.

翻译：海量的（开放）数据正被越来越多地用于对气候变化和全球流行病等危机议题进行论证，而数据可视化在将这些观点传递给更广泛的公众中发挥着核心作用。然而，可视化作品往往掩盖了其制作过程中涉及的诸多语境——从研究实验室中关于数据采集与共享的决策，到编辑部中关于讲述哪些数据故事的抉择。本研究以知名英语科普杂志《科学美国人》为案例，探究气候变化与COVID-19数据可视化在科普杂志中的制作过程。为此，我们运用数据之旅（Leonelli, 2020）这一分析概念，采用混合方法研究：核心通过对《科学美国人》编辑人员的访谈，辅以对精选图表的可视化分析。我们重点探讨了开放数据的可供性、协作式数据实践的作用，以及该杂志如何应对虚假信息并提升透明度。本研究通过揭示科学传播者的数据（可视化）实践，并论证数据之旅概念作为分析框架的可行性，提供了实证贡献。