Data journeys in popular science: Producing climate change and COVID-19 data visualizations at Scientific American
arxiv(2023)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要