We present an analysis of the representation of gender as a data dimension in data visualizations and propose a set of considerations around visual variables and annotations for gender-related data. Gender is a common demographic dimension of data collected from study or survey participants, passengers, or customers, as well as across academic studies, especially in certain disciplines like sociology. Our work contributes to multiple ongoing discussions on the ethical implications of data visualizations. By choosing specific data, visual variables, and text labels, visualization designers may, inadvertently or not, perpetuate stereotypes and biases. Here, our goal is to start an evolving discussion on how to represent data on gender in data visualizations and raise awareness of the subtleties of choosing visual variables and words in gender visualizations. In order to ground this discussion, we collected and coded gender visualizations and their captions from five different scientific communities (Biology, Politics, Social Studies, Visualisation, and Human-Computer Interaction), in addition to images from Tableau Public and the Information Is Beautiful awards showcase. Overall we found that representation types are community-specific, color hue is the dominant visual channel for gender data, and nonconforming gender is under-represented. We end our paper with a discussion of considerations for gender visualization derived from our coding and the literature and recommendations for large data collection bodies. A free copy of this paper and all supplemental materials are available at https://osf.io/v9ams/

翻译：我们分析了数据可视化中将性别作为数据维度的表征方式，并围绕性别相关数据的视觉变量与标注提出一系列考量。性别是从研究对象、调查参与者、乘客或顾客群体，以及学术研究中（尤其是社会学等学科）收集数据时的常见人口统计学维度。我们的工作与当前关于数据可视化伦理影响的讨论相呼应。可视化设计者在选择特定数据、视觉变量和文本标签时，可能有意或无意地固化刻板印象与偏见。本文旨在开启关于如何在数据可视化中表征性别数据的持续性讨论，并提高对性别可视化中选择视觉变量与措辞细微差别的认知。为奠定讨论基础，我们从五个科学社群（生物学、政治学、社会科学、可视化与人机交互）以及Tableau公共平台与“信息之美”奖项展出的图像中，收集并编码了性别可视化及其图注。总体发现包括：表征类型具有社群特异性；色相是性别数据的主导视觉通道；非二元性别的表征不足。最后，我们基于编码结果与相关文献，讨论了性别可视化的设计考量，并为大型数据采集机构提出建议。本文及所有补充材料免费开放于https://osf.io/v9ams/。