不像你想的那么容易--尝试创建自自底向上的的可视化图像类型学的经验和教训
Not As Easy As You Think -- Experiences and Lessons Learnt from Trying to Create a Bottom-Up Visualization Image Typology
摘要：
我们提出并讨论了为期两年的定性分析IEEE可视化（VIS）论文发表的图像的结果。具体来说，我们得到了13个可视化图像类型的类型学，编码来区分可视化和几个图像特征。分类过程需要的时间比我们最初想象的要多得多，也比我们最初想象的要困难得多。由此产生的类型学和图像分析可以服务于许多目的：研究社区的演变及其研究成果随着时间的推移，以促进教学目的的可视化图像的分类，以确定用于评估目的的视觉设计，或使可视化标准化的进展。除了类型学和图像表征之外，我们还提供了一个包含6，833个标记图像的数据集和一个在线工具，可用于探索和分析大量标记图像。因此，我们促进了对所使用的各种可视化的讨论，以及它们如何在我们的社区中发布和传播。
英文摘要：
We present and discuss the results of a two-year qualitative analysis of images published in IEEE Visualization (VIS) papers. Specifically, we derive a typology of 13 visualization image types, coded to distinguish visualizations and several image characteristics. The categorization process required much more time and was more difficult than we initially thought. The resulting typology and image analysis may serve a number of purposes: to study the evolution of the community and its research output over time, to facilitate the categorization of visualization images for the purpose of teaching, to identify visual designs for evaluation purposes, or to enable progress towards standardization in visualization. In addition to the typology and image characterization, we provide a dataset of 6,833 tagged images and an online tool that can be used to explore and analyze the large set of tagged images. We thus facilitate a discussion of the diverse visualizations used and how they are published and communicated in our community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
