The primary goal of visual data exploration tools is to enable the discovery of new insights. To justify and reproduce insights, the discovery process needs to be documented and communicated. A common approach to documenting and presenting findings is to capture visualizations as images or videos. Images, however, are insufficient for telling the story of a visual discovery, as they lack full provenance information and context. Videos are difficult to produce and edit, particularly due to the non‐linear nature of the exploratory process. Most importantly, however, neither approach provides the opportunity to return to any point in the exploration in order to review the state of the visualization in detail or to conduct additional analyses. In this paper we present CLUE (Capture, Label, Understand, Explain), a model that tightly integrates data exploration and presentation of discoveries. Based on provenance data captured during the exploration process, users can extract key steps, add annotations, and author “Vistories”, visual stories based on the history of the exploration. These Vistories can be shared for others to view, but also to retrace and extend the original analysis. We discuss how the CLUE approach can be integrated into visualization tools and provide a prototype implementation. Finally, we demonstrate the general applicability of the model in two usage scenarios: a Gapminder‐inspired visualization to explore public health data and an example from molecular biology that illustrates how Vistories could be used in scientific journals.

中文翻译：

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从视觉探索到讲故事，然后再回来


可视化数据探索工具的主要目标是发现新的见解。为了证明和再现见解，需要记录和传达发现过程。记录和呈现发现的常见方法是将可视化捕获为图像或视频。然而，图像不足以讲述视觉发现的故事，因为它们缺乏完整的出处信息和背景。视频很难制作和编辑，特别是由于探索过程的非线性性质。然而，最重要的是，这两种方法都没有提供返回探索中的任何点以详细检查可视化状态或进行其他分析的机会。在本文中，我们提出了 CLUE（捕获、标签、理解、解释），这是一个紧密集成数据探索和发现呈现的模型。根据探索过程中捕获的来源数据，用户可以提取关键步骤，添加注释，并根据探索历史创作“历史”视觉故事。这些历史可以共享给其他人查看，也可以回顾和扩展原始分析。我们讨论如何将 CLUE 方法集成到可视化工具中并提供原型实现​​。最后，我们展示了该模型在两种使用场景中的普遍适用性：受 Gapminder 启发的可视化，用于探索公共卫生数据，以及来自分子生物学的示例，说明如何在科学期刊中使用 Vitories。