van den Brandt Astrid, L'Yi Sehi, Nguyen Huyen N, Vilanova Anna, Gehlenborg Nils
IEEE Trans Vis Comput Graph. 2025 Jan;31(1):1180-1190. doi: 10.1109/TVCG.2024.3456298. Epub 2024 Dec 3.
Genomics experts rely on visualization to extract and share insights from complex and large-scale datasets. Beyond off-the-shelf tools for data exploration, there is an increasing need for platforms that aid experts in authoring customized visualizations for both exploration and communication of insights. A variety of interactive techniques have been proposed for authoring data visualizations, such as template editing, shelf configuration, natural language input, and code editors. However, it remains unclear how genomics experts create visualizations and which techniques best support their visualization tasks and needs. To address this gap, we conducted two user studies with genomics researchers: (1) semi-structured interviews (n=20) to identify the tasks, user contexts, and current visualization authoring techniques and (2) an exploratory study (n=13) using visual probes to elicit users' intents and desired techniques when creating visualizations. Our contributions include (1) a characterization of how visualization authoring is currently utilized in genomics visualization, identifying limitations and benefits in light of common criteria for authoring tools, and (2) generalizable design implications for genomics visualization authoring tools based on our findings on task- and user-specific usefulness of authoring techniques. All supplemental materials are available at https://osf.io/bdj4v/.
基因组学专家依靠可视化来从复杂的大规模数据集中提取并分享见解。除了用于数据探索的现成工具外，对于能够帮助专家创建定制可视化以进行见解探索和交流的平台的需求也日益增加。已经提出了各种用于创作数据可视化的交互技术，例如模板编辑、架子配置、自然语言输入和代码编辑器。然而，目前尚不清楚基因组学专家如何创建可视化，以及哪些技术最能支持他们的可视化任务和需求。为了填补这一空白，我们对基因组学研究人员进行了两项用户研究：（1）半结构化访谈（n = 20），以确定任务、用户背景和当前的可视化创作技术；（2）一项探索性研究（n = 13），使用视觉探针来引出用户在创建可视化时的意图和所需技术。我们的贡献包括：（1）描述了当前在基因组学可视化中如何利用可视化创作，根据创作工具的通用标准确定其局限性和优势；（2）基于我们对创作技术在任务和用户特定实用性方面的发现，为基因组学可视化创作工具提出可推广的设计建议。所有补充材料可在https://osf.io/bdj4v/获取。
