Chart corpora, which comprise data visualizations and their semantic labels, are crucial for advancing visualization research. However, the labels in most existing corpora are high-level (e.g., chart types), hindering their utility for broader applications in the era of AI. In this paper, we contribute VisAnatomy, a corpus containing 942 real-world SVG charts produced by over 50 tools, encompassing 40 chart types and featuring structural and stylistic design variations. Each chart is augmented with multi-level fine-grained labels on its semantic components, including each graphical element's type, role, and position, hierarchical groupings of elements, group layouts, and visual encodings. In total, VisAnatomy provides labels for more than 383k graphical elements. We demonstrate the richness of the semantic labels by comparing VisAnatomy with existing corpora. We illustrate its usefulness through four applications: semantic role inference for SVG elements, chart semantic decomposition, chart type classification, and content navigation for accessibility. Finally, we discuss research opportunities to further improve VisAnatomy.

Visualization designers (e.g., journalists or data analysts) often rely on examples to explore the space of possible designs, yet we have little insight into how examples shape data visualization design outcomes. While the effects of examples have been studied in other disciplines, such as web design or engineering, the results are not readily applicable to visualization due to inconsistencies in findings and challenges unique to visualization design. Towards bridging this gap, we conduct an exploratory experiment involving 32 data visualization designers focusing on the influence of five factors (timing, quantity, diversity, data topic similarity, and data schema similarity) on objectively measurable design outcomes (e.g., numbers of designs and idea transfers). Our quantitative analysis shows that when examples are introduced after initial brainstorming, designers curate examples with topics less similar to the dataset they are working on and produce more designs with a high variation in visualization components. Also, designers copy more ideas from examples with higher data schema similarities. Our qualitative analysis of participants' thought processes provides insights into why designers incorporate examples into their designs, revealing potential factors that have not been previously investigated. Finally, we discuss how our results inform how designers may use examples during design ideation as well as future research on quantifying designs and supporting example-based visualization design. All supplemental materials are available in our OSF repo.

Automatically generating data visualizations in response to human utterances on datasets necessitates a deep semantic understanding of the utterance, including implicit and explicit references to data attributes, visualization tasks, and necessary data preparation steps. Natural Language Interfaces (NLIs) for data visualization have explored ways to infer such information, yet challenges persist due to inherent uncertainty in human speech. Recent advances in Large Language Models (LLMs) provide an avenue to address these challenges, but their ability to extract the relevant semantic information remains unexplored. In this study, we evaluate four publicly available LLMs (GPT-4, Gemini-Pro, Llama3, and Mixtral), investigating their ability to comprehend utterances even in the presence of uncertainty and identify the relevant data context and visual tasks. Our findings reveal that LLMs are sensitive to uncertainties in utterances. Despite this sensitivity, they are able to extract the relevant data context. However, LLMs struggle with inferring visualization tasks. Based on these results, we highlight future research directions on using LLMs for visualization generation. Our supplementary materials have been shared on GitHub: https://github.com/hdi-umd/Semantic_Profiling_LLM_Evaluation.

Templates have emerged as an effective approach to simplifying the visualization design and programming process. For example, they enable users to quickly generate multiple visualization designs even when using complex toolkits like D3. However, these templates are often treated as rigid artifacts that respond poorly to changes made outside of the template’s established parameters, limiting user creativity. Preserving the user’s creative flow requires a more dynamic approach to template-based visualization design, where tools can respond gracefully to users’ edits when they modify templates in unexpected ways. In this paper, we leverage the structural similarities revealed by templates to design resilient support features for prototyping D3 visualizations: recommendations to suggest complementary interactions for a users’ D3 program; and code augmentation to implement recommended interactions with a single click, even when users deviate from pre-defined templates. We demonstrate the utility of these features in Mirny, a design-focused prototyping environment for D3. In a user study with 20 D3 users, we find that these automated features enable participants to prototype their design ideas with significantly fewer programming iterations. We also characterize key modification strategies used by participants to customize D3 templates. Informed by our findings and participants’ feedback, we discuss the key implications of the use of templates for interleaving visualization programming and design.

Examples are useful for inspiring ideas and facilitating implementation in visualization design. However, there is little understanding of how visualization designers use examples, and how computational tools may support such activities. In this paper, we contribute an exploratory study of current practices in incorporating visualization examples. We conducted semi-structured interviews with 15 university students and 15 professional designers. Our analysis focus on two core design activities: searching for examples and utilizing examples. We characterize observed strategies and tools for performing these activities, as well as major challenges that hinder designers' current workflows. In addition, we identify themes that cut across these two activities: criteria for determining example usefulness, curation practices, and design fixation. Given our findings, we discuss the implications for visualization design and authoring tools and highlight critical areas for future research.