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Design guidelines and best practices serve as references that support designers throughout the visualisation design process. While considerable effort has identified the elements that contribute to effective data visualisations, little attention has been paid to how language—scripts and reading direction, tool support, and cultural context also shape design decisions. As a result, assumptions of homogeneity persist, with visualisation practices predominantly benefiting users of English and left-to-right (LTR) scripts while overlooking the needs of over two billion Arabic script users. We investigate how Arabic-speaking visualisation practitioners design for right-to-left (RTL) scripts. We report on an analytical evaluation of seven popular GUI-based visualisation authoring tools using an Arabic dataset complemented by interviews with 11 Arabic-speaking practitioners across journalism, design, and data analysis. Our findings reveal that visualisation practitioners constantly negotiate tensions between Arabic reading conventions, “universal” LTR visual norms, and limited tool support for Arabic text, Eastern numerals, and maps. They engage in substantial labour, such as manually mirroring charts, fixing alignment issues, and stitching together multi-tool workflows, while making strategic compromises in language choice, interactivity, and chart type. Our tool analysis further reveals fragmented, inconsistent support for RTL mirroring, poor numeral rendering, and map defaults that encode geopolitical assumptions. In light of these findings, we discuss how RTL visualisation work is carried out under many constraints that affect agency and creativity. We argue that visualisation tools and defaults operationalise linguistic and geopolitical power in RTL contexts, and offer research directions and design implications that more robustly support RTL practitioners. Supplementary material: https://osf.io/8esbf

Visualizations and text often work together to support effective data communication. Despite this common paradigm, we know little about how the interplay of these modalities affects people’s data comprehension. We present a novel experimental paradigm to investigate multimodal data comprehension—the process of people comprehending information from multimodal visual and textual data—across both crowdsourced and think-aloud environments. Our methodology employs two sequential chains for presenting multimodal information—a visualization-first chain and a text-first chain—asking people to describe the data presented iteratively. By comparing how people’s data comprehension changes across the chain, we can assess the information contribution of each modality and how they shape subsequent comprehension. We found that the visualization-first chain facilitates exploratory comprehension with hypothesis-driven discovery, whereas the text-first chain yields confirmatory comprehension akin to framing effects where visualizations serve to reinforce and confirm observations drawn from text. Our findings provide empirical insights into multimodal information integration, with implications for designing more effective data-driven communication.

Annotations are widely used in information visualization to guide attention, clarify patterns, and support interpretation. We present a comprehensive survey of 191 research papers describing empirical studies, tools, techniques, and systems that incorporate annotations across various visualization contexts. Based on a structured analysis, we characterize annotations by their types, generation methods, and targets, and examine their use across four primary application domains: user engagement, storytelling, collaboration, and exploratory data analysis. We also discuss key trends, practical challenges, and open research directions. These findings offer a foundation for designing more effective annotation systems and advancing future research on annotation in visualization. An interactive web resource detailing the surveyed papers is available at https://shape-vis.github.io/annotation_star/.

Contemporary discourse on data communication has discussed logos (reason) and, more recently, ethos (credibility) extensively. While the concept of pathos (emotional appeal) has received growing attention in the visualization community in recent years, its connection to related concepts such as rhetoric and aesthetics remains underexplored. In this paper, we provide working definitions of these terms, contextualize them within data visualization, and explore their overlaps and differences in light of their historical development. This historical perspective offers a more holistic understanding of how these approaches in science and philosophy have evolved over time, contributing to a deeper comprehension of their integration into the design process. Drawing on Campbell's seven circumstances, we illustrate how pathos functions as a rhetorical strategy in contemporary data visualizations, examining the interplay of rhetorical strategies, aesthetic qualities, and offering our interpretation of how these elements work together.

In the digital age, readers value quantitative journalism that is clear, concise, analytical, and human-centred. To understand complex topics, they often piece together scattered facts from multiple articles. Visual storytelling can transform fragmented information into clear, engaging narratives, yet its use with unstructured online articles remains largely unexplored. To fill this gap, we present Compendia, an automated system that analyzes online articles in response to a user's query and generates a coherent data story tailored to the user's informational needs. Compendia addresses key challenges of storytelling from unstructured text through two modules covering: Online Article Retrieval, which gathers relevant articles; Data Fact Extraction, which identifies, validates, and refines quantitative facts; Fact Organization, which clusters and merges related facts into coherent thematic groups; and Visual Storytelling, which transforms the organized facts into narratives with visualizations in an interactive scrollytelling interface. We evaluated Compendia through a quantitative analysis, confirming the accuracy in fact extraction and organization, and through two user studies with 16 participants, demonstrating its usability, effectiveness, and ability to produce engaging visual stories for open-ended queries.

What do we value in our visualizations, and in the people who design them? Despite a growing body of work on critical data visualization, the conception of what it is to do ethical data visualization work can often be narrow (for instance, holding that our ethical duties are discharged merely by avoiding overtly lying or manipulating data), or entangled with potentially problematic implicit value structures (such as the assumption of the objectivity and neutrality of data, and so the designer’s role being merely the passive conveying of numbers as efficiently as possible). Yet, what it means to act ethically in data visualization is broad and multifaceted, and the virtues to which we should aspire as data visualization researchers and designers are worth explicating. We conducted an interview study with a broad spectrum of 20 experienced data visualization researchers, practitioners, and data artists to solicit their values and ethical considerations around doing visualization work. We report on a list of 68 values, organized into nine virtue clusters, that we encountered in our interviews. These virtues and values together describe a diverse space of matters of care and concern in data visualization: from the unease around the best use of visualization as a tool for persuasion, to the tightrope that visualization practitioners often walk between their professional responsibilities and their personal moral commitments. The virtues themselves, as well as our interviewees’ reflections on ethical practice, offer practitioners, researchers, and educators in data visualization a richer vocabulary for ethical reflection and provide a broader foundation for considering and applying visualization ethics.

Serious data visualization games combine data visualization with gameplay to help people engage with complex societal issues. Many of the real-world problems represented in such games involve competing values and systemic constraints, where undesirable outcomes may arise even when players make well-informed decisions. In these contexts, failure reveals the limits and trade-offs of systems in which data offers no right answer. However, failure has rarely been treated as a design resource for communicating systemic complexity. In this work, we explore what constitutes failure in serious data visualization games and how it can be designed. Through a corpus-based analysis of 33 games in which failure plays a meaningful role, we conceptualize three forms of failure, Avoidable, Mitigable, and Inevitable, capturing how failure emerges from interactions between player actions and systemic constraints. We further analyze how failure unfolds in gameplay using a modified narrative framework inspired by the three-act structure: Failure Emergence, Failure Experience, and Failure Response. Building on this analysis, we derive a design space with seven dimensions that articulate key strategies for designing failure in serious data visualization games. A workshop with 22 participants shows the effectiveness of the design space in supporting diverse design explorations, enabling designers to create games that communicate systemic constraints and societal trade-offs through gameplay.