Understanding the difference between small and big values spanning over several orders of magnitude can be difficult. In this demo, we introduce a proof of concept named AR Data Ribbons, an augmented reality visualization scaled so that even smaller values are visible but requiring people to unroll the biggest values of a bar chart going beyond its frame. While the smallest values appear as classic bars on a 2D canvas, participants are invited to pick up a roll handle and to unroll ribbons representing the biggest values of the dataset. Ribbons are then unfolded in the space surrounding the participant until reaching the length corresponding to the data value. We hope our design could lead to a greater engagement, creative ribbon output patterns and better data understanding.

Data videos combine visualization, animation, narration, and often background music to tell stories with data. While music is widely believed to enhance emotion and persuasion, its impact in data videos remains unexplored. We conducted a preregistered between-subjects experiment comparing six widely-viewed data videos with or without background music. Using Bayesian modeling and thematic analysis, we did not observe consistent measurable effects of background music on persuasion, engagement, or information recall. Qualitative responses revealed a more nuanced picture: some participants described the music as distracting or mismatched, while others reported that it enhanced enjoyment, supported focus, or strengthened emotional resonance when well aligned with the video’s tone. These findings suggest that the influence of background music in data videos is highly context-dependent, shaped by genre, familiarity, and its alignment with visual–narrative structure. We discuss possible reasons for the limited measurable effects observed in real-world videos and outline opportunities for future work on purpose-designed, incidental, or adaptive music for data-driven storytelling.

Computational notebooks, widely used for ad-hoc analysis and often shared with others, can be difficult to understand because the standard linear layout is not optimized for reading. In particular, related text, code, and outputs may be spread across the UI making it difficult to draw connections. In response, we introduce InterLink, a plugin designed to present the relationships between text, code, and outputs, thereby making notebooks easier to understand. In a formative study, we identify pain points and derive design requirements for identifying and navigating relationships among various pieces of information within notebooks. Based on these requirements, InterLink features a new layout that separates text from code and outputs into two columns. It uses visual links to signal relationships between text and associated code and outputs and offers interactions for navigating related pieces of information. In a user study with 12 participants, those using InterLink were 13.6% more accurate at finding and integrating information from complex analyses in computational notebooks. These results show the potential of notebook layouts that make them easier to understand.

Synchronous data-driven storytelling with network visualizations presents significant challenges due to the complexity of real-time manipulation of network components. While existing research addresses asynchronous scenarios, there is a lack of effective tools for live presentations. To address this gap, we developed TangibleNet, a projector-based AR prototype that allows presenters to interact with node-link diagrams using double-sided magnets during live presentations. The design process was informed by interviews with professionals experienced in synchronous data storytelling and workshops with 14 HCI/VIS researchers. Insights from the interviews helped identify key design considerations for integrating physical objects as interactive tools in presentation contexts. The workshops contributed to the development of a design space mapping user actions to interaction commands for node-link diagrams. Evaluation with 12 participants confirmed that TangibleNet supports intuitive interactions and enhances presenter autonomy, demonstrating its effectiveness for synchronous network-based data storytelling.

Computational notebooks are widely utilized for exploration and analysis. However, creating slides to communicate analysis results from these notebooks is quite tedious and time-consuming. Researchers have proposed automatic systems for generating slides from notebooks, which, however, often do not consider the process of users conceiving and organizing their messages from massive code cells. Those systems ask users to go directly into the slide creation process, which causes potentially ill-structured slides and burdens in further refinement. Inspired by the common and widely recommended slide creation practice: drafting outlines first and then adding concrete content, we introduce OutlineSpark, an AI-powered slide creation tool that generates slides from a slide outline written by the user. The tool automatically retrieves relevant notebook cells based on the outlines and converts them into slide content. We evaluated OutlineSpark with 12 users. Both the quantitative and qualitative feedback from the participants verify its effectiveness and usability.

Computational notebooks have become increasingly popular for exploratory data analysis due to their ability to support data exploration and explanation within a single document. Effective documentation for explaining chart findings during the exploration process is essential as it helps recall and share data analysis. However, documenting chart findings remains a challenge due to its time-consuming and tedious nature. While existing automatic methods alleviate some of the burden on users, they often fail to cater to users' specific interests. In response to these limitations, we present InkSight, a mixed-initiative computational notebook plugin that generates finding documentation based on the user's intent. InkSight allows users to express their intent in specific data subsets through sketching atop visualizations intuitively. To facilitate this, we designed two types of sketches, i.e., open-path and closed-path sketch. Upon receiving a user's sketch, InkSight identifies the sketch type and corresponding selected data items. Subsequently, it filters data fact types based on the sketch and selected data items before employing existing automatic data fact recommendation algorithms to infer data facts. Using large language models (GPT-3.5), InkSight converts data facts into effective natural language documentation. Users can conveniently fine-tune the generated documentation within InkSight. A user study with 12 participants demonstrated the usability and effectiveness of InkSight in expressing user intent and facilitating chart finding documentation.

Although visualization tools are widely available and accessible, not everyone knows the best practices and guidelines for creating accurate and honest visual representations of data. Numerous books and articles have been written to expose the misleading potential of poorly constructed charts and teach people how to avoid being deceived by them or making their own mistakes. These readings use various rhetorical devices to explain the concepts to their readers. In our analysis of a collection of books, online materials, and a design workshop, we identified six common explanation methods. To assess the effectiveness of these methods, we conducted two crowdsourced studies (each with N=125) to evaluate their ability to teach and persuade people to make design changes. In addition to these existing methods, we brought in the idea of Explorable Explanations, which allows readers to experiment with different chart settings and observe how the changes are reflected in the visualization. While we did not find significant differences across explanation methods, the results of our experiments indicate that, following the exposure to the explanations, the participants showed improved proficiency in identifying deceptive charts and were more receptive to proposed alterations of the visualization design. We discovered that participants were willing to accept more than 60% of the proposed adjustments in the persuasiveness assessment. Nevertheless, we found no significant differences among different explanation methods in convincing participants to accept the modifications.

Data videos are becoming increasingly popular in society and academia. Yet little is known about how to create endings that strengthen a lasting impression and persuasion. To fulfill the gap, this work aims to develop guidelines for data video endings by drawing inspiration from cinematic arts. To contextualize cinematic endings in data videos, 111 film endings and 105 data video endings are first analyzed to identify four common styles using the framework of ending punctuation marks. We conducted expert interviews (N=11) and formulated 20 guidelines for creating cinematic endings in data videos. To validate our guidelines, we conducted a user study where 24 participants were invited to design endings with and without our guidelines, which are evaluated by experts and the general public. The participants praise the clarity and usability of the guidelines, and results show that the endings with guidelines are perceived to be more understandable, impressive, and reflective.

Data storytelling explores how to communicate data insights to the general public engagingly and effectively. It combines the power of data visualizations and storytelling techniques and is popular in various media such as newspapers, interactive websites, and videos. Recently, virtual reality has brought new opportunities to enhance data storytelling with an incomparable sense of immersion. However, there exists a limited understanding of data stories in virtual reality (VR) as they are still in the early stage. In this paper, we investigated the idea of VR data videos by drawing inspiration from popular 3D data videos and studying how to transfer them from screens to VR. We systematically analyzed 100 highly-watched 3D data videos from Youtube and Tiktok channels to derive their design space. We then conducted a user study with 12 participants to explore the effects of four design factors on user experience, including varying camera angles, showing chart overview, animation, and using anchors. Specifically, participants watched 3D data videos in desktop and VR environments. We collected and analyzed their quantitative and qualitative feedback regarding the story’s understandability, memorability, engagement, and emotional effects. Results suggested that data videos in VR were significantly more appreciated than on desktops. We concluded with design implications for future applications and research on VR data videos.

Data visualizations have been increasingly used in oral presentations to communicate data patterns to the general public. Clear verbal introductions of visualizations to explain how to interpret the visually encoded information are essential to convey the takeaways and avoid misunderstandings. We contribute a series of studies to investigate how to effectively introduce visualizations to the audience with varying degrees of visualization literacy. We begin with understanding how people are introducing visualizations. We crowdsource 110 introductions of visualizations and categorize them based on their content and structures. From these crowdsourced introductions, we identify different introduction strategies and generate a set of introductions for evaluation. We conduct experiments to systematically compare the effectiveness of different introduction strategies across four visualizations with 1,080 participants. We find that introductions explaining visual encodings with concrete examples are the most effective. Our study provides both qualitative and quantitative insights into how to construct effective verbal introductions of visualizations in presentations, inspiring further research in data storytelling.

Data videos are an increasingly popular storytelling form. The opening of a data video critically influences its success as the opening either attracts the audience to continue watching or bores them to abandon watching. However, little is known about how to create an attractive opening. We draw inspiration from the openings of famous films to facilitate designing data video openings. First, by analyzing over 200 films from several sources, we derived six primary cinematic opening styles adaptable to data videos. Then, we consulted eight experts from the film industry to formulate 28 guidelines. To validate the usability and effectiveness of the guidelines, we asked participants to create data video openings with and without the guidelines, which were then evaluated by experts and the general public. Results showed that the openings designed with the guidelines were perceived to be more attractive, and the guidelines were praised for clarity and inspiration.

Data stories integrate compelling visual content to communicate data insights in the form of narratives. The narrative structure of a data story serves as the backbone that determines its expressiveness, and it can largely influence how audiences perceive the insights. Freytag's Pyramid is a classic narrative structure that has been widely used in film and literature. While there are continuous recommendations and discussions about applying Freytag's Pyramid to data stories, little systematic and practical guidance is available on how to use Freytag's Pyramid for creating structured data stories. To bridge this gap, we examined how existing practices apply Freytag's Pyramid by analyzing stories extracted from 103 data videos. Based on our findings, we proposed a design space of narrative patterns, data flows, and visual communications to provide practical guidance on achieving narrative intents, organizing data facts, and selecting visual design techniques through story creation. We evaluated the proposed design space through a workshop with 25 participants. Results show that our design space provides a clear framework for rapid storyboarding of data stories with Freytag's Pyramid.

Detecting and analyzing potential anomalous performances in cloud computing systems is essential for avoiding losses to customers and ensuring the efficient operation of the systems. To this end, a variety of automated techniques have been developed to identify anomalies in cloud computing. These techniques are usually adopted to track the performance metrics of the system (e.g., CPU, memory, and disk I/O), represented by a multivariate time series. However, given the complex characteristics of cloud computing data, the effectiveness of these automated methods is affected. Thus, substantial human judgment on the automated analysis results is required for anomaly interpretation. In this paper, we present a unified visual analytics system named CloudDet to interactively detect, inspect, and diagnose anomalies in cloud computing systems. A novel unsupervised anomaly detection algorithm is developed to identify anomalies based on the specific temporal patterns of the given metrics data (e.g., the periodic pattern). Rich visualization and interaction designs are used to help understand the anomalies in the spatial and temporal context. We demonstrate the effectiveness of CloudDet through a quantitative evaluation, two case studies with real-world data, and interviews with domain experts.