Amid rapid data streaming and declining mental health, there is a critical need to enhance the public’s ability to self-reflect and work on their mental wellbeing. Holding Your Mind is a series of artist-made personal data physicalizations that engage young students to record data relevant to their mental activity (electroencephalograph (EEG), heart rate, and narrative) during familiar tasks, and returned to them as personalized art sculptures to hold, interact with, and reflect. Our poster outlines the material characteristics through which Holding Your Mind sculptures embody mind data into body-relatable and perceptually enticing objects, procuring an embodied experience of self-cognition and reflection From the case of our sculptures, we draw preliminary insights on the use of artistic data embodiments to mediate the users’ relationship to self-tracking technology, reaching beyond quantification, to facilitate reflective practice for mental wellbeing. We lay groundwork for further study of data physicalization’s materiality in a mental wellbeing space, and its use in pre-clinical interventions helping foster a more aware and compassionate society.

The intersection of visualization and the humanities (VIS*H) is marked by a tension between chasing analytical "insight" and interpretive "meaning." The effectiveness of visualization techniques hinges on established evaluation frameworks that assess both analytical utility and communicative efficacy, creating a potential mismatch with the non-positivist, interpretive aims of humanities scholarship. To examine how this tension manifests in practice, we systematically surveyed 171 VIS*H design studies to analyze their evaluation workflows and rigor according to standard practice. Our findings reveal recurring flaws, such as an over-reliance on monomethod approaches, and show that higher-quality evaluations emerge from workflows that effectively triangulate diverse evidence. From these findings, we derive recommendations to refine quality and validation criteria for humanities visualizations, and juxtapose them to ongoing critical debates in the field, ultimately arguing for a paradigm shift that can reconcile the advantages of established validation techniques with the interpretive depth required for humanistic inquiry.

Abstract Visualization studies that examine age effects on task performance often attribute the performance gap between young adults (YA) and people in late adulthood (PLA) to declines in cognitive abilities, especially reduced visual working memory (VWM) capacity. However, the tasks and visualizations used in prior work typically involve many additional perceptual and cognitive processes, making it difficult to isolate the contribution of VWM decline to age differences in visualization performance. Here, we used a glyph‐based discrimination paradigm in which set size (number of glyphs) was manipulated to vary VWM demand systematically. Young adults (YA), mid‐age adults (MA), and PLA completed a two‐alternative forced‐choice task in which they judged which of two glyph‐based maps presented a higher overall risk level encoded by the glyphs. For each participant, we fitted psychometric functions to obtain accuracy and precision, and analyzed the response time. The results show that set size impaired performance similarly across age groups, and the marked accuracy drop at the largest set size indicated a practical upper limit on meaningful set size for this task. Contrary to expectations based on cognitive aging, PLA did not perform worse overall or show disproportionate costs at higher VWM demands. Instead, the MA group showed higher precision and was more strongly influenced by the distribution of individual glyph values across the visualization, raising the possibility that strategic or effortful processing may partly compensate for capacity limits. These findings indicate that, for this glyph‐based estimation task, age‐related VWM decline alone is unlikely to fully explain age differences in performance. Therefore, our findings offer little evidence for treating reduced VWM demand as the primary design goal for visualizations targeting PLA, in the absence of evidence that age‐related VWM limitations substantially constrain task performance.

Recent work suggests that shape can encode quantitative data via a mapping between value and spatial frequency (SF). However, the set-size effect when perceiving multiple SF based items remains unclear. While automatic feature extraction has been found to be less affected by set size (number of items in a group), higher-level processes for making perceptual decisions tend to require increased cognitive demand. To investigate the set-size effect on comparing integrated SF based items, we used a risk-based scenario to assess discrimination performance. Participants were asked to discriminate between pairs of maps containing multiple SF glyphs, in which each glyph represents one of four discrete levels (none, low, medium, high), forming an aggregate "risk strength" per map. The set size was also adjusted across conditions, ranging from small (3 items) to large (7 items). Discrimination sensitivity is modeled with a logistic function and response time with a mixed-effect linear model. Results show that smaller set sizes and lower overall strength enable more precise discrimination, with faster response times for larger differences between maps. Incorporating set size and overall strength into the logistic model, we found that these variables both independently and jointly influence discrimination sensitivity. We suggest these results point towards capacity-limited processes rather than purely automatic ensemble coding. Our findings highlight the importance of set size and overall signal strength when presenting multiple SF glyphs in data visualization.

With the acceleration of global ageing and the widespread adoption of electronic devices, modern visualization design must address the varying perceptual abilities across different age groups. Spatial frequency (SF) is commonly used as a stimulus dimension to assess perceptual ability in vision science, but its potential role as a guiding variable in visualization design, particularly for addressing age-related differences across the lifespan, remains largely unexplored. Many vision science studies have examined age-related spatial frequency perception, but with varying results. This review systematically analyses differences in experimental design and the reasons behind selecting experimental methods, which may be key to these differences. This study developed search terms and systematically searched the ACM, IEEE, PubMed, APA PsycINFO, SAGE, ScienceDirect, and Springer Nature databases. After rigorous screening, 33 studies were included and evaluated, with their similarities and differences compared across five dimensions. Cluster analysis further categorizes the literature into five groups, confirming that discrepancies in experimental outcomes largely stem from variations in psychological methods and participant characteristics. By considering the underlying reasons for these choices, we propose guidelines for designing perceptual experiments in which spatial frequency and age are manipulated factors. These recommendations provide theoretical support and practical insights for visualization design research that considers age-related perception of spatial frequency.

Ownership relationships of early printed books from the 15th century reveal complex patterns of distribution and possession, offering valuable insights for historical research. This paper presents OwnershipTracker, a visual analytics application developed to explore and trace these relationships using data from the Material Evidence in Incunabula (MEI) database. OwnershipTracker integrates bibliographic records, copy-specific data, and book provenance and ownership details, enabling users to uncover intricate ownership sequences over time. The application combines several visualization techniques, including network graphs to map connections between owners, timelines for temporal analysis, chord diagrams to quantify transfer patterns, and a distinctive, collaboratively designed spiderweb-like diagram highlighting converging and dispersing ownership transfers through specific owners. Developed iteratively with input from historical book researchers, the application underwent multiple refinements to align with domain research requirements. A summative evaluation with domain experts showcased the tool's ability to address the defined requirements and tasks. The final version of OwnershipTracker is deployed and accessible at: https://booktracker.nms.kcl.ac.uk/ownership.

Visualization design study is a widely adopted approach for developing tailored visual solutions to domain-specific problems through close interdisciplinary collaboration. While the visualization community has proposed generalizable frameworks, there is a growing need for domain-aware methodologies that address discipline-specific challenges and refine design study practices. To investigate how domain characteristics and collaborator roles influence the design study process, we conducted interviews with 15 experts, including domain specialists from the humanities, arts, applied sciences, and artificial intelligence, as well as visualization researchers and developers, with direct experience in design studies. Our findings reveal tensions and opportunities that arise from differing expectations, communication styles, and levels of engagement among collaborators at various stages of the design process, including problem formulation, co-design, and evaluation. We highlight how domain-specific norms and role dynamics shape collaboration and influence the trajectory of visualization projects. Based on these insights, we offer practical considerations to help visualization researchers anticipate domain-specific challenges, foster mutual understanding, and adapt their methods accordingly. Our study contributes to ongoing efforts to support more context-sensitive, sustainable, and inclusive design study practices across diverse application domains.

Visualizing event timelines for collaborative text writing is an important application for navigating and understanding such data, as time passes and the size and complexity of both text and timeline increase. They are often employed by applications such as code repositories and collaborative text editors. In this article, we present a visualization tool to explore historical records of writing of legislative texts, which were discussed and voted on by an assembly of representatives. Our visualization focuses on event timelines from text documents that involve multiple people and different topics, allowing for observation of different proposed versions of said text or tracking data provenance of given text sections, while highlighting the connections between all elements involved. We also describe the process of designing such a tool alongside domain experts, with three steps of evaluation being conducted to verify the effectiveness of our design.

Grounded theory (GT) is a research methodology that entails a systematic workflow for theory generation grounded on emergent data. In this article, we juxtapose GT workflows with typical workflows in visualization and visual analytics (VIS), unveiling the characteristics shared by these workflows. We explore the research landscape of VIS to study where GT is applied to generate VIS theories, explicitly as well as implicitly. We discuss "why" GT can potentially play a significant role in VIS. We outline a "how" methodology for conducting GT research in VIS, which addresses the need for theoretical advancement in VIS while benefiting from other methods and techniques in VIS. We illustrate this "how" methodology with a use case of adopting GT approaches in studying visualization guidelines.

This article presents a review and analysis of evaluation practices within the visualization and visual analytics (VIS) domain, with a focus on domain application work accepted at the IEEE VIS conference from 2018 to 2022. Through the analysis of 140 pertinent article, we establish a detailed classification principle for evaluation practices, using the Who, When, What, and How indicators. This principle covers facets such as analysis methods, targets, scenarios, participant expertise, and stages of occurrence. By systematically categorizing the application domains presented in these works, we apply our established classification principle to discern and categorize the evaluation practices within them, identifying the prevailing characteristics and trends. The article explores the variety of evaluation methods employed across different application domains and observes the distinctions in their usage. In conclusion, we provide insights and highlight concerns for conducting evaluations in upcoming domain application research. Our findings are intended to inform and guide subsequent studies in a similar context.

This study builds on past research bridging spatial visualization, psychology, and information visualization to holistically inform visualization design. We investigate the effects of chosen disciplines in psychology and math & computer science, combined with cognitive abilities and demographic differences, on visual tasks by measuring estimation of Pearson's correlation coefficient in scatterplots. Results reveal mathematicians demonstrate greater accuracy, benefiting from domain expertise. However, psychologists with high spatial skills outperform some mathematicians with lower spatial skills. Spatial visualization, level of education, and age (inversely) correlated with quicker and more accurate responses. Findings prove that domain expertise and spatial cognition affect correlation judgments in scatterplots, supporting that individual differences should inform visualization design. This work introduces psychologists as a new target domain for visualization research and reveals the impact of combined effects of cognitive abilities and domain on the estimation and manipulation of Pearson's correlation coefficient.

Abstract In natural language processing (NLP), text classification tasks are increasingly fine‐grained, as datasets are fragmented into a larger number of classes that are more difficult to differentiate from one another. As a consequence, the semantic structures of datasets have become more complex, and model decisions more difficult to explain. Existing tools, suited for coarse‐grained classification, falter under these additional challenges. In response to this gap, we worked closely with NLP domain experts in an iterative design‐and‐evaluation process to characterize and tackle the growing requirements in their workflow of developing fine‐grained text classification models. The result of this collaboration is the development of SemLa, a novel Visual Analytics system tailored for 1) dissecting complex semantic structures in a dataset when it is spatialized in model embedding space, and 2) visualizing fine‐grained nuances in the meaning of text samples to faithfully explain model reasoning. This paper details the iterative design study and the resulting innovations featured in SemLa. The final design allows contrastive analysis at different levels by unearthing lexical and conceptual patterns including biases and artifacts in data. Expert feedback on our final design and case studies confirm that SemLa is a useful tool for supporting model validation and debugging as well as data annotation.

The circulation of historical books has always been an area of interest for historians. However, the data used to represent the journey of a book across different places and times can be difficult for domain experts to digest due to buried geographical and chronological features within text-based presentations. This situation provides an opportunity for collaboration between visualization researchers and historians. This paper describes a design study where a variant of the Nine-Stage Framework [46] was employed to develop a Visual Analytics (VA) tool called DanteExploreVis. This tool was designed to aid domain experts in exploring, explaining, and presenting book trade data from multiple perspectives. We discuss the design choices made and how each panel in the interface meets the domain requirements. We also present the results of a qualitative evaluation conducted with domain experts. The main contributions of this paper include: 1) the development of a VA tool to support domain experts in exploring, explaining, and presenting book trade data; 2) a comprehensive documentation of the iterative design, development, and evaluation process following the variant Nine-Stage Framework; 3) a summary of the insights gained and lessons learned from this design study in the context of the humanities field; and 4) reflections on how our approach could be applied in a more generalizable way.

This study builds on past research to present a domain-specific empirical investigation of artists and math & computer scientists on their respective relationships to, perceptions of, and interactions with data visualization. We conducted a three-phase study utilizing mixed-methods to investigate performance on visual and text representations of data between domains. Our findings evidenced how math & computer scientists are proficient utilizing text representations of data while artists benefit more from visual chart representations. Finally, we present perspectives from artists to gain an understanding of their approach to visual and mathematical tasks. Our findings indicate that artists are especially adept at statistical visual tasks and that development of cognitive skills could be fostered by individuals to potentially benefit visualization task performance.

Abstract Historical records from democratic processes and negotiation of constitutional texts are a complex type of data to navigate due to the many different elements that are constantly interacting with one another: people, timelines, different proposed documents, changes to such documents, and voting to approve or reject those changes. In particular, voting records can offer various insights about relationships between people of note in that historical context, such as alliances that can form and dissolve over time and people with unusual behavior. In this paper, we present a toolset developed to aid users in exploring relationships in voting records from a particular domain of constitutional conventions. The toolset consists of two elements: a dataset visualizer, which shows the entire timeline of a convention and allows users to investigate relationships at different moments in time via dimensionality reduction, and a person visualizer, which shows details of a given person's activity in that convention to aid in understanding the behavior observed in the dataset visualizer. We discuss our design choices and how each tool in those elements works towards our goals, and how they were perceived in an evaluation conducted with domain experts.

This paper introduces design patterns for dashboards to inform dashboard design processes. Despite a growing number of public examples, case studies, and general guidelines there is surprisingly little design guidance for dashboards. Such guidance is necessary to inspire designs and discuss tradeoffs in, e.g., screenspace, interaction, or information shown. Based on a systematic review of 144 dashboards, we report on eight groups of design patterns that provide common solutions in dashboard design. We discuss combinations of these patterns in "dashboard genres" such as narrative, analytical, or embedded dashboard. We ran a 2-week dashboard design workshop with 23 participants of varying expertise working on their own data and dashboards. We discuss the application of patterns for the dashboard design processes, as well as general design tradeoffs and common challenges. Our work complements previous surveys and aims to support dashboard designers and researchers in co-creation, structured design decisions, as well as future user evaluations about dashboard design guidelines. Detailed pattern descriptions and workshop material can be found online: https://dashboarddesignpatterns.github.io.

Understanding one's audience is foundational to creating high impact visualization designs. However, individual differences and cognitive abilities influence interactions with information visualization. Different user needs and abilities suggest that an individual's background could influence cognitive performance and interactions with visuals in a systematic way. This study builds on current research in domain-specific visualization and cognition to address if domain and spatial visualization ability combine to affect performance on information visualization tasks. We measure spatial visualization and visual task performance between those with tertiary education and professional profile in business, law & political science, and math & computer science. We conducted an online study with 90 participants using an established psychometric test to assess spatial visualization ability, and bar chart layouts rotated along Cartesian and polar coordinates to assess performance on spatially rotated data. Accuracy and response times varied with domain across chart types and task difficulty. We found that accuracy and time correlate with spatial visualization level, and education in math & computer science can indicate higher spatial visualization. Additionally, we found that motivational differences between domains could contribute to increased levels of accuracy. Our findings indicate discipline not only affects user needs and interactions with data visualization, but also cognitive traits. Our results can advance inclusive practices in visualization design and add to knowledge in domain-specific visual research that can empower designers across disciplines to create effective visualizations.

We present the design and development of a data visualization service (RAMPVIS) in response to the urgent need to support epidemiological modeling workflows during the COVID-19 pandemic. Facing a set of demanding requirements and several practical challenges, our small team of volunteers had to rely on existing knowledge and components of services computing, while thinking on our feet in configuring services composition and adopting suitable approaches to services engineering. Through developing the RAMPVIS service, we have gained useful experience of ensuring conformation to services computing standards, enabling rapid development and early deployment, and facilitating effective and efficient maintenance and operation with limited resources. This experience can be valuable to the ongoing effort for combating the COVID-19 pandemic, and provides a blueprint for visualization service development when future needs for visual analytics arise during emergency response.

In the process of developing an infrastructure for providing visualization and visual analytics (VIS) tools to epidemiologists and modeling scientists, we encountered a technical challenge for applying a number of visual designs to numerous datasets rapidly and reliably with limited development resources. In this paper, we present a technical solution to address this challenge. Operationally, we separate the tasks of data management, visual designs, and plots and dashboard deployment in order to streamline the development workflow. Technically, we utilize: an ontology to bring datasets, visual designs, and deployable plots and dashboards under the same management framework; multi-criteria search and ranking algorithms for discovering potential datasets that match a visual design; and a purposely-design user interface for propagating each visual design to appropriate datasets (often in tens and hundreds) and quality-assuring the propagation before the deployment. This technical solution has been used in the development of the RAMPVIS infrastructure for supporting a consortium of epidemiologists and modeling scientists through visualization.

Abstract Detecting similarity between texts is a frequently encountered text mining task. Because the measurement of similarity is typically composed of a number of metrics, and some measures are sensitive to subjective interpretation, a generic detector obtained using machine learning often has difficulties balancing the roles of different metrics according to the semantic context exhibited in a specific collection of texts. In order to facilitate human interaction in a visual analytics process for text similarity detection, we first map the problem of pairwise sequence comparison to that of image processing, allowing patterns of similarity to be visualized as a 2D pixelmap. We then devise a visual interface to enable users to construct and experiment with different detectors using primitive metrics, in a way similar to constructing an image processing pipeline. We deployed this new approach for the identification of commonplaces in 18th‐century literary and print culture. Domain experts were then able to make use of the prototype system to derive new scholarly discoveries and generate new hypotheses.

Abstract In this paper, we present an empirical study designed to evaluate the hypothesis that humans’ soft knowledge can enhance the cost‐benefit ratio of a visualization process by reducing the potential distortion. In particular, we focused on the impact of three classes of soft knowledge: (i) knowledge about application contexts, (ii) knowledge about the patterns to be observed (i.e., in relation to visualization task), and (iii) knowledge about statistical measures. We mapped these classes into three control variables, and used real‐world time series data to construct stimuli. The results of the study confirmed the positive contribution of each class of knowledge towards the reduction of the potential distortion, while the knowledge about the patterns prevents distortion more effectively than the other two classes.

This research measures human performance in inferring the functional types (i.e., home, work, leisure and transport) of locations in geo-location data using different visual representations of the data (textual, static and animated visualizations) along with different amounts of data (1, 3 or 5 day(s)). We first collected real life geo-location data from tweets. We then asked the data owners to tag their location points, resulting in ground truth data. Using this dataset we conducted an empirical study involving 45 participants to analyze how accurately they could infer the functional location of the original data owners under different conditions, i.e., three data representations, three data densities and four location types. The study results indicate that while visual techniques perform better than textual ones, the functional locations of human activities can be inferred with a relatively high accuracy even using only textual representations and a low density of location points. Workplace was more easily inferred than home while transport was the functional location with the highest accuracy. Our results also showed that it was easier to infer functional locations from data exhibiting more stable and consistent mobility patterns, which are thus more vulnerable to privacy disclosures. We discuss the implications of our findings in the context of privacy preservation and provide guidelines to users and companies to help preserve and safeguard people's privacy.

Abstract Previous empirical studies for comparing parallel coordinates plots and scatter plots showed some uncertainty about their relative merits. Some of these studies focused on the task of value retrieval, where visualization usually has a limited advantage over reading data directly. In this paper, we report an empirical study that compares user performance, in terms of accuracy and response time, in the context of four different visualization tasks, namely value retrieval, clustering, outlier detection, and change detection. In order to evaluate the relative merits of the two types of plots with a common base line (i.e., reading data directly), we included three forms of stimuli, data tables, scatter plots, and parallel coordinate plots. Our results show that data tables are better suited for the value retrieval task, while parallel coordinates plots generally outperform the two other visual representations in three other tasks. Subjective feedbacks from the users are also consistent with the quantitative analyses. As visualization is commonly used for aiding multiple observational and analytical tasks, our results provided new evidence to support the prevailing enthusiasm for parallel coordinates plots in the field of visualization.

Abstract In this paper, we present a user‐centered design study on poetry visualization. We develop a rule‐based solution to address the conflicting needs for maintaining the flexibility of visualizing a large set of poetic variables and for reducing the tedium and cognitive load in interacting with the visual mapping control panel. We adopt Munzner's nested design model to maintain high‐level interactions with the end users in a closed loop. In addition, we examine three design options for alleviating the difficulty in visualizing poems latitudinally. We present several example uses of poetry visualization in scholarly research on poetry.

In written and spoken communications, figures of speech (e.g., metaphors and synecdoche) are often used as an aid to help convey abstract or less tangible concepts. However, the benefits of using rhetorical illustrations or embellishments in visualization have so far been inconclusive. In this work, we report an empirical study to evaluate hypotheses that visual embellishments may aid memorization, visual search and concept comprehension. One major departure from related experiments in the literature is that we make use of a dual-task methodology in our experiment. This design offers an abstraction of typical situations where viewers do not have their full attention focused on visualization (e.g., in meetings and lectures). The secondary task introduces "divided attention", and makes the effects of visual embellishments more observable. In addition, it also serves as additional masking in memory-based trials. The results of this study show that visual embellishments can help participants better remember the information depicted in visualization. On the other hand, visual embellishments can have a negative impact on the speed of visual search. The results show a complex pattern as to the benefits of visual embellishments in helping participants grasp key concepts from visualization.

The different visual cues present in a document - such as spatial intervals and positions, contrast in font families, sizes and weights - combine to form the document's visual hierarchy. This hierarchy is essential to the reader, allowing scanning and comprehension; in contrast, this information is often ignored by machine processing. At the same time, the document structure is often not available in a machine readable form due to the ways documents were originally created or later transformed. This paper addresses the challenge of automatic document repurposing - applying styling and formatting from one 'implicitly' structured document to another, whilst preserving the underlying visual hierarchy. Using visual perception analysis, the proportionality mapping is established, according to which the original document content is transformed into the new style without breaking the original hierarchical structure. Spatial relationships, location and frequency analysis are then used to fine-tune the transformation.

Colour realism plays an important role in computer graphics and visualization. In this paper, we present a new approach to direct volume rendering based on the Kubelka-Munk theory of diffuse reflectance. We show that not only the Kubelka-Munk theory facilitates a correct spectral volume rendering integral suitable for both solid objects and amorphous matters in volume datasets, but also provides volume visualization with more accurate optical effects than the traditional volume rendering integral based on the RGBα accumulation. We discuss the design of transfer functions for specifying absorption and scattering coefficients, and the use of post-illumination for integrating pre-processed reflectance images in real time. We demonstrate the optical realism achieved by this approach with a combination of several natural and artificial colour datasets.