Ke Er Amy Zhang

We conduct a deconstructive reading of a qualitative interview study with 17 visual data journalists from newsrooms across the globe. We borrow a deconstruction approach from literary critique to explore the instability of meaning in language and reveal implicit beliefs in words and ideas. Through our analysis we surface two sets of opposing implicit beliefs in visual data journalism: objectivity/subjectivity and humanism/mechanism. We contextualize these beliefs through a genealogical analysis, which brings deconstruction theory into practice by providing a historic backdrop for these opposing perspectives. Our analysis shows that these beliefs held within visual data journalism are not self-enclosed but rather a product of external societal forces and paradigm shifts over time. Through this work, we demonstrate how thinking with critical theories such as deconstruction and genealogy can reframe "success" in visual data storytelling and diversify visualization research outcomes. These efforts push the ways in which we as researchers produce domain knowledge to examine the sociotechnical issues of today’s values towards datafication and data visualization. All supplemental materials for this work are available at osf.io/5fr48.

Several uncertainties emerge in each component of the visual analytics (VA) cycle that hinder the user’s ability to make efficient and effective decisions, e.g., through missing data, model approximations, or visual mappings. Well-known VA strategies aim first at making users aware of these uncertainties, often through visual means. When visuals alone are insufficient to accurately quantify or communicate uncertainties, VA designers may rely on guidance to support users’ understanding of these uncertainties throughout the VA cycle. While prior VA research has attempted to conceptualize guidance, the ability and mechanisms for guidance to comprehensively address different sources of uncertainties remain an open question. In this survey, we characterize the relationships between uncertainties and guidance in VA literature. Our key contribution is a taxonomic framework that relates uncertainty sources to relevant guidance strategies and their respective profiles, i.e., roles, scopes, and features. Through this taxonomy, we discuss how guidance addresses uncertainties, identify research gaps, and promote a more comprehensive understanding of guidance strategies to support effective design for uncertainty in VA. Our survey underscores the effectiveness of guidance in navigating uncertainties with context-aware and multi-scope strategies. We highlight challenging opportunities for further research in this space, especially in the adjacent areas of accessibility and onboarding, and suggest new research areas, such as narrative and persuasion guidance to support uncertainty awareness in VA.

Public health studies commonly use questionnaires to get insights into human health. The Norwegian Mother, Father and Child Cohort Study (MoBa) is a longitudinal study that captures self-reported data on pregnancy symptoms across gestation, in particular also with a potential for an improved understanding of hyperemesis gravidarum (HG), a severe pregnancy complication. Although missing values are prevalent in these data and can hinder the analysis of the collected information, researchers studying MoBa data hypothesize that missingness within observed responses may signal HG. We present a design study exploring the problem space of hypothesis generation regarding relationships between missingness response patterns in the MoBa data and HG. Through this study, we also introduce Voidlytics, a visualization tool that supports hypothesis generation. It facilitates the exploration of missingness response patterns, the explicit formulation of assumptions about how suggestive of HG specific patterns are, as well as testing these assumptions. We provide initial evidence that the tool may support the generation of hypotheses about how indicative of HG missingness response patterns are. Supplementary materials are available at osf.io/3ycp5.

Data Humanism is a human-centered design approach that emphasizes the personal, contextual, and imperfect nature of data. Despite its growing influence among practitioners, the 13 principles outlined in Giorgia Lupi’s visual manifesto remain loosely defined in research contexts, creating a gap between design practice and systematic application. Through a mixed-methods approach, including a systematic literature review, multimedia analysis, and expert interviews, we present a characterization of Data Humanism principles for visualization researchers. Our characterization provides concrete definitions that maintain interpretive flexibility in operationalizing design choices. We validate our work through direct consultation with Lupi. Moreover, we leverage the characterization to decode a visualization work, mapping Data Humanism principles to specific visual design choices. Our work creates a common language for human-centered visualization, bridging the gap between practice and research for future applications and evaluations.

We propose a design space for data melodification, where standard visualization idioms and fundamental data characteristics map to rhetorical devices of music for a more affective experience of data. Traditional data sonification transforms data into sound by mapping it to different parameters such as pitch, volume, and duration. Often and regrettably, this mapping leaves behind melody, harmony, rhythm and other musical devices that compose the centuries-long persuasive and expressive power of music. What results is the occasional, unintentional sense of tinnitus and horror film-like impending doom caused by a disconnect between the semantics of data and sound. Through this work we ask, can the aestheticization of sonification through (classical) music theory make data simultaneously accessible, meaningful, and pleasing to one’s ears?

Measures such as the Global Peace Index represent peace and conflict through numerical composite indicators, e.g., military expenditure or political instability. While these indexes quantify international peace and conflict to make them comparable and interpretable, they often do so at the expense of subjective nuance and lived realities. This project questions statistical peace and conflict and reimagines how these measures can be more textured and human. We borrow from deconstruction theory, an approach from literary critique that explores the instability of meaning in language to reveal implicit beliefs in words and ideas, to analyze the methodology/rationale behind data production and visualization of the metrics of peace. Through this approach, we aim to explore the following questions: 1) How are peace and conflict framed through data and its absence? 2) What and whose definitions of peace and conflict are privileged? 3) What definitions are not represented and remain invisible and implicit? Inspired by design work valuing humanistic elements in media and in literature, we will develop a collaborative, exploratory graphical display that serves both as a documentation of our dialogues and as a visual articulation of our deconstructive analysis.

For the Bio+MedVis Redesign Challenge at IEEE VIS 2025, we propose a dashboard visualization design that enables neuroscientists and clinicians to explore, analyze, and communicate phosphorus magnetic resonance spectroscopy (31P-MRS) data. Despite the importance of 31P-MRS data visualization for the early detection of neurological conditions, current visualization practices make it challenging to identify and interpret relationships found in such data, particularly across different time points and patients. It is equally challenging for MRS researchers and their stakeholders to learn to interpret spectral data. We address these challenges by leveraging visualization types that are familiar and routinely used in MRS research, organizing and tiering them in an interactive dashboard according to task priority. Additional figures and a video walkthrough of our design are available on OSF.

The time-honored practice of medical illustration and visualization, has, like nearly all other disciplines, seen changes in its tooling and development pipeline in step with technological and societal developments. At its core, however, medical visualization remains a discipline focused on telling stories about biology and medicine. The story we tell in this work assumes a more distant vantage point to tell a story about the biomedical storytellers themselves. Our story peers over the shoulders of two medical illustrators in the middle of a project to illustrate a procedure in one of the small blood vessels around the heart, and through the medium of an online chat explores the dialogue, tensions, and goals of such projects in the digital age. We adopt the two-column format of the CHI template, as it is more reminiscent of the width of our usual messaging windows while working. The second part of our submission reflects on these tensions and modes of storytelling from an HCI and Visualization-situated perspective.

Genome-wide association studies (GWAS) are critical to identifying genetic variations associated with a particular trait or disease. It is important to cultivate an awareness of GWAS in the general public as members of this group are key participants of these studies. However, low genetic data literacy and trust in the sharing of genetic data pose challenges to learning and engaging with GWAS concepts. In this design study, we explore design strategies for the public communication of GWAS data. As part of this study, we present an interactive visual prototype that explores the use of narrative structure, linked visualizations through scrollytelling, and plain language to onboard and communicate genetic concepts to a GWAS-naive audience.

Visual representations are essential to scientific research and teaching, playing a role in conceptual understanding, knowledge generation, and the communication of discovery and change. Undergraduate students are expected to interpret, use, and create visual representations so they can make their thinking explicit when engaging in discourse with the scientific community. Despite the importance of visualization in the biosciences, students often learn visualization skills in an ad hoc fashion without a clear framework. We used a mixed-methods sequential explanatory study design to explore and assess the pedagogical needs of undergraduate biology students (n = 53), instructors (n = 13), and teaching assistants (n = 8) in visual science communication education. Key themes were identified using inductive grounded theory methods. We found that extrinsic motivations, namely time, financial resources, and grading practices, contribute to a lack of guidance, support, and structure as well as ambiguous expectations and standards perceived by students and instructors. Biology and science visualization instructors cite visual communication assessments as a way of developing and evaluating students’ higher-order thinking skills in addition to their communication competencies. An output of this research, the development of a learning module, the Visual Science Communication Toolkit, is discussed along with design considerations for developing resources for visual science communication education.

Visualizations play a critical role in discovering, understanding, interpreting, synthesizing, and communicating scientific knowledge. Effective scientific visualization requires careful attention to a number of factors, in particular, a faithful translation of scientific evidence, understanding of the communication needs of the target audience, and skillful application of visualization design principles. As a result, science visualization projects require a team of contributors with specialized knowledge and technical expertise. Regardless of team size and structure, a clear definition and appreciation of the design process as well as an understanding of the responsibilities of each contributor are imperative to the success of a project. Gaps in understanding often result in conflict between visualizers and stakeholders, compromising the quality of the scientific visualization. Although many companies have developed their own process through trial and error over years of experience, to date, there is no formalized framework for scientific visualization that details the steps of the process and the contributions of each individual. Informed by our examination of case studies, frameworks, and our collective experience as practitioners, we propose a framework tailored to the design, production, and evaluation of scientific visualization that aims to support practitioners in meeting their objectives and facilitating conversations that allow others to better understand the impact of the design process on the final product. We explore underlying drivers of decision-making within the visualization design space, describe the activities and outputs that impact decisions made about the final visualization, and discuss potential applications and limitations of this framework in practice.