Jane Lydia Adams

In systems genetics and other multi‐omics research, exploring high‐dimensional relationships among molecular and physiological variables across individuals poses significant challenges. We present the Gridded Trees interface, a novel interactive visualization tool designed to facilitate the exploration of conditional inference trees, which are hierarchical models of relationships in these complex datasets. Traditional static tools struggle to reveal patterns in tree‐structured data, but the Gridded Trees interface provides interactive, coordinated views, allowing users to navigate between overview and detail, filter data dynamically, and compare molecular‐physiological relationships across subgroups. By combining filtering techniques, strip plots, Sankey diagrams, and small multiples, the Gridded Trees interface enhances exploratory data analysis and supports hypothesis generation. In our systems genetics research use case, this tool has revealed significant associations among microbial populations and addiction‐related behavioral traits in genetically diverse mice. The Gridded Trees interface suggests broad potential for visualizing hierarchical and multipartite data across domains. A preprint of this paper as well as Supplemental Materials are available on OSF at https://osf.io/9emn5/.

We introduce three data visualization projects related to systems genetics and addiction research. Historically, the challenge of biology research has been one of data collection. However, as measurement becomes easier, the challenge shifts towards data sensemaking. Here, researchers are asking questions about the relationships between genes, traits, and the microbiome, using visualization for data exploration. This work has implications for visualization of high-dimensional data in other domains, as it shows how statistical methods can support visualization filtering, aggregation, and information hierarchies to explore dense data.

We present a provocation towards teaching development operations (“DevOps") and other infrastructure concepts in the course of collegiate data visualization instruction. We survey 65 syllabi from semester-long, college-level data visualization courses, with an eye toward languages and platforms used, as well as mentions of deployment related terms. Results convey significant variability in language and tooling used in curricula. We identify a distinct lack of discussions around ‘DevOps for DataVis’ scaffolding concepts such as version control, package management, server infrastructure, high-performance computing, and machine learning data pipelines. We acknowledge the challenges of adding supplemental information to already dense curricula, and the expectation that prior or concurrent classes should provide this computer science background. We propose a group community effort to create one free ‘course’ or ‘wiki’ as a living reference on the ways these broader DevOps concepts relate directly to data visualizationspecifically.

We present a mixed methods user study evaluating augmented reality (AR) as a visualization technique for use in astronomy journal publications. This work is motivated by the highly spatial nature of scientific visualizations employed in astronomy, including spatial reasoning tasks for hypothesis generation and scientific communications. In this 52-person user study, we evaluate two AR approaches (one traditional tabletop projection and the other with a 'tangible' aid) as spatial 3D visualization techniques, as compared to a baseline 3D rendering on a phone. We identify a significant difference in mental and physical workload between the two AR conditions in men and women. Qualitatively, through thematic coding of interviews, we identify notable observed differences ranging from device-specific physical challenges, to subdomain-specific utility within astronomy. The confluence of quantitative and qualitative results suggest a tension between workload and engagement when comparing non-AR and AR technologies. We summarize these findings and contribute them for reference in data visualization research furthering novel scientific communications in astronomy journal publications.

We present a survey of the design space of player-created strategic visualizations for video games, to better understand how players make sense of complex game mechanics and incorporate feedback from fellow players. We present several examples of these visualizations, and contrast them to developer-created visualizations, both in information presentation and purpose. We find that there is a rich community-building aspect to visualization development within game 'fandoms', facilitated by cross-platform exchange and iterative development, including: social media, informational 'wikis', and in-game modifications ('modding'). Finally, we consider player-created visualizations in the context of a broader imagining about the future of visualization development for non-game but analogous strategic applications. We maintain a collection of tagged and categorized examples of player-created video game visualizations.

I intend to make a scientific contribution of my subjective experience as a single unit of self-described 'artist' leveraging artificial intelligence as an assistive visual creation tool, in the hopes that it may provide some inspiration or deeper meaning for fellow artists and computer scientists in this medium. First, I will provide some background on my personal history thus far as an artist. Neither artist nor scientist can exist in a vaccuum, so I then will provide some (albeit a non-exhaustive list of) related work that has helped me contextualize my own work and thinking in this area. I often consider my methods in the creative process chronologically, so I have divided that section according to the loose structure of my artistic workflow. These foundations provide a fertile grounding for discussion around topics of subject matter, reception, community, and ethics. I then conclude with some ideas for future work in the realms of theory of authorship, explainability tooling, and research framing.

Increasingly, visualization practitioners are working with, using, and studying private and sensitive data. There can be many stakeholders interested in the resulting analyses—but widespread sharing of the data can cause harm to individuals, companies, and organizations. Practitioners are increasingly turning to differential privacy to enable public data sharing with a guaranteed amount of privacy. Differential privacy algorithms do this by aggregating data statistics with noise, and this now-private data can be released visually with differentially private scatterplots. While the private visual output is affected by the algorithm choice, privacy level, bin number, data distribution, and user task, there is little guidance on how to choose and balance the effect of these parameters. To address this gap, we had experts examine 1,200 differentially private scatterplots created with a variety of parameter choices and tested their ability to see aggregate patterns in the private output (i.e. the visual utility of the chart). We synthesized these results to provide easy-to-use guidance for visualization practitioners releasing private data through scatterplots. Our findings also provide a ground truth for visual utility , which we use to benchmark automated utility metrics from various fields. We demonstrate how multi-scale structural similarity (MS-SSIM), the metric most strongly correlated with our study's utility results, can be used to optimize parameter selection .

Generative text‐to‐image models (as exemplified by DALL‐E, MidJourney, and Stable Diffusion) have recently made enormous technological leaps, demonstrating impressive results in many graphical domains—from logo design to digital painting to photographic composition. However, the quality of these results has led to existential crises in some fields of art, leading to questions about the role of human agency in the production of meaning in a graphical context. Such issues are central to visualization, and while these generative models have yet to be widely applied in visualization, it seems only a matter of time until their integration is manifest. Seeking to circumvent similar ponderous dilemmas, we attempt to understand the roles that generative models might play across visualization. We do so by constructing a framework that characterizes what these technologies offer at various stages of the visualization workflow, augmented and analyzed through semi‐structured interviews with 21 experts from related domains. Through this work, we map the space of opportunities and risks that might arise in this intersection, identifying doomsday prophecies and delicious low‐hanging fruits that are ripe for research.

Increasingly, visualization practitioners are working with, using, and studying private and sensitive data. There can be many stakeholders interested in the resulting analyses—but widespread sharing of the data can cause harm to individuals, companies, and organizations. Practitioners are increasingly turning to differential privacy to enable public sharing of data with a guaranteed amount of privacy. Differential privacy algorithms do this by aggregating data statistics with noise, and this now-private data can be released visually with differentially private scatterplots. While the private visual output is affected by the algorithm choice, privacy level, bin number, data distribution, and user task, there is little guidance on how to choose and balance the effect of these parameters. To address this gap, we had experts examine 1,200 differentially private scatterplots created with a variety of parameter choices and tested their ability to see aggregate patterns in the private output (i.e. the visual utility of the chart). We synthesized these results to provide easy-to-use guidance for visualization practitioners releasing private data through scatterplots. Our findings also provide a ground truth for visual utility, which we use to benchmark automated utility metrics from a variety of fields. We demonstrate how multi-scale structural similarity (MS-SSIM), the metric most strongly correlated with our study’s utility results, can be used to optimize parameter selection. A free copy of this paper along with all supplemental materials is available at https://osf.io/wej4s/.

Generative text-to-image models (as exemplified by DALL-E, MidJourney, and Stable Diffusion) have recently made enormous technological leaps, demonstrating impressive results in many graphical domains—from logo design to digital painting and photographic composition. However, the quality of these results has led to existential crises in some fields of art, leading to questions about the role of human agency in the production of meaning in a graphical context. Such issues are central to visualization, and while these generative models have yet to be widely applied to visualization, it seems only a matter of time until their integration is manifest. Seeking to circumvent similar ponderous dilemmas, we attempt to understand the roles that generative models might play across visualization. We do so by constructing a framework that characterizes what these technologies offer at various stages of the visualization workflow, augmented and analyzed through semi-structured interviews with 21 experts from related domains. Throughthis work, we map the space of opportunities and risks that might arise in this intersection, identifying doomsday prophecies and delicious low-hanging fruits that are ripe for research.