nn Walks in the Fictional Woods

This paper presents a novel exploration of the interaction between generative AI models, visualization, and narrative generation processes, using OpenAI's GPT as a case study. Drawing on Umberto Eco's ``Six Walks in the Fictional Woods'', we engender a speculative, transdisciplinary scientific narrative plentiful with references and links to relevant talks. To enrich our exposition, we present a visualization prototype to analyze storyboarded narratives, and extensive conversations with ChatGPT. Our paper is thoroughly decorated with thoughtful decorations that try to encode meaning and complement the narrative.Comment: this is a submission for alt.vis 202

Ways of Visualizing Curves

International audienceThis paper reviews the many ways curves are used to encode data in information visualization. As part of our review, we introduce a curve-based visualization framework where data can be encoded in two major ways: i) through a curve’s shape (a process we call embedding) and ii) through a curve’s local visual attributes (a process we call enrichment). Our framework helps describing and organizing the rich design space of curve-based data visualizations, and offer inspiration for novel data visualizations

Keeping Track of Time:The Role of Spatial and Embodied Cognition in the Comprehension of Nonlinear Storyworlds

ABSTRACT: What allows an audience to make sense of stories with complex nonlinear time structures that are radically different from everyday experience? To address this question, we distinguish between two types of narrative nonlinearity: nonlinear storytelling (a non-chronological presentation of events in the narration) and nonlinear storyworlds (nonlinearity as a feature of the narrated world, for instance by way of time-travel or temporal loops). With most scholarly attention focusing on the former, here we focus on the latter, as the question of what allows audiences to make sense of strange and impossible storyworld temporalities has remained somewhat overlooked. Drawing on the available research on text comprehension, we first discuss how both strategies of nonlinearity affect narrative comprehension differently. We then ask what cognitive abilities allow spectators to engage with nonlinear storyworlds. Drawing on insights from conceptual metaphor theory and mental timeline theory, we propose that the comprehension of nonlinear storyworlds is facilitated by the cognitive ability to mentally represent time in terms of space. By metaphorically blending spatial and embodied concepts into narrative timelines, strategies of spatial mental representation allow spectators to conceive and comprehend various forms of phenomenologically non-experienceable time structures—a hypothesis we seek to demonstrate through several cases of nonlinear storyworlds from contemporary complex cinema

Scaling Up Medical Visualization : Multi-Modal, Multi-Patient, and Multi-Audience Approaches for Medical Data Exploration, Analysis and Communication

Medisinsk visualisering er en av de mest applikasjonsrettede områdene av visualiseringsforsking. Tett samarbeid med medisinske eksperter er nødvendig for å tolke medisinsk bildedata og lage betydningsfulle visualiseringsteknikker og visualiseringsapplikasjoner. Kreft er en av de vanligste dødsårsakene, og med økende gjennomsnittsalder i i-land øker også antallet diagnoser av gynekologisk kreft. Moderne avbildningsteknikker er et viktig verktøy for å vurdere svulster og produsere et økende antall bildedata som radiologer må tolke. I tillegg til antallet bildemodaliteter, øker også antallet pasienter, noe som fører til at visualiseringsløsninger må bli skalert opp for å adressere den økende kompleksiteten av multimodal- og multipasientdata. Dessuten er ikke medisinsk visualisering kun tiltenkt medisinsk personale, men har også som mål å informere pasienter, pårørende, og offentligheten om risikoen relatert til visse sykdommer, og mulige behandlinger. Derfor har vi identifisert behovet for å skalere opp medisinske visualiseringsløsninger for å kunne håndtere multipublikumdata. Denne avhandlingen adresserer skaleringen av disse dimensjonene i forskjellige bidrag vi har kommet med. Først presenterer vi teknikkene våre for å skalere visualiseringer i flere modaliteter. Vi introduserer en visualiseringsteknikk som tar i bruk små multipler for å vise data fra flere modaliteter innenfor et bildesnitt. Dette lar radiologer utforske dataen effektivt uten å måtte bruke flere sidestilte vinduer. I det neste steget utviklet vi en analyseplatform ved å ta i bruk «radiomic tumor profiling» på forskjellige bildemodaliteter for å analysere kohortdata og finne nye biomarkører fra bilder. Biomarkører fra bilder er indikatorer basert på bildedata som kan forutsi variabler relatert til kliniske utfall. «Radiomic tumor profiling» er en teknikk som genererer mulige biomarkører fra bilder basert på første- og andregrads statistiske målinger. Applikasjonen lar medisinske eksperter analysere multiparametrisk bildedata for å finne mulige korrelasjoner mellom kliniske parameter og data fra «radiomic tumor profiling». Denne tilnærmingen skalerer i to dimensjoner, multimodal og multipasient. I en senere versjon la vi til funksjonalitet for å skalere multipublikumdimensjonen ved å gjøre applikasjonen vår anvendelig for livmorhalskreft- og prostatakreftdata, i tillegg til livmorkreftdataen som applikasjonen var designet for. I et senere bidrag fokuserer vi på svulstdata på en annen skala og muliggjør analysen av svulstdeler ved å bruke multimodal bildedata i en tilnærming basert på hierarkisk gruppering. Applikasjonen vår finner mulige interessante regioner som kan informere fremtidige behandlingsavgjørelser. I et annet bidrag, en digital sonderingsinteraksjon, fokuserer vi på multipasientdata. Bildedata fra flere pasienter kan sammenlignes for å finne interessante mønster i svulstene som kan være knyttet til hvor aggressive svulstene er. Til slutt skalerer vi multipublikumdimensjonen med en likhetsvisualisering som er anvendelig for forskning på livmorkreft, på bilder av nevrologisk kreft, og maskinlæringsforskning på automatisk segmentering av svulstdata. Som en kontrast til de allerede fremhevete bidragene, fokuserer vårt siste bidrag, ScrollyVis, hovedsakelig på multipublikumkommunikasjon. Vi muliggjør skapelsen av dynamiske og vitenskapelige “scrollytelling”-opplevelser for spesifikke eller generelle publikum. Slike historien kan bli brukt i spesifikke brukstilfeller som kommunikasjon mellom lege og pasient, eller for å kommunisere vitenskapelige resultater via historier til et generelt publikum i en digital museumsutstilling. Våre foreslåtte applikasjoner og interaksjonsteknikker har blitt demonstrert i brukstilfeller og evaluert med domeneeksperter og fokusgrupper. Dette har ført til at noen av våre bidrag allerede er i bruk på andre forskingsinstitusjoner. Vi ønsker å evaluere innvirkningen deres på andre vitenskapelige felt og offentligheten i fremtidige arbeid.Medical visualization is one of the most application-oriented areas of visualization research. Close collaboration with medical experts is essential for interpreting medical imaging data and creating meaningful visualization techniques and visualization applications. Cancer is one of the most common causes of death, and with increasing average age in developed countries, gynecological malignancy case numbers are rising. Modern imaging techniques are an essential tool in assessing tumors and produce an increasing number of imaging data radiologists must interpret. Besides the number of imaging modalities, the number of patients is also rising, leading to visualization solutions that must be scaled up to address the rising complexity of multi-modal and multi-patient data. Furthermore, medical visualization is not only targeted toward medical professionals but also has the goal of informing patients, relatives, and the public about the risks of certain diseases and potential treatments. Therefore, we identify the need to scale medical visualization solutions to cope with multi-audience data. This thesis addresses the scaling of these dimensions in different contributions we made. First, we present our techniques to scale medical visualizations in multiple modalities. We introduced a visualization technique using small multiples to display the data of multiple modalities within one imaging slice. This allows radiologists to explore the data efficiently without having several juxtaposed windows. In the next step, we developed an analysis platform using radiomic tumor profiling on multiple imaging modalities to analyze cohort data and to find new imaging biomarkers. Imaging biomarkers are indicators based on imaging data that predict clinical outcome related variables. Radiomic tumor profiling is a technique that generates potential imaging biomarkers based on first and second-order statistical measurements. The application allows medical experts to analyze the multi-parametric imaging data to find potential correlations between clinical parameters and the radiomic tumor profiling data. This approach scales up in two dimensions, multi-modal and multi-patient. In a later version, we added features to scale the multi-audience dimension by making our application applicable to cervical and prostate cancer data and the endometrial cancer data the application was designed for. In a subsequent contribution, we focus on tumor data on another scale and enable the analysis of tumor sub-parts by using the multi-modal imaging data in a hierarchical clustering approach. Our application finds potentially interesting regions that could inform future treatment decisions. In another contribution, the digital probing interaction, we focus on multi-patient data. The imaging data of multiple patients can be compared to find interesting tumor patterns potentially linked to the aggressiveness of the tumors. Lastly, we scale the multi-audience dimension with our similarity visualization applicable to endometrial cancer research, neurological cancer imaging research, and machine learning research on the automatic segmentation of tumor data. In contrast to the previously highlighted contributions, our last contribution, ScrollyVis, focuses primarily on multi-audience communication. We enable the creation of dynamic scientific scrollytelling experiences for a specific or general audience. Such stories can be used for specific use cases such as patient-doctor communication or communicating scientific results via stories targeting the general audience in a digital museum exhibition. Our proposed applications and interaction techniques have been demonstrated in application use cases and evaluated with domain experts and focus groups. As a result, we brought some of our contributions to usage in practice at other research institutes. We want to evaluate their impact on other scientific fields and the general public in future work.Doktorgradsavhandlin

A survey on visualization techniques to narrate interpersonal interactions between sportsmen

Technological advances have resulted in rapid growth in personal activity data documentation. Chronological activity data flowing can narrate a story. Writing a story needs the means to visualize interactions to know the relationships between characters. This article explores time-oriented data visualization techniques. Exploration aims to investigate visualizations that might be used to narrate activities about interpersonal interactions. We map data visualizations based on the completeness of story elements and shape flexibility. Based on the analysis of visualization techniques, we are looking for a flow visualization technique from events that can describe the event in detai

A survey on visualization techniques to narrate interpersonal interactions between sportsmen

Technological advances have resulted in rapid growth in personal activity data documentation. Chronological activity data flowing can narrate a story. Writing a story needs the means to visualize interactions to know the relationships between characters. This article explores time-oriented data visualization techniques. Exploration aims to investigate visualizations that might be used to narrate activities about interpersonal interactions. We map data visualizations based on the completeness of story elements and shape flexibility. Based on the analysis of visualization techniques, we are looking for a flow visualization technique from events that can describe the event in detai

Unlocking Sustainability with Visualizations: Driving the Driven through the Whys and Hows

Visualizations have been broadly employed to help individuals understand complex environmental issues and encourage sustainable behaviors. However, sustainability knowledge only sometimes transpires to actual green practices. In this study, we explain the effects of post-trip visualized storytelling on eco-driving behaviors. We conducted a laboratory experiment involving eye-tracking and driving simulation. This study contributes to the literature by unraveling the impact of visualized narratives on behaviors and demonstrating eco-driving behaviors in multiple manifestations