70 research outputs found
Data Feel: Exploring Visual Effects in Video Games to Support Sensemaking Tasks
This paper explores the use of visual effects common in video games that
support a range of tasks that are similar in many ways to analysis tasks
supported in visual analytics tools. While some visual effects are meant to
increase engagement or to support a game's overall visual design, we find that
in many games visual effects are used throughout gameplay in order to assist a
player in reasoning about the game world. In this work, we survey popular games
across a range of categories (from casual games to "Triple A" games), focusing
specifically on visual effects that support a player's sensemaking within the
game world. Based on our analysis of these games, we identify a range of tasks
that could benefit from the use of "data feel," and advocate for the continued
investigation of visual effects and their application in data visualization
software tools.Comment: 7 pages, 5 figures, VIS4DH 202
Procedural Montage: A Design Trace of Reflection and Refraction
Narrative media may vary the adjacency of fixed textual passages to drive rhizomatic readings through a montage procedure. We present the design of “exul mater”, a hypertext fiction which locates perlocutionary acts in virtual spaces and resonant gaps. We reflect on sculptural fiction, the (de)formance of complex systems, and tarot reading as methods of layering metaphorical blends into polysemous juxtapositional elements. exul mater consists of one set of such elements and their pairwise juxtapositions, as presented through an interface which supports higher-order ‘gap-filling’ reading(s). We draw on peer feedback to address challenges to readability arising from the narrative application of procedural montage
TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral Therapy
We introduce TempoCave, a novel visualization application for analyzing
dynamic brain networks, or connectomes. TempoCave provides a range of
functionality to explore metrics related to the activity patterns and modular
affiliations of different regions in the brain. These patterns are calculated
by processing raw data retrieved functional magnetic resonance imaging (fMRI)
scans, which creates a network of weighted edges between each brain region,
where the weight indicates how likely these regions are to activate
synchronously. In particular, we support the analysis needs of clinical
psychologists, who examine these modular affiliations and weighted edges and
their temporal dynamics, utilizing them to understand relationships between
neurological disorders and brain activity, which could have a significant
impact on the way in which patients are diagnosed and treated. We summarize the
core functionality of TempoCave, which supports a range of comparative tasks,
and runs both in a desktop mode and in an immersive mode. Furthermore, we
present a real-world use case that analyzes pre- and post-treatment connectome
datasets from 27 subjects in a clinical study investigating the use of
cognitive behavior therapy to treat major depression disorder, indicating that
TempoCave can provide new insight into the dynamic behavior of the human brain
Dynamic Influence Networks for Rule-based Models
We introduce the Dynamic Influence Network (DIN), a novel visual analytics
technique for representing and analyzing rule-based models of protein-protein
interaction networks. Rule-based modeling has proved instrumental in developing
biological models that are concise, comprehensible, easily extensible, and that
mitigate the combinatorial complexity of multi-state and multi-component
biological molecules. Our technique visualizes the dynamics of these rules as
they evolve over time. Using the data produced by KaSim, an open source
stochastic simulator of rule-based models written in the Kappa language, DINs
provide a node-link diagram that represents the influence that each rule has on
the other rules. That is, rather than representing individual biological
components or types, we instead represent the rules about them (as nodes) and
the current influence of these rules (as links). Using our interactive DIN-Viz
software tool, researchers are able to query this dynamic network to find
meaningful patterns about biological processes, and to identify salient aspects
of complex rule-based models. To evaluate the effectiveness of our approach, we
investigate a simulation of a circadian clock model that illustrates the
oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres
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