Visualization is a powerful technique for analysis and communication of complex, multidimensional, and time-varying data. However, it can be difficult to manually synthesize a coherent narrative in a chart or graph due to the quantity of visualized attributes, a variety of salient features, and the awareness required to interpret points of interest (POls). We present Temporal Summary Images (TSIs) as an approach for both exploring this data and creating stories from it. As a visualization, a TSI is composed of three common components: (1) a temporal layout, (2) comic strip-style data snapshots, and (3) textual annotations. To augment user analysis and exploration, we have developed a number of interactive techniques that recommend relevant data features and design choices, including an automatic annotations workflow. As the analysis and visual design processes converge, the resultant image becomes appropriate for data storytelling. For validation, we use a prototype implementation for TSIs to conduct two case studies with large-scale, scientific simulation datasets.
Figure 1: An analyst is using Constellations to investigate results generated by previous analysts. Constellations organizes these visualizations with projection and clustering. Adjusting the data coverage, encoding choice, and keywords sliders changes how pairwise chart similarities are scored and updates the projected layout and cluster groupings. Several charts are tagged to show how their positions change. AbstractMany data problems in the real world are complex and require multiple analysts working together to uncover embedded insights by creating chart-driven data stories. How, as a subsequent analysis step, do we interpret and learn from these collections of charts? We present Chart Constellations, a system to interactively support a single analyst in the review and analysis of data stories created by other collaborative analysts. Instead of iterating through the individual charts for each data story, the analyst can project, cluster, filter, and connect results from all users in a meta-visualization approach. Constellations supports deriving summary insights about prior investigations and supports the exploration of new, unexplored regions in the dataset. To evaluate our system, we conduct a user study comparing it against data science notebooks. Results suggest that Constellations promotes the discovery of both broad and high-level insights, including theme and trend analysis, subjective evaluation, and hypothesis generation.
Lead (Pb) is a commonly found heavy metal due to its historical applications. Recent studies have associated the early-life Pb exposure with the onset of various neurodegenerative disease. The molecular mechanisms of Pb conferring long-term neurotoxicity, however, is yet to be elucidated. In this study, we explored the persistency of alteration in epigenetic marks that arise from exposure to low dose of Pb using a combination of image-based and gene expression analysis. Using SH-SY5Y as a model cell line, we observed significant alterations in global 5-methycytosine (5mC) and histone 3 lysine 27 tri-methylation (H3K27me3) and histone 3 lysine 9 tri-methylation (H3K9me3) levels in a dose-dependent manner immediately after Pb exposure. The changes are partially associated with alterations in epigenetic enzyme expression levels. Long term culturing (14 days) after cease of exposure revealed persistent changes in 5mC, partial recovery in H3K9me3 and overcompensation in H3K27me3 levels. The observed alterations in H3K9me3 and H3K27me3 are reversed after neuronal differentiation, while reduction in 5mC levels are amplified with significant changes in patterns as identified via texture clustering analysis. Moreover, correlation analysis demonstrates a strong positive correlation between trends of 5mC alteration after differentiation and neuronal morphology. Collectively, our results suggest that exposure to low dose of Pb prior to differentiation can result in persistent epigenome alterations that can potentially be responsible for observed phenotypic changes.
In team-based workplaces, reviewing and reflecting on the content from a previously held meeting can lead to better planning and preparation. However, ineffective meeting summaries can impair this process, especially when participants have difficulty remembering what was said and what its context was. To assist with this process, we introduce MeetingVis, a visual narrative-based approach to meeting summarization. MeetingVis is composed of two primary components: (1) a data pipeline that processes the spoken audio from a group discussion, and (2) a visual-based interface that efficiently displays the summarized content. To design MeetingVis, we create a taxonomy of relevant meeting data points, identifying salient elements to promote recall and reflection. These are mapped to an augmented storyline visualization, which combines the display of participant activities, topic evolutions, and task assignments. For evaluation, we conduct a qualitative user study with five groups. Feedback from the study indicates that MeetingVis effectively triggers the recall of subtle details from prior meetings: all study participants were able to remember new details, points, and tasks compared to an unaided, memory-only baseline. This visual-based approaches can also potentially enhance the productivity of both individuals and the whole team.
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