Order of Magnitude Markers: An Empirical Study on Large Magnitude Number Detection

Borgo, Rita; Dearden, Joel; Jones, Mark W.

doi:10.1109/tvcg.2014.2346428

Cited by 18 publications

(24 citation statements)

References 19 publications

(27 reference statements)

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“…A similar design idea of using scientific notation to represent large range data was recently studied by Borgo et al [10]. However, there has been no direct transfer of empirical study results from 2D to 3D to validate similar visual effects.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the visualization researchers or practitioners currently have little guidance on which encodings are more appropriate for which tasks and on whether encoding methods can adapt to hardware display features. Though there is extensive research on textual display in IRVEs [6], [7], [8] and glyph designs for two-dimensional (2D) data comprehension [9], [10], [11], [12], the perception of data in three-dimensional (3D) space is different. For example, perspectives in 3D change size perceptions [13] that may or may not be corrected [14]; also, occlusion influences encoding approaches and the choices of display characteristics [15].…”

Section: Introductionmentioning

confidence: 99%

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Validation of SplitVectors Encoding for Quantitative Visualization of Large-Magnitude-Range Vector Fields

Zhao

Bryant

Griffin

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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We designed and evaluated SplitVectors, a new vector field display approach to help scientists perform new discrimination tasks on large-magnitude-range scientific data shown in three-dimensional (3D) visualization environments. SplitVectors uses scientific notation to display vector magnitude, thus improving legibility. We present an empirical study comparing the SplitVectors approach with three other approaches - direct linear representation, logarithmic, and text display commonly used in scientific visualizations. Twenty participants performed three domain analysis tasks: reading numerical values (a discrimination task), finding the ratio between values (a discrimination task), and finding the larger of two vectors (a pattern detection task). Participants used both mono and stereo conditions. Our results suggest the following: (1) SplitVectors improve accuracy by about 10 times compared to linear mapping and by four times to logarithmic in discrimination tasks; (2) SplitVectors have no significant differences from the textual display approach, but reduce cluttering in the scene; (3) SplitVectors and textual display are less sensitive to data scale than linear and logarithmic approaches; (4) using logarithmic can be problematic as participants' confidence was as high as directly reading from the textual display, but their accuracy was poor; and (5) Stereoscopy improved performance, especially in more challenging discrimination tasks.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of SplitVectors Encoding for Quantitative Visualization of Large-Magnitude-Range Vector Fields

Zhao

Bryant

Griffin

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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“…Here the visualized gene expression level is represented using the order of magnitude markers approach (OOMM) to fit the large-dynamic range expression data [ 18 ]. An expression level is represented using the scientific notation of A × 10 B , where A is the digit (a real) and B is the exponent (an integer).…”

Section: Resultsmentioning

confidence: 99%

PathRings: a web-based tool for exploration of ortholog and expression data in biological pathways

et al. 2015

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BackgroundHigh-throughput methods are generating biological data on a vast scale. In many instances, genomic, transcriptomic, and proteomic data must be interpreted in the context of signaling and metabolic pathways to yield testable hypotheses. Since humans can interpret visual information rapidly, a means for interactive visual exploration that lets biologists interpret such data in a comprehensive and exploratory manner would be invaluable. However, humans have limited memory capacity. Current visualization tools have limited viewing and manipulation capabilities to address complex data analysis problems, and visual exploratory tools are needed to reduce the high mental workload imposed on biologists.ResultsWe present PathRings, a new interactive web-based, scalable biological pathway visualization tool for biologists to explore and interpret biological pathways. PathRings integrates metabolic and signaling pathways from Reactome in a single compound graph visualization, and uses color to highlight genes and pathways affected by input data. Pathways are available for multiple species and analysis of user-defined species or input is also possible. PathRings permits an overview of the impact of gene expression data on all pathways to facilitate visual pattern finding. Detailed pathways information can be opened in new visualizations while maintaining the overview, that form a visual exploration provenance. A dynamic multi-view bubbles interface is designed to support biologists’ analytical tasks by letting users construct incremental views that further reflect biologists’ analytical process. This approach decomposes complex tasks into simpler ones and automates multi-view management.ConclusionsPathRings has been designed to accommodate interactive visual analysis of experimental data in the context of pathways defined by Reactome. Our new approach to interface design can effectively support comparative tasks over substantially larger collection than existing tools. The dynamic interaction among multi-view dataset visualization improves the data exploration. PathRings is available free at http://raven.anr.udel.edu/~sunliang/PathRings and the source code is hosted on Github: https://github.com/ivcl/PathRings.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0585-1) contains supplementary material, which is available to authorized users.

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“…(Task E); We also study Rate of Change (Task C). A user study by Borgo et al [5] has studied the performance of Elementary tasks with respect to reading bar charts.…”

Section: Exploration and Analysismentioning

confidence: 99%

TimeNotes: A Study on Effective Chart Visualization and Interaction Techniques for Time-Series Data

Walker

Borgo

Jones

2016

IEEE Trans. Visual. Comput. Graphics

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Collecting sensor data results in large temporal data sets which need to be visualized, analyzed, and presented. Onedimensional time-series charts are used, but these present problems when screen resolution is small in comparison to the data. This can result in severe over-plotting, giving rise for the requirement to provide effective rendering and methods to allow interaction with the detailed data. Common solutions can be categorized as multi-scale representations, frequency based, and lens based interaction techniques. In this paper, we comparatively evaluate existing methods, such as Stack Zoom [15] and ChronoLenses [38], giving a graphical overview of each and classifying their ability to explore and interact with data. We propose new visualizations and other extensions to the existing approaches. We undertake and report an empirical study and a field study using these techniques.

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Order of Magnitude Markers: An Empirical Study on Large Magnitude Number Detection

Cited by 18 publications

References 19 publications

Validation of SplitVectors Encoding for Quantitative Visualization of Large-Magnitude-Range Vector Fields

Validation of SplitVectors Encoding for Quantitative Visualization of Large-Magnitude-Range Vector Fields

PathRings: a web-based tool for exploration of ortholog and expression data in biological pathways

TimeNotes: A Study on Effective Chart Visualization and Interaction Techniques for Time-Series Data

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