NSF IIS 1528203

Technologies for Creating Explanatory and Exploratory Animations from Scientific Data


Principal Investigator: Kwan-Liu Ma, University of California at Davis
Program Manager: Maria Zemankova

Period: October 1, 2015 - September 30, 2018 (no cost extension to September 30, 2020)

Project Summary

Animation is a powerful, expressive medium for visual explanations and for telling stories with data. Scientists make extensive use of animations to explain their findings and to illustrate complex phenomena. By presenting time as time, animation is one of the most natural ways to illustrate how objects evolve and interact, and how they change in shape, size, position, and spatial relationship to other objects over time. Both commercial and open-source visualization tools offer a wealth of visualization techniques, enabling scientists to explore their data and to generate individual images to capture key aspects of the subject under study. However, most visualization packages include very limited support for creating explanatory animations. As a result, scientists who wish to use animations to illustrate their findings must spend considerable amounts of time learning how to produce animations, often using external software packages, or turn to professional animators or production specialists for assistance. This research aims to develop adequate support for composing animation content and constructing scientific video narratives, and also extend explanatory animation to exploratory animation and study its usability. This project will thus have a significant impact on both the visualization researchers and users. The new concepts introduced in this project will inspire others to also develop similar and even better support for storytelling using visualization. More users will benefit from such advanced visualization technologies leading to high productivity in their work.

This research will introduce key technologies that can greatly increase scientists’ ability to make visualization animations and video narratives for storytelling. To facilitate scientific narrations using animations, this project will design a semi-automatic animation generation system tightly coupled with the interactive data exploration and visualization process. That is to make the process of animation and storytelling a first class citizen within exploratory data visualization tools. This will allow the scientists to focus on gaining insight from their data, and the visualization tools should assist them in assembling findings together into a coherent story for presentation. This project will design methods to choose views, camera paths, lighting, transitions, etc. for users. Methods for users to interact with animations will also be designed, instead of passively watching, to achieve new levels of inspection and apprehension. Explorable images, a powerful and novel concept introduced for realizing exploratory animation, enables multidimensional data exploration using a medium comparable to a video in terms of compactness and simplicity. The task of realizing these novel concepts and designs, and integrating them into scientists’ workflows and tools, will be challenging. This research will conduct extensive evaluation of the animation support, with the participation of domain scientists who are prospective users of the new technology. The lessons learned in this project may establish guidelines for the effective use of animation in explaining complex phenomena, and suggest a new framework for next-generation visualization systems. The project results will be disseminated to the visualization community and beyond through annual conferences, workshops, and tutorials, and also through the project website (http://vis.cs.ucdavis.edu/NSF/IIS1528203), which will include project status updates and deliverables such as images, videos, and prototype software.

NSF Award Abstract Page

Participants

  • Kwan-Liu Ma, PI, UC Davis
  • Christopher Bryan, PhD Student, UC Davis (Received his PhD degree in August 2018 and joined Arizona State University as an assistant professor since August 2018)
  • Oleg Igouchkine, PhD Student, UC Davis
  • Min Shih, PhD Student, UC Davis (Received his PhD degree in August 2018)
  • Franz Sauer, PhD Student, UC Davis (Received his PhD degree in June 2017)
  • Keshav Dasu, PhD Student, UC Davis
  • Jianping Li, PhD Student, UC Davis (Received his PhD degree in August 2020)
  • Jacqueline Chu, MS Student, UC Davis (Received her M.S. degree in August 2016)
  • Chien-Hsin Hsueh, MS Student, UC Davis (Received his M.S. degree in August 2016)
  • Sandra Bae, MS Student, UC Davis (Was UCD undergrad when participating in REU)
  • Shidara Hidekazu, BS Student, UC Davis (Paricipated in REU and received his BS in August 2018)
  • Yuting Han, Undergraduate Student, UC Davis (Received her B.S. degree 2017)
  • Gregory Guterman, Undergraduate Student, UC Davis (Received his B.S. degree in June 2016)
  • Yuqi Yang, MS Student, UC Davis (Was UCD undergrad in REU and received her B.S. degree in June 2019)
  • Chuan Wang, PhD Student, UC Davis (Received her PhD degree in December 2020)
  • Kirby Zhou, BS Student, UC Davis (REU)

Publications

  1. Jianping Li and Kwan-Liu Ma. P6: A Declarative Language for Integrating Machine Learning in Visual Analytics. IEEE Transactions on Visualization and Computer Graphics (VAST 2020 preprint). [pdf]

  2. Keshav Dasu, Kwan-Liu Ma, Joyce Ma, Jennifer Frazier. Sea of Genes: Combining Animation and Narrative Strategies to Visualize Metagenomic Data for Museums. IEEE Transactions on Visualization and Computer Graphics (preprint) [pdf]

  3. Jianping Kelvin Li and Kwan-Liu Ma. P5: Portable Progressive Parallel Processing Pipeline for Interactive Data Analysis and Visualization. Accepted by IEEE VIS/InfoVis 2019. To appear in IEEE Transactions on Visualization and Computer Graphics 26(1):1151-1160 (2020). [pdf]

  4. Takanori Fujiwara, Jia-Kai Chou, Shilpika, Panpan Xu, Liu Ren, Kwan-Liu Ma. An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data. IEEE Transactions on Visualization and Computer Graphics 26(1):418-428 (2020) [pdf]

  5. Jianping Kelvin Li and Kwan-Liu Ma. P4: Portable Parallel Processing Pipelines for Interactive Information Visualization. IEEE Transactions on Visualization and Computer Graphics 26(3):1548-1561 (2020) [pdf]

  6. Min Shih, Charles Rozhon, and Kwan-Liu Ma. A Declarative Grammar of Flexible Volume Visualization Pipelines. Presented at IEEE SciVis 2018. IEEE Transactions on Visualization and Computer Graphics 25(1):1050-1059 (2019) [DOI]

  7. Keshav Dasu, Takanori Fujiwara, and Kwan-Liu Ma. An Organic Visual Metaphor for Public Understanding of Conditional Co-occurrences. In Proceedings of IEEE SciVis 2018.

  8. Jacqueline Chu, Chris Bryan, Min Shih, Leonardo Ferrer, Kwan-Liu Ma. Navigable Videos for Presenting Scientific Data on Affordable Head-Mounted Displays. In Proceedings of ACM Multimedia Systems (MMSys 2017), pp. 250-260. DOI [Video]

  9. Chris Bryan, Gregory Guterman, Kwan-Liu Ma, Harris Lewin, Denis Larkin, Jaebum Kim, Jian Ma, and Marta Farre (2017). Synteny Explorer: An Interactive Visualization Application for Teaching Genome Evolution. IEEE Transactions on Visualization and Computer Graphics (also VIS2016 Proceedings). 23(1):711-720 (2017). DOI [video]

  10. Christopher Bryan, Kwan-Liu Ma, and Jonathan Woodring (2017). Temporal Summary Images: An Approach to Narrative Visualization via Interactive Annotation Generation and Placement. IEEE Transactions on Visualization and Computer Graphics. 23(1):511-520 (2017). DOI [video]

  11. Franz Sauer, Tyson Neuroth, Jacqueline Chu, and Kwan-Liu Ma (2016). Audience-Targeted Design Considerations for Effective Scientific Storytelling. IEEE Computing in Science and Engineering. 18(6):68-76 (2016). DOI

  12. Chien-Hsin Hsueh, Jacqueline Chu, Kwan-Liu Ma, Joyce Ma, and Jennifer Frazier. Fostering comparisons: Designing an interactive exhibit that visualizes marine animal behaviors. In Proceedings of 2016 IEEE Pacific Visualization Symposium, pp. 259-263.
    [DOI] [video]

  13. Chien-Hsin Hsueh, Jia-Kai Chou, Kwan-Liu Ma. A study of using motion for comparative visualization. In Proceedings of 2016 IEEE Pacific Visualization Symposium. pp. 219-223.
    [DOI]

Visualization Demonstrations

  • Sea of Genes, 2019
    [video]

  • Demonstration of creating animations for data visualization with P5's declarative language, InfoVis 2019.
    [video]

  • A Visualization of Two-Stage Autoignition of n-dodecane
    Yucong Chris Ye, Min Shih, Franz Sauer, Kwan-Liu Ma, Giulio Borghesi, Alexander Krisman, and Jacqueline Chen
    Winner, Visual Data Storytelling Contest, IEEE PacificVis 2018
    [video]

  • Bullies and Victims: How Bullying Incidents Vary in Grades and How They are Reported
    Oh-Hyun Kwon, Jia-Kai Chou, and Kwan-Liu Ma
    Winner, Visual Data Storytelling Contest, IEEE PacificVis 2017
    [demo]

  • Navigable Videos, 2017
    Jacqueline Chu et al.
    [Video]

  • Synteny Explorer, 2017
    Chris Bryan et al.
    [video]

  • Ocean Track, 2016
    Chien-Hsin Hsueh et al.
    [video]

Software

  • P4: Portable Parallel Processing Pipelines
    A GPU accelerated JavaScript library for data processing and interactive visualization
    [Code]

  • P5: Portable Progressive Parallel Processing Pipelines
    P5 is a JavaScript toolkit for progressive parallel processing and visualization. P5 leverages P4 for parallel data processing and rendering, and provides an intuitive API for implementing progressive workflows.
    [Code]

  • P6 is a declarative language for integrating machine learning in visual analytics
    [Code]

Acknowledgments

The materials presented at this website are based upon work supported by the National Science Foundation under Grant No. 1528203. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the PI and do not necessarily reflect the views of the National Science Foundation.

Last Updated, Decembeer 28, 2020