Presented by Marc Streit, Institute of Computer Graphics, Johannes Kepler University LinzChaired by Tom Freeman
The primary goal of visual data exploration tools is to enable the discovery of new insights. To justify and reproduce insights, the discovery process needs to be documented and communicated. A common approach to documenting and presenting findings is to capture visualizations as images or videos. Images, however, are insufficient for telling the story of a visual discovery, as they lack full provenance information and context. Videos are difficult to produce and edit, particularly due to the nonlinear nature of the exploration process. Most importantly, however, neither approach provides the opportunity to return to any point in the exploration in order to review the state of the visualization in detail or to conduct additional analyses.
In this talk, I will introduce our efforts to more tightly integrate biomedical data exploration with the presentation of discoveries. Based on provenance data captured during the exploration process, users can extract key steps, add annotations, and author 'Vistories', visual stories based on the history of the exploration. These Vistories can be shared for others to view, but also to retrace and extend the original analysis. I will demonstrate how such methods can increase the reproducibility of cancer research and drug discovery.
The presented work is part of the Caleydo project
, which is a long-running collaboration between JKU Linz, Harvard University and the University of Utah.Marc Streit is a tenured Associate Professor at the Institute of Computer Graphics at Johannes Kepler University Linz where he leads the Visual Data Science group. He finished his PhD at the Institute for Computer Graphics and Vision at Graz University of Technology in early 2011 and moved to Linz later that year. In 2012 he was a visiting researcher at the Center for Biomedical Informatics (CBMI) at Harvard Medical School. As part of a Fulbright scholarship for research and lecturing he was a visiting professor at the Visual Computing Group at Harvard Paulson School in 2014. Marc also teaches courses at the Imperial College Business School and Salzburg University of Applied Sciences.
His scientific areas of interest include visualization, visual analytics, and biological data visualization, where he is particularly interested in the integrated analysis of large heterogeneous data. Together with his team he develops novel visual analysis tools for cancer research, drug discovery, and other biomedical applications. Most of his research is embedded in the open-source project Caleydo, where he is one of the project leaders and founding-members. Since 2016 he is a CEO of the JKU spin-off company datavisyn.
Marc won Best Paper Awards at InfoVis'13, BioVis’12, InfoVis’11, GI’10 and Honorable Mention Awards at EuroVis'16, CHI'14, InfoVis'14 and EuroVis’12. He is a co-author of the Nature Methods Points of View column. In 2013 he co-edited the Special Issue on Visual Analytics in the IEEE Computer journal. Additionally, he actively contributes to the scientific community by serving on the organizing and program committee of several scientific events as well as by acting as a reviewer for high-quality journals and conferences. He was program chair of the IEEE Visualization in Data Science Symposium and papers and now general chair of BioVis, the Symposium on Biological Data Visualization.