D04 | Quantitative Aspects of Immersive Analytics for the Life Sciences

Prof. Dr. Falk Schreiber, University of Konstanz
Email | Website

Falk Schreiber

Dr. Lewis L. Chuang, LMU
Email | Website

Lewis L. Chuang

Dr. Karsten Klein, University of Konstanz – Email | Website

Michael Aichem, University of Konstanz – Email | Website

Immersive Analytics (IA) is an emerging field that studies technologies facilitating a deep cognitive, perceptual and/or emotional involvement of humans when understanding and reasoning with data. The goal of this project is to investigate and quantify the impact of such technologies on immersion, and the role of immersion for data analytics. We aim to further develop the Immersive Analytics methodology and investigate the applicability of IA approaches to research tasks in the life sciences, with a particular focus on quantitative aspects of immersive analytics. We will design  immersive environments for selected applications of the life sciences  and develop new methodologies that allow us to put the human in the loop for an immersive experience during an analytics workflow.

Research Questions

How can we quantify immersion in an analytics process, and how can we quantify the impact of immersion?

How can we best support analytics and decision making tasks with Immersive Analytics approaches facilitated by new technologies?

What are new potentials and benets that IA brings for tasks in the life sciences, and how can we quantify them?

Fig. 1: Immersive Analytics (IA)

Publications

  1. M. Klapperstueck et al., “Contextuwall: Multi-site Collaboration Using Display Walls,” Journal of Visual Languages & Computing, vol. 46, pp. 35–42, 2018.
  2. Y. Zhu et al., “Genome-scale Metabolic Modeling of Responses to Polymyxins in Pseudomonas Aeruginosa,” GigaScience, vol. 7, no. 4, pp. 16:1-16:18, 2018.
  3. M. de Ridder, K. Klein, and J. Kim, “A Review and Outlook on Visual Analytics for Uncertainties in Functional Magnetic Resonance Imaging,” Brain Informatics, vol. 5, no. 2, pp. 5:1-5:19, 2018.
  4. V. Yoghourdjian, T. Dwyer, K. Klein, K. Marriott, and M. Wybrow, “Graph Thumbnails: Identifying and Comparing Multiple Graphs at a Glance,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 12, pp. 3081–3095, 2018.
  5. K. Marriott et al., Immersive Analytics, vol. 11190. Springer International Publishing, 2018.
  6. M. de Ridder, K. Klein, and J. Kim, “Temporaltracks: Visual Analytics for Exploration of 4D fMRI Time-series Coactivation,” in Proceedings of the Computer Graphics International Conference (CGI), 2017, pp. 13:1-13:6.
  7. H. T. Nim et al., “Design Considerations for Immersive Analytics of Bird Movements Obtained by Miniaturised GPS Sensors,” in Proceedings of the Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM), 2017.
  8. T. Chandler et al., “Immersive Analytics,” in Proceedings of the IEEE Symposium on Big Data Visual Analytics (BDVA), 2015, pp. 73–80.
  9. H. Rolletschek et al., “Metabolic Architecture of the Cereal Grain and its Relevance to Maximize Carbon Use Efficiency,” Plant Physiology, vol. 169, no. 3, pp. 1698–1713, 2015.
  10. H. Rohn et al., “VANTED v2: A Framework for Systems Biology Applications,” BMC Systems Biology, vol. 6, pp. 139:1-139:13, 2012.