Reducing the Analytical Bottleneck for Domain Scientists: Lessons from a Climate Data Visualization Case Study

The gap between large-scale data production rate and the rate of generation of data-driven scientific insights has led to an analytical bottleneck in scientific domains like climate, biology, and so on. This is primarily due to the lack of innovative analytical tools that can help scientists efficiently analyze and explore alternative hypotheses about the data and communicate their findings effectively to a broad audience. In this article, by reflecting on a set of successful collaborative research efforts between with a group of climate scientists and visualization researchers, the authors introspect how interactive visualization can help reduce the analytical bottleneck for domain scientists.

[1]  Mengchen Liu,et al.  A survey on information visualization: recent advances and challenges , 2014, The Visual Computer.

[2]  Alex T. Pang,et al.  Approaches to uncertainty visualization , 1996, The Visual Computer.

[3]  C. C. Law,et al.  ParaView: An End-User Tool for Large-Data Visualization , 2005, The Visualization Handbook.

[4]  James J. Thomas,et al.  Defining Insight for Visual Analytics , 2009, IEEE Computer Graphics and Applications.

[5]  Jonathan C. Roberts,et al.  Visual comparison for information visualization , 2011, Inf. Vis..

[6]  Miriah D. Meyer,et al.  Information visualisation for science and policy: engaging users and avoiding bias. , 2014, Trends in ecology & evolution.

[7]  Valerio Pascucci,et al.  Visualizing High-Dimensional Data: Advances in the Past Decade , 2017, IEEE Transactions on Visualization and Computer Graphics.

[8]  Daniel A. Keim,et al.  Bridging the Gap of Domain and Visualization Experts with a Liaison , 2015, EuroVis.

[9]  Jacques Bertin,et al.  Semiology of Graphics - Diagrams, Networks, Maps , 2010 .

[10]  Yaxing Wei,et al.  Bridging Theory with Practice: An Exploratory Study of Visualization Use and Design for Climate Model Comparison , 2015, IEEE Transactions on Visualization and Computer Graphics.

[11]  Yaxing Wei,et al.  Visual Reconciliation of Alternative Similarity Spaces in Climate Modeling , 2014, IEEE Transactions on Visualization and Computer Graphics.

[12]  P. Rheingans,et al.  SimilarityExplorer : A Visual Inter-Comparison Tool for Multifaceted Climate Data , 2013 .

[13]  Cláudio T. Silva,et al.  Using maximum topology matching to explore differences in species distribution models , 2015, 2015 IEEE Scientific Visualization Conference (SciVis).

[14]  James H. Faghmous,et al.  A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science , 2014, Big Data.

[15]  Min Chen,et al.  Conceptualizing Visual Uncertainty in Parallel Coordinates , 2012, Comput. Graph. Forum.

[16]  Huan Liu,et al.  Subspace clustering for high dimensional data: a review , 2004, SKDD.

[17]  Daniel A. Keim,et al.  The Role of Uncertainty, Awareness, and Trust in Visual Analytics , 2016, IEEE Transactions on Visualization and Computer Graphics.

[18]  Martin Krzywinski,et al.  Points of view: Storytelling , 2013, Nature Methods.

[19]  Yaxing Wei,et al.  UV-CDAT: Analyzing Climate Datasets from a User's Perspective , 2013, Computing in Science & Engineering.

[20]  Jock D. Mackinlay,et al.  The structure of the information visualization design space , 1997, Proceedings of VIZ '97: Visualization Conference, Information Visualization Symposium and Parallel Rendering Symposium.

[21]  Luke J. Gosink,et al.  VIMTEX: A Visualization Interface for Multivariate, Time‐Varying, Geological Data Exploration , 2015, Comput. Graph. Forum.