To improve the future, we must reflect on our past. The IEEE VIS Test of Time Award is an accolade given to recognize articles published at previous conferences whose contents are still vibrant and useful today and have had a major impact and influence within and beyond the visualization community.
Vis & SciVis
Committee: Hamish Carr, Eduard Gröller (Chair), Xiaoru Yuan, Gerik Scheuermann (Chair), Deborah Silver
2000 (25-Years Vis ToT Award):
A flow-guided streamline seeding strategy
Vivek Verma, David L. Kao, Alex Pang
10.1109/VISUAL.2000.885690
The paper introduced the most commonly used strategy for placing streamlines in flow fields. The problem of selecting streamlines to capture flow behaviour is as old as the concept of streamlines itself - seed points are always needed to calculate and display them. This paper incorporates topological structures by explicitly seeding around critical points in a predefined pattern. This approach strikes a balance between topological structures, spatial cover age and visual aesthetics, using a non-iterative seeding method based on a Poisson disk distribution. This technique is relatively simple and efficient, which is one of the reasons why it has received more than 250 citations. Even after 25 years, Verma et al.’s topology-guided strategy is still in use.
2013 (12-Years SciVis ToT Award):
Contour Boxplots: A Method for Characterizing Uncertainty in Feature Sets from Simulation Ensembles
Ross T. Whitaker, Mahsa Mirzargar, Robert M. Kirby
10.1109/TVCG.2013.143
The paper introduced a groundbreaking statistical and visualization framework for analyzing ensembles of contours - generalizing functional boxplots to non-scalar, shape-based data. By extending the concept of data depth to sets, the authors enabled robust statistical aggregation and uncertainty quantification for complex features beyond simple scalar fields. This work bridged uncertainty visualization and statistical shape analysis, influencing diverse domains from fluid dynamics to weather forecasting. Its impact endures, inspiring numerous follow-up techniques, including Curve Boxplots, and shaping how the community approaches statistical analysis and visualization of geometric and topological features in simulation science. The paper has since been widely cited, underscoring its lasting relevance and influence.
2014 (11-Years SciVis ToT Award):
Fixed-Rate Compressed Floating-Point Arrays
Peter Lindstrom
10.1109/TVCG.2014.2346458
Scientific visualisation often deals with large amounts of floating-point data, which can generate significant quantities of data when describing graphics output. This article introduces a fast, computationally efficient, nearly lossless compression scheme for blocks of 4d values in d dimensions. The main advantage over other methods is the ability to define a fixed number of bits for each compressed block. This enables fast random access to the compressed data and paves the way for the rapid visualisation of large, compressed datasets. This paper has influenced several other compression schemes and is often referenced as an exemplary method for dealing with large scientific or geometric datasets.
InfoVis
Committee: Natalia Andrienko, Enrico Bertini, Min Chen (Chair), Tim Dwyer, Daniel Weiskopf
2005 (20-Years InfoVis ToT Award):
Graph-theoretic scagnostics
Leland Wilkinson, Anushka Anand, Robert L. Grossman
10.1109/INFVIS.2005.1532142
In this highly influential work, the authors established the mathematical and algorithmic foundations of the now well-known scagnostics techniques for visualizing high-dimensional multivariate data. While they duly acknowledged John and Paul Tukey for the original idea, they not only formalized the concept of scagnostics but also introduced a suite of graph-theoretic measures to enable feature-driven visualization. The impact of this work extends well beyond scatter plots, setting a gold standard for visualizing complex datasets through the analysis of visual features in their graphical representations.
2015 (10-Years InfoVis ToT Award):
Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations
Kanit Wongsuphasawat, Dominik Moritz, Anushka Anand, Jock D. Mackinlay, Bill Howe, Jeffrey Heer
10.1109/TVCG.2015.2467191
This work represents a major landmark in the development of visualization technology, transforming from human-led interactive visualization to a mixed-initiative approach, with which visualization plots are automatically generated as machine-led recommendations. In the paper, the authors presented not only the novel concept, but also the Voyager system for demonstrating the concept, including its architecture, algorithmic components, and visual design, together with a study for comparing it with another system. With the advancement of AI, the mixed-initiative approach is even more relevant today.
VAST
Committee: Jeffrey Heer, Helwig, Hauser, Ross Maciejewski, Shixia Liu (Chair)
2015 (10-Years VAST ToT Award):
Reducing Snapshots to Points: A Visual Analytics Approach to Dynamic Network Exploration
Stef van den Elzen, Danny Holten, Jorik Blaas, Jarke J. van Wijk
10.1109/TVCG.2015.2468078
This paper establishes a new paradigm for analyzing dynamic networks by reducing network snapshots to points in a two-dimensional space for interactive exploration. Its innovative dual-view design enables users to identify stable states, recurring patterns, and transitional behaviors, advancing temporal network analysis beyond animation and small multiples. Beyond its technical novelty, the paper has proven broadly influential, inspiring follow-up research in temporal graph analysis, embedding-based visualization, and state-space abstraction. It stands as a compelling example of technique-driven innovation that has shaped the visualization community’s perspective on dynamic network analysis.