Ensemble methods are widely used to simulate complex non-linear systems and to estimate forecast uncertainty. However, visualizing and analyzing ensemble data is challenging, in particular when multimodality arises, i.e., ...

Explaining the predictions of a deep neural network (DNN) in image classification is an active area of research. Many methods focus on localizing pixels, or groups of pixels, which maximize a relevance metric for the ...

This work presents Air Quality Temporal Analyser (AQTA), an interactive system to support visual analyses of air quality data with time. This interactive AQTA allows the seamless integration of predictive models and detailed ...

Oceanic eddies, which are highly mass-coherent vortices traveling through the earth's waters, are of special interest for their mixing properties. Therefore, large-scale ensemble simulations are performed to approximate ...

A comprehensive study of the Earth System and its different environments requires understanding of multi-dimensional data acquired with a multitude of different sensors or produced by various models. Here we present a ...

Farm-scale cultivation of macroalgae for the production of renewable biofuel depends on complex ocean hydrodynamics and also on the availability of different essential nutrients. To better understand such conditions that ...

Analysis of overlapping spatial data sets is a challenging problem with tension between clearly identifying individual surfaces and exploring significant overlaps/conflicts. One area where this problem occurs is when dealing ...

Vector plots can directly visualize both temporal variation and spatial distribution, so it is interesting to use this type of plot for displaying multivariate time series. However, vector plots cannot reveal global temporal ...

In the last decade, the popularity of ESports has grown rapidly. The financial leader in the tournament scene is Dota2, a complex and strategic multiplayer game. Analysis and exploration of game data could lead to better ...