DevLan/vispubs huggingface dataset:
Cytosplore: Interactive Immune Cell Phenotyping for Large Single-Cell Datasets
Authors: Thomas Höllt, Nicola Pezzotti, Vincent van Unen, Frits Koning, Elmar Eisemann, Boudewijn P. F. Lelieveldt, Anna Vilanova
Published at EuroVis, 2016

To understand how the immune system works, one needs to have a clear picture of its cellular compositon and the cells' corresponding properties and functionality. Mass cytometry is a novel technique to determine the properties of single‐cells with unprecedented detail. This amount of detail allows for much finer differentiation but also comes at the cost of more complex analysis. In this work, we present Cytosplore, implementing an interactive workflow to analyze mass cytometry data in an integrated system, providing multiple linked views, showing different levels of detail and enabling the rapid definition of known and unknown cell types. Cytosplore handles millions of cells, each represented as a high‐dimensional data point, facilitates hypothesis generation and confirmation, and provides a significant speed up of the current workflow. We show the effectiveness of Cytosplore in a case study evaluation.
Decoupled Shading for Real-time Heterogeneous Volume Illumination
Authors: Y. Zhang, Kwan-Liu Ma
Published at EuroVis, 2016

Existing real‐time volume rendering techniques which support global illumination are limited in modeling distinct realistic appearances for classified volume data, which is a desired capability in many fields of study for illustration and education. Directly extending the emission‐absorption volume integral with heterogeneous material shading becomes unaffordable for real‐time applications because the high‐frequency view‐dependent global lighting needs to be evaluated per sample along the volume integral. In this paper, we present a decoupled shading algorithm for multi‐material volume rendering that separates global incident lighting evaluation from per‐sample material shading under multiple light sources. We show how the incident lighting calculation can be optimized through a sparse volume integration method. The quality, performance and usefulness of our new multi‐material volume rendering method is demonstrated through several examples.
Designing Multiple Coordinated Visualizations for Tablets
Authors: Ramik Sadana, John T. Stasko
Published at EuroVis, 2016

The use of multiple coordinated views (MCV) in data visualization provides analytic power because it allows a person to explore data under a variety of different perspectives. Since this design pattern utilizes multiple visualizations and requires coordinated interactions across the views, a clever use of screen space is vital and many synchronized interface operations must be provided. Bringing this design pattern to tablet computers is challenging due to their small display size and the absence of keyboard and mouse input. In this article, we explain important design considerations for MCV visualization on tablets and describe a prototype MCV visualization system we have built for the iPad. The design is based on the principles of maximizing screen space for data presentation, promoting consistent interactions across visualizations, and minimizing occlusion from a person's hands.
Dynamic Change Arcs to Explore Model Forecasts
Authors: C. St. Jean, Colin Ware, Rose F. Gamble
Published at EuroVis, 2016

In many planning applications, a computational model is used to make predictions about the effects of management or engineering decisions. To understand the implications of alternative scenarios, a user typically adjusts one or more of the input parameters, runs the model, and examines the outcomes using simple charts. For example, a time series showing changes in productivity or revenue might be generated. While this approach can be effective in showing the projected effects of changes to the model's input parameters, it fails to show the mechanisms that cause those changes. In order to promote understanding of model mechanics using a simple graphical device, we propose dynamic change arcs. Dynamic change arcs graphically reveal the internal model structure as cause and effect linkages. They are signed to show both positive and negative effects. We implemented this concept using a species interaction model developed for fisheries management based on a system of Lotka‐Volterra equations. The model has 10 economically important fish species and incorporates both predation and competition between species. The model predicts that changing the catch of one species can sometimes result in changes in biomass of another species through multi‐step causal chains. The dynamic change arcs make it possible to interpret the resulting complex causal chains and interaction effects. We carried out an experiment to evaluate three alternative forms of arcs for portraying causal connections in the model. The results show that all linkage representations enabled participants to reason better about complex chains of causality than not showing linkages. However, none of them were significantly better than the others.
Enhancing Scatterplots with Multi-Dimensional Focal Blur
Authors: Joachim Staib, Sebastian Grottel, Stefan Gumhold
Published at EuroVis, 2016

Scatterplots directly depict two dimensions of multi‐dimensional data points, discarding all other information. To visualize all data, these plots are extended to scatterplot matrices, which distribute the information of each data point over many plots. Problems arising from the resulting visual complexity are nowadays alleviated by concepts like filtering and focus and context. We present a method based on depth of field that contains both aspects and injects information from all dimensions into each scatterplot. Our approach is a natural generalization of the commonly known focus effects from optics. It is based on a multidimensional focus selection body. Points outside of this body are defocused depending on their distance. Our method allows for a continuous transition from data points in focus, over regions of blurry points providing contextual information, to visually filtered data. Our algorithm supports different focus selection bodies, blur kernels, and point shapes. We present an optimized GPU‐based implementation for interactive exploration and show the usefulness of our approach on several data sets.
Evaluating Viewpoint Entropy for Ribbon Representation of Protein Structure
Authors: Julian Heinrich, Jenny Vuong, Christopher J. Hammang, A. Wu, Markus Rittenbruch, James M. Hogan, Margot Brereton, Seán I. O'Donoghue
Published at EuroVis, 2016

While many measures of viewpoint goodness have been proposed in computer graphics, none have been evaluated for ribbon representations of protein secondary structure. To fill this gap, we conducted a user study on Amazon's Mechanical Turk platform, collecting human viewpoint preferences from 65 participants for 4 representative superfamilies of protein domains. In particular, we evaluated viewpoint entropy, which was previously shown to be a good predictor for human viewpoint preference of other, mostly non‐abstract objects. In a second study, we asked 7 experts in molecular biology to find the best viewpoint of the same protein domains and compared their choices with viewpoint entropy.Our results indicate that viewpoint entropy overall is a significant predictor of human viewpoint preference for ribbon representations of protein secondary structure. However, the accuracy depends on the type and composition of the structure: while most participants agree on good viewpoints for structures with mainly beta sheets, viewpoint preference varies considerably for complex arrangements of alpha helices. Finally, experts tend to choose viewpoints of both low and high viewpoint entropy to emphasize different aspects of the respective structure.
Exploratory Visual Analysis for Animal Movement Ecology
Authors: Aidan Slingsby, E. Emiel van Loon
Published at EuroVis, 2016

Movement ecologists study animals' movement to help understand their behaviours and interactions with each other and the environment. Data from GPS loggers are increasingly important for this. These data need to be processed, segmented and summarised for further visual and statistical analysis, often using predefined parameters. Usually, this process is separate from the subsequent visual and statistical analysis, making it difficult for these results to inform the data processing and to help set appropriate scale and thresholds parameters. This paper explores the use of highly interactive visual analytics techniques to close the gap between processing raw data and exploratory visual analysis. Working closely with animal movement ecologists, we produced requirements to enable data characteristics to be determined, initial research questions to be investigated, and the suitability of data for further analysis to be assessed. We design visual encodings and interactions to meet these requirements and provide software that implements them. We demonstrate these techniques with indicative research questions for a number of bird species, provide software, and discuss wider implications for animal movement ecology.
Exploring and Comparing Clusterings of Multivariate Data Sets Using Persistent Homology
Authors: Bastian Rieck, Heike Leitte
Published at EuroVis, 2016

Clustering algorithms support exploratory data analysis by grouping inputs that share similar features. Especially the clustering of unlabelled data is said to be a fiendishly difficult problem, because users not only have to choose a suitable clustering algorithm but also a suitable number of clusters. The known issues of existing clustering validity measures comprise instabilities in the presence of noise and restrictive assumptions about cluster shapes. In addition, they cannot evaluate individual clusters locally. We present a new measure for assessing and comparing different clusterings both on a global and on a local level. Our measure is based on the topological method of persistent homology, which is stable and unbiased towards cluster shapes. Based on our measure, we also describe a new visualization that displays similarities between different clusterings (using a global graph view) and supports their comparison on the individual cluster level (using a local glyph view). We demonstrate how our visualization helps detect different—but equally valid—clusterings of data sets from multiple application domains.
Exploring Items and Features with IF, FI-Tables
Authors: Paul van der Corput, Jarke J. van Wijk
Published at EuroVis, 2016

The exploration of high‐dimensional data is challenging because humans have difficulty to understand more than three dimensions. We present a new visualization concept that enables users to explore such data and, specifically, to learn about important items and features that are unknown or overlooked, based on the items and features that are already known. The visualization consists of two juxtaposed tables: an IF‐Table, showing all items with a selection of features; and an FI‐Table, showing all features with a selection of items. This enables the user to limit the number of visible items and features to those needed for the exploration. The interaction is kept simple: each selection of items and features results in a complete overview of similar and relevant items and features.
Faceted Views of Varying Emphasis (FaVVEs): a framework for visualising multi-perspective small multiples
Authors: Roger Beecham, Chris Rooney, S. Meier, Jason Dykes, Aidan Slingsby, Cagatay Turkay, Jo Wood, B. L. William Wong
Published at EuroVis, 2016

Many datasets have multiple perspectives – for example space, time and description – and often analysts are required to study these multiple perspectives concurrently. This concurrent analysis becomes difficult when data are grouped and split into small multiples for comparison. A design challenge is thus to provide representations that enable multiple perspectives, split into small multiples, to be viewed simultaneously in ways that neither clutter nor overload. We present a design framework that allows us to do this. We claim that multi‐perspective comparison across small multiples may be possible by superimposing perspectives on one another rather than juxtaposing those perspectives side‐by‐side. This approach defies conventional wisdom and likely results in visual and informational clutter. For this reason we propose designs at three levels of abstraction for each perspective. By flexibly varying the abstraction level, certain perspectives can be brought into, or out of, focus. We evaluate our framework through laboratory‐style user tests. We find that superimposing, rather than juxtaposing, perspective views has little effect on performance of a low‐level comparison task. We reflect on the user study and its design to further identify analysis situations for which our framework may be desirable. Although the user study findings were insufficiently discriminating, we believe our framework opens up a new design space for multi‐perspective visual analysis.
Formalizing Emphasis in Information Visualization
Authors: Kyle Wm. Hall, Charles Perin, Peter G. Kusalik, Carl Gutwin, Sheelagh Carpendale
Published at EuroVis, 2016

We provide afresh look at the use and prevalence of emphasis effects in Infovis. Through a survey of existing emphasis frameworks, we extract a set‐based approach that uses visual prominence to link visually and algorithmically diverse emphasis effects. Visual prominence provides a basis for describing, comparing and generating emphasis effects when combined with a set of general features of emphasis effects. Therefore, we use visual prominence and these general features to construct a new mathematical Framework for Information Visualization Emphasis, FIVE. The concepts we introduce to describe FIVE unite the emphasis literature and point to several new research directions for emphasis in information visualization.
From Visual Exploration to Storytelling and Back Again
Authors: Samuel Gratzl, Alexander Lex, Nils Gehlenborg, N. Cosgrove, Marc Streit
Published at EuroVis, 2016

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 non‐linear nature of the exploratory 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 paper we present CLUE (Capture, Label, Understand, Explain), a model that tightly integrates data exploration and 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. We discuss how the CLUE approach can be integrated into visualization tools and provide a prototype implementation. Finally, we demonstrate the general applicability of the model in two usage scenarios: a Gapminder‐inspired visualization to explore public health data and an example from molecular biology that illustrates how Vistories could be used in scientific journals.
GEMSe: Visualization-Guided Exploration of Multi-channel Segmentation Algorithms
Authors: Bernhard Fröhler, Torsten Möller, Christoph Heinzl
Published at EuroVis, 2016

We present GEMSe, an interactive tool for exploring and analyzing the parameter space of multi‐channel segmentation algorithms. Our targeted user group are domain experts who are not necessarily segmentation specialists. GEMSe allows the exploration of the space of possible parameter combinations for a segmentation framework and its ensemble of results. Users start with sampling the parameter space and computing the corresponding segmentations. A hierarchically clustered image tree provides an overview of variations in the resulting space of label images. Details are provided through exemplary images from the selected cluster and histograms visualizing the parameters and the derived output in the selected cluster. The correlation between parameters and derived output as well as the effect of parameter changes can be explored through interactive filtering and scatter plots. We evaluate the usefulness of GEMSe through expert reviews and case studies based on three different kinds of datasets: A synthetic dataset emulating the combination of 3D X‐ray computed tomography with data from K‐Edge spectroscopy, a three‐channel scan of a rock crystal acquired by a Talbot‐Lau grating interferometer X‐ray computed tomography device, as well as a hyperspectral image.
Glyphs for Asymmetric Second-Order 2D Tensors
Authors: Nicholas Seltzer, Gordon L. Kindlmann
Published at EuroVis, 2016

Tensors model a wide range of physical phenomena. While symmetric tensors are sufficient for some applications (such as diffusion), asymmetric tensors are required, for example, to describe differential properties of fluid flow. Glyphs permit inspecting individual tensor values, but existing tensor glyphs are fully defined only for symmetric tensors. We propose a glyph to visualize asymmetric second‐order two‐dimensional tensors. The glyph includes visual encoding for physically significant attributes of the tensor, including rotation, anisotropic stretching, and isotropic dilation. Our glyph design conserves the symmetry and continuity properties of the underlying tensor, in that transformations of a tensor (such as rotation or negation) correspond to analogous transformations of the glyph. We show results with synthetic data from computational fluid dynamics.
Hierarchical Stochastic Neighbor Embedding
Authors: Nicola Pezzotti, Thomas Höllt, Boudewijn P. F. Lelieveldt, Elmar Eisemann, Anna Vilanova
Published at EuroVis, 2016

In recent years, dimensionality‐reduction techniques have been developed and are widely used for hypothesis generation in Exploratory Data Analysis. However, these techniques are confronted with overcoming the trade‐off between computation time and the quality of the provided dimensionality reduction. In this work, we address this limitation, by introducing Hierarchical Stochastic Neighbor Embedding (Hierarchical‐SNE). Using a hierarchical representation of the data, we incorporate the well‐known mantra of Overview‐First, Details‐On‐Demand in non‐linear dimensionality reduction. First, the analysis shows an embedding, that reveals only the dominant structures in the data (Overview). Then, by selecting structures that are visible in the overview, the user can filter the data and drill down in the hierarchy. While the user descends into the hierarchy, detailed visualizations of the high‐dimensional structures will lead to new insights. In this paper, we explain how Hierarchical‐SNE scales to the analysis of big datasets. In addition, we show its application potential in the visualization of Deep‐Learning architectures and the analysis of hyperspectral images.
How Ordered Is It? On the Perceptual Orderability of Visual Channels
Authors: David H. S. Chung, Daniel Archambault, Rita Borgo, Darren J. Edwards, Robert S. Laramee, Min Chen 0001
Published at EuroVis, 2016

The design of effective glyphs for visualisation involves a number of different visual encodings. Since spatial position is usually already specified in advance, we must rely on other visual channels to convey additional relationships for multivariate analysis. One such relationship is the apparent order present in the data. This paper presents two crowdsourcing empirical studies that focus on the perceptual evaluation of orderability for visual channels, namely Bertin's retinal variables. The first study investigates the perception of order in a sequence of elements encoded with different visual channels. We found evidence that certain visual channels are perceived as more ordered (for example, value) while others are perceived as less ordered (for example, hue) than the measured order present in the data. As a result, certain visual channels are more/less sensitive to disorder. The second study evaluates how visual orderability affects min and max judgements of elements in the sequence. We found that visual channels that tend to be perceived as ordered, improve the accuracy of identifying these values.
In Situ Methods, Infrastructures, and Applications on High Performance Computing Platforms
Authors: Andrew C. Bauer, Hasan Abbasi, James P. Ahrens, Hank Childs, Berk Geveci, Scott Klasky, Kenneth Moreland, Patrick O'Leary, Venkatram Vishwanath, Brad Whitlock, E. Wes Bethel
Published at EuroVis, 2016

The considerable interest in the high performance computing (HPC) community regarding analyzing and visualization data without first writing to disk, i. e., in situ processing, is due to several factors. First is an I/O cost savings, where data is analyzed/visualized while being generated, without first storing to a filesystem. Second is the potential for increased accuracy, where fine temporal sampling of transient analysis might expose some complex behavior missed in coarse temporal sampling. Third is the ability to use all available resources, CPU's and accelerators, in the computation of analysis products. This STAR paper brings together researchers, developers and practitioners using in situ methods in extreme‐scale HPC with the goal to present existing methods, infrastructures, and a range of computational science and engineering applications using in situ analysis and visualization.
Interactive 3D Force-Directed Edge Bundling
Authors: Daniel Zielasko, Benjamin Weyers, Bernd Hentschel 0001, Torsten W. Kuhlen
Published at EuroVis, 2016

Interactive analysis of 3D relational data is challenging. A common way of representing such data are node‐link diagrams as they support analysts in achieving a mental model of the data. However, naïve 3D depictions of complex graphs tend to be visually cluttered, even more than in a 2D layout. This makes graph exploration and data analysis less efficient. This problem can be addressed by edge bundling. We introduce a 3D cluster‐based edge bundling algorithm that is inspired by the force‐directed edge bundling (FDEB) algorithm [HvW09b] and fulfills the requirements to be embedded in an interactive framework for spatial data analysis. It is parallelized and scales with the size of the graph regarding the runtime. Furthermore, it maintains the edge's model and thus supports rendering the graph in different structural styles. We demonstrate this with a graph originating from a simulation of the function of a macaque brain.