DevLan/vispubs huggingface dataset:
A Survey of Visualization and Analysis in High-Resolution Connectomics
Authors: Johanna Beyer, Jakob Troidl, Saeed Boorboor, Markus Hadwiger, Arie E. Kaufman, Hanspeter Pfister
Published at EuroVis, 2022

The field of connectomics aims to reconstruct the wiring diagram of Neurons and synapses to enable new insights into the workings of the brain. Reconstructing and analyzing the Neuronal connectivity, however, relies on many individual steps, starting from high‐resolution data acquisition to automated segmentation, proofreading, interactive data exploration, and circuit analysis. All of these steps have to handle large and complex datasets and rely on or benefit from integrated visualization methods. In this state‐of‐the‐art report, we describe visualization methods that can be applied throughout the connectomics pipeline, from data acquisition to circuit analysis. We first define the different steps of the pipeline and focus on how visualization is currently integrated into these steps. We also survey open science initiatives in connectomics, including usable open‐source tools and publicly available datasets. Finally, we discuss open challenges and possible future directions of this exciting research field.
A Typology of Guidance Tasks in Mixed-Initiative Visual Analytics Environments
Authors: Ignacio Pérez-Messina, Davide Ceneda, Mennatallah El-Assady, Silvia Miksch, Fabian Sperrle
Published at EuroVis, 2022

Guidance has been proposed as a conceptual framework to understand how mixed‐initiative visual analytics approaches can actively support users as they solve analytical tasks. While user tasks received a fair share of attention, it is still not completely clear how they could be supported with guidance and how such support could influence the progress of the task itself. Our observation is that there is a research gap in understanding the effect of guidance on the analytical discourse, in particular, for the knowledge generation in mixed‐initiative approaches. As a consequence, guidance in a visual analytics environment is usually indistinguishable from common visualization features, making user responses challenging to predict and measure. To address these issues, we take a system perspective to propose the notion of guidance tasks and we present it as a typology closely aligned to established user task typologies. We derived the proposed typology directly from a model of guidance in the knowledge generation process and illustrate its implications for guidance design. By discussing three case studies, we show how our typology can be applied to analyze existing guidance systems. We argue that without a clear consideration of the system perspective, the analysis of tasks in mixed‐initiative approaches is incomplete. Finally, by analyzing matchings of user and guidance tasks, we describe how guidance tasks could either help the user conclude the analysis or change its course.
AirLens: Multi-Level Visual Exploration of Air Quality Evolution in Urban Agglomerations
Authors: Dezhan Qu, Cheng Lv, Yiming Lin, Huijie Zhang, Rong Wang
Published at EuroVis, 2022

The precise prevention and control of air pollution is a great challenge faced by environmental experts in recent years. Understanding the air quality evolution in the urban agglomeration is important for coordinated control of air pollution. However, the complex pollutant interactions between different cities lead to the collaborative evolution of air quality. The existing statistical and machine learning methods cannot well support the comprehensive analysis of the dynamic air quality evolution. In this study, we propose AirLens, an interactive visual analytics system that can help domain experts explore and understand the air quality evolution in the urban agglomeration from multiple levels and multiple aspects. To facilitate the cognition of the complex multivariate spatiotemporal data, we first propose a multi‐run clustering strategy with a novel glyph design for summarizing and understanding the typical pollutant patterns effectively. On this basis, the system supports the multi‐level exploration of air quality evolution, namely, the overall level, stage level and detail level. Frequent pattern mining, city community extraction and useful filters are integrated into the system for discovering significant information comprehensively. The case study and positive feedback from domain experts demonstrate the effectiveness and usability of AirLens.
An Interactive Approach for Identifying Structure Definitions
Authors: Natalia Mikula, Tom Dörffel, Daniel Baum, Hans-Christian Hege
Published at EuroVis, 2022

Our ability to grasp and understand complex phenomena is essentially based on recognizing structures and relating these to each other. For example, any meteorological description of a weather condition and explanation of its evolution recurs to meteorological structures, such as convection and circulation structures, cloud fields and rain fronts. All of these are spatiotemporal structures, defined by time‐dependent patterns in the underlying fields. Typically, such a structure is defined by a verbal description that corresponds to the more or less uniform, often somewhat vague mental images of the experts. However, a precise, formal definition of the structures or, more generally, of the concepts is often desirable, e.g., to enable automated data analysis or the development of phenomenological models. Here, we present a systematic approach and an interactive tool to obtain formal definitions of spatiotemporal structures. The tool enables experts to evaluate and compare different structure definitions on the basis of data sets with time‐dependent fields that contain the respective structure. Since structure definitions are typically parameterized, an essential part is to identify parameter ranges that lead to desired structures in all time steps. In addition, it is important to allow a quantitative assessment of the resulting structures simultaneously. We demonstrate the use of the tool by applying it to two meteorological examples: finding structure definitions for vortex cores and center lines of temporarily evolving tropical cyclones.Ideally, structure definitions should be objective and applicable to as many data sets as possible. However, finding such definitions, e.g., for the common atmospheric structures in meteorology, can only be a long‐term goal. The proposed procedure, together with the presented tool, is just a first systematic approach aiming at facilitating this long and arduous way.
Barrio: Customizable Spatial Neighborhood Analysis and Comparison for Nanoscale Brain Structures
Authors: Jakob Troidl, Corrado Calì, Eduard Gröller, Hanspeter Pfister, Markus Hadwiger, Johanna Beyer
Published at EuroVis, 2022

High‐resolution electron microscopy imaging allows neuroscientists to reconstruct not just entire cells but individual cell substructures (i.e., cell organelles) as well. Based on these data, scientists hope to get a better understanding of brain function and development through detailed analysis of local organelle neighborhoods. In‐depth analyses require efficient and scalable comparison of a varying number of cell organelles, ranging from two to hundreds of local spatial neighborhoods. Scientists need to be able to analyze the 3D morphologies of organelles, their spatial distributions and distances, and their spatial correlations. We have designed Barrio as a configurable framework that scientists can adjust to their preferred workflow, visualizations, and supported user interactions for their specific tasks and domain questions. Furthermore, Barrio provides a scalable comparative visualization approach for spatial neighborhoods that automatically adjusts visualizations based on the number of structures to be compared. Barrio supports small multiples of spatial 3D views as well as abstract quantitative views, and arranges them in linked and juxtaposed views. To adapt to new domain‐specific analysis scenarios, we allow the definition of individualized visualizations and their parameters for each analysis session. We present an in‐depth case study for mitochondria analysis in neuronal tissue and demonstrate the usefulness of Barrio in a qualitative user study with neuroscientists.
Branch Decomposition-Independent Edit Distances for Merge Trees
Authors: Florian Wetzels, Heike Leitte, Christoph Garth
Published at EuroVis, 2022

Edit distances between merge trees of scalar fields have many applications in scientific visualization, such as ensemble analysis, feature tracking or symmetry detection. In this paper, we propose branch mappings, a novel approach to the construction of edit mappings for merge trees. Classic edit mappings match nodes or edges of two trees onto each other, and therefore have to either rely on branch decompositions of both trees or have to use auxiliary node properties to determine a matching. In contrast, branch mappings employ branch properties instead of node similarity information, and are independent of predetermined branch decompositions. Especially for topological features, which are typically based on branch properties, this allows a more intuitive distance measure which is also less susceptible to instabilities from small‐scale perturbations. For trees with 𝒪(n) nodes, we describe an 𝒪(n4) algorithm for computing optimal branch mappings, which is faster than the only other branch decomposition‐independent method in the literature by more than a linear factor. Furthermore, we compare the results of our method on synthetic and real‐world examples to demonstrate its practicality and utility.
Chart Question Answering: State of the Art and Future Directions
Authors: Enamul Hoque, Parsa Kavehzadeh, Ahmed Masry
Published at EuroVis, 2022

Information visualizations such as bar charts and line charts are very common for analyzing data and discovering critical insights. Often people analyze charts to answer questions that they have in mind. Answering such questions can be challenging as they often require a significant amount of perceptual and cognitive effort. Chart Question Answering (CQA) systems typically take a chart and a natural language question as input and automatically generate the answer to facilitate visual data analysis. Over the last few years, there has been a growing body of literature on the task of CQA. In this survey, we systematically review the current state‐of‐the‐art research focusing on the problem of chart question answering. We provide a taxonomy by identifying several important dimensions of the problem domain including possible inputs and outputs of the task and discuss the advantages and limitations of proposed solutions. We then summarize various evaluation techniques used in the surveyed papers. Finally, we outline the open challenges and future research opportunities related to chart question answering.
CorpusVis: Visual Analysis of Digital Sheet Music Collections
Authors: Matthias Miller, Julius Rauscher, Daniel A. Keim, Mennatallah El-Assady
Published at EuroVis, 2022

Manually investigating sheet music collections is challenging for music analysts due to the magnitude and complexity of underlying features, structures, and contextual information. However, applying sophisticated algorithmic methods would require advanced technical expertise that analysts do not necessarily have. Bridging this gap, we contribute CorpusVis, an interactive visual workspace, enabling scalable and multi‐faceted analysis. Our proposed visual analytics dashboard provides access to computational methods, generating varying perspectives on the same data. The proposed application uses metadata including composers, type, epoch, and low‐level features, such as pitch, melody, and rhythm. To evaluate our approach, we conducted a pair‐analytics study with nine participants. The qualitative results show that CorpusVis supports users in performing exploratory and confirmatory analysis, leading them to new insights and findings. In addition, based on three exemplary workflows, we demonstrate how to apply our approach to different tasks, such as exploring musical features or comparing composers.
DanmuVis: Visualizing Danmu Content Dynamics and Associated Viewer Behaviors in Online Videos
Authors: Shuai Chen 0001, Sihang Li, Yanda Li, Junlin Zhu, Juanjuan Long, Siming Chen 0001, Jiawan Zhang, Xiaoru Yuan
Published at EuroVis, 2022

Danmu (Danmaku) is a unique social media service in online videos, especially popular in Japan and China, for viewers to write comments while watching videos. The danmu comments are overlaid on the video screen and synchronized to the associated video time, indicating viewers' thoughts of the video clip. This paper introduces an interactive visualization system to analyze danmu comments and associated viewer behaviors in a collection of videos and enable detailed exploration of one video on demand. The watching behaviors of viewers are identified by comparing video time and post time of viewers' danmu. The system supports analyzing danmu content and viewers' behaviors against both video time and post time to gain insights into viewers' online participation and perceived experience. Our evaluations, including usage scenarios and user interviews, demonstrate the effectiveness and usability of our system.
Effective Use of Likert Scales in Visualization Evaluations: A Systematic Review
Authors: Laura South, David Saffo, Olga Vitek, Cody Dunne, Michelle A. Borkin
Published at EuroVis, 2022

Likert scales are often used in visualization evaluations to produce quantitative estimates of subjective attributes, such as ease of use or aesthetic appeal. However, the methods used to collect, analyze, and visualize data collected with Likert scales are inconsistent among evaluations in visualization papers. In this paper, we examine the use of Likert scales as a tool for measuring subjective response in a systematic review of 134 visualization evaluations published between 2009 and 2019. We find that papers with both objective and subjective measures do not hold the same reporting and analysis standards for both aspects of their evaluation, producing less rigorous work for the subjective qualities measured by Likert scales. Additionally, we demonstrate that many papers are inconsistent in their interpretations of Likert data as discrete or continuous and may even sacrifice statistical power by applying nonparametric tests unnecessarily. Finally, we identify instances where key details about Likert item construction with the potential to bias participant responses are omitted from evaluation methodology reporting, inhibiting the feasibility and reliability of future replication studies. We summarize recommendations from other fields for best practices with Likert data in visualization evaluations, based on the results of our survey. A full copy of this paper and all supplementary material are available at https://osf.io/exbz8/.
Exploring Effects of Ecological Visual Analytics Interfaces on Experts' and Novices' Decision-Making Processes: A Case Study in Air Traffic Control
Authors: Elmira Zohrevandi, Carl A. L. Westin, Katerina Vrotsou, Jonas Lundberg
Published at EuroVis, 2022

Operational demands in safety‐critical systems impose a risk of failure to the operators especially during urgent situations. Operators of safety‐critical systems learn to make decisions effectively throughout extensive training programs and many years of experience. In the domain of air traffic control, expensive training with high dropout rates calls for research to enhance novices' ability to detect and resolve conflicts in the airspace. While previous researchers have mostly focused on redesigning training instructions and programs, the current paper explores possible benefits of novel visual representations to improve novices' understanding of the situations as well as their decision‐making process. We conduct an experimental evaluation study testing two ecological visual analytics interfaces, developed in a previous study, as support systems to facilitate novice decision‐making. The main contribution of this paper is threefold. First, we describe the application of an ecological interface design approach to the development of two visual analytics interfaces. Second, we perform a human‐in‐the‐loop experiment with forty‐five novices within a simplified air traffic control simulation environment. Third, by performing an expert‐novice comparison we investigate the extent to which effects of the proposed interfaces can be attributed to the subjects' expertise. The results show that the proposed ecological visual analytics interfaces improved novices' understanding of the information about conflicts as well as their problem‐solving performance. Further, the results show that the beneficial effects of the proposed interfaces were more attributable to the visual representations than the users' expertise.
Exploring How Visualization Design and Situatedness Evoke Compassion in the Wild
Authors: Luiz Morais, Nazareno Andrade, D. Sousa
Published at EuroVis, 2022

This work explores how the design and situatedness of data representations affect people's compassion with a case study concerning harassment episodes in a public place. Results contribute to advancing the understanding of how visualizations can evoke emotions and their impact on prosocial behaviors, such as helping people in need. Recent literature examined the effect of different on‐screen data representations on emotion or prosociality, but little has been done concerning visualizations shown in a public place — especially a space contextually relevant to the data — or presented through unconventional media formats such as physical marks. We conducted two in‐the‐wild studies to investigate how different factors affect people's self‐reported compassion and intention to donate. We compared three ways of presenting data about the harassment cases: (1) communicating data only verbally; (2) using a printed poster with aggregated information; and (3) using a physicalization with detailed information about each story. We found that the physicalization influenced people to donate more than only hearing about the data, but it is unclear if the same applied to the poster visualization. Also, passers‐by reported a likely small increase in compassion when they saw the physicalization instead of the poster. We also examined the role of situatedness by showing the physicalization in a site that is not contextually relevant to the data. Our results suggest that people had a similar intention to donate and levels of compassion in both places. Those findings may indicate that using specific visualization designs to support campaigns about sensitive causes (e.g., sexual harassment) can increase the emotional response of passers‐by and may motivate them to help, independently of where the data representation is shown. Finally, this work also informs on the strengths and weaknesses of using research in the wild to evaluate data visualizations in public spaces.
Exploring Multivariate Event Sequences with an Interactive Similarity Builder
Authors: Shaobin Xu, Minghui Sun, Zhengtai Zhang, Hao Xue
Published at EuroVis, 2022

Similarity‐based exploration is an effective method in knowledge discovery. Faced with multivariate event sequence data (MVES), developing a satisfactory similarity measurement for a specific question is challenging because of the heterogeneity introduced by numerous attributes with different data formats, coupled with their associations. Additionally, the absence of effective validation feedback makes judging the goodness of a measurement scheme a time‐consuming and error‐prone procedure. To free analysts from tedious programming to concentrate on the exploration of MVES data, this paper introduces an interactive similarity builder, where analysts can use visual building blocks for assembling similarity measurements in a drag‐and‐drop and incremental fashion. Based on the builder, we further propose a visual analytics framework that provides multi‐granularity visual validations for measurement schemes and supports a recursive workflow for refining the focus set. We illustrate the power of our prototype through a case study and a user study with real‐world datasets. Results suggest that the system improves the efficiency of developing similarity measurements and the usefulness of exploring MVES data.
How accessible is my visualization? Evaluating visualization accessibility with Chartability
Authors: Frank Elavsky, Cynthia L. Bennett, Dominik Moritz
Published at EuroVis, 2022

Novices and experts have struggled to evaluate the accessibility of data visualizations because there are no common shared guidelines across environments, platforms, and contexts in which data visualizations are authored. Between non‐specific standards bodies like WCAG, emerging research, and guidelines from specific communities of practice, it is hard to organize knowledge on how to evaluate accessible data visualizations. We present Chartability, a set of heuristics synthesized from these various sources which enables designers, developers, researchers, and auditors to evaluate data‐driven visualizations and interfaces for visual, motor, vestibular, neurological, and cognitive accessibility. In this paper, we outline our process of making a set of heuristics and accessibility principles for Chartability and highlight key features in the auditing process. Working with participants on real projects, we found that data practitioners with a novice level of accessibility skills were more confident and found auditing to be easier after using Chartability. Expert accessibility practitioners were eager to integrate Chartability into their own work. Reflecting on Chartability's development and the preliminary user evaluation, we discuss tradeoffs of open projects, working with high‐risk evaluations like auditing projects in the wild, and challenge future research projects at the intersection of visualization and accessibility to consider the broad intersections of disabilities.
Hybrid Touch/Tangible Spatial Selection in Augmented Reality
Authors: Mickaël Sereno, Stéphane Gosset, Lonni Besançon, Tobias Isenberg 0001
Published at EuroVis, 2022

We study tangible touch tablets combined with Augmented Reality Head‐Mounted Displays (AR‐HMDs) to perform spatial 3D selections. We are primarily interested in the exploration of 3D unstructured datasets such as cloud points or volumetric datasets. AR‐HMDs immerse users by showing datasets stereoscopically, and tablets provide a set of 2D exploration tools. Because AR‐HMDs merge the visualization, interaction, and the users' physical spaces, users can also use the tablets as tangible objects in their 3D space. Nonetheless, the tablets' touch displays provide their own visualization and interaction spaces, separated from those of the AR‐HMD. This raises several research questions compared to traditional setups. In this paper, we theorize, discuss, and study different available mappings for manual spatial selections using a tangible tablet within an AR‐HMD space. We then study the use of this tablet within a 3D AR environment, compared to its use with a 2D external screen.