Quality Metrics and Reordering Strategies for Revealing Patterns in BioFabric Visualizations.

Visualizing relational data is crucial for understanding complex connections between entities in social networks, political affiliations, or biological interactions. Well-known representations like node-link diagrams and adjacency matrices offer valuable insights, but their effectiveness relies on the ability to identify patterns in the underlying topological structure. Reordering strategies and layout algorithms play a vital role in the visualization process since the arrangement of nodes, edges, or cells influences the visibility of these patterns.

An Analysis of the Interplay and Mutual Benefits of Grounded Theory and Visualization.

Grounded theory (GT) is a research methodology that entails a systematic workflow for theory generation grounded on emergent data. In this paper, we juxtapose GT workflows with typical workflows in visualization and visual analytics (VIS), unveiling the characteristics shared by these workflows. We explore the research landscape of VIS to study where GT is applied to generate VIS theories, explicitly as well as implicitly.

AI-smartphone markerless motion capturing of hip, knee, and ankle joint kinematics during countermovement jumps.

Recently, AI-driven skeleton reconstruction tools that use multistage computer vision pipelines were designed to estimate 3D kinematics from 2D video sequences. In the present study, we validated a novel markerless, smartphone video-based artificial intelligence (AI) motion capture system for hip, knee, and ankle angles during countermovement jumps (CMJs). Eleven participants performed six CMJs.

TreEducation: A Visual Education Platform for Teaching Treemap Layout Algorithms.

Treemaps are a powerful tool for representing hierarchical data in a space-efficient manner and are used in various domains, including network security or software development. However, interpreting the topology encoded by nested rectangles can be challenging, particularly compared to tree-structured representations like node-link diagrams or icicle plots. To address this challenge, we introduce TreEducation, a visual education platform designed to improve the visualization literacy skills required for reading treemaps among non-expert users.

Comparative Evaluation of Animated Scatter Plot Transitions.

Scatter plots are popular for displaying 2D data, but in practice, many data sets have more than two dimensions. For the analysis of such multivariate data, it is often necessary to switch between scatter plots of different dimension pairs, e.g.

SkiVis: Visual Exploration and Route Planning in Ski Resorts.

Optimal ski route selection is a challenge based on a multitude of factors, such as the steepness, compass direction, or crowdedness. The personal preferences of every skier towards these factors require individual adaptations, which aggravate this task. Current approaches within this domain do not combine automated routing capabilities with user preferences, missing out on the possibility of integrating domain knowledge in the analysis process.

Grand Challenges in Visual Analytics Applications.

In the past two decades, research in visual analytics (VA) applications has made tremendous progress, not just in terms of scientific contributions, but also in real-world impact across wide-ranging domains including bioinformatics, urban analytics, and explainable AI. Despite these success stories, questions on the rigor and value of VA application research have emerged as a grand challenge. This article outlines a research and development agenda for making VA application research more rigorous and impactful.

FS/DS: A Theoretical Framework for the Dual Analysis of Feature Space and Data Space.

With the surge of data-driven analysis techniques, there is a rising demand for enhancing the exploration of large high-dimensional data by enabling interactions for the joint analysis of features (i.e., dimensions).

Automorphism Faithfulness Metrics for Symmetric Graph Drawings.

In this article, we present new quality metrics for symmetric graph drawing based on group theory. Roughly speaking, the new metrics are faithfulness metrics, i.e.

Visual Comparison of Networks in VR.

Networks are an important means for the representation and analysis of data in a variety of research and application areas. While there are many efficient methods to create layouts for networks to support their visual analysis, approaches for the comparison of networks are still underexplored. Especially when it comes to the comparison of weighted networks, which is an important task in several areas, such as biology and biomedicine, there is a lack of efficient visualization approaches.

Investigating the Sketchplan: A Novel Way of Identifying Tactical Behavior in Massive Soccer Datasets.

Coaches and analysts prepare for upcoming matches by identifying common patterns in the positioning and movement of the competing teams in specific situations. Existing approaches in this domain typically rely on manual video analysis and formation discussion using whiteboards; or expert systems that rely on state-of-the-art video and trajectory visualization techniques and advanced user interaction. We bridge the gap between these approaches by contributing a light-weight, simplified interaction and visualization system, which we conceptualized in an iterative design study with the coaching team of a European first league soccer team.

The Role of Interactive Visualization in Fostering Trust in AI.

The increasing use of artificial intelligence (AI) technologies across application domains has prompted our society to pay closer attention to AI's trustworthiness, fairness, interpretability, and accountability. In order to foster trust in AI, it is important to consider the potential of interactive visualization, and how such visualizations help build trust in AI systems. This manifesto discusses the relevance of interactive visualizations and makes the following four claims: i) trust is not a technical problem, ii) trust is dynamic, iii) visualization cannot address all aspects of trust, and iv) visualization is crucial for human agency in AI.

Multiscale Visualization: A Structured Literature Analysis.

Multiscale visualizations are typically used to analyze multiscale processes and data in various application domains, such as the visual exploration of hierarchical genome structures in molecular biology. However, creating such multiscale visualizations remains challenging due to the plethora of existing work and the expression ambiguity in visualization research. Up to today, there has been little work to compare and categorize multiscale visualizations to understand their design practices.

VisInReport: Complementing Visual Discourse Analytics Through Personalized Insight Reports.

We present VisInReport, a visual analytics tool that supports the manual analysis of discourse transcripts and generates reports based on user interaction. As an integral part of scholarly work in the social sciences and humanities, discourse analysis involves an aggregation of characteristics identified in the text, which, in turn, involves a prior identification of regions of particular interest. Manual data evaluation requires extensive effort, which can be a barrier to effective analysis.

Generation of Recombinant Primary Human B Lymphocytes Using Non-Viral Vectors.

Although the development of gene delivery systems based on non-viral vectors is advancing, it remains a challenge to deliver plasmid DNA into human blood cells. The current "gold standard", namely linear polyethyleneimine (l-PEI 25 kDa), in particular, is unable to produce transgene expression levels >5% in primary human B lymphocytes. Here, it is demonstrated that a well-defined 24-armed poly(2-dimethylamino) ethyl methacrylate (PDMAEMA, 755 kDa) nano-star is able to reproducibly elicit high transgene expression (40%) at sufficient residual viability (69%) in primary human B cells derived from tonsillar tissue.

The Value of Immersive Visualization.

In recent years, research on immersive environments has experienced a new wave of interest, and immersive analytics has been established as a new research field. Every year, a vast amount of different techniques, applications, and user studies are published that focus on employing immersive environments for visualizing and analyzing data. Nevertheless, immersive analytics is still a relatively unexplored field that needs more basic research in many aspects and is still viewed with skepticism.

Perspectives on the 2 × 2 Matrix: Solving Semantically Distinct Problems Based on a Shared Structure of Binary Contingencies.

Cognition is both empowered and limited by representations. The matrix lens model explicates tasks that are based on frequency counts, conditional probabilities, and binary contingencies in a general fashion. Based on a structural analysis of such tasks, the model links several problems and semantic domains and provides a new perspective on representational accounts of cognition that recognizes representational isomorphs as opportunities, rather than as problems.

Dynamic Risk Perceptions in Times of Avian and Seasonal Influenza Epidemics: A Repeated Cross-Sectional Design.

Infectious diseases pose a serious threat to humans. Therefore, it is crucial to understand how accurately people perceive these risks. However, accuracy can be operationalized differently depending on the standard of comparison.

Task-Based Visual Interactive Modeling: Decision Trees and Rule-Based Classifiers.

Visual analytics enables the coupling of machine learning models and humans in a tightly integrated workflow, addressing various analysis tasks. Each task poses distinct demands to analysts and decision-makers. In this survey, we focus on one canonical technique for rule-based classification, namely decision tree classifiers.

MultiSegVA: Using Visual Analytics to Segment Biologging Time Series on Multiple Scales.

Segmenting biologging time series of animals on multiple temporal scales is an essential step that requires complex techniques with careful parameterization and possibly cross-domain expertise. Yet, there is a lack of visual-interactive tools that strongly support such multi-scale segmentation. To close this gap, we present our MultiSegVA platform for interactively defining segmentation techniques and parameters on multiple temporal scales.

Visual Analytics for Temporal Hypergraph Model Exploration.

Many processes, from gene interaction in biology to computer networks to social media, can be modeled more precisely as temporal hypergraphs than by regular graphs. This is because hypergraphs generalize graphs by extending edges to connect any number of vertices, allowing complex relationships to be described more accurately and predict their behavior over time. However, the interactive exploration and seamless refinement of such hypergraph-based prediction models still pose a major challenge.

Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs.

The overview-driven visual analysis of large-scale dynamic graphs poses a major challenge. We propose Multiscale Snapshots, a visual analytics approach to analyze temporal summaries of dynamic graphs at multiple temporal scales. First, we recursively generate temporal summaries to abstract overlapping sequences of graphs into compact snapshots.

Toward Mass Video Data Analysis: Interactive and Immersive 4D Scene Reconstruction.

The technical progress in the last decades makes photo and video recording devices omnipresent. This change has a significant impact, among others, on police work. It is no longer unusual that a myriad of digital data accumulates after a criminal act, which must be reviewed by criminal investigators to collect evidence or solve the crime.

Virtual Reality Potentiates Emotion and Task Effects of Alpha/Beta Brain Oscillations.

The progress of technology has increased research on neuropsychological emotion and attention with virtual reality (VR). However, direct comparisons between conventional two-dimensional (2D) and VR stimulations are lacking. Thus, the present study compared electroencephalography (EEG) correlates of explicit task and implicit emotional attention between 2D and VR stimulation.

PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers.

PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms.