Eye movements play an important role during social interaction, for instance, by indicating phases of mutual and joint attention. This type of gaze behavior has been researched extensively in pair collaboration but has been far less studied in the investigation of group activities. Further, the analysis of attention is often decoupled from speech, even though they are closely linked in social interactions. We introduce a visualization to facilitate joint analysis of gaze and speech among multiple participants during social activities. Our proposed social interaction graphs are two-layered arc diagrams that visualize pairwise relationships among participants. Our evaluation is based on two datasets of multiplayer tabletop gaming sessions recorded with five players. We present a traditional evaluation using eye-tracking metrics as a baseline and compare it with the additional findings possible with our approach.

BACKGROUND: With the exponential growth of public RNA-seq datasets, meta-analysis has become a crucial tool for integrating studies to increase statistical power and identify consistent biological patterns. However, conventional p value combination methods were not designed for large-scale integration and exhibit a critical flaw: as more datasets are added, the rate of false positives increases dramatically. This issue stems from the disproportionate influence of extremely low p values from just a few individual studies, which can create a misleading signal of overall significance and undermine the reliability of the findings. RESULTS: Here, we introduce a robust meta-analysis framework that incorporates p value capping, cutoff thresholding, and bagging to alleviate this issue. We implemented this framework using Stouffer’s method, termed the hybrid Stouffer (hStouffer) method. The proposed method demonstrates a reduced false positive rate while preserving high sensitivity. Furthermore, the validation showed that the DEGs identified by hStouffer accurately reflect the underlying biological phenomena, making it an essential tool for leveraging the full potential of expanding genomic databases to understand complex biological processes. CONCLUSIONS: The hStouffer method provides a statistically robust and biologically coherent solution for large-scale RNA-seq meta-analysis. By effectively controlling for technical artifacts and false discoveries, it enables researchers to extract more reliable and meaningful insights from complex, aggregated transcriptomic data.

In digital humanities (DH) and cultural heritage (CH), visualization-based storytelling (VBS) has become an important approach for structuring, interpreting, and communicating cultural data and research results. The specific characteristics of these domains – historically oriented data, high levels of semantic ambiguity and uncertainty, interpretive rather than purely analytical goals, and deep expertise in narrative theory and practice – create both distinct requirements for VBS design and distinct opportunities that existing frameworks and tools only partially address. Yet the body of work that has emerged in response to these requirements remains scattered across disciplines and venues, with no systematic account of current practice and no consolidated view of open challenges.Against this backdrop, we survey DH and CH work on story designs and VBS tools to identify trends and recurring patterns, promising practices, and open challenges. We contribute by (1) synthesizing storytelling design spaces into a framework tailored to VBS in DH and CH, (2) mapping existing approaches to generate a field-level picture of practices and gaps, and (3) highlighting future areas of concern and inquiry for VBS in relation to domain-specific epistemic questions. Overall, this survey seeks to consolidate an emerging community of practice and offer a shared analytical foundation for future research and design.

The visualization and interactive exploration of geo-referenced networks poses challenges if the network’s nodes are not evenly distributed. Our approach proposes new ways of realizing animated transitions for exploring such networks from an ego-perspective. We aim to reduce the required screen estate while maintaining the viewers’ mental map of distances and directions. A preliminary study provides first insights of the comprehensiveness of animated geographic transitions regarding directional relationships between start and end point in different projections. Two use cases showcase how ego-perspective graph exploration can be supported using less screen space than previous approaches.

Abstract Sports visualization has developed into an active research field over the last decades. Many approaches focus on analyzing movement data recorded from unstructured situations, such as soccer. For the analysis of choreographed activities like formation dancing, however, the goal differs, as dancers follow specific formations in coordinated movement trajectories. To date, little work exists on how visual analytics methods can support such choreographed performances. To fill this gap, we introduce a new visual approach for planning and assessing dance choreographies. In terms of planning choreographies, we contribute a web application with interactive authoring tools and views for the dancers' positions and orientations, movement trajectories, poses, dance floor utilization, and movement distances. For assessing dancers' real‐world movement trajectories, extracted by manual bounding box annotations, we developed a timeline showing aggregated trajectory deviations and a dance floor view for detailed trajectory comparison. Our approach was developed and evaluated in collaboration with dance instructors, showing that introducing visual analytics into this domain promises improvements in training efficiency for the future.

The COVID-19 pandemic fundamentally changed the nature of work by shifting most in-person work to a predominantly remote modality as a way to limit the spread of the coronavirus. In the process, the shift to working-from-home rapidly forced the large-scale adoption of groupware technologies. Although prior empirical research examined the experience of working-from-home within small-scale groups and for targeted kinds of work, the pandemic provides HCI and CSCW researchers with an unprecedented opportunity to understand the psycho-social impacts of a universally mandated work-from-home experience rather than an autonomously chosen one. Drawing on boundary theory and a methodological approach grounded in humanistic geography, we conducted a qualitative analysis of Reddit data drawn from two work-from-home-related subreddits between March 2020 and January 2021. In this paper, we present a characterization of the challenges and solutions discussed within these online communities for adapting work to a hybrid or fully remote modality, managing reconfigured work-life boundaries, and reconstructing the home's sense of place to serve multiple, sometimes conflicting roles. We discuss how these findings suggest an emergent interplay among adapted work practice, reimagined physical (and virtual) spaces, and the establishment and continual re-negotiation of boundaries as a means for anticipating the long-term impact of COVID on future conceptualizations of productivity and work.

In a microservice system, runtime changes such as failures, deployments, or self-adaptation can trigger the system to transition from one steady state to another, i.e., exhibiting transient behavior. To assess a system’s quality, it is imperative that this transient behavior is specified in non-functional requirements and that stakeholders can analyze whether these requirements are met. Yet, there is little support for either specifying transient behavior as a non-functional requirement or analyzing how such a requirement is met in production. We aim to make these two tasks more accessible by utilizing novel human-computer interaction methods. To this end, we developed TransVis, an approach for specifying and analyzing transient behavior based on chatbot interactions and visualizations of the systems’ resilience. We examined the effectiveness of our approach by conducting an exploratory expert study on a prototypical implementation. The study revealed that the developed visualizations are effective for specifying and exploring transient behavior. Participants found especially helpful the feature to compare specifications with the actual behavior. However, the integration of a chatbot did not prove effective for our use cases. In conclusion, our approach is capable of supporting stakeholders in the exploration and specification of transient behavior.

Even though load testing is an established technique to assess load-related quality properties of software systems, it is applied only seldom and with questionable results. Indeed, configuring, executing, and interpreting results of a load test require high effort and expertise. Since chatbots have shown promising results for interactively supporting complex tasks in various domains (including software engineering), we hypothesize that chatbots can provide developers suitable support for load testing. In this paper, we present PerformoBot, our chatbot for configuring and running load tests. In a natural language conversation, PerformoBot guides developers through the process of properly specifying the parameters of a load test, which is then automatically executed by PerformoBot using a state-of-the-art load testing tool. After the execution, PerformoBot provides developers a report that answers the respective concern. We report on results of a user study that involved 47 participants, in which we assessed our tool's acceptance and effectiveness. We found that participants in the study, particularly those with a lower level of expertise in performance engineering, had a mostly positive view of PerformoBot.