Branches within clusters can represent meaningful subgroups that should be explored. In general, automatically detecting branching structures within clusters requires analysing the distances between data points and a ...

In this paper, we present an empirical demonstration of a prompt-based learning approach, which utilizes pre-trained Large Language Models to generate visualization specifications from user queries expressed in natural ...

In recent years, proteins have been shown to carry many more post-translational modifications (PTMs) than originally thought. The visualization of proteins along with their PTMs facilitates exploration and understanding ...

We present ORD-Xplore, an approach to bridge gaps between digital editions, which represent valuable collections of multiple digitized research artifacts. However, digital editions often co-exist isolated, making it difficult ...

We present Gleaner, an automatic dashboard design system that optimizes the design in terms of four design criteria, namely Specificity, Interestingness, Diversity, and Coverage. With these criteria, Gleaner not only ...

We present an exploratory study of semantically resonant colors for combinations of categories. The goal is to support color selection of multi-labeled classes of classified data. We asked participants to assign colors to ...

Pediatric chronic kidney disease (CKD) increases the risk of cardiovascular disease, stroke and other life-threatening conditions. Monitoring blood pressure in CKD patients is crucial to managing these risks. 24-hour ...

Criminal networks have proven to be highly resilient against law enforcement interventions. This resiliency has driven researchers to investigate these networks further. However, the obtained insights reaching law enforcement ...

Quali-quantitative methods provide ways for interrogating Convolutional Neural Networks (CNN). For it, we propose a dashboard using a quali-quantitative method based on quantitative metrics and saliency maps. By those ...