In today's data-rich environment, visualization literacy-the ability to understand and communicate information through charts-is increasingly important. However, constructing effective charts can be challenging due to the numerous design choices involved. Off-the-shelf systems and libraries produce charts with carefully selected defaults that users may not be aware of, making it hard to increase their visualization literacy with those systems. In addition, traditional ways of improving visualization literacy, such as textbooks and tutorials, can be burdensome as they require sifting through a plethora of resources. To address this challenge, we designed Iguanodon, an easy-to-use game application that complements the traditional methods of improving visualization construction literacy. In our game application, users interactively choose whether to apply design choices, which we assign to sub-tasks that must be optimized to create an effective chart. The application offers multiple game variations to help users learn how different design choices should be applied to construct effective charts. Furthermore, our approach easily adapts to different visualization design guidelines. We describe the application's design and present the results of a user study with 37 participants. Our findings indicate that our game-based approach supports users in improving their visualization literacy.

In this work, we propose an interactive visual approach for the exploration and formation of structural relationships in embeddings of high-dimensional data. These structural relationships, such as item sequences, associations of items with groups, and hierarchies between groups of items, are defining properties of many real-world datasets. Nevertheless, most existing methods for the visual exploration of embeddings treat these structures as second-class citizens or do not take them into account at all. In our proposed analysis workflow, users explore enriched scatterplots of the embedding, in which relationships between items and/or groups are visually highlighted. The original high-dimensional data for single items, groups of items, or differences between connected items and groups are accessible through additional summary visualizations. We carefully tailored these summary and difference visualizations to the various data types and semantic contexts. During their exploratory analysis, users can externalize their insights by setting up additional groups and relationships between items and/or groups. We demonstrate the utility and potential impact of our approach by means of two use cases and multiple examples from various domains.

SUMMARY: A main task in computational cancer analysis is the identification of patient subgroups (i.e. cohorts) based on metadata attributes (patient stratification) or genomic markers of response (biomarkers). Coral is a web-based cohort analysis tool that is designed to support this task: Users can interactively create and refine cohorts, which can then be compared, characterized and inspected down to the level of single items. Coral visualizes the evolution of cohorts and also provides intuitive access to prevalence information. Furthermore, findings can be stored, shared and reproduced via the integrated session management. Coral is pre-loaded with data from over 128 000 samples from the AACR Project GENIE, the Cancer Genome Atlas and the Cell Line Encyclopedia. AVAILABILITY AND IMPLEMENTATION: Coral is publicly available at https://coral.caleydoapp.org. The source code is released at https://github.com/Caleydo/coral. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.