Coral: a web-based visual analysis tool for creating and characterizing cohorts.

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.

Marjorie: Visualizing Type 1 Diabetes Data to Support Pattern Exploration.

In this work we propose Marjorie, a visual analytics approach to address the challenge of analyzing patients' diabetes data during brief regular appointments with their diabetologists. Designed in consultation with diabetologists, Marjorie uses a combination of visual and algorithmic methods to support the exploration of patterns in the data. Patterns of interest include seasonal variations of the glucose profiles, and non-periodic patterns such as fluctuations around mealtimes or periods of hypoglycemia (i.e., glucose levels below the normal range). We introduce a unique representation of glucose data based on modified horizon graphs and hierarchical clustering of adjacent carbohydrate or insulin entries. Semantic zooming allows the exploration of patterns on different levels of temporal detail. We evaluated our solution in a case study, which demonstrated Marjorie's potential to provide valuable insights into therapy parameters and unfavorable eating habits, among others. The study results and informal feedback collected from target users suggest that Marjorie effectively supports patients and diabetologists in the joint exploration of patterns in diabetes data, potentially enabling more informed treatment decisions. A free copy of this paper and all supplemental materials are available at https://osf.io/34t8c/.

Loops: Leveraging Provenance and Visualization to Support Exploratory Data Analysis in Notebooks.

Exploratory data science is an iterative process of obtaining, cleaning, profiling, analyzing, and interpreting data. This cyclical way of working creates challenges within the linear structure of computational notebooks, leading to issues with code quality, recall, and reproducibility. To remedy this, we present Loops, a set of visual support techniques for iterative and exploratory data analysis in computational notebooks. Loops leverages provenance information to visualize the impact of changes made within a notebook. In visualizations of the notebook provenance, we trace the evolution of the notebook over time and highlight differences between versions. Loops visualizes the provenance of code, markdown, tables, visualizations, and images and their respective differences. Analysts can explore these differences in detail in a separate view. Loops not only makes the analysis process transparent but also supports analysts in their data science work by showing the effects of changes and facilitating comparison of multiple versions. We demonstrate our approach's utility and potential impact in two use cases and feedback from notebook users from various backgrounds. This paper and all supplemental materials are available at https://osf.io/79eyn.

Iguanodon: A Code-Breaking Game for Improving Visualization Construction Literacy.

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.

Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings.

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.