The aim of visualization is to support humans in dealing with large and complex information structures, to make these structures more comprehensible, facilitate exploration, and enable knowledge dis- covery. However, users often have problems reading and interpreting data from visualizations, in particular when they experience them for the first time. A lack of visualization literacy, i.e., knowledge in terms of domain, data, visual encoding, interaction, and also analyti- cal methods can be observed. To support users in learning how to use new digital technologies, the concept of onboarding has been successfully applied in other domains. However, it has not received much attention from the visualization community so far. With our position paper, we aim to work towards filling this gap by proposing a design space of onboarding in the context of visualization.

Storing analytical provenance generates a knowledge base with a large potential for recalling previous results and guiding users in future analyses. However, without extensive manual creation of meta information and annotations by the users, search and retrieval of analysis states can become tedious. We present KnowledgePearls, a solution for efficient retrieval of analysis states that are structured as provenance graphs containing automatically recorded user interactions and visualizations. As a core component, we describe a visual interface for querying and exploring analysis states based on their similarity to a partial definition of a requested analysis state. Depending on the use case, this definition may be provided explicitly by the user by formulating a search query or inferred from given reference states. We explain our approach using the example of efficient retrieval of demographic analyses by Hans Rosling and discuss our implementation for a fast look-up of previous states. Our approach is independent of the underlying visualization framework. We discuss the applicability for visualizations which are based on the declarative grammar Vega and we use a Vega-based implementation of Gapminder as guiding example. We additionally present a biomedical case study to illustrate how KnowledgePearls facilitates the exploration process by recalling states from earlier analyses.

A major challenge in data-driven biomedical research lies in the collection and representation of data provenance information to ensure that findings are reproducibile. In order to communicate and reproduce multi-step analysis workflows executed on datasets that contain data for dozens or hundreds of samples, it is crucial to be able to visualize the provenance graph at different levels of aggregation. Most existing approaches are based on node-link diagrams, which do not scale to the complexity of typical data provenance graphs. In our proposed approach, we reduce the complexity of the graph using hierarchical and motif-based aggregation. Based on user action and graph attributes, a modular degree-of-interest (DoI) function is applied to expand parts of the graph that are relevant to the user. This interest-driven adaptive approach to provenance visualization allows users to review and communicate complex multi-step analyses, which can be based on hundreds of files that are processed by numerous workflows. We have integrated our approach into an analysis platform that captures extensive data provenance information, and demonstrate its effectiveness by means of a biomedical usage scenario.

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Most tabular data visualization techniques focus on overviews, yet many practical analysis tasks are concerned with investigating individual items of interest. At the same time, relating an item to the rest of a potentially large table is important. In this work we present Taggle, a tabular visualization technique for exploring and presenting large and complex tables. Taggle takes an item-centric, spreadsheet-like approach, visualizing each row in the source data individually using visual encodings for the cells. At the same time, Taggle introduces data-driven aggregation of data subsets. The aggregation strategy is complemented by interaction methods tailored to answer specific analysis questions, such as sorting based on multiple columns and rich data selection and filtering capabilities. We demonstrate Taggle using a case study conducted by a domain expert on complex genomics data analysis for the purpose of drug discovery.