In today's data-driven world, effective communication of data and insights is crucial. However, understanding and interpreting visualizations require a certain level of "visualization literacy." This doctoral thesis explores how to support users with low visualization literacy in interpreting data visualizations through visualization onboarding. To answer this question, a descriptive design space was developed to answer key questions related to visualization onboarding. Design considerations for visualization onboarding concepts were also derived from the empirical studies of the papers we surveyed. We explored different onboarding methods by developing four onboarding concepts and tested them through usability studies with MTurk workers and students to evaluate their impact on user performance and experience. Furthermore, we developed a visualization onboarding concept and validated it iteratively through a cognitive walkthrough with experts. A rapid prototyping process followed this to create VisAhoi, a JavaScript library for semi-automatically generating onboarding instructions for VA tools. To apply our visualization onboarding concept, we conducted two design studies. The first study focused on integrating VisAhoi into a visual analytics tool for analyzing high-throughput screening data in biomedical R&D. The second study involved a self-explanatory visualization onboarding concept for data-journalistic use cases. Finally, we provide design actions to guide the design and development of visualization onboarding. Visualization onboarding is just one aspect of user assistance. Another component of user assistance is guidance, which involves providing users with concrete suggestions and support for the next steps in the exploration. We illustrated how visualization onboarding and guidance could assist users in various analysis phases by introducing a descriptive model.
