Data visualization is one of the most important steps in data mining and modeling (Keim 2001; Sellars et al. 2013; Morse et al. 2017). Science has evolved toward computer-and data-based methods, but new tools and methods are still needed to better capture, analyze, model, and visualize scientific information (Fay 2009). Some tools, techniques, and approaches that were standard in data analysis and presentation several decades or even years ago now need to be updated and improved to support better interpretation of the results and effective communication with the scientific community and the society. In addition, new models and algorithms require high-level graphics and visualization tools to illustrate results in a powerful way. For example, tools that help interpret the results of machine learning and deep learning models, which are frequently based on big data and highly complicated algorithms, are needed. Additionally, with novel approaches, data visualization literacy (DVL), which is the ability to confidently use a given data visualization technique and to extract information from data visualizations, must be evaluated (Börner et al. 2019). It is a common opinion that with the sophisticated tools at hand, a data analyst can easily obtain results, but the key problem is presenting those new results to the public. This is a question related to the efficiency of the message delivery and information flow between scientists and professionals to the end user or audience. This step needs to be as effective as possible because the majority of research is conducted with public funds (which are expected to be returned as new discoveries and information), and the end-user group encompasses nonprofessionals or specialists in other branches of science and industry. Moreover, it is necessary to visualize the outcomes in an easy-to-absorb form to increase their practical value and promote further developments in the respective field.