PLEASE NOTE: This is an in-process DRAFT, not yet a publication. Thank you for understanding. Please email the authors if you have questions.
Visualization is critical to the work of nearly all scientists, but not all scientists are equally adept at visualization. Software defaults and instinct often guide scientists’ visualization designs. However, not all software is optimized for visualization design, and instinct does not always guide us to good functional design choices. Most software is not developed in collaboration with design or science professionals, and software developers are not always trained in cognitive and perceptual sciences to assess the implications of their design choices on humans. Furthermore, while instinct in visualization design can produce outcomes that please a personal aesthetic, it may not lead to correctly understood and interpreted visualizations. In other words; many scientists are accidentally producing ineffective visualizations because they are not aware of the body of knowledge emerging from empirical studies on how people experience visualizations. There is a knowledge transfer gap between the knowledge in visualization research domain and those who ‘practice’ the art. This paper distills and synthesizes the literature on visualization research into (potentially) practicable rules of thumb optimized for scientists and data journalists who are not trained in visualization design. Specifically, we offer the reader a set of questions they need to ask themselves before making visualization decisions.