# Enhancing Color Scales for Active Data Physicalizations
## 1 Introduction  
Traditionally, data have been visualized through digital data visualizations, but current advances in materials science and digital fabrication are enabling new ways to represent and interact with data physically [1]. The greater affordability of 3D printing technology for a broader profile of users and applications can be attributed to the combination of material innovation, open-source collaboration, market competition, standardization, efficiency improvements, and community support. In this context, Data Physicalization is the area that investigates how physical objects can encode data through their physical (geometry and materials) and visual characteristics [2, 3]. Part of the challenge of designing a physicalization of data is choosing the materials that will compose it and how these materials (physical and visual variables) will map and communicate the data appropriately [4]. There are many ways of encoding data in data physicalizations, some similar to digital data visualizations, making use of the visual channel to visually identify varieties of color, size, shape, etc., and other encodings make use of different perception channels such as touch, hearing, smell, and taste, which can provide the user with a multisensory experience of exploring data [5]. According to [6], there are still many open questions for designing and creating good data physicalization that is enjoyable, promotes user engagement, and represents and communicates data efficiently. Some of these open questions are about what coding variables are available to compose the design of data physicalization, how they can mapand encode data efficiently, what criteria could be helpful to evaluate them, what methods could be used to assess the data physicalization, etc. Well-established studies in the information visualization area, such as visual variables, lead us to believe these fundamentals can be fully applied to data physicalization without any adaptations or refinements. However, the physical context of physicalizations presents challenges beyond the digital environment, which can contribute to an erroneous perception of visual variables and, therefore, lead to low efficiency in data communication [5]. The works of [2,6] pointed out several challenges and opportunities for the development of data physicalizations, including the need to develop empirical guidelines similar to those for digital data visualizations, considering the use of physical and digital variables, to answer questions about the precision in the representation of values by these variables, including which of them can best represent quantitative, ordinal, or nominal data, and questions related to the pre-attention of these variables, as well as their combined use, among others. Color is one of the most used visual variables in information visualization and data physicalization to encode and communicate data [1,6]. Although the use of color is based on well-established theories in information visualization, these fundamentals are still being consolidated in data physicalization, and evaluations are necessary [6]. It is noteworthy that color perception can be influenced by the type of material that composes the physical artifact, the characteristics of the light-emitting device, and ambient light. If these issues are not addressed, information communication in data physicalization can fail. At first, an easy way to overcome these issues could be to use digital color palettes [7, 8, 9, 10], or define them manually, to be applied directly to data physicalization, leading to the low efficiency of the color to communicate data [6]. Therefore, this article proposes and presents a method for evaluating and selecting colors for active data physicalization which includes the choice of colors for forming categorical, sequential, and divergent color palettes. The approach also includes the evaluation of the proposed colors concerning the material that makes up the data physicalization and the light-emitting device.