We present the design and prototype implementation of a scientific visualization language called Zifazah for composing 3D visualizations of diffusion tensor magnetic resonance imaging (DT-MRI or DTI) data. Unlike existing tools allowing flexible customization of data visualizations that are programmer-oriented, we focus on domain scientists as end users in order to enable them to freely compose visualizations of their scientific data set. We analyzed end-user descriptions extracted from interviews with neurologists and physicians conducting clinical practices using DTI about how they would build and use DTI visualizations to collect syntax and semantics for the language design, and have discovered the elements and structure of the proposed language. Zifazah makes use of the initial set of lexical terms and semantics to provide a declarative language in the spirit of intuitive syntax and usage. This work contributes three, among others, main design principles for scientific visualization language design as well as a practice of such language for DTI visualization with Zifazah. First, Zifazah incorporated visual symbolic mapping based on color, size and shape, which is a sub-set of Bertin's taxonomy migrated to scientific visualizations. Second, Zifazah is defined as a spatial language whereby lexical representation of spatial relationship for 3D object visualization and manipulations, which is characteristic of scientific data, can be programmed. Third, built on top of Bertin's semiology, flexible data encoding specifically for scientific visualizations is integrated in our language in order to allow end users to achieve optimal visual composition at their best. Along with sample scripts representative of our language design features, some new DTI visualizations as the running results created by end users using the novel visualization language have also been presented.
Recently Metoyer et. al. @cite_29 report from an exploratory study a set of design implications for the design of visualization languages and toolkits. More specifically, their findings inform visualization language design through the way end users describe visualizations and their inclination to using ambiguous and relative, instead of definite and absolute, terms that can be refined later via a feedback loop provided by the language. Emphatically, their findings also disclose that end users tend to express in generally high-level semantics. During the design of our visualization language, we have benefited from these findings and actually have reflected them in the development of .
@cite_29
2147699512
Tools exist for people to create visualizations with their data; however, they are often designed for programmers or they restrict less technical people to pre-defined templates. This can make creating novel, custom visualizations difficult for the average person. For example, existing tools typically do not support syntax or interaction techniques that are natural to end users. To explore how to support a more natural production of data visualizations by end users, we conducted an exploratory study to illuminate the structure and content of the language employed by end users when describing data visualizations. We present our findings from the study and discuss their design implications for future visualization languages and toolkits.