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1. Contextual Visualization

   https://doi.org/10.1109/MCG.2018.2874782  

   Borland, David; Wang, Wenyuan; Gotz, David; Rhyne, Theresa-Marie (November 2018, IEEE Computer Graphics and Applications)
2. Physical Visualization Design

   https://doi.org/10.1145/3318464.3384711  

   Ramjit, Lana; Kong, Zhaoning; Netravali, Ravi; Wu, Eugene (January 2020, SIGMOD Demo)

   null (Ed.)
3. Metabolite Fragmentation Visualization

   https://doi.org/10.54808/JSCI.20.05.138  

   Kwak, Myungjae; Molina, Matthew; Arnold, Spencer; Woodward, Andrew; An, Jin-Young; Nuckels, Estelle; Wang, Yingfeng (October 2022, Journal of Systemics, Cybernetics and Informatics)

   Tandem mass spectrometry (MS/MS) is a popular technology for identifying small molecules involved in metabolism, better known as metabolites. Coupled with liquid chromatography (LC), LC-MS/MS instruments first separate, ionize, and fragment metabolites, then measure mass-to-charge ratios (m/z) and intensities of metabolite fragments. Understanding metabolite fragmentation is crucial to develop computational tools for identifying metabolites based on this spectroscopic data. Metabolite fragmentation patterns have large variations making it especially difficult for computer scientists to design and implement metabolite identification approaches. To address this interdisciplinary challenge, this article presents FragView, a web-based application providing the web service for visualizing metabolite fragmentation. Users can break chemical bonds to produce metabolite fragments and export 3D fragment structures for 3D printing. Developing FragView is an opportunity for exposing student participants to this interdisciplinary bioinformatics project. This paper summarizes the experience of training student participants in bootcamps and designing the implementation plan based on student backgrounds. Students were exposed to project meeting discussions on coding and raw data visualization and visited a lab with an LC-MS/MS instrument. FragView is an open source, freely accessible tool, released under the GPLv3 license. We will continue to improve and update FragView in the future based on feedback. 

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4. Visualization (nD, VR, AR)

   https://doi.org/10.1061/9780784482421.012  

   Xu, Lei; Francisco, Abigail; Mohammadi, Neda; Taylor, John E. (June 2019, Development of a Virtual Reality Integrated Community-Scale Eco-Feedback System)

   A growing number of community energy initiatives have enlarged energy-related social networks to the community level. Information provision is deemed as an important role in such programs while energy data disclosure offers a great opportunity to promote energy savings by engaging energy-related actors. However, it is crucial to communicate this data in an effective way. In this research, we develop a virtual reality (VR) integrated eco-feedback system that enables both occupants and facility managers to interact with real-time energy consumption data represented in a community scale 3D immersive environment. This paper presents the detailed front-end and back-end design and development of this novel VR-integrated eco-feedback system using Georgia Tech’s campus as a test case for implementation. The VR-integrated community scale eco-feedback system is capable of visually characterizing differences in energy consumption across a large number of buildings of different types, and will be tested by users in future research. This research, when deployed broadly in cities, may help promote energy-aware behaviors of occupants and timely intervention strategies to achieve energy savings in urban areas. 

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5. DIVI: Dynamically Interactive Visualization

   https://doi.org/10.1109/TVCG.2023.3327172  

   Snyder, Luke S.; Heer, Jeffrey (January 2024, IEEE Transactions on Visualization and Computer Graphics)

   Dynamically Interactive Visualization (DIVI) is a novel approach for orchestrating interactions within and across static visualizations. DIVI deconstructs Scalable Vector Graphics charts at runtime to infer content and coordinate user input, decoupling interaction from specification logic. This decoupling allows interactions to extend and compose freely across different tools, chart types, and analysis goals. DIVI exploits positional relations of marks to detect chart components such as axes and legends, reconstruct scales and view encodings, and infer data fields. DIVI then enumerates candidate transformations across inferred data to perform linking between views. To support dynamic interaction without prior specification, we introduce a taxonomy that formalizes the space of standard interactions by chart element, interaction type, and input event. We demonstrate DIVI's usefulness for rapid data exploration and analysis through a usability study with 13 participants and a diverse gallery of dynamically interactive visualizations, including single chart, multi-view, and cross-tool configurations. 

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