- Algorithmic simplification
- Artistic design
- Data abstraction
- Data structure
- Dataset
- Deployment
- Design methodology
- Evaluation methodology
- Formalism
- Guideline
- Interaction technique
- Lessons from failure
- Mechanism
- New domain and problem
- Performance analysis
- Presentation
- Qualitative empirical finding
- Replication
- Survey
- Task abstraction
- Taxonomy and conceptual model
- Toolkit / Language / Architecture
- Visual data analysis methodology
- Vision
- Visual representation
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Yuan, L., Haroz, S., & Franconeri, S. (2018). Perceptual proxies for extracting averages in data visualizations. Psychonomic Bulletin & Review, 1–8.
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Padilla, L. M., Creem-Regehr, S. H., Hegarty, M., & Stefanucci, J. K. (2018). Decision making with visualizations: a cognitive framework across disciplines. Cognitive Research: Principles and Implications, 3(1), 29.
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Borkin, M. A., Vo, A. A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., & Pfister, H. (2013). What makes a visualization memorable?. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2306–2315.
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Rensink, R. A. (2014). On the prospects for a science of visualization. In Handbook of human centric visualization (pp. 147–175). Springer, New York, NY.
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Szafir, D. A., Haroz, S., Gleicher, M., & Franconeri, S. (2016). Four types of ensemble coding in data visualizations. Journal of Vision, 16(5), 11:1–19.
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Green, T. M., Ribarsky, W., & Fisher, B. (2009). Building and applying a human cognition model for visual analytics. Information Visualization, 8(1), 1-13.
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Haroz, S., & Whitney, D. (2012). How capacity limits of attention influence information visualization effectiveness. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2402–2410.
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Navarro, F., Castillo, S., Serón, F. J., & Gutierrez, D. (2011). Perceptual considerations for motion blur rendering. ACM Transactions on Applied Perception (TAP), 8(3), 20.