During this period data visualization consolidated integrated, interactive approaches that combine multiple coordinated views and dimensional stacking to reveal multivariate patterns. Focus+context and space-efficient representations enabled researchers to navigate large information spaces while preserving detail and context. User-driven encoding and customization of visual representations allowed analysts to tailor glyphs and mappings to domain tasks, and three-dimensional as well as multi-view visualization spaces supported layered spatial layouts for large databases. Historical Significance: Foundational contributions include The Box Plot: A Simple Visual Method to Interpret Data, which introduced a compact summary using quartiles and medians to support rapid pattern recognition and outlier detection. The application visualization system AVS provided an extensible environment that accelerated toolkit development for scientific visualization. The Visual Display of Quantitative Information codified design principles emphasizing clarity and minimalism, and Java Treeview offered an open-source tool catalyzing community-driven bioinformatics visualization, shaping subsequent design practices and contributing to the rise of visual analytics.