VESA: A Visualization-Enabled Search Application for Exploratory Dataset Discovery

The increasing complexity and scale of scientific datasets demand advanced tools for efficient discovery and exploration. Traditional search systems often fall short in addressing the multidimensional nature of data and their inherent relationships, limiting their effectiveness for exploratory search. This paper presents the visualization-enabled search application (VESA), a backend-agnostic visual analytics framework that supports interactive and multidimensional exploration of heterogeneous scientific metadata. VESA enables cross-repository data discovery through configurable adapter interfaces that map repository-specific metadata onto a minimal set of common discovery dimensions, enabling seamless ingestion and consistent processing. At the frontend, coordinated visualizations, including spatial, temporal, and relational views, support exploratory search and sensemaking.

To demonstrate the functionality and usefulness of the system, a software prototype is developed and applied to Earth System Science repositories, showing how heterogeneous sources can be integrated and explored through a unified interface. The framework is evaluated against established design guidelines and further validated through an online user study. In addition, adapters for two data repositories are implemented, illustrating how different backends can be connected to the system. Results indicate positive user reception, highlighting VESA’s usability, low learning curve, and its potential to enhance data discovery workflows through interactive visual exploration.