A Backend Agnostic Visualisation Enabled Search Application (VESA)

VESA v1.0.0 – Initial Public Release

This release presents the first stable version of the Visualization Enabled Search Application (VESA), a backend-agnostic visual analytics framework for exploratory metadata search across heterogeneous scientific repositories.

Key Features

• Backend-agnostic architecture supporting multiple repositories
• Metadata harmonization via IDataAdapter interface
• Configurable repository integration through proxy APIs
• Synchronization and ingestion pipeline for scalable data integration
• Interactive visualization dashboard supporting:
  • Spatial exploration (Map)
  • Temporal analysis (Timeline)
  • Relational insights (Chord diagram)
  • Keyword-based exploration (Word cloud)
• Coordinated multiple views for exploratory search and sensemaking

Architecture Highlights

• Separation of data retrieval, processing, and visualization layers
• Modular application logic for dataset, author, and keyword services
• Visualization transformers for structured data preparation
• Unified data access interface for frontend interaction

Use Case

Demonstrated using PANGAEA and GBIF repository Adaptor.

Notes

This version corresponds to the implementation used in the VESA research prototype and associated publication.

A Backend Agnostic Visualisation Enabled Search Application (VESA)

VESA v1.0.0 – Initial Public Release

This release presents the first stable version of the Visualization Enabled Search Application (VESA), a backend-agnostic visual analytics framework for exploratory metadata search across heterogeneous scientific repositories.

Key Features

• Backend-agnostic architecture supporting multiple repositories
• Metadata harmonization via IDataAdapter interface
• Configurable repository integration through proxy APIs
• Synchronization and ingestion pipeline for scalable data integration
• Interactive visualization dashboard supporting:
  • Spatial exploration (Map)
  • Temporal analysis (Timeline)
  • Relational insights (Chord diagram)
  • Keyword-based exploration (Word cloud)
• Coordinated multiple views for exploratory search and sensemaking

Architecture Highlights

• Separation of data retrieval, processing, and visualization layers
• Modular application logic for dataset, author, and keyword services
• Visualization transformers for structured data preparation
• Unified data access interface for frontend interaction

Use Case

Demonstrated using PANGAEA and GBIF repository Adaptor.

Notes

This version corresponds to the implementation used in the VESA research prototype and associated publication.