DataLens: Enhancing Dataset Discovery via Network Topologies

TL;DR

DataLens is a web platform that improves dataset discovery by integrating network visualizations and faceted search, enabling users to explore connections between resources more effectively.

Contribution

It introduces a novel combination of network-based visualizations with faceted search for enhanced dataset exploration and discovery.

Findings

Users highly value visualization tools, especially network-based exploration.

The platform supports multi-perspective data exploration.

User feedback guides future refinements.

Abstract

The rapid growth of publicly available textual resources, such as lexicons and domain-specific corpora, presents challenges in efficiently identifying relevant resources. While repositories are emerging, they often lack advanced search and exploration features. Most search methods rely on keyword queries and metadata filtering, which require prior knowledge and fail to reveal connections between resources. To address this, we present DataLens, a web-based platform that combines faceted search with advanced visualization techniques to enhance resource discovery. DataLens offers network-based visualizations, where the network structure can be adapted to suit the specific analysis task. It also supports a chained views approach, enabling users to explore data from multiple perspectives. A formative user study involving six data practitioners revealed that users highly value visualization tools-especially network-based exploration-and offered insights to help refine our approach to better support dataset search.