OldVisOnline: Curating a Dataset of Historical Visualizations

TL;DR

OldVisOnline presents the first large-scale curated dataset of 13,000 historical visualizations from multiple digital libraries, enabling new research opportunities in visualization and history through browsing, labeling, and analysis.

Contribution

This work introduces a comprehensive workflow for collecting, processing, and labeling historical visualizations, and provides a publicly accessible dataset and system for research and analysis.

Findings

Successfully curated 13,000 visualizations from seven digital libraries.

Developed semi-automatic labeling to distinguish visualizations from artifacts.

Proposed usage scenarios and research opportunities with the dataset.

Abstract

With the increasing adoption of digitization, more and more historical visualizations created hundreds of years ago are accessible in digital libraries online. It provides a unique opportunity for visualization and history research. Meanwhile, there is no large-scale digital collection dedicated to historical visualizations. The visualizations are scattered in various collections, which hinders retrieval. In this study, we curate the first large-scale dataset dedicated to historical visualizations. Our dataset comprises 13K historical visualization images with corresponding processed metadata from seven digital libraries. In curating the dataset, we propose a workflow to scrape and process heterogeneous metadata. We develop a semi-automatic labeling approach to distinguish visualizations from other artifacts. Our dataset can be accessed with OldVisOnline, a system we have built to browse and label historical visualizations. We discuss our vision of usage scenarios and research opportunities with our dataset, such as textual criticism for historical visualizations. Drawing upon our experience, we summarize recommendations for future efforts to improve our dataset.