Generating High Dimensional Test Data for Topological Data Analysis

This peer-reviewed publication was produced as part of graduate research conducted at the University of Cincinnati College of Engineering and Applied Science.

The work develops methods for generating synthetic high-dimensional datasets that can be used to evaluate and benchmark topological data analysis algorithms under controlled conditions.

The resulting framework supports more rigorous experimental validation, algorithm comparison, and reproducibility within the topological data analysis research community.