Curation and featurization of multiple topological materials databases

The discovery of topological materials has advanced rapidly due to high-throughput computation and machine learning, but research progress is hampered by inconsistent classification standards and fragmented data resources. Existing databases differ in computational methods, material coverage, and labeling criteria, making it difficult to compare findings across studies. To overcome these challenges, we present a unified topological materials dataset that systematically combines and reconciles two major databases: Materiae and the Topological Materials Database. This dataset provides consistent topological classifications for 35608 materials, accessible through the Materials Galaxy platform for interactive exploration and available for bulk download via MatElab. We describe the featurization methodology that converts crystal structures into 4710 machine-learning-ready descriptors and present a comprehensive analysis of topological material distributions. This work serves as a complete guide for accessing, utilizing, and interpreting this unified resource, designed to enable reproducible machine learning applications and accelerate the discovery of topological materials.