Landslide dams and their subsequent outburst floods represent cascading geohazards with profound socio-economic and morphological impacts. However, the widespread absence of dynamic breaching parameters in existing global inventories severely constrains quantitative hydrodynamic modeling and downstream risk assessment. To bridge this critical data void, this study presents a comprehensive global landslide dam dataset encompassing 902 rigorously vetted events spanning before 2020. Moving beyond traditional static cataloging, the assembled dataset integrates 11 fundamental morphological and triggering parameters with 6 highly transient breaching metrics. Notably, it significantly improves the data availability of historically scarce variables, including peak discharge, released water volume, and three-dimensional breach geometries. Spatially, the database achieves global coverage, with the highest data densities clustered within the Alpine-Himalayan and Circum-Pacific active belts. To objectively account for observational limitations and chronological biases across different technological eras, a point-by-point Data Quality Flag (DQF) system is incorporated into the dataset, transparently classifying the spatial, geometric, and hydrodynamic uncertainties for every cataloged event. This multi-dimensional and structurally transparent inventory provides a robust empirical foundation for future machine-learning-based hazard susceptibility mapping and physically-based dam-breach simulations. The dataset is publicly available at Zenodo <a href="https://doi.org/10.5281/zenodo.19198720" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.19198720</a> (Jiang et al. 2026).