Résumé : Tephrochronology studies the deposits of explosive volcanic eruptions in the stratigraphic record. The Southern (SVZ, 33–46° S) and Austral (AVZ, 49–55° S) Volcanic Zones of the Andes are two very active volcanic zones where tephrochronology is of great use. There, it can be used to improve chronologies of paleoenvironmental records in Patagonia, an area providing valuable records at global scale; as well as to identify areas likely to be affected by volcanic eruptions in the future, essential for producing volcanic hazard maps. The close proximity of many volcanic centers with recurrent explosive activity, which have very similar geochemical compositions, and very often poor age constraints, represent a challenge for the study of tephrochronology in the region. In addition to this, the ever-growing amount of tephrochronological information in the area, dispersed in different types of publications which vary greatly in format, makes the integration of the data produced by different actors, and consecutively its interpretation, increasingly difficult. Here we address this issue by compiling the BOOM! dataset, which integrates ∼30 years of research on 32 active volcanic centers and 132 different eruptions, which took place during the last 20,000 years. To help users find and reuse data in the large dataset, we developed an online platform which provides user-friendly tools for exploring it, and helps users download subsets of it. To integrate this very heterogeneous information, special attention was given to include information which allows users to evaluate data quality and comparability, as well as to provide tools in the explorer for users to filter data by different criteria. The integration of this dataset opens new perspectives for the development of novel visualizations of tephrochronological data, for example, to better understand the multidimensional uncertainties associated with it. For example, uncertainties associated with analytical precision, with age estimates of both tephra deposits and volcanic eruptions, and of tephra classification. Additionally, it allows for the use of robust statistical tools to correlate tephra deposits, including those based on machine learning algorithms, which are here explored.