The Volcanic Information System (VIS) is a detection algorithm designed to identify and characterize explosive volcanic eruptions based on long-range infrasound observations (De Negri et al., 2024). VIS utilizes data from a single array to detect infrasound emitted by ongoing volcanic eruptions and applies normalized thresholds to parameterize the eruption. This process generates records of increased volcanic activity. The parameterization relies on the Infrasound Parameter (IP), a detection algorithm initially developed for local infrasound early warning systems (Ulivieri et al., 2013; Ripepe et al., 2018), and later adapted for long-range observations (Marchetti et al., 2019; LePichon et al., 2021; Gheri et al., 2023). The IP is the core component of VIS and enables the characterization of infrasound signals in terms of signal strength and persistency (see Gheri et al., 2023 and Gheri et al., in press). This dataset presents the results from VIS applied to data collected from 16 International Monitoring System (IMS) arrays over a period of ten years (2010-2019), as detailed in Gheri et al. (in press). VIS is a binary system that reflects the status of a volcano based on long-range infrasound data over time. When the range-corrected IP exceeds a predefined threshold, a VIS record is generated, potentially indicating a volcanic eruption. Each VIS record is identified by its start time (tstart) and end time (tend), corresponding to the infrasound signals radiated by the volcano. The dataset includes 419 VIS records associated with volcanic eruptions, based on comparisons with Global Volcanism Program (GVP) bulletin reports. These records correspond to volcanoes located within 2000 km of the IMS arrays. For each .mat file, the following information is provided: tstart: onset time of the eruption tend: end time of the eruption Psmean: pressure amplitude at the source Ndet: signal persistency Range-corrected IP: Infrasound Parameter for the event. This dataset provides valuable insights into volcanic activity, combining infrasound data with eruption timelines to enhance monitoring capabilities. The University of Florence acknowledges the contribution of the National Recovery and Resilience Plan, Mission 4 Component 2 -- Investment 1.4 -- NATIONAL CENTER FOR HPC, BIG DATA AND QUANTUM COMPUTING -- funded by the European Union -- NextGenerationEU -- CUP B83C22002830001.