The advent of the Industrial Internet of Things (IIoT) has led to the availability of huge amounts of data, that can be used to train advanced Machine Learning algorithms to perform tasks such as Anomaly Detection, Fault Classification and Predictive Maintenance. Most of them are already capable of logging warnings and alarms occurring during operation. Turning this data, which is easy to collect, into meaningful information about the health state of machinery can have a disruptive impact on the improvement of efficiency and up-time. The provided dataset consists of a sequence of alarms logged by packaging equipment in an industrial environment. The collection includes data logged by 20 machines, deployed in different plants around the world, from 2019-02-21 to 2020-06-17. There are 154 distinct alarm codes, whose distribution is highly unbalanced.