Anonymized scientific-technical database from the ML-AMAR monitoring campaign

==1 Abstract

The project ML-AMAR (Machine Learning Applications in Marine Engineering) aims to develop machine learning tools to optimize the lifecycle management of ships, from design and operation to maintenance and structural monitoring.

This document contains the anonymized scientific-technical database from the ML-AMAR monitoring campaign. It has been generated to support the validation of the meteorological forecasting, seakeeping behavior, and life-cycle analysis tools developed within the project. The database integrates inertial signals, GNSS data, and external metoceanographic variables, providing a traceable record of the vessel’s real operational performance along commercial routes.

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