Facing an ever-increasing amount of traffic at sea, many research centres, international organisations, and industrials have favoured and developed sensors together with detection techniques for the monitoring, analysis, and visualisation of sea movements. The Automatic Identification System (AIS) is one of the electronic systems that enable ships to broadcast their position and nominative information via radio communication. In addition to these systems, the understanding of maritime activities and their impact on the environment also requires contextual maritime data capturing additional features to ships' kinematic from complementary data sources (environmental, contextual, geographical, …). The dataset described in this paper contains ship information collected through the AIS, prepared together with spatially and temporally correlated data characterising the vessels, the area where they navigate and the situation at sea. The dataset contains four categories of data: navigation data, vessel-oriented data, geographic data, and environmental data. It covers a time span of six months, from October 1st, 2015 to March 31st, 2016 and provides ship positions over the Celtic sea, the North Atlantic Ocean, the English Channel, and the Bay of Biscay (France). The dataset is proposed for an easy integration with relational databases. This relies on the widespread and open source relational database management system PostgreSQL, with the adjunction of the geospatial extension PostGIS for the treatment of all spatial features of the dataset.

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The Automatic Identification System (AIS) was initially designed for safety and security of navigation purposes. However it was progressively also used for other objectives, such as surveillance, and thus led to the discovery of behaviors such as the falsification of the AIS messages by people that have been carrying out illegal activities and will to keep their activities up in an hidden way. In addition, the messages contain erroneous data and undergo spoofing attacks. The paper introduces the quality dimensions of data that shall be used in a quality assessment of AIS messages, in order to point out the dubious ones. The principles of a methodological approach for the detection of such data errors and falsifications are introduced.