Estimating impacts of offshore windfarm construction on marine mammals requires data on displacement in relation to different noise levels and sources. Using echolocation detectors and noise recorders, we investigated harbour porpoise behavioural responses to piling noise during the 10-month foundation installation of a North Sea windfarm. Current UK guidance assumes total displacement within 26 km of pile driving. In contrast, we recorded a 50 % probability of response within 7.4 km (95 % CI = 5.7 – 9.4) at the first location piled, decreasing to 1.3 km (95 % CI = 0.2 – 2.8) by the final location; representing 28 % (95 % CI = 21 – 35) and 18 % (95 % CI = 13 – 23) displacement of individuals within 26 km. Distance proved as good a predictor of responses as audiogram weighted received levels, presenting a more practicable variable for environmental assessments. Critically, acoustic deterrent device (ADD) use and vessel activity increased response levels. Policy and management to minimise impacts of renewables on cetaceans have concentrated on pile-driving noise. Our results highlight the need to consider trade-offs between efforts to reduce far-field behavioural disturbance and near-field injury through ADD use.
Data from: Harbour porpoise responses to pile-driving diminish over time
The schema of the dataset is provided below: · t: the time at which the message was received (UTC) · shipid: the anonymized id of the ship · lon: the longitude of the current ship position · lat: the latitude of the current ship position · heading: (see: https://en.wikipedia.org/wiki/Course_(navigation)) · course: the direction in which the ship moves (see: https://en.wikipedia.org/wiki/Course_(navigation)) · speed: the speed of the ship (measured in knots) · shiptype: AIS reported ship-type · destination: AIS reported destination
Single Ground Based AIS Receiver Vessel Tracking Dataset
Environmental and AIS data collected during the second phase of EUMR TNA experiments using the CMRE LOON testbed. Environmental data consists of temperature measured across the water column; sound velocity measured close to the surface and close to the sea bottom; meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain). The environmental dataset is complemented with Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy).
Environmental and AIS data collected during the EUMarineRobots Trans-National Access activities experiments using the NATO STO-CMRE Littoral Ocean Observatory Network testbed (Release 2)
The advent of Big Data and streaming technologies has resulted in a swarm of voluminous, heterogeneous information, especially in the domains of Internet of Things (IoT) and transportation. Focusing on the maritime field, we present a dataset that contains vessel position information transmitted by vessels of different types and collected via the Automatic Identification System (AIS). The AIS dataset comes along with spatially and temporally correlated data about the vessels and the area of interest, including weather information. It covers a time span of over 2.5 years, from May 9th, 2017 to December 26th, 2019 and provides anonymised vessel positions within the wider area of the port of Piraeus (Greece), one of the busiest ports in Europe and worldwide. The dataset consists of over 244 million AIS records, an average of more than 10,000 records per hour, which makes it an ideal input for large-scale mobility data processing and analytics purposes.
The Piraeus AIS Dataset for Large-scale Maritime Data Analytics
With an ever-increasing number of vessels at sea, the modelling, analysis and visualisation of maritime traffic are of paramount importance to support the monitoring tasks of maritime stakeholders. Sensors have been developed in this respect to track vessels and capture the maritime traffic at the global scale. The Automatic Identification System (AIS) is transmitting maritime positional and nominative information at highest frequency rate, making it a valuable source for maritime traffic modelling. From an original AIS dataset covering the area of Brest, France, we extracted a set of 17 maritime routes, connecting ports in this area. Two different representations for the routes are provided: (1) clusters of AIS contacts, and (2) route prototypes, representing the nominal trajectory of the vessels following the route. Additionally, a set of tracklets (built by five consecutive AIS contacts from the same vessel trajectory) has been extracted from the set of routes and the original dataset, and labelled either with the route name to which they belong or as off-route tracklets. This dataset provides thus some ground truth on the routes followed by vessels and is aimed at testing and validating vessel-to-route or track-to-route association algorithms.
Maritime routes and vessel tracklet dataset for vessel-to-route association
Definition of Automatic Identification System (AIS) given by Wikipedia.
Automatic Identification System (AIS) wikipedia
Automatic Identification System (AIS) - Ships positions - download - 12nm from baseline Data set from the Norwegian Coastal Administration Data can be downloaded directly from https://ais-public.kystverket.no/ais-download/ (free registration).
Automatic Identification System (AIS) - Ships positions - download - 12nm from baseline
This Research Object gathers information about publicly available AIS datasets.
Fouilloux, Anne, Jørgen Schartum Dokken, and Thomas Roehr. "AIS public datasets." ROHub. Jan 19 ,2023. https://w3id.org/ro-id/0b1fcfe6-ab98-4df7-be19-e952de9776c6.
This dataset contains ships' information collected though the Automatic Identification System, integrated with a set of complementary data having spatial and temporal dimensions aligned. 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 ships positions within Celtic sea, the Channel and Bay of Biscay (France). The dataset is proposed with predefined integration and querying principles for relational databases. These rely on the widespread and free relational database management system PostgreSQL, with the adjunction of the PostGIS extension, for the treatment of all spatial features proposed in the dataset.
Heterogeneous Integrated Dataset for Maritime Intelligence, Surveillance, and Reconnaissance
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Economy, business and finance/Economic sector/Computing and information technology/Hardware