This dataset corresponds to 6 months of AIS data of vessels steaming in the area of the Ushant traffic separation scheme (in Brittany, West of France). This is an area with one of the highest traffic density in the world, with a clear separation scheme with two navigation lanes. Different kinds of vessels are present in the area, from cargos and tankers with high velocity and straight routes to sailing boats or fishing vessels with low speed and different sailing directions. As such, the area is highly monitored to avoid collision or grounding, and a better analysis and understanding of the different ship behaviors is of prime importance.The whole trajectory data set consists in 18,603 trajectories, gathering overall more than 7 millions GPS observations. Only trajectories having more than 30 points were kept, time lag between two consecutive observations ranges between 5 seconds and 15 hours, with 95% of time lags below 3 minutes.Authors would like to thank CLS (Collecte Localisation Satellites) and Erwan Guegueniat for providing the raw data that allowed building this dataset.This work has been supported by DGA through the ANR/Astrid SESAME project (ref: ANR-16-ASTR-0026).

What is NOAA?

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fc5ae637bae31e1943c58136f51cf353d%2F_62d1a093-a086-41ec-966f-768c6ce0b785_s.jpeg?generation=1734049700915857&alt=media" alt="">

The National Oceanic and Atmospheric Administration (abbreviated as NOAA /ˈnoʊ.ə/ NOH-ə) is a US scientific and regulatory agency charged with forecasting weather, monitoring oceanic and atmospheric conditions, charting the seas, conducting deep-sea exploration, and managing fishing and protection of marine mammals and endangered species in the US exclusive economic zone. The agency is part of the United States Department of Commerce and is headquartered in Silver Spring, Maryland.

From Wikipedia

What is AIS data?

The automatic identification system, or AIS, transmits a ship’s position so that other ships are aware of its position. The International Maritime Organization and other management bodies require large ships, including many commercial fishing vessels, to broadcast their position with AIS in order to avoid collisions. Each year, more than 400,000 AIS devices broadcast vessel location, identity, course and speed information. Ground stations and satellites pick up this information, making vessels trackable even in the most remote areas of the ocean.

https://globalfishingwatch.org/faqs/what-is-ais/

NOAA AIS Data

Vessel traffic data, or Automatic Identification System (AIS) data, are collected by the U.S. Coast Guard through an onboard navigation safety device that transmits the location and characteristics of large vessels for tracking in real time. The MarineCadastre.gov project team has worked with the Coast Guard and NOAA’s Office of Coast Survey to repurpose and make available some of the most important data for use in ocean planning applications.

From https://coast.noaa.gov/digitalcoast/training/ais.html

Data Dictionary

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2F1af61e6fe2afbb698f6af58085f5637a%2FAISDataDictionary.png?generation=1719076791476332&alt=media" alt="">

Other Data Dictionaries

• MMSI Country code prefixes
• Navigational Status Codes
• Ship Types
• Vessel Types

Source

https://coast.noaa.gov/htdata/CMSP/AISDataHandler/2024/index.html

Notes

• Original data consists of daily data and was 100GB+. What I did was I separated the ship from the AIS data, plus broke it down per month of a year for a smaller footprint. I also converted it to parquet file format.
• There are some erroneous lines that are being encountered when importing. I removed them but this is like very, very, very, small that it's insignificant

Citations

I did not create the dataset

I just made the data more accessible and easier to utilize from an end user's perspective. All credits to NOAA and the AIS methodology.

For citation of NOAA, go here

More info here

Image

Image Generated with Bing

See the whole series

• 2014 🇺🇸 US Coast Guard + NOAA AIS Dataset
• 2015 🇺🇸 US Coast Guard + NOAA AIS Dataset
• 2016 🇺🇸 US Coast Guard + NOAA AIS Dataset
• 2017 🇺🇸 US Coast Guard + NOAA AIS Dataset
• 2018 🇺🇸 US Coast Guard + NOAA AIS Dataset
• 2019 🇺🇸 US Coast Guard + NOAA AIS Dataset
• 2020 🇺🇸 US Coast Guard + NOAA AIS Dataset
• 2021 🇺🇸 US Coast Guard + NOAA AIS Dataset
• 2022 🇺🇸 US Coast Guard + NOAA AIS Dataset
• 2023 🇺🇸 US Coast Guard + NOAA AIS Dataset
• 2024 🇺🇸 US Coast Guard + NOAA AIS Dataset

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

Vessel traffic data or Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. In the U.S. the Coast Guard and industry collect AIS data, which can also be used for a variety of coastal management purposes. NOAA and BOEM have worked jointly to make available these data from the U.S. Coast Guards national network of AIS receivers. The original records were filtered to a one-minute frequency rate and were subsetted to depict the location and description of vessels broadcasting within the Exclusive Economic Zone. MarineCadastre.gov AIS data are divided by month and Universal Transverse Mercator (UTM) zone.

A vessel track shows the _location and characteristics of commercial and recreational boats as a sequence of positions transmitted by an Automatic Identification System (AIS). AIS signals are susceptible to interference and this can result in a gap within a vessel track. The distribution, type, and frequency of vessel tracks are a useful aid to understanding the risk of conflicting uses within a certain geographic area. The vessel track positions in this data set are collected and recorded from land-based antennas as part of a national network operated by the U.S. Coast Guard.

This item is part of the collection "AIS Trajectories from Danish Waters for Abnormal Behavior Detection"

DOI: https://doi.org/10.11583/DTU.c.6287841

Using Deep Learning for detection of maritime abnormal behaviour in spatio temporal trajectories is a relatively new and promising application. Open access to the Automatic Identification System (AIS) has made large amounts of maritime trajectories publically avaliable. However, these trajectories are unannotated when it comes to the detection of abnormal behaviour. The lack of annotated datasets for abnormality detection on maritime trajectories makes it difficult to evaluate and compare suggested models quantitavely. With this dataset, we attempt to provide a way for researchers to evaluate and compare performance.
We have manually labelled trajectories which showcase abnormal behaviour following an collision accident. The annotated dataset consists of 521 data points with 25 abnormal trajectories. The abnormal trajectories cover amoung other; Colliding vessels, vessels engaged in Search-and-Rescue activities, law enforcement, and commercial maritime traffic forced to deviate from the normal course

These datasets consists of labelled trajectories for the purpose of evaluating unsupervised models for detection of abnormal maritime behavior. For unlabelled datasets for training please refer to the collection. Link in Related publications.

The dataset is an example of a SAR event and cannot not be considered representative of a large population of all SAR events.

The dataset consists of a total of 521 trajectories of which 25 is labelled as abnormal. the data is captured on a single day in a specific region. The remaining normal traffic is representative of the traffic during the winter season. The normal traffic in the ROI has a fairly high seasonality related to fishing and leisure sailing traffic.

The data is saved using the pickle format for Python. Each dataset is split into 2 files with naming convention:

datasetInfo_XXX
data_XXX

Files named "data_XXX" contains the extracted trajectories serialized sequentially one at a time and must be read as such. Please refer to provided utility functions for examples. Files named "datasetInfo" contains Metadata related to the dataset and indecies at which trajectories begin in "data_XXX" files.

The data are sequences of maritime trajectories defined by their; timestamp, latitude/longitude position, speed, course, and unique ship identifer MMSI. In addition, the dataset contains metadata related to creation parameters. The dataset has been limited to a specific time period, ship types, moving AIS navigational statuses, and filtered within an region of interest (ROI). Trajectories were split if exceeding an upper limit and short trajectories were discarded. All values are given as metadata in the dataset and used in the naming syntax.

Naming syntax: data_AIS_Custom_STARTDATE_ENDDATE_SHIPTYPES_MINLENGTH_MAXLENGTH_RESAMPLEPERIOD.pkl

See datasheet for more detailed information and we refer to provided utility functions for examples on how to read and plot the data.

Vessel traffic data, or Automatic Identification System (AIS) data, are collected by the U.S. Coast Guard through an onboard navigation safety device that transmits and monitors the location and characteristics of large vessels in U.S. and international waters in real time. In the U.S., the Coast Guard and commercial vendors collect AIS data, which can also be used for a variety of coastal planning purposes.The Bureau of Ocean Energy Management (BOEM) and the National Oceanic and Atmospheric Administration (NOAA) have worked jointly to repurpose and make available some of the most important records from the U.S. Coast Guard’s national network of AIS receivers. Information such as location, time, ship type, speed, length, beam, and draft have been extracted from the raw data and prepared for analyses in desktop GIS software.Vessel tracks show the location and characteristics of commercial, recreational, and other marine vessels as a sequence of positions transmitted by AIS. AIS signals are susceptible to interference, and this can result in a gap within a vessel track. Vessels can have one or more tracks of any length. Furthermore, tracks will not necessarily start or stop at a well-defined port, or when a vessel is not in motion.The distribution, type, and frequency of vessel tracks are a useful aid to understanding the risk of conflicting uses within a certain geographic area and are an efficient and spatially unbiased indicator of vessel traffic. These tracks are used to build respective AIS Vessel Transit Counts layers, summarized at a 100-meter grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell.This item is curated by the MarineCadastre.gov team. Find more information at marinecadastre.gov.

The proposed AIS dataset encompasses a substantial temporal span of 20 months, spanning from April 2021 to December 2022. This extensive coverage period empowers analysts to examine long-term trends and variations in vessel activities. Moreover, it facilitates researchers in comprehending the potential influence of external factors, including weather patterns, seasonal variations, and economic conditions, on vessel traffic and behavior within the Finnish waters.

This dataset encompasses an extensive array of data pertaining to vessel movements and activities encompassing seas, rivers, and lakes. Anticipated to be comprehensive in nature, the dataset encompasses a diverse range of ship types, such as cargo ships, tankers, fishing vessels, passenger ships, and various other categories.

The AIS dataset exhibits a prominent attribute in the form of its exceptional granularity with a total of 2 293 129 345 data points. The provision of such granular information proves can help analysts to comprehend vessel dynamics and operations within the Finnish waters. It enables the identification of patterns and anomalies in vessel behavior and facilitates an assessment of the potential environmental implications associated with maritime activities.

Please cite the following publication when using the dataset:

TBD

The publication is available at: TBD

A preprint version of the publication is available at TBD

csv file structure

YYYY-MM-DD-location.csv

This file contains the received AIS position reports. The structure of the logged parameters is the following: [timestamp, timestampExternal, mmsi, lon, lat, sog, cog, navStat, rot, posAcc, raim, heading]

timestamp I beleive this is the UTC second when the report was generated by the electronic position system (EPFS) (0-59, or 60 if time stamp is not available, which should also be the default value, or 61 if positioning system is in manual input mode, or 62 if electronic position fixing system operates in estimated (dead reckoning) mode, or 63 if the positioning system is inoperative).

timestampExternal The timestamp associated with the MQTT message received from www.digitraffic.fi. It is assumed this timestamp is the Epoch time corresponding to when the AIS message was received by digitraffic.fi.

mmsi MMSI number, Maritime Mobile Service Identity (MMSI) is a unique 9 digit number that is assigned to a (Digital Selective Calling) DSC radio or an AIS unit. Check https://en.wikipedia.org/wiki/Maritime_Mobile_Service_Identity

lon Longitude, Longitude in 1/10 000 min (+/-180 deg, East = positive (as per 2's complement), West = negative (as per 2's complement). 181= (6791AC0h) = not available = default)

lat Latitude, Latitude in 1/10 000 min (+/-90 deg, North = positive (as per 2's complement), South = negative (as per 2's complement). 91deg (3412140h) = not available = default)

sog Speed over ground in 1/10 knot steps (0-102.2 knots) 1 023 = not available, 1 022 = 102.2 knots or higher

cog Course over ground in 1/10 = (0-3599). 3600 (E10h) = not available = default. 3 601-4 095 should not be used

navStat Navigational status, 0 = under way using engine, 1 = at anchor, 2 = not under command, 3 = restricted maneuverability, 4 = constrained by her draught, 5 = moored, 6 = aground, 7 = engaged in fishing, 8 = under way sailing, 9 = reserved for future amendment of navigational status for ships carrying DG, HS, or MP, or IMO hazard or pollutant category C, high speed craft (HSC), 10 = reserved for future amendment of navigational status for ships carrying dangerous goods (DG), harmful substances (HS) or marine pollutants (MP), or IMO hazard or pollutant category A, wing in ground (WIG); 11 = power-driven vessel towing astern (regional use); 12 = power-driven vessel pushing ahead or towing alongside (regional use); 13 = reserved for future use, 14 = AIS-SART (active), MOB-AIS, EPIRB-AIS 15 = undefined = default (also used by AIS-SART, MOB-AIS and EPIRB-AIS under test)

rot ROTAIS Rate of turn

• 0 to +126 = turning right at up to 708 deg per min or higher
• 0 to -126 = turning left at up to 708 deg per min or higher
• Values between 0 and 708 deg per min coded by ROTAIS = 4.733 SQRT(ROTsensor) degrees per min where ROTsensor is the Rate of Turn as input by an external Rate of Turn Indicator (TI). ROTAIS is rounded to the nearest integer value.
• +127 = turning right at more than 5 deg per 30 s (No TI available)
• -127 = turning left at more than 5 deg per 30 s (No TI available)
• -128 (80 hex) indicates no turn information available (default).

ROT data should not be derived from COG information.

posAcc Position accuracy, The position accuracy (PA) flag should be determined in accordance with the table below:

• 1 = high (<= 10 m)
• 0 = low (> 10 m)
• 0 = default

See https://www.navcen.uscg.gov/?pageName=AISMessagesA#RAIM

raim RAIM-flag Receiver autonomous integrity monitoring (RAIM) flag of electronic position fixing device; 0 = RAIM not in use = default; 1 = RAIM in use. See Table https://www.navcen.uscg.gov/?pageName=AISMessagesA#RAIM

Check https://en.wikipedia.org/wiki/Receiver_autonomous_integrity_monitoring

heading True heading, Degrees (0-359) (511 indicates not available = default)

YYYY-MM-DD-metadata.csv

This file contains the received AIS metadata: the ship static and voyage related data. The structure of the logged parameters is the following: [timestamp, destination, mmsi, callSign, imo, shipType, draught, eta, posType, pointA, pointB, pointC, pointD, name]

timestamp The timestamp associated with the MQTT message received from www.digitraffic.fi. It is assumed this timestamp is the Epoch time corresponding to when the AIS message was received by digitraffic.fi.

destination Maximum 20 characters using 6-bit ASCII; @@@@@@@@@@@@@@@@@@@@ = not available For SAR aircraft, the use of this field may be decided by the responsible administration

mmsi MMSI number, Maritime Mobile Service Identity (MMSI) is a unique 9 digit number that is assigned to a (Digital Selective Calling) DSC radio or an AIS unit. Check https://en.wikipedia.org/wiki/Maritime_Mobile_Service_Identity

callSign 7?=?6 bit ASCII characters, @@@@@@@ = not available = default Craft associated with a parent vessel, should use “A” followed by the last 6 digits of the MMSI of the parent vessel. Examples of these craft include towed vessels, rescue boats, tenders, lifeboats and liferafts.

imo 0 = not available = default – Not applicable to SAR aircraft

• 0000000001-0000999999 not used
• 0001000000-0009999999 = valid IMO number;
• 0010000000-1073741823 = official flag state number.

Check: https://en.wikipedia.org/wiki/IMO_number

shipType

• 0 = not available or no ship = default
• 1-99 = as defined below
• 100-199 = reserved, for regional use
• 200-255 = reserved, for future use Not applicable to SAR aircraft

Check https://www.navcen.uscg.gov/pdf/AIS/AISGuide.pdf and https://www.navcen.uscg.gov/?pageName=AISMessagesAStatic

draught In 1/10 m, 255 = draught 25.5 m or greater, 0 = not available = default; in accordance with IMO Resolution A.851 Not applicable to SAR aircraft, should be set to 0

eta Estimated time of arrival; MMDDHHMM UTC

• Bits 19-16: month; 1-12; 0 = not available = default
• Bits 15-11: day; 1-31; 0 = not available = default
• Bits 10-6: hour; 0-23; 24 = not available = default
• Bits 5-0: minute; 0-59; 60 = not available = default

For SAR aircraft, the use of this field may be decided by the responsible administration

posType Type of electronic position fixing device

• 0 = undefined (default)
• 1 = GPS
• 2 = GLONASS
• 3 = combined

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. In the U.S. the Coast Guard and industry collect AIS data, which can also be used for a variety of coastal planning purposes. NOAA and BOEM have worked jointly to re-task and make available some of the most important records from the U.S. Coast Guard's national network of AIS receivers. This dataset represents annual vessel transit counts summarized at a 100 m by 100 m geographic area. A single transit is counted each time a vessel track passes through, starts, or stops within a 100 m grid cell.

The Automatic Identification System (AIS) allows vessels to share identification, characteristics, and location data through self-reporting. This information is periodically broadcast and can be received by other vessels with AIS transceivers, as well as ground or satellite sensors. Since the International Maritime Organisation (IMO) mandated AIS for vessels above 300 gross tonnage, extensive datasets have emerged, becoming a valuable resource for maritime intelligence.

Maritime collisions occur when two vessels collide or when a vessel collides with a floating or stationary object, such as an iceberg. Maritime collisions hold significant importance in the realm of marine accidents for several reasons:

Injuries and fatalities of vessel crew members and passengers.

Environmental effects, especially in cases involving large tanker ships and oil spills.

Direct and indirect economic losses on local communities near the accident area.

Adverse financial consequences for ship owners, insurance companies and cargo owners including vessel loss and penalties.

As sea routes become more congested and vessel speeds increase, the likelihood of significant accidents during a ship's operational life rises. The increasing congestion on sea lanes elevates the probability of accidents and especially collisions between vessels.

The development of solutions and models for the analysis, early detection and mitigation of vessel collision events is a significant step towards ensuring future maritime safety. In this context, a synthetic vessel proximity event dataset is created using real vessel AIS messages. The synthetic dataset of trajectories with reconstructed timestamps is generated so that a pair of trajectories reach simultaneously their intersection point, simulating an unintended proximity event (collision close call). The dataset aims to provide a basis for the development of methods for the detection and mitigation of maritime collisions and proximity events, as well as the study and training of vessel crews in simulator environments.

The dataset consists of 4658 samples/AIS messages of 213 unique vessels from the Aegean Sea. The steps that were followed to create the collision dataset are:

Given 2 vessels X (vessel_id1) and Y (vessel_id2) with their current known location (LATITUDE [lat], LONGITUDE [lon]):

Check if the trajectories of vessels X and Y are spatially intersecting.

If the trajectories of vessels X and Y are intersecting, then align temporally the timestamp of vessel Y at the intersect point according to X’s timestamp at the intersect point. The temporal alignment is performed so the spatial intersection (nearest proximity point) occurs at the same time for both vessels.

Also for each vessel pair the timestamp of the proximity event is different from a proximity event that occurs later so that different vessel trajectory pairs do not overlap temporarily.

Two csv files are provided. vessel_positions.csv includes the AIS positions vessel_id, t, lon, lat, heading, course, speed of all vessels. Simulated_vessel_proximity_events.csv includes the id, position and timestamp of each identified proximity event along with the vessel_id number of the associated vessels. The final sum of unintended proximity events in the dataset is 237. Examples of unintended vessel proximity events are visualized in the respective png and gif files.

The research leading to these results has received funding from the European Union's Horizon Europe Programme under the CREXDATA Project, grant agreement n° 101092749.

Automatic Identification System (AIS) Market Report is Segmented by Application (Fleet Management, Vessel Tracking, Maritime Security, and Other Applications), Platform (Vessel-Based and On-Shore), and Geography (North America, Europe, Asia-Pacific, Latin America, and Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

Terrestrial vessel automatic identification system (AIS) data was collected around Ålesund, Norway in 2020, from multiple receiving stations with unsynchronized clocks. Features are 'mmsi', 'imo', 'length', 'latitude', 'longitude', 'sog', 'cog', 'true_heading', 'datetime UTC', 'navigational status', and 'message number'. Compact parquet files can be turned into data frames with python's pandas library. Data is irregularly sampled because of the navigational status. The preprocessing script for training the machine learning models can be found here. There you will find gathered dozen of trainable models and hundreds of datasets. Visit this website for more information about the data. If you have additional questions, please find our information in the links below:

• Luka Grgičević
• Ottar Laurits Osen

The AIS dataset is provided by the National Oceanic and Atmospheric Administration (NOAA), spanning from January 2020 to December 2020. The trajectories of 140 individual vessels (including tankers and cargo) were collected. Weather and ocean conditions for the same period are obtained from the National Data Buoy Center (NDBC), collected from 15 buoys.

Dataset

This dataset was created by Singhkunal

Released under MIT

Contents

Ship position data from a satellite-based Automatic Identification System (AIS) were obtained jointly by PacIOOS (J. Potemra), SOEST/ORE of the University of Hawaii (E. Roth), and the Papahanaumokuakea Marine National Monument (PNMN) (D. Graham) through a one-time purchase from ORBCOMM LLC. The purchase agreement was made in late 2012 and was for a 30-by-30 degree section of historical AIS data that included the region of the Hawaiian Islands. The data include AIS long and unchecked reports for a one year period: August 2011 through mid-August 2012. The raw, monthly GPS files were locally converted to NetCDF for the PacIOOS data servers. Due to vendor constraints, release of the raw data is limited.

Nowadays, a multitude of tracking systems produce massive amounts of maritime data on a daily basis. The most commonly used is the Automatic Identification System (AIS), a collaborative, self-reporting system that allows vessels to broadcast their identification information, characteristics and destination, along with other information originating from on-board devices and sensors, such as location, speed and heading. AIS messages are broadcast periodically and can be received by other vessels equipped with AIS transceivers, as well as by on the ground or satellite-based sensors.

Since becoming obligatory by the International Maritime Organisation (IMO) for vessels above 300 gross tonnage to carry AIS transponders, large datasets are gradually becoming available and are now being considered as a valid method for maritime intelligence [4].There is now a growing body of literature on methods of exploiting AIS data for safety and optimisation of seafaring, namely traffic analysis, anomaly detection, route extraction and prediction, collision detection, path planning, weather routing, etc., [5].

As the amount of available AIS data grows to massive scales, researchers are realising that computational techniques must contend with difficulties faced when acquiring, storing, and processing the data. Traditional information systems are incapable of dealing with such firehoses of spatiotemporal data where they are required to ingest thousands of data units per second, while performing sub-second query response times.

Processing streaming data seems to exhibit similar characteristics with other big data challenges, such as handling high data volumes and complex data types. While for many applications, big data batch processing techniques are sufficient, for applications such as navigation and others, timeliness is a top priority; making the right decision steering a vessel away from danger, is only useful if it is a decision made in due time. The true challenge lies in the fact that, in order to satisfy real-time application needs, high velocity, unbounded sized data needs to be processed in constraint, in relation to the data size and finite memory. Research on data streams is gaining attention as a subset of the more generic Big Data research field.

Research on such topics requires an uncompressed unclean dataset similar to what would be collected in real world conditions. This dataset contains all decoded messages collected within a 24h period (starting from 29/02/2020 10PM UTC) from a single receiver located near the port of Piraeus (Greece). All vessels identifiers such as IMO and MMSI have been anonymised and no down-sampling procedure, filtering or cleaning has been applied.

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

AIS nmea sample dataset used for testing AIS decoding library. This dataset was originally published in https://github.com/aduvenhage/ais-decoder as a sample test dataset. The original dataset can be found at https://github.com/aduvenhage/ais-decoder/blob/master/data/nmea-sample.txt

This St. Louis County, MN Aquatic Invasive Species Risk Assessment Tool was funded by St. Louis County’s 2015 State AIS Prevention Aid award. This project was completed December, 2017 by Josh Dumke and Kristi Nixon at the Natural Resources Research Institute in Duluth, University of Minnesota Duluth, MN. We used spatial, environmental, and lakeshore development data to assess the risk which lakes have in receiving new aquatic invasive species (AIS). Included in this database are worksheets of Waterbody Master, Water Chemistry, Boater Surveys, Collaborator Point Surveys, Contributors and Data Sources, Access List, About and Citations, and Metadata. If you have a version of this file from before November 20, 2020, please re-download the dataset and delete prior versions.

This dataset contains the anonymised transit line data used to create the UK shipping density grid produced as part of MMO1066. See AIS data processing methodology developed by ABPmer under MMO project number 1066, entitled Mapping UK Shipping Density and Routes from AIS Open Source Data and Methods