An underwater observation dataset for fish classification and fishery assessment

Scientific Data
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Abstract

Using Dual-Frequency Identification Sonar (DIDSON), fishery acoustic observation data was collected from the Ocqueoc River, a tributary of Lake Huron in northern Michigan, USA. Data were collected March through July 2013 and 2016 and included the identification, via technology or expert analysis, of eight fish species as they passed through the DIDSON’s field of view. A set of short DIDSON clips containing identified fish was curated. Additionally, two other datasets were created that include visualizations of the acoustic data and longer DIDSON clips. These datasets could complement future research characterizing the abundance and behavior of valued fishes such as walleye (Sander vitreus) or white sucker (Catostomus commersonii) or invasive fishes such as sea lamprey (Petromyzon marinus) or European carp (Cyprinus carpio). Given the abundance of DIDSON data and the fact that a portion of it is labeled, these data could aid in the creation of machine learning tools from DIDSON data, particularly for invasive sea lamprey which are amply represented and a destructive invader of the Laurentian Great Lakes.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title An underwater observation dataset for fish classification and fishery assessment
Series title Scientific Data
DOI 10.1038/sdata.2018.190
Volume 5
Year Published 2018
Language English
Publisher Nature
Contributing office(s) Great Lakes Science Center
Description Article number: 180190; 8 p.
First page 1
Last page 8
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