DOI: 10.5061/dryad.98g01
title: Data from: Expert, crowd, students or algorithm: who holds the key to deep-sea imagery ‘big data’ processing?

description: 1. Recent technological development has increased our capacity to study
 the deep sea and the marine benthic realm, particularly with the
 development of multidisciplinary seafloor observatories. Since 2006, Ocean
 Networks Canada cabled observatories, has acquired nearly 65 TB and over
 90,000 hours of video data from seafloor cameras and Remotely Operated
 Vehicles (ROVs). Manual processing of these data is time-consuming and
 highly labour-intensive, and cannot be comprehensively undertaken by
 individual researchers. These videos contain valuable information for
 faunal and environmental monitoring, and are a crucial source of
 information for assessing natural variability and ecosystem responses to
 increasing human activity in the deep sea. 2. In this study, we compared
 the performance of three groups of humans and one computer vision
 algorithm in counting individuals of the commercially important sablefish
 (or black cod) Anoplopoma fimbria, in recorded video from a cabled camera
 platform at 900 m depth in a submarine canyon in the Northeast Pacific.
 The first group of human observers were untrained volunteers recruited via
 a crowdsourcing platform and the second were experienced university
 students, who performed the task in the context of an ichthyology class.
 Results were validated against counts obtained from a scientific expert.
 3. All groups produced relatively accurate results in comparison to the
 expert and all succeeded in detecting patterns and periodicities in fish
 abundance data. Trained volunteers displayed the highest accuracy and the
 algorithm the lowest. 4. As seafloor observatories increase in number
 around the world, this study demonstrates the value of a hybrid
 combination of crowdsourcing and computer vision techniques, as a tool to
 help process large volumes of imagery to support basic research and
 environmental monitoring. Reciprocally, by engaging large numbers of
 online participants in deep-sea research, this approach can contribute
 significantly to ocean literacy and informed citizen input to policy
 development.
description: expert_dataContains all the data acquired by the PhD student referred as the expert. Details of the columns are as follows: Date: Date of video acquisition Time: Time of video acquisition Counts: Number of sablefish in the video MatchDateTime: Date and Time used to match the counts among the different groupscrowd_dataContains all the data acquired by Citizen Scientists using the Digital Fisher crowdsourcing platform. Details of the columns are as follows: UserID: unique ID attributed to each observer in the Ocean Networks Canada digital infrastructure database. Date: Date of video acquisition Time: Time of video acquisition Counts: Number of sablefish in the video MatchDateTime: Date and Time used to match the counts among the different groupsalgorithm_dataContains all the data acquired using the computer vision algorithm. Details of the columns are as follows: Video: Name of the video in the Ocean Networks Canada digital infrastructure database. Date: Date of video acquisition Time: Time of video acquisition Counts: Number of sablefish in the video MatchDateTime: Date and Time used to match the counts among the different groupsstudents_dataContains all the data acquired by the group of students. Details of the columns are as follows: UserID: unique ID attributed to each observer in the Ocean Networks Canada digital infrastructure database. Date: Date of video acquisition Time: Time of video acquisition Counts: Number of sablefish in the video MatchDateTime: Date and Time used to match the counts among the different groups