8 research outputs found
Expert, Crowd, Students or Algorithm: who holds the key to deep-sea imagery ‘big data’ processing?
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, have 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 are a crucial source of information for assessing natural variability and ecosystem responses to increasing human activity in the deep sea.
2.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 for their 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
Data from: Expert, crowd, students or algorithm: who holds the key to deep-sea imagery ‘big data’ processing?
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
expert_data
Contains 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 group
crowd_data
Contains 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 group
students_data
Contains 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 group
algorithm_data
Contains 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 group
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One thousand plant transcriptomes and the phylogenomics of green plants
Abstract: Green plants (Viridiplantae) include around 450,000–500,000 species1, 2 of great diversity and have important roles in terrestrial and aquatic ecosystems. Here, as part of the One Thousand Plant Transcriptomes Initiative, we sequenced the vegetative transcriptomes of 1,124 species that span the diversity of plants in a broad sense (Archaeplastida), including green plants (Viridiplantae), glaucophytes (Glaucophyta) and red algae (Rhodophyta). Our analysis provides a robust phylogenomic framework for examining the evolution of green plants. Most inferred species relationships are well supported across multiple species tree and supermatrix analyses, but discordance among plastid and nuclear gene trees at a few important nodes highlights the complexity of plant genome evolution, including polyploidy, periods of rapid speciation, and extinction. Incomplete sorting of ancestral variation, polyploidization and massive expansions of gene families punctuate the evolutionary history of green plants. Notably, we find that large expansions of gene families preceded the origins of green plants, land plants and vascular plants, whereas whole-genome duplications are inferred to have occurred repeatedly throughout the evolution of flowering plants and ferns. The increasing availability of high-quality plant genome sequences and advances in functional genomics are enabling research on genome evolution across the green tree of life