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A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA
The sustainable management of fisheries and aquaculture requires an understanding of how these activities interact with natural fish populations. GoPro cameras were used to collect an underwater video data set on and around shellfish aquaculture farms in an estuary in the NE Pacific from June to Aug...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10643608/ https://www.ncbi.nlm.nih.gov/pubmed/37957151 http://dx.doi.org/10.1038/s41597-023-02557-6 |
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author | Farrell, Dara M. Ferriss, Bridget Sanderson, Beth Veggerby, Karl Robinson, Lauren Trivedi, Anusua Pathak, Shreyaan Muppalla, Sreya Wang, Jane Morris, Dan Dodhia, Rahul |
author_facet | Farrell, Dara M. Ferriss, Bridget Sanderson, Beth Veggerby, Karl Robinson, Lauren Trivedi, Anusua Pathak, Shreyaan Muppalla, Sreya Wang, Jane Morris, Dan Dodhia, Rahul |
author_sort | Farrell, Dara M. |
collection | PubMed |
description | The sustainable management of fisheries and aquaculture requires an understanding of how these activities interact with natural fish populations. GoPro cameras were used to collect an underwater video data set on and around shellfish aquaculture farms in an estuary in the NE Pacific from June to August 2017 and June to August 2018 to better understand habitat use by the local fish and crab communities. Images extracted from these videos were labeled to produce a data set that is suitable for use in training computer vision models. The labeled data set contains 77,739 images sampled from the collected video; 67,990 objects (fishes and crustaceans) have been annotated in 30,384 images (the remainder have been annotated as “empty”). The metadata of the data set also indicates whether a physical magenta filter was used during video collection to counteract reduced visibility. These data have the potential to help researchers address system-level and in-depth regional shellfish aquaculture questions related to ecosystem services and shellfish aquaculture interactions. |
format | Online Article Text |
id | pubmed-10643608 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106436082023-11-13 A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA Farrell, Dara M. Ferriss, Bridget Sanderson, Beth Veggerby, Karl Robinson, Lauren Trivedi, Anusua Pathak, Shreyaan Muppalla, Sreya Wang, Jane Morris, Dan Dodhia, Rahul Sci Data Data Descriptor The sustainable management of fisheries and aquaculture requires an understanding of how these activities interact with natural fish populations. GoPro cameras were used to collect an underwater video data set on and around shellfish aquaculture farms in an estuary in the NE Pacific from June to August 2017 and June to August 2018 to better understand habitat use by the local fish and crab communities. Images extracted from these videos were labeled to produce a data set that is suitable for use in training computer vision models. The labeled data set contains 77,739 images sampled from the collected video; 67,990 objects (fishes and crustaceans) have been annotated in 30,384 images (the remainder have been annotated as “empty”). The metadata of the data set also indicates whether a physical magenta filter was used during video collection to counteract reduced visibility. These data have the potential to help researchers address system-level and in-depth regional shellfish aquaculture questions related to ecosystem services and shellfish aquaculture interactions. Nature Publishing Group UK 2023-11-13 /pmc/articles/PMC10643608/ /pubmed/37957151 http://dx.doi.org/10.1038/s41597-023-02557-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Farrell, Dara M. Ferriss, Bridget Sanderson, Beth Veggerby, Karl Robinson, Lauren Trivedi, Anusua Pathak, Shreyaan Muppalla, Sreya Wang, Jane Morris, Dan Dodhia, Rahul A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA |
title | A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA |
title_full | A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA |
title_fullStr | A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA |
title_full_unstemmed | A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA |
title_short | A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA |
title_sort | labeled data set of underwater images of fish and crab species from five mesohabitats in puget sound wa usa |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10643608/ https://www.ncbi.nlm.nih.gov/pubmed/37957151 http://dx.doi.org/10.1038/s41597-023-02557-6 |
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