Advancing long-term phytoplankton biodiversity assessment in the North Sea using an imaging approach.
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| Main Authors: | , , , , , , , |
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| Format: | Artículo científico |
| Language: | en |
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Scientific data
2025
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| _version_ | 1868266111378653186 |
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| author | Lagaisse, Rune Dillen, Nick Bakeev, Dias Decrop, Wout Focke, Paul Mortelmans, Jonas Muyle, Julie Deneudt, Klaas |
| author_facet | Lagaisse, Rune Dillen, Nick Bakeev, Dias Decrop, Wout Focke, Paul Mortelmans, Jonas Muyle, Julie Deneudt, Klaas Lagaisse, Rune Dillen, Nick Bakeev, Dias Decrop, Wout Focke, Paul Mortelmans, Jonas Muyle, Julie Deneudt, Klaas |
| collection | PubMed - marine biology |
| contents | Advancing long-term phytoplankton biodiversity assessment in the North Sea using an imaging approach. Lagaisse, Rune Dillen, Nick Bakeev, Dias Decrop, Wout Focke, Paul Mortelmans, Jonas Muyle, Julie Deneudt, Klaas Biodiversity North Sea Phytoplankton Neural Networks, Computer This paper presents a high spatial and temporal resolution microphytoplankton long-term biodiversity assessment for the southern bight of the North Sea obtained by FlowCam imaging. We describe the extension of the time series with the release of over six years of new quality-controlled data as well as a taxonomic revision of previously published data leading to 92 newly recognized groups. We also describe the latest fine-tuning of sampling and laboratory processing protocols leading to a more robust methodological framework while maintaining time series continuity. The implementation of semi-automated data pipelines, leveraging convolutional neural networks, allows to deal with the high influx of biodiversity imaging data and metadata. Data and provenance metadata are annually published under a CC-BY license in trusted repositories. This current open access, high-resolution 7 year-long dataset serves as a valuable tool for studying phytoplankton communities in the Belgian Part of the North Sea. |
| format | Artículo científico |
| id | pubmed_41402358 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | Scientific data |
| record_format | pubmed |
| spellingShingle | Advancing long-term phytoplankton biodiversity assessment in the North Sea using an imaging approach. Lagaisse, Rune Dillen, Nick Bakeev, Dias Decrop, Wout Focke, Paul Mortelmans, Jonas Muyle, Julie Deneudt, Klaas Biodiversity North Sea Phytoplankton Neural Networks, Computer Advancing long-term phytoplankton biodiversity assessment in the North Sea using an imaging approach. Lagaisse, Rune Dillen, Nick Bakeev, Dias Decrop, Wout Focke, Paul Mortelmans, Jonas Muyle, Julie Deneudt, Klaas Biodiversity North Sea Phytoplankton Neural Networks, Computer This paper presents a high spatial and temporal resolution microphytoplankton long-term biodiversity assessment for the southern bight of the North Sea obtained by FlowCam imaging. We describe the extension of the time series with the release of over six years of new quality-controlled data as well as a taxonomic revision of previously published data leading to 92 newly recognized groups. We also describe the latest fine-tuning of sampling and laboratory processing protocols leading to a more robust methodological framework while maintaining time series continuity. The implementation of semi-automated data pipelines, leveraging convolutional neural networks, allows to deal with the high influx of biodiversity imaging data and metadata. Data and provenance metadata are annually published under a CC-BY license in trusted repositories. This current open access, high-resolution 7 year-long dataset serves as a valuable tool for studying phytoplankton communities in the Belgian Part of the North Sea. |
| title | Advancing long-term phytoplankton biodiversity assessment in the North Sea using an imaging approach. |
| topic | Biodiversity North Sea Phytoplankton Neural Networks, Computer |
| url | https://pubmed.ncbi.nlm.nih.gov/41402358/ |