LifeWatch observatory data: phytoplankton observations in the Belgian Part of the North Sea
This paper describes a phytoplankton data series generated through systematic observations in the Belgian Part of the North Sea (BPNS). Phytoplankton samples are collected during multidisciplinary sampling campaigns, visiting nine nearshore stations with monthly frequency, and an additional eight offshore stations on a seasonal basis.The data series contain taxon specific phytoplankton densities determined by analysis with the Flow Cytometer And Microscope (FlowCAM®) and associated image based classification. The classification is performed by two separate semi-automated classification systems... Mehr ...
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Dokumenttyp: | Artikel |
Erscheinungsdatum: | 2020 |
Verlag/Hrsg.: |
Pensoft Publishers
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Schlagwörter: | phytoplankton / Belgium / marine / LifeWatch Belgium / FlowCAM / image recognition |
Sprache: | unknown |
Permalink: | https://search.fid-benelux.de/Record/base-26533149 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://zenodo.org/record/4391506 |
This paper describes a phytoplankton data series generated through systematic observations in the Belgian Part of the North Sea (BPNS). Phytoplankton samples are collected during multidisciplinary sampling campaigns, visiting nine nearshore stations with monthly frequency, and an additional eight offshore stations on a seasonal basis.The data series contain taxon specific phytoplankton densities determined by analysis with the Flow Cytometer And Microscope (FlowCAM®) and associated image based classification. The classification is performed by two separate semi-automated classification systems, followed by manual validation by taxonomic experts. To date 637,819 biological particles have been collected and identified, yielding a large dataset of validated phytoplankton images. The collection and processing of the 2017–2018 dataset is described, along with its data curation, quality control and data storage. In addition, the classification of images using image classification algorithms based on convolutional neural networks (CNN) from 2019 onwards is also described. Data is published in a standardized format together with environmental parameters, accompanied by extensive metadata descriptions, and finally labelled with digital identifiers for traceability. The data are published under a CC‐BY 4.0 license, allowing use of the data under the condition of providing the reference to the original source.