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Advancing long-term phytoplankton biodiversity assessment in the North Sea using an imaging approach. <i>Scientific Data 12</i>: 1989. <a href=\"https://dx.doi.org/10.1038/s41597-025-06278-w\" target=\"_blank\">https://dx.doi.org/10.1038/s41597-025-06278-w</a>","BEntID":434612,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":0,"RefStringPartII":". <i>Scientific Data 12</i>: 1989. <a href=\"https://dx.doi.org/10.1038/s41597-025-06278-w\" target=\"_blank\">https://dx.doi.org/10.1038/s41597-025-06278-w</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Lagaisse, R.; Dillen, N.; Bakeev, D.; Decrop, W.; Focke, P.; Mortelmans, J.; Muyle, J.; Deneudt, K.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Lagaisse, R. <i>et al.</i>","Englishabstract":"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. 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