IFCB Uto 2021 JERICO-RI Gulf of Finland Pilot Supersite

The data set available here is published with article “Kraft et al. (2022). Towards operational phytoplankton recognition with automated high-throughput imaging, near real-time data processing, and convolutional neural networks. Front Mar. Sci. 9. Doi: 10.3389/fmars.2022.867695” and if used for further purposes, the article should be cited accordingly. The data set contains approximately 150 000 images belonging to 50 different classes (~57 000) + unclassifiable (~94 000) consisting mainly of phytoplankton. The images can be used to validate classifier model performance with data from natural samples. The images were collected with an Imaging FlowCytobot from a continuous deployment in 2021 at the Utö Atmospheric and Marine Research Station operated by Finnish Environment Institute and Finnish Meteorological Institute. The images were manually annotated by expert taxonomists. 

Citation: Kraft, K., Haraguchi, L., Velhonoja, O., Seppälä, J. (2022). SYKE-plankton_IFCB_Utö_2021. https://doi.org/10.23728/B2SHARE.7C273B6F409C47E98A868D6517BE3AE3

Published: September 17, 2025 at 08:37

URL: https://ipt.vliz.be/eurobis/resource?r=syke-ifcb