DOI: 10.23728/b2share.7c273b6f409c47e98a868d6517be3ae3
title: SYKE-plankton_IFCB_Utö_2021

description: 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 (IFCB, McLane Research Laboratories, Inc., U.S., Olson and Sosik, 2007) from a continuous deployment in 2021at the Utö Atmospheric and Marine Research Station (59°46.84’ N, 21°22.13’ E; Laakso et al., 2018; Kraft et al., 2021) operated by Finnish Environment Institute and Finnish Meteorological Institute. The images were manually annotated by expert taxonomists. The data was used for validating CNN model performance for natural samples. The sample selection targeted on one sample per week from continuous operation between January to December 2021. Due to scarcity of some classes additional samples were selected from expected seasons. The selected samples were manually inspected: all classifications were assessed (confirmed or corrected) and all identifiable images that were left under the thresholds were labeled. The unidentifiable images that were left without an assigned class were considered as unclassified. More detailed explanation and example images can be found from the publication Kraft et al. 2022. The zipped folder contains 50 different folders, and the images are located in the class-specific folders. Additionally, there is also a folder of the unclassifiable images (not belonging to any of those 50 classes). The work utilized SYKE and FMI marine research infrastructure as a part of the national FINMARI RI consortium. The work was partly funded by Tiina and Antti Herlin Foundation (personal grant for KK), Academy of Finland project FASTVISION (grant no. 321980), Academy of Finland project FASTVISION-plus (grant no. 339355), JERICO-S3 project, funded by the European Commission’s H2020 Framework Programme under grant agreement No. 871153, and PHIDIAS project, funded by the European Union's Connecting Europe Facility under grant agreement INEA/CEF/ICT/A2018/1810854.
description: The class list separated in taxonomic groups Cyanophyceae Aphanizomenon flosaquae Aphanothece paralleliformis Chroococcales Chroococcus sp. Dolichospermum sp. / Anabaenopsis sp. Dolichospermum sp. / Anabaenopsis sp. coiled Merismopedia sp. Nodularia spumigena Oscillatoriales Snowella sp. / Woronichinia sp. Cryptophyceae Cryptomonadales Cryptophyceae / Teleaulax sp. Euglenophyceae Euglenophyceae Eutreptiella sp. Dinophyceae Amylax triacantha Dinophyceae Dinophysis acuminata Gonyaulax verior Gymnodiniales Gymnodinium like cells Heterocapsa rotundata Heterocapsa triquetra Peridiniella catenata chain Peridiniella catenata single Prorocentrum cordatum Diatomophyceae Centrales Ceratoneis closterium Chaetoceros sp. chain Chaetoceros sp. single Cyclotella choctawhatcheeana Licmophora sp. Melosira arctica Nitzschia paleacea Pauliella taeniata Pennales thick Pennales thin Skeletonema marinoi Thalassiosira levanderi Chrysophyceae Pseudopedinella sp. Uroglenopsis sp. Chlorophyta Chlorococcales Cymbomonas tetramitiformis Monoraphidium contortum Oocystis sp. Pyramimonas sp. Other Katablepharis remigera Ciliates Ciliata Mesodinium rubrum Additional classes Beads Heterocyte