IFCB Uto 2021 JERICO-RI Gulf of Finland Pilot Supersite

<figure class="table"><table><tbody><tr><td>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.&nbsp;</td></tr></tbody></table></figure>

Source https://data.blue-cloud.org/search-details?step=~0128990D7A6E0BE10AC41E213F8F26152EB3D337F9B
Metadata Access https://data.blue-cloud.org/api/collections/8990D7A6E0BE10AC41E213F8F26152EB3D337F9B
Publisher Blue-Cloud Data Discovery & Access service; EurOBIS - EMODnet Biology
Contributor Finnish Environment Institute; Flanders Marine Institute (VLIZ)
Publication Year 2024
OpenAccess true
Contact blue-cloud-support(at)maris.nl
Discipline Marine Science
Spatial Coverage (21.370W, 59.780S, 21.370E, 59.780N)