Annotated IFCB plankton images from the Mediterranean Sea

The Imaging FlowCytobot (IFCB), supported by machine learning-based classifications, has revolutionized plankton research by automating plankton monitoring and considerably increasing sampling resolution. However, building a training set of labeled IFCB images to train the machine learning algorithms remains time-consuming and challenging. Consequently, there is a growing demand within the IFCB user community for shared datasets of taxonomically annotated IFCB images. Currently, such datasets are scarce and lack Mediterranean coverage. This data descriptor introduces MedPlanktonSet, a dataset comprising 77,271 taxonomically annotated IFCB images provided with their associated features. Data were collected from November 2022 to February 2025 at six stations in the Gulf of Naples (Western Mediterranean Sea) and the IFCB images were classified into 139 categories. MedPlanktonSet will support the development of various machine learning classifiers, 3D plankton reconstructions, training of plankton taxonomists and trait-based ecological studies. Ultimately, by facilitating the use of IFCBs with their associated classifiers, MedPlanktonSet will contribute to advancing research on plankton biodiversity and ecology.

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Publication Details

Journal
Scientific Data
Published
2025-10-24
DOI
https://doi.org/10.1038/s41597-025-05973-y
Primary Topic
Isotope Analysis in Ecology
Type
article
Field-Weighted Citation Impact
0.00

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article

Annotated IFCB plankton images from the Mediterranean Sea

Diana Sarno, Emilie Houliez, Eleonora Scalco, Leonilde Roselli et al.
Scientific Data
Isotope Analysis in Ecology
article

Annotated IFCB plankton images from the Mediterranean Sea

Diana Sarno, Emilie Houliez, Eleonora Scalco, Leonilde Roselli, Melpomeni Sofia Mente
article en

Abstract

The Imaging FlowCytobot (IFCB), supported by machine learning-based classifications, has revolutionized plankton research by automating plankton monitoring and considerably increasing sampling resolution. However, building a training set of labeled IFCB images to train the machine learning algorithms remains time-consuming and challenging. Consequently, there is a growing demand within the IFCB user community for shared datasets of taxonomically annotated IFCB images. Currently, such datasets are scarce and lack Mediterranean coverage. This data descriptor introduces MedPlanktonSet, a dataset comprising 77,271 taxonomically annotated IFCB images provided with their associated features. Data were collected from November 2022 to February 2025 at six stations in the Gulf of Naples (Western Mediterranean Sea) and the IFCB images were classified into 139 categories. MedPlanktonSet will support the development of various machine learning classifiers, 3D plankton reconstructions, training of plankton taxonomists and trait-based ecological studies. Ultimately, by facilitating the use of IFCBs with their associated classifiers, MedPlanktonSet will contribute to advancing research on plankton biodiversity and ecology.

Scientific DataVol. 12(1)
Stazione Zoologica Anton Dohrn (IT), Parco Nazionale d'Abruzzo (IT), National Biodiversity Institute (CR), University of Palermo (IT)
Stazione Zoologica Anton Dohrn
Openalex Percentile: Top 70%
Isotope Analysis in Ecology
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