seanoe:55741 Irisson Jean-Olivier Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France irisson@normalesup.org Stemmann Lars Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France stemmann@obs-vlfr.fr 2026-03-12 ISO 19115:2003/19139 1.0 ZooScanNet: plankton images captured with the ZooScan 2024-09 2026-03-12 Elineau Amanda, Desnos Corinne, Jalabert Laetitia, Olivier Marion, Romagnan Jean-Baptiste, Costa Brandao Manoela, Lombard Fabien, Llopis Natalia, Courboulès Justine, Caray-Counil Louis, Serranito Bruno, Irisson Jean-Olivier, Picheral Marc, Gorsky Gaby, Stemmann Lars (2024). ZooScanNet: plankton images captured with the ZooScan. SEANOE. https://doi.org/10.17882/55741 Plankton was sampled with various nets, from bottom or 500m depth to the surface, in many oceans of the world. Samples were imaged with a ZooScan. The full images were processed with ZooProcess which generated regions of interest (ROIs) around each individual object and a set of associated features measured on the object (see Gorsky et al 2010 for more information). The same objects were re-processed to compute features with the scikit-image toolbox http://scikit-image.org. The 1,451,745 resulting objects were sorted by a limited number of operators, following a common taxonomic guide, into 98 taxa, using the web application EcoTaxa http://ecotaxa.obs-vlfr.fr. For the purpose of training machine learning classifiers, the images in each class were split into training, validation, and test sets, with proportions 70%, 15% and 15%. The folder ZooScanNet_data.tar contains : taxa.csv.gz Table of the classification of each object in the dataset, with columns : - objid: unique object identifier in EcoTaxa (integer number) - taxon_level1: taxonomic name corresponding to the level 1 classification - lineage_level1: taxonomic lineage corresponding to the level 1 classification - taxon_level2: name of the taxon corresponding to the level 2 classification  - plankton: if the object is a plankton or not (boolean) - set: class of the image corresponding to the taxon (train : training, val : validation, or test) - img_path: local path of the image corresponding to the taxon (of level 1), named according to the object id features_native.csv.gz Table of metadata of each object including the different features processed by ZooProcess. All features are computed on the object only, not the background. All area/length measures are in pixels. All grey levels are in encoded in 8 bits (0=black, 255=white). With columns: - objid: unique object identifier in EcoTaxa (integer number) And 48 features: - area - mean - stddev - mode - min/max - perim. - width,height  - major,minor - circ. - feret - intden - median - skew,kurt - %area - area_exc - fractal - skelarea - slope - histcum1,2,3 - nb1,2,3 - symetrieh,symetriev - symetriehc,symetrievc - convperim,convarea - fcons - thickr:  - esd - elongation - range - centroids - sr - perimareaexc - feretareaexc - perimferet/perimmajor - circex - cdexc See the “ZooScan” sheet - OBJECT metadata, annotation and measurements - , at https://doi.org/10.5281/zenodo.14704250 for definitions. features_skimage.csv.gz Table of morphological features recomputed with skimage.measure.regionprops on the ROIs produced by ZooProcess. See http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops for documentation. inventory.tsv Tree view of the taxonomy and number of images in each taxon, displayed as text. With columns : - lineage_level1: taxonomic lineage corresponding to the level 1 classification - taxon_level1: name of the taxon corresponding to the level 1 classification - n: number of objects in each taxon class          2. Second folder ZooScanNet_imgs.tar contains : imgs Directory containing images of each object, named according to the object id objid and sorted in subdirectories according to their taxon.         3. And : map.png Map of the sampling locations, to give an idea of the diversity sampled in this dataset.   Data collection and processing was supported by the recurrent funding and facilities of the Observatoire Océanologique de Villefranche-sur-Mer and by Tara Expeditions, MOOSE, OCEANOMICS, Parc Naturel Marin d’Iroise, and LEFE DL-PIC and Belmont Forum project WWWPIC. Elineau Amanda Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France Desnos Corinne Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France Jalabert Laetitia Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France Olivier Marion Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France ROMAGNAN Jean-Baptiste Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France COSTA BRANDAO Manoela Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France Lombard Fabien Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France Llopis Natalia Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France Courboulès Justine Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France Caray-Counil Louis Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France Serranito Bruno Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France Irisson Jean-Olivier Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France irisson@normalesup.org Picheral Marc Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France Gorsky Gaby Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France Stemmann Lars Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France stemmann@obs-vlfr.fr SEANOE https://www.seanoe.org/data/00446/55741/illustration.gif Zooplankton imaged by the ZooScan plankton image ZooScan WP2 Bongo Juday-Bogorov Régent Biological oceanography Biological oceanography SeaDataNet Parameter Disciplines 2025-07-29 geonetwork.thesaurus.external.parameter.NVS.P08 /Observational data/in-situ Type de jeux de donnée ODATIS 2025-12-09 geonetwork.thesaurus.local.theme.type_jeux_donnee CC-BY-NC (Creative Commons - Attribution, No commercial usage) While all identifications have been reviewed by at least one human operator, we cannot fully guarantee the correctness of each of the >1.4M identifications, a few mistakes might remain. Benchmark of plankton images classification: emphasizing features extraction over classifier complexity 2026 Copernicus GmbH Panaïotis Thelma Amblard Emma Boniface-Chang Guillaume Dulac-Arnold Gabriel Woodward Benjamin Irisson Jean-Olivier 10.5194/essd-18-945-2026 Benchmark of plankton images classification: emphasizing features extraction over classifier complexity 2025 Copernicus GmbH Panaïotis Thelma Amblard Emma Boniface-Chang Guillaume Dulac-Arnold Gabriel Woodward Benjamin Irisson Jean-Olivier 10.5194/essd-2025-309 PlanktonTNet: Rethinking Plankton Classification from a Global View with Swin-Transformer 2025 IEEE Yuan Dekun Qi Yanping Zhang Jie Li Zhongwei 10.1109/oceans58557.2025.11104797 Metazoan zooplankton in the Bay of Biscay: 16 years of individual sizes and abundances from the ZooScan and ZooCAM imaging systems 2023 Copernicus GmbH GRANDREMY NINA BOURRIAU PAUL DACHÉ EDWIN DANIELOU MARIE-MADELEINE DORAY MATHIEU DUPUY CHRISTINE FOREST BERTRAND JALABERT LAETITIA HURET MARTIN LE MESTRE SOPHIE NOWACZYK ANTOINE PETITGAS PIERRE PINEAU PHILIPPE ROUXEL JUSTIN TARDIVEL MORGAN ROMAGNAN JEAN-BAPTISTE 10.5194/essd-2023-187 In-domain versus out-of-domain transfer learning in plankton image classification 2023 Springer Science and Business Media LLC Maracani Andrea Pastore Vito Paolo Natale Lorenzo Rosasco Lorenzo Odone Francesca 10.1038/s41598-023-37627-7 Machine Learning for the Study of Plankton and Marine Snow from Images 2022 Annual Reviews Irisson Jean-Olivier Ayata Sakina-Dorothée Lindsay Dhugal J. Karp-Boss Lee Stemmann Lars 10.1146/annurev-marine-041921-013023 Coupling Imaging and Omics in Plankton Surveys: State-of-the-Art, Challenges, and Future Directions 2022 Frontiers Media SA Pierella Karlusich Juan José Lombard Fabien Irisson Jean-Olivier Bowler Chris Foster Rachel A. 10.3389/fmars.2022.878803 MorphoCluster: Efficient Annotation of Plankton Images by Clustering 2020 MDPI AG Schröder Simon-Martin Kiko Rainer Koch Reinhard 10.3390/s20113060 Globally Consistent Quantitative Observations of Planktonic Ecosystems 2019 Frontiers Media SA LOMNARD FABIEN BOSS EMMANUEL WAITE ANYA M. VOGT MEIKE UITZ JULIA STEMMAN LARS SOSIK HEIDI M. SCHULZ JAN ROMAGNAN JEAN-BAPTISTE PICHERAL MARC PEARLMAN JAY OHMAN MARK D NIEHOFF BARBARA MÖLLER KLAS O MILOSLAVICH PATRICIA LARA-LPEZ ANA KUDELA RAPHAEL LOPES RIBENS M KIKO RAINER KARP-BOSS LEE JAFFE JULES S IVERSEN MORTEN H IRISSON JEAN-OLIVIER FENNEL KATJA HAUSS HELENA GUIDI LIONEL GORSKY GABY GIERING SARAH L.C. GAUBE PETER GALLAGER SCOTT DUBELAAR GEORGE COWEN ROBERT K CARLOTTI FRANÇOIS BRISENO-AVENA CHRISTIAN BERLINE LÉO BENOIT-BIRD KELLY BAX NICHOLAS BATTEN SONIA AYATA SAKINA DOROTHÉE ARTIGAS LUIS FELIPE APPELTANS WARD 10.3389/fmars.2019.00196 Plankton and environmental monitoring dataset from the Iroise Marine Natural Park (NE Atlantic, 2010-2023) 2025 SEANOE Drago Laetitia Cailliau Caroline Pouline Patrick Beker Beatriz Jalabert Laëtitia Romagnan Jean-Baptiste Ayata Sakina-Dorothée 10.17882/105465 FlowCAMNet : plankton images captured with the FlowCAM 2024 SEANOE Jalabert Laetitia Signoret Guillaume Caray-Counil Louis Vilain Marion Martins Emmanuelle Lombard Fabien Picheral Marc Irisson Jean-Olivier 10.17882/101961 ISIISNet : plankton images captured with the ISIIS (In-situ Ichthyoplankton Imaging System) 2024 SEANOE Panaïotis Thelma Caray-Counil Louis Jalabert Laetitia Irisson Jean-Olivier 10.17882/101950 UVP6Net : plankton images captured with the UVP6 2024 SEANOE Picheral Marc Jalabert Laetitia Motreuil Solène Courchet Lucas Carray-Counil Louis Ricour Florian Panaiotis Thelma Petit Flavien Elineau Amanda 10.17882/101948 PELGAS Bay of Biscay ZooScan zooplankton Dataset (2004-2016) 2023 SEANOE Grandrémy Nina Bourriau Paul Daché Edwin Danielou Marie-Madeleine Doray Mathieu Dupuy Christine Huret Martin Jalabert Laetitia Le Mestre Sophie Nowaczyk Antoine Petitgas Pierre Pineau Philippe Raphalen Elio Romagnan Jean-Baptiste 10.17882/94052 PELGAS Bay of Biscay ZooCAM zooplankton Dataset (2016-2019) 2023 SEANOE Grandrémy Nina Bourriau Paul Danielou Marie-Madeleine Doray Mathieu Dupuy Christine Forest Bertrand Huret Martin Le Mestre Sophie Nowaczyk Antoine Petitgas Pierre Pineau Philippe Rouxel Justin Tardivel Morgan Romagnan Jean-Baptiste 10.17882/94040 oceans -142.58 160.0 -60.55 48.55 1995-01-04 2019-10-23 IMAGE https://www.seanoe.org/data/00446/55741/#113141 WWW:DOWNLOAD-1.0-link--download Processed data 1995-2019 data - 9 GB https://www.seanoe.org/data/00446/55741/ WWW:DOWNLOAD-1.0-link--download Processed data 2009-2017 data [RESTRICTED] - 9 GB https://doi.org/10.17882/55741 WWW:LINK-1.0-http--metadata-URL DOI of the product https://www.seanoe.org/data/00446/55741/ rel-canonical Seanoe Nets: WP2: 200µm diam 57cm, Bongo: 300µm diam 57cm, Juday-Bogorov: 330µm diam 50cm, Régent: 680µm diam 100cm. ZooScan and Zooprocess: Gorsky G, Ohman MD, Picheral M, Gasparini S, Stemmann L, Romagnan JB, Cawood A, Pesant S, García-Comas C, Prejger F. Digital zooplankton image analysis using the ZooScan integrated system. Journal of plankton research. 2010 Mar 1;32(3):285-303.