seanoe:107583 Irisson Jean-Olivier Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, F-06230 Villefranche-sur-Mer, France jean-olivier.irisson@imev-mer.fr 2025-10-24 ISO 19115:2003/19139 1.0 A global consistent database of plankton and detritus from in situ imaging by the Underwater Vision Profiler 5 2025-07-11 2025-10-24 Nocera Ariadna, Stemmann Lars, Babin Marcel, Biard Tristan, Coustenoble Julie, Carlotti François, Coppola Laurent, Courchet Lucas, Drago Laetitia, Elineau Amanda, Guidi Lionel, Hauss Helena, Jalabert Laëtitia, Karp-Boss Lee, Kiko Rainer, Laget Marion, Lombard Fabien, McDonnell Andrew, Merland Camille, Motreuil Solène, Panaïotis Thelma, Picheral Marc, Rogge Andreas, Waite Anya, Irisson Jean-Olivier (2025). A global consistent database of plankton and detritus from in situ imaging by the Underwater Vision Profiler 5. SEANOE. https://doi.org/10.17882/107583 Dataset summary Plankton and detritus are essential components of the Earth’s oceans influencing biogeochemical cycles and carbon sequestration. Climate change impacts their composition and marine ecosystems as a whole. To improve our understanding of these changes, standardized observation methods and integrated global datasets are needed to enhance the accuracy of ecological and climate models. Here, we present a global dataset for plankton and detritus obtained by two versions of the Underwater Vision Profiler 5 (UVP5). This release contains the images classified in 33 homogenized categories, as well as the metadata associated with them, reaching 3,114 profiles and ca. 8 million objects acquired between 2008-2018 at global scale. The geographical distribution of the dataset is unbalanced, with the Equatorial region (30° S - 30° N) being the most represented, followed by the high latitudes in the northern hemisphere and lastly the high latitudes in the Southern Hemisphere. Detritus is the most abundant category in terms of concentration (90%) and biovolume (95%), although its classification in different morphotypes is still not well established. Copepoda was the most abundant taxa within the plankton, with Trichodesmium colonies being the second most abundant. The two versions of UVP5 (SD and HD) have different imagers, resulting in a different effective size range to analyse plankton and detritus from the images (HD objects >600 µm, SD objects >1 mm) and morphological properties (grey levels, etc.) presenting similar patterns, although the ranges may differ. A large number of images of plankton and detritus will be collected in the future by the UVP5, and the public availability of this dataset will help it being utilized as a training set for machine learning and being improved by the scientific community. This will reduce uncertainty by classifying previously unclassified objects and expand the classification categories, ultimately enhancing biodiversity quantification. Data tables The data set is organised according to: - samples : Underwater Vision Profiler 5 profiles, taken at a given point in space and time. - objects : individual UVP images, taken at a given depth along the each profile, on which various morphological features were measured and that where then classified taxonomically in EcoTaxa. samples and objects have unique identifiers. The sample_id is used to link the different tables of the data set together. All files are Tab separated values, UTF8 encoded, gzip compressed. samples.tsv.gz - sample_id    <int>    unique sample identifier - sample_name    <text>    original sample identifier - project    <text>    EcoPart project title - lat, lon    <float>    location [decimal degrees] - datetime    <text>    date and time of start of profile [ISO 8601: YYYY-MM-DDTHH:MM:SSZ] - pixel_size    <float>    size of one pixel [mm] - uvp_model    <text>    version of the UVP: SD: standard definition, ZD: zoomed, HD: high definition samples_volume.tsv.gz Along a profile, the UVP takes many images, each of a fixed volume. The profiles are cut into 5 m depth bins in which the number of images taken is recorded and hence the imaged volume is known. This is necessary to compute concentrations. - sample_id    <int>    unique sample identifier - mid_depth_bin    <float>    middle of the depth bin (2.5 = from 0 to 5 m depth) [m] - water_volume_imaged    <float>    volume imaged = number of full images × unit volume [L] objects.tsv.gz - object_id    <int>     unique object identifier - object_name    <text>     original object identifier - sample_id    <int>     unique sample identifier - depth    <float>    depth at which the image was taken [m] - mid_depth_bin    <float>    corresponding depth bin [m]; to match with samples_volumes - taxon    <text>     original taxonomic name as in EcoTaxa; is not consistent across projects - lineage    <text>     taxonomic lineage corresponding to that name - classif_author    <text>     unique, anonymised identifier of the user who performed this classification - classif_datetime    <text>     date and time at which the classification was - group    <text>     broader taxonomic name, for which the identification is consistent over the whole dataset - group_lineage    <text>     taxonomic lineage corresponding to this broader group - area_mm2    <float>    measurements on the object, in real worl units (i.e. comparable across the whole dataset) … - major_mm    <float> - area    <float>    measurements on the objet, in [pixels] and therefore not directly comparable among the different UVP models and units - mean    <float> … - skeleton_area    <float> properties_per_bin.tsv.gz The information above allows to compute concentrations, biovolumes, and average grey level within a given depth bin. The code to do so is in `summarise_objects_properties.R`. - sample_id    <int>     unique sample identifier - depth_range    <text>     range of depth over which the concentration/biovolume are computed: (start,end], in [m] where `(` means not including, `]` means including - group    <text>     broad taxonomic group - concentration    <float>    concentration [ind/L] - biovolume    <float>    biovolume [mm3/L] - avg_grey    <float>    average grey level of particles [no unit; 0 is black, 255 is white] ODV_biovolumes.txt, ODV_concentrations.txt, ODV_grey_levels.txt This is the same information as above, formatted in a way that Ocean Data View https://odv.awi.de can read. In ODV, go to Import > ODV Spreadsheet and accept all default choices. Images The images are provided in a separate, much larger, zip file. They are stored with the format `sample_id/object_id.jpg`, where `sample_id` and `object_id` are the integer identifiers used in the data tables above. Nocera Ariadna Centro para el Estudio de Sistemas Marinos, CCT-CENPAT-CONICET, U9120ACD, Puerto Madryn, Chubut, Argentina Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, F-06230 Villefranche-sur-Mer, France Stemmann Lars Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, F-06230 Villefranche-sur-Mer, France Babin Marcel Département de Biologie, Université Laval, Québec, Canada Biard Tristan Laboratoire d’Océanologie et de Géosciences (LOG), Université du Littoral Côte d’Opale, Université Lille, CNRS, IRD, UMR 8187, Wimereux, France Coustenoble Julie Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, F-06230 Villefranche-sur-Mer, France Carlotti François Mediterranean Institute of Oceanography, Aix-Marseille Université, Université de Toulon, CNRS, IRD, UMR 7294, Marseille, France Coppola Laurent Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, F-06230 Villefranche-sur-Mer, France Sorbonne Université, CNRS, OSU STAMAR, UAR2017, 4 Place Jussieu, 75252 Paris CEDEX 05, France Courchet Lucas Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, F-06230 Villefranche-sur-Mer, France Drago Laetitia Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, F-06230 Villefranche-sur-Mer, France Elineau Amanda Sorbonne Université, CNRS, Institut de la Mer de Villefranche, IMEV, F-06230 Villefranche-sur-Mer, France Guidi Lionel Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, F-06230 Villefranche-sur-Mer, France Hauss Helena GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany NORCE Norwegian Research Centre, Bergen, Norway Jalabert Laëtitia Sorbonne Université, CNRS, Institut de la Mer de Villefranche, IMEV, F-06230 Villefranche-sur-Mer, France Karp-Boss Lee School of Marine Sciences, University of Maine, Orono, ME, USA Kiko Rainer Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, F-06230 Villefranche-sur-Mer, France GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany Laget Marion Laboratoire d’Océanologie et de Géosciences (LOG), Université du Littoral Côte d’Opale, Université Lille, CNRS, IRD, UMR 8187, Wimereux, France Lombard Fabien Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, F-06230 Villefranche-sur-Mer, France McDonnell Andrew College of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Fairbanks, Alaska, USA Merland Camille Sorbonne Université, CNRS, Institut de la Mer de Villefranche, IMEV, F-06230 Villefranche-sur-Mer, France Motreuil Solène Sorbonne Université, CNRS, Institut de la Mer de Villefranche, IMEV, F-06230 Villefranche-sur-Mer, France Panaïotis Thelma Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, F-06230 Villefranche-sur-Mer, France Picheral Marc Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, F-06230 Villefranche-sur-Mer, France Rogge Andreas Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany Waite Anya Department of Oceanography, Dalhousie University, Halifax, Nova Scotia, Canada Irisson Jean-Olivier Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, F-06230 Villefranche-sur-Mer, France jean-olivier.irisson@imev-mer.fr https://www.seanoe.org/data/00964/107583/illustration.jpg plankton particles Underwater Vision Profiler image Biological oceanography H2020 TRIATLAS (Agreement: 817578) H2020 AtlantECO (Agreement: 862923) H2020 Blue-Cloud (Agreement: 862409) Contaminants Zooplankton ODATIS aggregation parameters and Essential Variable names 2025-07-29 geonetwork.thesaurus.external.theme.NVS.OD1 Biological oceanography SeaDataNet Parameter Disciplines 2025-07-29 geonetwork.thesaurus.external.parameter.NVS.P08 A global consistent database of plankton and detritus from in situ imaging by the Underwater Vision Profiler 5 2025 Copernicus GmbH Nocera Ariadna Celina Stemmann Lars Babin Marcel Biard Tristan Coustenoble Julie Carlotti François Coppola Laurent Courchet Lucas Drago Laetitia Elineau Amanda Guidi Lionel Hauss Helena Jalabert Laëtitia Karp-Boss Lee Kiko Rainer Laget Manon Lombard Fabien McDonnell Andrew Merland Camille Motreuil Solène Panaïotis Thelma Picheral Marc Rogge Andreas Waite Anya Irisson Jean-Olivier 10.5194/essd-2025-522 Global Distribution of Zooplankton Biomass Estimated by In Situ Imaging and Machine Learning 2022 Frontiers Media SA Drago Laetitia Panaïotis Thelma Irisson Jean-Olivier Babin Marcel Biard Tristan Carlotti François Coppola Laurent Guidi Lionel Hauss Helena Karp-Boss Lee Lombard Fabien McDonnell Andrew M. P. Picheral Marc Rogge Andreas Waite Anya M. Stemmann Lars Kiko Rainer 10.3389/fmars.2022.894372 Correcting estimations of copepod volume from two‐dimensional images 2022 Wiley Dubois Cédric Irisson Jean‐Olivier Debreuve Eric 10.1002/lom3.10492 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 10.5194/essd-14-4315-2022 10.1111/geb.13741 The global marine particle size distribution dataset obtained with the Underwater Vision Profiler 5 - version 1 2021 PANGAEA Kiko Rainer Picheral Marc Antoine David Babin Marcel Berline L Biard Tristan Boss Emmanuel Brandt Peter Carlotti F Christiansen Svenja Coppola Laurent de la Cruz Leandro Diamond-Riquier Emilie de Madron Xavier Durrieu Elineau A Gorsky G Guidi Lionel Hauss Helena Irisson Jean-Olivier Karp-Boss Lee Karstensen Johannes Kim Dong-gyun Lekanoff Rachel M Lombard Fabien Lopes Rubens M Marec Claudie McDonnell Andrew Niemeyer Daniela Noyon Margaux O'Daly Stephanie Ohman Mark D Pretty Jessica L Rogge Andreas Searson Sarah Shibata Masashi Tanaka Yuji Tanhua Toste Taucher Jan Trudnowska Emilia Turner Jessie S Waite Anya M Stemmann Lars 10.1594/pangaea.924375 10.17600/8010090 10.17600/15001200 10.17600/13020010 10.18142/131 10.18142/235 10.17600/14007500 oceans -168.5 176.0 -64.5 80.5 TEXTE IMAGE https://www.seanoe.org/data/00964/107583/data/120871.zip WWW:DOWNLOAD-1.0-link--download Quality controlled data Data tables - 1 GB https://www.seanoe.org/data/00964/107583/data/121258.zip WWW:DOWNLOAD-1.0-link--download Quality controlled data Images - 33 GB https://doi.org/10.17882/107583 WWW:LINK-1.0-http--metadata-URL DOI of the product https://www.seanoe.org/data/00964/107583/ rel-canonical Seanoe