The data-set is composed of three tables, Environmental variables, Phytoplankton ( in log+1 abundance)  and the coordinates of the station used in the study. They are the processed data.

This dataset contains the biological outputs of a global ocean simulation coupling dynamics and biogeochemistry at ¼° over the year 2019. The simulation has been performed using the coupled circulation/ecosystem model NEMO/PISCES (https://www.nemo-ocean.eu/), which is here enhanced to perform an ensemble simulation with explicit simulation of modeling uncertainties in the physics and in the biogeochemistry. This dataset is one of the 40 members of the ensemble simulation. This study was part of the Horizon Europe project SEAMLESS (https://seamlessproject.org/Home.html), with the general objective of improving the analysis and forecast of ecosystem indicators.   See Popov et al. (https://os.copernicus.org/articles/20/155/2024/) for more details on the study.

The dataset dcm_dtb.txt contains bio-optical measurements and environmental parameters associated with  Deep Chlorophyll Maxima (DCM) acquired by BGC-Argo profiling floats. For each BGC-Argo profile the data files includes the World Meteorological Organization (WMO) and profile numbers, the Data Assembly Center (DAC), the geographical position (LON and LAT), the date of the profile in Julian Day (JULD) and in YYYY-MM-DD format; the region of the profile (REGION, acronyms detailed in the region.txt file), the DCM zonal attribution (ZONE, acronyms detailed in the zone.txt file), the vertical resolution of measurements of the concentration of the chlorophyll a [Chla] and of the backscattering coefficient (bbp) within the 250 first meters, the Mixed Layer Depth (MLD, m), the qualification of the vertical profile (DCM_TYPE) as Deep Biomass Maximum (3), Deep photoAcclimation Maximum (2), or presenting no DCM (1); the depth of the DCM (DCM_DEPTH); the chlorophyll a concentration (CHLA_DCM, mg chla m-3 ) the backscattering coefficient (BBP_DCM, m-1), and the Brunt-Vaisala frequency (N2_DCM) at the DCM depth; the nitracline depth (NCLINE_DEPTH, m) and steepness (NCLINE_STEEP, µmol NO3 m-3 m-1), the mean nitrate concentration within the Mixed Layer (NO3_MEAN_MLD, µmol NO3 m-3), the mean daily Photosynthetically Available Radiation in the Mixed Layer (MEAN_IPAR_MLD, E m -1 d -1), the daily Photosynthetically Available Radiation at the nitracline depth (IPAR_NCLINE, E m-2 d-1);  and the [Chla] measured by satellite (CHLA_SAT, mg chla m-3). The dataset shape_NASTG_ASEW.txt contains the seasonal median, the first and third quartiles of the [Chla] and of the bbp profiles for the North Atlantic Subtropical Gyre and Atlantic SubEquatorial Waters regions. The dataset climato_NASTG_ASEW.txt contains the monthly mean and standard deviations of the DCM depth (DCM_depth), the isolume depth of daily Photosynthetically Available Radiation of 20 E m-2 d-1 (iPAR_20), the nitracline depth, and the Mixed Layer Depth (MLD) for the profiles within the North Atlantic Subtropical Gyre and Atlantic SubEquatorial Waters regions.  The qualification and processing of the BGC-Argo profiles, as well as the DCM detection (DCM_TYPE) and the estimation of the environmental parameters, were applied as described from Cornec, M., Claustre, H., Mignot, A., Guidi, L., Lacour, L., Poteau, A., D’Ortenzio, F.,Gentili, B., Schmechtig, C., (to be updated.) Deep Chlorophyll Maxima in the global ocean: occurrences, drivers and characteristics. Global Biogeochemical Cycles, to be updated The [Chla] satellite variable was obtained by the match of each BGC-Argo profile with a L3S [Chla] product from the Ocean Colour-Climate Change Initiative v4.0 database merging observations from MERIS, MODIS, VIIRS and SeaWiFs, at a monthly and 4x4-km-pixel resolution, up to December 31, 2019 (ftp://oc-cci-data:ELaiWai8ae@oceancolour.org/occci-v4.2/).

'''Short description:''' Le modèle biogéochimique ECO-MARS3D sur la façade Manche Atlantique (PREVIMER_B1-ECOMARS3D-MANGA4000) est un modèle 3D de résolution spatiale 4km qui fournit les concentrations de nutriments et de plancton toutes les heures sur 30 niveaux (fenêtre de prévision à 4 jours). '''Paramètres calculés :''' Les paramètres calculés sont les suivants : * SAL : sea_water_salinity * TEMP : sea_water_temperature * suspended_inorganic_particulate_matter : mass_concentration_of_suspended_matter_in_sea_water * nanopicoplankton_nitrogen : mole_concentration_of_nanoplankton_expressed_as_nitrogen_in_sea_water * diatom_nitrogen : mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_water * dinoflagellate_nitrogen : mole_concentration_of_dinoflagellates_expressed_as_nitrogen_in_sea_water * microzooplankton_nitrogen : mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_water * mesozooplankton_nitrogen : mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_water * colonial_phaeocystis_nitrogen : mole_concentration_of_colonial_phaeocystis_expressed_as_nitrogen_in_sea_water * phaeocystis_mucus : concentration_of_phaeocystis_mucus_expressed_as_mass_in_sea_water * ammonium : mole_concentration_of_ammonium_in_sea_water * nitrate : mole_concentration_of_nitrate_in_sea_water * dissolved_silicate : mole_concentration_of_silicate_in_sea_water * dissolved_phosphate : mole_concentration_of_phosphate_in_sea_water * dissolved_oxygen : dissolved_oxygen_in_water_column * cumulative_nanoflagellate_carbon_production : cumulative_nanoflagellate_production_expressed_as_carbon_in_sea_water * cumulative_diatom_carbon_production : cumulative_diatom_production_expressed_as_carbon_in_sea_water * cumulative_dinoflagellate_carbon_production : cumulative_dinoflagellate_production_expressed_as_carbon_in_sea_water * cumulative_phaeocystis_carbon_production : cumulative_phaeocystis_production_expressed_as_carbon_in_sea_water * organic_nitrogen_benth : mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_benthos Les paramètres diagnostiques calculés sont les suivants : * XE : sea_surface_height_above_geoid * maximum_de_diat : maximum_diatom_mass_concentration_in_sea_water * maximum_de_dino : maximum_dinoflagellate_mass_concentration_in_sea_water * maximum_de_nano : maximum_nanoflagellate_mass_concentration_in_sea_water * grad_vert_salinite : maximum_vertical_gradient_of_sea_water_salinity * grad_vert_temp : maximum_vertical_gradient_of_sea_water_temperature * extinction_lumineuse : light_extinction_in_sea_water * prod_diat : cumulated_production_of_diatoms_in_sea_water_column_expressed_in_carbon * prod_dino : cumulated_production_of_dinoflagellates_in_sea_water_column_expressed_in_carbon * prod_nano : cumulated_production_of_nanoflagellates_in_sea_water_column_expressed_in_carbon * chlorophylle_a : chlorophyll_mass_concentration_in_sea_water * prod_cumul_chloro : cumulated_total_production_in_sea_water_column_expressed_in_carbon * maximum_de_phaeocystis : maximum_phaeocystis_mass_concentration_in_sea_water * prod_phaeocystis : cumulated_production_of_phaeocystis_in_sea_water_column_expressed_in_carbon * oxygen_saturation : oxygen_saturation * ammoniumGIRON_tracer_sign: mole_concentration_of_ammonium_in_sea_waterGIRON_tracer_sign * ammoniumGIRON_tracer_age: mole_concentration_of_ammonium_in_sea_waterGIRON_tracer_age * nitrateGIRON_tracer_sign: mole_concentration_of_nitrate_in_sea_waterGIRON_tracer_sign * nitrateGIRON_tracer_age: mole_concentration_of_nitrate_in_sea_waterGIRON_tracer_age * nanopicoplankton_nitrogenGIRON_tracer_sign: mole_concentration_of_nanoplankton_expressed_as_nitrogen_in_sea_waterGIRON_tracer_sign * nanopicoplankton_nitrogenGIRON_tracer_age: mole_concentration_of_nanoplankton_expressed_as_nitrogen_in_sea_waterGIRON_tracer_age * diatom_nitrogenGIRON_tracer_sign: mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_waterGIRON_tracer_sign * diatom_nitrogenGIRON_tracer_age: mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_waterGIRON_tracer_age * dinoflagellate_nitrogenGIRON_tracer_sign: mole_concentration_of_dinoflagellates_expressed_as_nitrogen_in_sea_waterGIRON_tracer_sign * dinoflagellate_nitrogenGIRON_tracer_age: mole_concentration_of_dinoflagellates_expressed_as_nitrogen_in_sea_waterGIRON_tracer_age * microzooplankton_nitrogenGIRON_tracer_sign: mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_waterGIRON_tracer_sign * microzooplankton_nitrogenGIRON_tracer_age: mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_waterGIRON_tracer_age * mesozooplankton_nitrogenGIRON_tracer_sign: mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_waterGIRON_tracer_sign * mesozooplankton_nitrogenGIRON_tracer_age: mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_waterGIRON_tracer_age * detrital_nitrogenGIRON_tracer_sign: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_waterGIRON_tracer_sign * detrital_nitrogenGIRON_tracer_age: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_waterGIRON_tracer_age * colonial_phaeocystis_nitrogenGIRON_tracer_sign: mole_concentration_of_colonial_phaeocystis_expressed_as_nitrogen_in_sea_waterGIRON_tracer_sign * colonial_phaeocystis_nitrogenGIRON_tracer_age: mole_concentration_of_colonial_phaeocystis_expressed_as_nitrogen_in_sea_waterGIRON_tracer_age * phaeocystis_cells_nitrogenGIRON_tracer_sign: mole_concentration_of_phaeocystis_cells_expressed_as_nitrogen_in_sea_waterGIRON_tracer_sign * phaeocystis_cells_nitrogenGIRON_tracer_age: mole_concentration_of_phaeocystis_cells_expressed_as_nitrogen_in_sea_waterGIRON_tracer_age * organic_nitrogen_benthGIRON_tracer_sign: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_benthosGIRON_tracer_sign * organic_nitrogen_benthGIRON_tracer_age: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_benthosGIRON_tracer_age * phytoplankton_sign_N_GIRON: nitrogen_fraction_in_phytoplankton_from_source_GIRON * phytoplankton_age_N_GIRON: age_of_nitrogen_fraction_in_phytoplankton_from_source_GIRON * ammoniumLOIRE_tracer_sign: mole_concentration_of_ammonium_in_sea_waterLOIRE_tracer_sign * ammoniumLOIRE_tracer_age: mole_concentration_of_ammonium_in_sea_waterLOIRE_tracer_age * nitrateLOIRE_tracer_sign: mole_concentration_of_nitrate_in_sea_waterLOIRE_tracer_sign * nitrateLOIRE_tracer_age: mole_concentration_of_nitrate_in_sea_waterLOIRE_tracer_age * nanopicoplankton_nitrogenLOIRE_tracer_sign: mole_concentration_of_nanoplankton_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_sign * nanopicoplankton_nitrogenLOIRE_tracer_age: mole_concentration_of_nanoplankton_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_age * diatom_nitrogenLOIRE_tracer_sign: mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_sign * diatom_nitrogenLOIRE_tracer_age: mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_age * dinoflagellate_nitrogenLOIRE_tracer_sign: mole_concentration_of_dinoflagellates_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_sign * dinoflagellate_nitrogenLOIRE_tracer_age: mole_concentration_of_dinoflagellates_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_age * microzooplankton_nitrogenLOIRE_tracer_sign: mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_sign * microzooplankton_nitrogenLOIRE_tracer_age: mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_age * mesozooplankton_nitrogenLOIRE_tracer_sign: mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_sign * mesozooplankton_nitrogenLOIRE_tracer_age: mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_age * detrital_nitrogenLOIRE_tracer_sign: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_sign * detrital_nitrogenLOIRE_tracer_age: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_age * colonial_phaeocystis_nitrogenLOIRE_tracer_sign: mole_concentration_of_colonial_phaeocystis_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_sign * colonial_phaeocystis_nitrogenLOIRE_tracer_age: mole_concentration_of_colonial_phaeocystis_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_age * phaeocystis_cells_nitrogenLOIRE_tracer_sign: mole_concentration_of_phaeocystis_cells_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_sign * phaeocystis_cells_nitrogenLOIRE_tracer_age: mole_concentration_of_phaeocystis_cells_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_age * organic_nitrogen_benthLOIRE_tracer_sign: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_benthosLOIRE_tracer_sign * organic_nitrogen_benthLOIRE_tracer_age: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_benthosLOIRE_tracer_age * phytoplankton_sign_N_LOIRE: nitrogen_fraction_in_phytoplankton_from_source_LOIRE * phytoplankton_age_N_LOIRE: age_of_nitrogen_fraction_in_phytoplankton_from_source_LOIRE * ammoniumSEINE_tracer_sign: mole_concentration_of_ammonium_in_sea_waterSEINE_tracer_sign * ammoniumSEINE_tracer_age: mole_concentration_of_ammonium_in_sea_waterSEINE_tracer_age * nitrateSEINE_tracer_sign: mole_concentration_of_nitrate_in_sea_waterSEINE_tracer_sign * nitrateSEINE_tracer_age: mole_concentration_of_nitrate_in_sea_waterSEINE_tracer_age * nanopicoplankton_nitrogenSEINE_tracer_sign: mole_concentration_of_nanoplankton_expressed_as_nitrogen_in_sea_waterSEINE_tracer_sign * nanopicoplankton_nitrogenSEINE_tracer_age: mole_concentration_of_nanoplankton_expressed_as_nitrogen_in_sea_waterSEINE_tracer_age * diatom_nitrogenSEINE_tracer_sign: mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_waterSEINE_tracer_sign * diatom_nitrogenSEINE_tracer_age: mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_waterSEINE_tracer_age * dinoflagellate_nitrogenSEINE_tracer_sign: mole_concentration_of_dinoflagellates_expressed_as_nitrogen_in_sea_waterSEINE_tracer_sign * dinoflagellate_nitrogenSEINE_tracer_age: mole_concentration_of_dinoflagellates_expressed_as_nitrogen_in_sea_waterSEINE_tracer_age * microzooplankton_nitrogenSEINE_tracer_sign: mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_waterSEINE_tracer_sign * microzooplankton_nitrogenSEINE_tracer_age: mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_waterSEINE_tracer_age * mesozooplankton_nitrogenSEINE_tracer_sign: mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_waterSEINE_tracer_sign * mesozooplankton_nitrogenSEINE_tracer_age: mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_waterSEINE_tracer_age * detrital_nitrogenSEINE_tracer_sign: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_waterSEINE_tracer_sign * detrital_nitrogenSEINE_tracer_age: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_waterSEINE_tracer_age * colonial_phaeocystis_nitrogenSEINE_tracer_sign: mole_concentration_of_colonial_phaeocystis_expressed_as_nitrogen_in_sea_waterSEINE_tracer_sign * colonial_phaeocystis_nitrogenSEINE_tracer_age: mole_concentration_of_colonial_phaeocystis_expressed_as_nitrogen_in_sea_waterSEINE_tracer_age * phaeocystis_cells_nitrogenSEINE_tracer_sign: mole_concentration_of_phaeocystis_cells_expressed_as_nitrogen_in_sea_waterSEINE_tracer_sign * phaeocystis_cells_nitrogenSEINE_tracer_age: mole_concentration_of_phaeocystis_cells_expressed_as_nitrogen_in_sea_waterSEINE_tracer_age * organic_nitrogen_benthSEINE_tracer_sign: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_benthosSEINE_tracer_sign * organic_nitrogen_benthSEINE_tracer_age: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_benthosSEINE_tracer_age * phytoplankton_sign_N_SEINE: nitrogen_fraction_in_phytoplankton_from_source_SEINE * phytoplankton_age_N_SEINE: age_of_nitrogen_fraction_in_phytoplankton_from_source_SEINE

The PHYTOBS-MARCOBOLO dataset comprises long-term time series on marine microphytoplankton, from 2003 to 2021, along the entire French metropolitan coastline. Microphytoplankton data cover microscopic taxonomic identifications and counts. The PHYTOBS-MARCOBOLO dataset corresponds to a dataset extracted from the PHYTOBS network (DOI:10.17882/85178). The PHYTOBS network provides the scientific community and stakeholders with validated and qualified data on the biomass, abundance and composition of marine microphytoplankton in coastal and lagoon waters, with the aim of supporting scientific research. PHYTOBS-MARCOBOLO is a dataset used as part of the Horizon Europe MARCO-BOLO project (https://marcobolo-project.eu/), in which we are currently working to understand and analyze multi-decadal trends in coastal and marine biodiversity on a European scale. The PHYTOBS-MARCOBOLO dataset gathers data from 18 sampling sites, selected from the PHYTOBS-Network dataset according to requirements of time series quality and geographical location of sampling sites established as part of the MARCO-COLO project. This dataset was also formatted according to a template imposed for the European project.

This REPHY dataset includes long-term time series on marine phytoplankton and physico-chemical measures, since 1987, along the whole French metropolitan coast. Some of these data are shared with those of the following regional networks: SRN (Hauts-de-France), RHLN (Normandy), ARCHYD (Arcachon), RSLHYD (Mediterranean lagoons). REPHY dataset from overseas departments (Martinique, Guadeloupe and French Guiana in West Atlantic waters; Reunion Island and Mayotte in Indian Ocean) will be available later. Phytoplankton data essentially cover microscopic taxonomic identifications and counts, but also pigments measures and flux cytometry measures in few regions. Physico-chemical measures include temperature, salinity, turbidity, dissolved oxygen, nutrients and chlorophylle a. The whole dataset is available, but is also divided into regions: North Sea + Channel + Atlantic, Mediterranean. For each of these two regional datasets, one includes only phytoplankton counts (PHYTO), the other (HYDRO) includes physico-chemical measures, pigments and flux cytometry measures.

Plankton was sampled with a Continuous Underway Fish Egg Sampler (CUFES, 315µm mesh size) at 4 m below the surface, and a WP2 net (200µm mesh size) from 100m to the surface, or 5 m above the sea floor to the surface when the depth was < 100 m, in the Bay of Biscay. The full images were processed with the ZooCAM software and the embedded Matrox Imaging Library (Colas et a., 2018) which generated regions of interest (ROIs) around each individual object and a set of features measured on the object. The same objects were re-processed to compute features with the scikit-image library http://scikit-image.org. The 1, 286, 590 resulting objects were sorted by a limited number of operators, following a common taxonomic guide, into 93 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 archive 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 morphological features computed by ZooCAM. 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 : - area : object's surface - area_exc : object surface excluding white pixels - area_based_diameter : object's Area Based Diameter: 2 * (object_area/pi)^(1/2) - meangreyobjet : mean image grey level - modegreyobjet : modal object grey level - sigmagrey : object grey level standard deviation - mingrey : minimum object grey level - maxgrey : maximum object grey level - sumgrey : object grey level integrated density: object_mean*object_area - breadth : breadth of the object along the best fitting ellipsoid minor axis - length : breadth of the object along the best fitting ellipsoid majorr axis - elongation : elongation index: object_length/object_breadth - perim : object's perimeter - minferetdiam : minimum object's feret diameter - maxferetdiam : maximum object's feret diameter - meanferetdiam : average object's feret diameter - feretelongation : elongation index: object_maxferetdiam/object_minferetdiam - compactness : Isoperimetric quotient: the ration of the object's area to the area of a circle having the same perimeter - intercept0, intercept45 , intercept90, intercept135 : the number of times that a transition from background to foreground occurs a the angle 0ø, 45ø, 90ø and 135ø for the entire object - convexhullarea : area of the convex hull of the object - convexhullfillratio : ratio object_area/convexhullarea - convexperimeter : perimeter of the convex hull of the object - n_number_of_runs : number of horizontal strings of consecutive foreground pixels in the object - n_chained_pixels : number of chained pixels in the object - n_convex_hull_points : number of summits of the object's convex hull polygon - n_number_of_holes : number of holes (as closed white pixel area) in the object - roughness : measure of small scale variations of amplitude in the object's grey levels - rectangularity : ratio of the object's area over its best bounding rectangle's area - skewness : skewness of the object's grey level distribution - kurtosis : kurtosis of the object's grey level distribution - fractal_box : fractal dimension of the object's perimeter - hist25, hist50, hist75 : grey level value at quantile 0.25, 0.5 and 0.75 of the object's grey levels normalized cumulative histogram - valhist25, valhist50, valhist75 : sum of grey levels at quantile 0.25, 0.5 and 0.75 of the object's grey levels normalized cumulative histogram - nobj25, nobj50, nobj75 : number of objects after thresholding at the object_valhist25, object_valhist50 and object_valhist75 grey level - symetrieh :index of horizontal symmetry - symetriev : index of vertical symmetry - skelarea : area of the object skeleton - thick_r : maximum object's thickness/mean object's thickness - cdist : distance between the mass and the grey level object's centroids features_skimage.csv.gz Table of morphological features recomputed with skimage.measure.regionprops on the ROIs produced by ZooCAM. 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 group map.png Map of the sampling locations, to give an idea of the diversity sampled in this dataset. imgs Directory containing images of each object, named according to the object id objid and sorted in subdirectories according to their taxon.

The Sir Alister Hardy Foundation for Ocean Science (SAHFOS) is an international charity that operates the Continuous Plankton Recorder (CPR) Survey. The dataset covers the North Atlantic and the North Sea on since 1958.

This dataset contains the pictures used for morphometric measurements, as well as the elemental compositon and production rates data, of planktonic Rhizaria. Specimens were collected in the bay of Villefranche-sur-Mer in May 2019 and during the P2107 cruise in the California Current in July-August 2021. Analyses of the data can be found at https://github.com/MnnLgt/Elemental_composition_Rhizaria.

This benchmark dataset contains the physical data used as predictors to reconstruct global chlorophyll-a concentrations (Chl, a proxy of phytoplankton biomass) in Roussillon et al., as well as the reference satellite Chl target fields. The nine physical predictors' data (Short-Wave radiations, Sea Surface Temperature, Sea Level Anomaly, Zonal and meridional surface currents, Zonal and meridional surface wind stress, Bathymetry, Binary continental mask) were extracted from publicly available datasets over [1998-2015] and resampled to the same spatio-temporel resolution as Chl, i.e. monthly on a 1°x1° grid between 50°N and 50°S. Missing values were gap-filled using the heat diffusion equation. Each variable was normalized by substracting its mean from the original values and dividing by its standard deviation over [1998-2015]. This dataset was used to train and validate the Multi-Mode Convolutional Neural network (CNNMM8) introduced in Roussillon et al. ; reconstructed monthly Chl fields over the [2012-2015] test period are also provided here. We hope this benchmark dataset can help to promote the improvements of methods as well as the emergence of new ideas, as building datasets is sometimes more time-consuming than the implementation of machine learning tools themselves. This would also facilitate the quantitative comparison of models performances' on the exact same datasets.