ZooScanNet: plankton images captured with the ZooScan

DOI

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 1,433,278 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). 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: taxonomic name. Ambiguous names are made unique by including the name of the parent taxon in parentheses, after the name of the taxon. - lineage: full taxonomic lineage corresponding to this taxon. features_native.csv.gz Table of morphological features computed 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: same as above - area: area - mean: mean grey - stddev: standard deviation of greys - mode: modal grey - min: minimum grey - max: maximum grey - perim.: perimeter - width,height dimensions - major,minor: length of major,minor axis of the best fitting ellipse - circ.: circularity: 4pi(area/perim.^2) - feret: maximal feret diameter - intden: integrated density: meanarea - median: median grey - skew,kurt: skewness,kurtosis of the histogram of greys - %area: proportion of the image corresponding to the object - area_exc: area excluding holes - fractal: fractal dimension of the perimeter - skelarea: area of the one-pixel wide skeleton of the image - slope: slope of the cumulated histogram of greys - histcum1,2,3: grey level at quantiles 0.25, 0.5, 0.75 of the histogram of greys - nb1,2,3: number of objects after thresholding at the grey levels above - symetrieh,symetriev: index of horizontal,vertical symmetry - symetriehc,symetrievc: same but after thresholding at level histcum1 - convperim,convarea: perimeter,area of the convex hull of the object - fcons: contrast - thickr: thickness ratio: maximum thickness/mean thickness - elongation: elongation index: major/minor - range: range of greys: max-min - meanpos: relative position of the mean grey: (max-mean)/range - cv: coefficient of variation of greys: 100(stddev/mean) - sr: index of variation of greys: 100*(stddev/range) - perimferet: index of the relative complexity of the perimeter: perim/feret - perimmajor: index of the relative complexity of the perimeter: perim/major 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.txt Tree view of the taxonomy and number of images in each taxon, displayed as text. 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.

Identifier
DOI https://doi.org/10.17882/55741
Metadata Access http://www.seanoe.org/oai/OAIHandler?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:seanoe.org:55741
Provenance
Creator 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
Publisher SEANOE
Publication Year 2018
Rights CC-BY-NC
OpenAccess true
Contact SEANOE
Representation
Resource Type Dataset
Discipline Marine Science