ZooScanNet: plankton images captured with the ZooScan

Date 2018-07-03
Temporal extent 2009-09-07 -2017-07-19
Author(s) Elineau Amanda1, Desnos Corinne1, Jalabert Laetitia1, Olivier Marion1, Romagnan Jean-Baptiste1, Costa Brandao ManoelaORCID1, Lombard Fabien1, Llopis Natalia1, Courboulès Justine1, Caray-Counil Louis1, Serranito Bruno1, Irisson Jean-OlivierORCID1, Picheral Marc1, Gorsky Gaby1, Stemmann LarsORCID1
Affiliation(s) 1 : Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, 06230 Villefranche-sur-mer, France
DOI 10.17882/55741
Publisher SEANOE
Keyword(s) plankton, image, ZooScan, WP2, Bongo, Juday-Bogorov, Régent
Abstract

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: mean*area

  • 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.

Licence CC-BY-NC
Utilisation 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.
Acknowledgements 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.
Sensor metadata

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.

 

Data
File Size Format Processing Access
57398.tar 9 GB IMAGE Processed data Open access
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How to cite 

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 (2018). ZooScanNet: plankton images captured with the ZooScan. SEANOE. https://doi.org/10.17882/55741