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%.

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

• 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
• esd: Equivalent Spherical Diameter
• elongation: elongation index: major/minor
• range: range of greys: max-min
• centroids : sqrt(pow(xm-x,2)+ pow(ym-y,2))
• sr: index of variation of greys: 100*(stddev/range)
• perimareaexc : perim/(sqrt(area_exc))
• feretareaexc : feret/(sqrt(area_exc))
• perimferet: index of the relative complexity of the perimeter: perim/feret
• perimmajor: index of the relative complexity of the perimeter: perim/major
• circex: (4*PI*area_exc)/(pow(perim,2))
• cdexc: (1/(sqrt(area_exc))) * sqrt(pow(xm-x,2)+pow(ym-y,2)

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 

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.