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%.

The folder ZooScanNet_data.tar 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 metadata of each object including the different features processed 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: unique object identifier in EcoTaxa (integer number) And 48 features:

- area

- mean

- stddev

- mode

- min/max

- perim.

- width,height 

- major,minor

- circ.

- feret

- intden

- median

- skew,kurt

- %area

- area_exc

- fractal

- skelarea

- slope

- histcum1,2,3

- nb1,2,3

- symetrieh,symetriev

- symetriehc,symetrievc

- convperim,convarea

- fcons

- thickr: 

- esd

- elongation

- range

- centroids

- sr

- perimareaexc

- feretareaexc

- perimferet/perimmajor

- circex

- cdexc

See the “ZooScan” sheet - OBJECT metadata, annotation and measurements - , at https://doi.org/10.5281/zenodo.14704250 for definitions.

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 

 

      2. Second folder ZooScanNet_imgs.tar contains :

imgs

Directory containing images of each object, named according to the object id objid and sorted in subdirectories according to their taxon.

 

      3. And :

map.png

Map of the sampling locations, to give an idea of the diversity sampled in this dataset.