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 1,451,745 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.

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 2024
Rights CC-BY-NC
OpenAccess true
Contact SEANOE
Representation
Resource Type Dataset
Discipline Marine Science