|Author(s)||Sauger Carine1, Quinquis Jerome1, Kellner Kristell2, Heude-Berthelin Clothilde2, Lepoittevin Mélanie3, Elie Nicolas4, Dubroca Laurent1|
|Affiliation(s)||1 : Institut Français de Recherche pour l’Exploitation de la Mer, IFREMER, Laboratoire Ressources Halieutiques de Port-en-Bessin, 14520, Port-en-Bessin-Huppain, France
2 : Université de Caen Normandie, Biologie des Organismes et Ecosystèmes Aquatiques (BOREA) MNHN, Sorbonne Université, UCN, CNRS-7208, IRD, UA, team EMERGE, Esplanade de la Paix, 14032, Caen, France
3 : Normandie Université, 14400, Caen, France
4 : Normandie Université, UNICAEN, SF 4206 ICORE, CMABIO3, 14000, Caen, France
|Source||Scientific Data (2052-4463) (Springer Science and Business Media LLC), 2020-05 , Vol. 7 , N. 1 , P. 165 (8p.)|
The North Sea plaice, Pleuronectes platessa (Linnaeus, 1758), is a commonly studied commercial flatfish with poorly known ovarian histology. The following dataset is a collection of female plaice gonad images and their corresponding histological slides, collected during a complete season of the plaice’s reproduction cycle. Stereology was used to determine the percentage of different structures found throughout the ovaries. Inter-agent calibrations were accomplished in order to harmonize the stereological readings, and were based on a comprehensive reading protocol and histological lexicon that were specifically written for the plaice’s ovaries. The distribution and homogeneity of the different cell types found throughout the ovaries were also evaluated. This dataset can be used to automate the stereological reading process (through statistical learning methods for example) or to objectively determine the plaice’s maturity phase, and link that information to either macroscopic measurements or through image analysis of the full ovaries.
Sauger Carine, Quinquis Jerome, Kellner Kristell, Heude-Berthelin Clothilde, Lepoittevin Mélanie, Elie Nicolas, Dubroca Laurent (2020). A macroscopic and stereological imaging dataset of Pleuronectes platessa ovaries. Scientific Data, 7(1), 165 (8p.). Publisher's official version : https://doi.org/10.1038/s41597-020-0505-8 , Open Access version : https://archimer.ifremer.fr/doc/00631/74342/