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High-resolution Microscopic Image Dataset of Freshwater Plankton in Japanese Lakes and Reservoirs (FREPJ): I. Zooplankton

dataset
posted on 2024-11-22, 00:10 authored by Yurie OtakeYurie Otake, Aoi Osone, Wataru Makino, Koichi Ito, Takafumi Aoki, Kanta Miura, Yoshinobu Hayakawa, Ryotaro Yoshida, Satoshi Ichise, Akihiro Tsuji, Jotaro Urabe

“Zooplankton images” is a dataset recorded in “High-resolution Microscopic Image Dataset of Freshwater Plankton in Japanese Lakes and Reservoirs (FREPJ): I. Zooplankton” by “Yurie Ohtake, Aoi Osone, Wataru Makino, Koichi Ito, Takafumi Aoki, Kanta Miura, Yoshinobu Hayakawa, RyotaroYoshida, Satoshi Ichise, Akihiro Tuji and Jotaro Urabe”. It contains a total of 88,653 images of 214 freshwater zooplankton taxa collected from 87 lakes and reservoirs located in different areas of the Japanese archipelago. 

In the dataset, the images are stored in separate 40x and 100x folders, named “images_40” and “images_100” respectively. In these two folders, each folder name corresponds to a taxonomic label (phylum, class, order, family, genus, and species) and contains the images labeled with them. 

To obtain these images, a single large photograph for was first taken by scanning each zooplankton sample with a high-resolution camera installed in an intelligent microscope. Then, each plankton individual was cropped and extracted from the photograph as a single image, classified and labeled with multiple taxonomic ranks (phylum, class, order, family, genus, and species), and stored in the dataset. 

Metadata of this dataset is shown as “meta_data.docx”. Table S1 shows the information of sampling sites and date. Table S2 shows links between labels attached to each plankton image and classification information. Table S3 and S4 show the sampling location and sampling date for each individual plankton image in 40x and 100x folders. The present dataset will be useful not only as an atlas of freshwater zooplankton in Japan, but also for the construction, training, and evaluation of an automatic plankton identification and enumeration system based on machine learning.

Funding

The Environmental Restoration and Conservation Agency

History

Corresponding author email address

jurabe07@gmail.com

Copyright

© 2024 National Museum of Nature and Science

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    国立科学博物館研究報告B類(植物学)/Bulletin of the National Museum of Nature and Science. Series B, Botany

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