Nolte Kristopher, Agboli Eric, Garcia Gabriela Azambuja, Badolo Athanase, Becker Norbert, Loc Do Huy, Dworrak Tarja Viviane, Eguchi Jacqueline, Eisenbarth Albert, de Freitas Rafael Maciel, Doumna-Ndalembouly Ange Gatien, Heitmann Anna, Jansen Stephanie, Jöst Artur, Jöst Hanna, Kiel Ellen, Meyer Alexandra, Pfitzner Wolf-Peter, Saathoff Joy, Schmidt-Chanasit Jonas, Sulesco Tatiana, Tokatlian Artin, Velavan Thirumalaisamy P, Villacañas de Castro Carmen, Wehmeyer Magdalena Laura, Zahouli Julien, Sauer Felix Gregor, Lühken Renke
Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany.
Laboratório de Mosquitos Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil.
Sci Data. 2025 Apr 29;12(1):715. doi: 10.1038/s41597-025-05043-3.
Accurate identification of mosquito species is essential for effective vector control and mitigation of mosquito-borne disease outbreaks. Traditional morphological identification requires highly specialized personnel and is time-consuming, while molecular techniques can be cost-effective and dependent on comprehensive genetic information. Wing geometric morphometry has emerged as a promising alternative, leveraging detailed geometric measurements of wing shapes and vein patterns to distinguish between species and detect intraspecies variations. This paper presents a curated dataset of 18,104 mosquito wing images, collected from 10,500 mosquito specimens, annotated with extensive meta-information, designed to support research in wing geometric morphometry and the development of machine learning models, ultimately supporting efforts in vector surveillance and research.
准确识别蚊种对于有效控制病媒和减轻蚊媒疾病爆发至关重要。传统的形态学鉴定需要高度专业化的人员且耗时，而分子技术虽具有成本效益，但依赖全面的遗传信息。翅几何形态测量法已成为一种有前景的替代方法，它利用对翅形和翅脉模式的详细几何测量来区分物种并检测种内变异。本文展示了一个精心整理的数据集，包含从10,500个蚊子标本收集的18,104张蚊子翅膀图像，并标注了丰富的元信息，旨在支持翅几何形态测量研究以及机器学习模型的开发，最终为病媒监测和研究提供支持。
