von Patrick Höller ; Renke Lühken ; Felix G. Sauer ; Carmen Villacañas de Castro ; Norbert Becker ; Hanna Jöst ; Wolf Peter Pfitzner ; Jonas Schmidt-Chanasit ; Anna Heitmann ; Stephanie Jansen
von Kristopher Nolte ; Eric Agboli ; Gabriela Azambuja Garcia ; Athanase Badolo ; Norbert Becker ; Do Huy Loc ; Tarja Viviane Dworrak ; Jacqueline Eguchi ; Albert Eisenbarth ; Rafael Maciel de Freitas ; Ange Gatien Doumna-Ndalembouly ; Anna Heitmann ; Stephanie Jansen ; Artur Jöst ; Hanna Jöst ; Ellen Kiel ; Alexandra Meyer ; Wolf-Peter Pfitzner ; Joy Saathoff ; Jonas Schmidt-Chanasit ; Tatiana Sulesco ; Artin Tokatlian ; Thirumalaisamy P. Velavan ; Carmen Villacañas de Castro ; Magdalena Laura Wehmeyer ; Julien Zahouli ; Felix Gregor Sauer ; Renke Lühken
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.
Scientific data London : Nature Publ. Group, 2014 12(2025), 1, Artikel-ID 715, Seite 1-6 Online-Ressource
