Nolte, K.; Agboli, E.; Azambuja Garcia, G.; Badolo, A.; Becker, N.; Do Huy, L.; Dworrak, T. V.; Eguchi, J.; Eisenbarth, A.; Maciel de Freitas, R.; Doumna-Ndalembouly, A. G.; Heitmann, A.; Jansen, S.; Joest, A.; Joest, H.; Kiel, E.; Meyer, A.; Pfitzner, W.-P.; Saathoff, J.; Schmidt-Chanasit, J.; Sulesco, T.; Tokatlian, A.; Velavan, T. P.; Villacanas de Castro, C.; Wehmeyer, M. L.; Zahouli, J.; Sauer, F. G.; Luehken, R.

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.