Živko Južnič-Zonta, Isis Sanpera-Calbet, Roger Eritja, John R B Palmer, Agustí Escobar, Joan Garriga, Aitana Oltra, Alex Richter-Boix, Francis Schaffner, Alessandra Della Torre, Miguel Ángel Miranda, Marion Koopmans, Luisa Barzon, Frederic Bartumeus Ferre
{"title":"Mosquito alert: leveraging citizen science to create a GBIF mosquito occurrence dataset.","authors":"Živko Južnič-Zonta, Isis Sanpera-Calbet, Roger Eritja, John R B Palmer, Agustí Escobar, Joan Garriga, Aitana Oltra, Alex Richter-Boix, Francis Schaffner, Alessandra Della Torre, Miguel Ángel Miranda, Marion Koopmans, Luisa Barzon, Frederic Bartumeus Ferre","doi":"10.46471/gigabyte.54","DOIUrl":null,"url":null,"abstract":"<p><p>The Mosquito Alert dataset includes occurrence records of adult mosquitoes collected worldwide in 2014-2020 through Mosquito Alert, a citizen science system for investigating and managing disease-carrying mosquitoes. Records are linked to citizen science-submitted photographs and validated by entomologists to determine the presence of five targeted European mosquito vectors: <i>Aedes albopictus</i>, <i>Ae. aegypti</i>, <i>Ae. japonicus</i>, <i>Ae. koreicus</i>, and <i>Culex pipiens</i>. Most records are from Spain, reflecting Spanish national and regional funding, but since autumn 2020 substantial records from other European countries are included, thanks to volunteer entomologists coordinated by the AIM-COST Action, and to technological developments to increase scalability. Among other applications, the Mosquito Alert dataset will help develop citizen science-based early warning systems for mosquito-borne disease risk. It can also be reused for modelling vector exposure risk, or to train machine-learning detection and classification routines on the linked images, to assist with data validation and establishing automated alert systems.</p>","PeriodicalId":73157,"journal":{"name":"GigaByte (Hong Kong, China)","volume":"2022 ","pages":"gigabyte54"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9930537/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"GigaByte (Hong Kong, China)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46471/gigabyte.54","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The Mosquito Alert dataset includes occurrence records of adult mosquitoes collected worldwide in 2014-2020 through Mosquito Alert, a citizen science system for investigating and managing disease-carrying mosquitoes. Records are linked to citizen science-submitted photographs and validated by entomologists to determine the presence of five targeted European mosquito vectors: Aedes albopictus, Ae. aegypti, Ae. japonicus, Ae. koreicus, and Culex pipiens. Most records are from Spain, reflecting Spanish national and regional funding, but since autumn 2020 substantial records from other European countries are included, thanks to volunteer entomologists coordinated by the AIM-COST Action, and to technological developments to increase scalability. Among other applications, the Mosquito Alert dataset will help develop citizen science-based early warning systems for mosquito-borne disease risk. It can also be reused for modelling vector exposure risk, or to train machine-learning detection and classification routines on the linked images, to assist with data validation and establishing automated alert systems.
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