Morphometric dataset of Varanus salvator for non-invasive sex identification using machine learning

• Main Authors: Arif Azlan Alymann, Imann Azlan Alymann, Song-Quan Ong, Mohd Uzair Rusli, Abu Hassan Ahmad, Hasber Salim
• Format: Article
• Language: English; English
• Published: Springer Nature 2024
• Subjects: QL1-991 Zoology; QL801-950.9 Anatomy
• Online Access: https://eprints.ums.edu.my/id/eprint/41439/1/ABSTRACT.pdf; https://eprints.ums.edu.my/id/eprint/41439/2/FULL%20TEXT.pdf; https://eprints.ums.edu.my/id/eprint/41439/; http://dx.doi.org/10.1038/s41597-024-03172-9
• Institution: Universiti Malaysia Sabah

Reliable sex identifcation in Varanus salvator traditionally relied on invasive methods like genetic analysis or dissection, as less invasive techniques such as hemipenes inversion are unreliable. Given the ecological importance of this species and skewed sex ratios in disturbed habitats, a dataset...