Morphometric dataset of Varanus salvator for non-invasive sex identification using machine learning
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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
Springer Nature
2024
|
| Subjects: | |
| 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 |
| _version_ | 1825715866133594112 |
|---|---|
| author | Arif Azlan Alymann Imann Azlan Alymann Song-Quan Ong Mohd Uzair Rusli Abu Hassan Ahmad Hasber Salim |
| author_facet | Arif Azlan Alymann Imann Azlan Alymann Song-Quan Ong Mohd Uzair Rusli Abu Hassan Ahmad Hasber Salim |
| author_sort | Arif Azlan Alymann |
| collection | UMS |
| description | 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 that allows ecologists or zoologists to study the sex determination of the lizard is crucial. We present a new dataset containing morphometric measurements of V. salvator individuals from the skin trade, with sex confrmed by dissection post- measurement. The dataset consists of a mixture of primary and secondary data such as weight, skull size, tail length, condition etc. and can be used in modelling studies for ecological and conservation research to monitor the sex ratio of this species. Validity was demonstrated by training and testing six machine learning models. This dataset has the potential to streamline sex determination, ofering a non-invasive alternative to complement existing methods in V. salvator research, mitigating the need for invasive procedures |
| first_indexed | 2024-12-09T00:52:31Z |
| format | Article |
| id | ums.eprints-41439 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-12-09T00:52:31Z |
| publishDate | 2024 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | ums.eprints-414392024-10-16T06:17:28Z https://eprints.ums.edu.my/id/eprint/41439/ Morphometric dataset of Varanus salvator for non-invasive sex identification using machine learning Arif Azlan Alymann Imann Azlan Alymann Song-Quan Ong Mohd Uzair Rusli Abu Hassan Ahmad Hasber Salim QL1-991 Zoology QL801-950.9 Anatomy 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 that allows ecologists or zoologists to study the sex determination of the lizard is crucial. We present a new dataset containing morphometric measurements of V. salvator individuals from the skin trade, with sex confrmed by dissection post- measurement. The dataset consists of a mixture of primary and secondary data such as weight, skull size, tail length, condition etc. and can be used in modelling studies for ecological and conservation research to monitor the sex ratio of this species. Validity was demonstrated by training and testing six machine learning models. This dataset has the potential to streamline sex determination, ofering a non-invasive alternative to complement existing methods in V. salvator research, mitigating the need for invasive procedures Springer Nature 2024 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/41439/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/41439/2/FULL%20TEXT.pdf Arif Azlan Alymann and Imann Azlan Alymann and Song-Quan Ong and Mohd Uzair Rusli and Abu Hassan Ahmad and Hasber Salim (2024) Morphometric dataset of Varanus salvator for non-invasive sex identification using machine learning. Scientific Data, 11 (1). pp. 1-5. ISSN 2052-4463 http://dx.doi.org/10.1038/s41597-024-03172-9 |
| spellingShingle | QL1-991 Zoology QL801-950.9 Anatomy Arif Azlan Alymann Imann Azlan Alymann Song-Quan Ong Mohd Uzair Rusli Abu Hassan Ahmad Hasber Salim Morphometric dataset of Varanus salvator for non-invasive sex identification using machine learning |
| title | Morphometric dataset of Varanus salvator for non-invasive sex identification using machine learning |
| title_full | Morphometric dataset of Varanus salvator for non-invasive sex identification using machine learning |
| title_fullStr | Morphometric dataset of Varanus salvator for non-invasive sex identification using machine learning |
| title_full_unstemmed | Morphometric dataset of Varanus salvator for non-invasive sex identification using machine learning |
| title_short | Morphometric dataset of Varanus salvator for non-invasive sex identification using machine learning |
| title_sort | morphometric dataset of varanus salvator for non invasive sex identification using machine learning |
| topic | QL1-991 Zoology QL801-950.9 Anatomy |
| url | https://eprints.ums.edu.my/id/eprint/41439/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/41439/2/FULL%20TEXT.pdf |
| work_keys_str_mv | AT arifazlanalymann morphometricdatasetofvaranussalvatorfornoninvasivesexidentificationusingmachinelearning AT imannazlanalymann morphometricdatasetofvaranussalvatorfornoninvasivesexidentificationusingmachinelearning AT songquanong morphometricdatasetofvaranussalvatorfornoninvasivesexidentificationusingmachinelearning AT mohduzairrusli morphometricdatasetofvaranussalvatorfornoninvasivesexidentificationusingmachinelearning AT abuhassanahmad morphometricdatasetofvaranussalvatorfornoninvasivesexidentificationusingmachinelearning AT hasbersalim morphometricdatasetofvaranussalvatorfornoninvasivesexidentificationusingmachinelearning |