Collection Overview
Scientific Data has launched a Guest-Edited Collection on Computer vision for animal studies.
Computer vision is advancing rapidly as AI tools grow in performance and sophistication, but progress remains limited by the availability of high‑quality training data.
This Collection aims to showcase descriptions of datasets in computer vision related to animal studies, covering use cases such as identification, welfare monitoring, phenotyping, and occurrence data. All settings, behaviours, and organism types will be considered. Submissions are also welcomed on data standards to support FAIR metadata and annotation in the area.
This Collection supports and amplifies research related to: SDG 15: Life on land.
This will be a Collection of data descriptors and will be open for submissions from all authors – on the condition that the manuscripts fall within the scope of the Collection and of Scientific Data more generally. We are welcoming submissions until 1st February 2027.
Why is this Collection important?
"I am excited about this Collection because computer vision is transforming animal studies in agriculture, biodiversity, conservation, and public health. By automating camera trap and aerial image analysis for domestic animal health and endangered species monitoring, and by accelerating digitization and morphometric studies in natural history collections, these technologies greatly reduce manual effort. Computer vision also enables rapid identification and surveillance of medically important vectors and animals, such as mosquitoes, ticks, and rodents, which are linked to disease outbreaks. This Collection can foster interdisciplinary collaboration and showcase innovations that advance ecological research, conservation, agriculture, and disease surveillance globally."
- Dr. Song-Quan Ong, Guest Editor
Why submit to a collection?
Collections like this one help promote high-quality science. They are led by Guest Editors, who are experts in their fields, and In-House Editors and are supported by a dedicated team of Commissioning Editors and Managing Editors at Springer Nature. Collection manuscripts typically see higher citations, downloads, and Altmetric scores and provide a one-stop-shop on a cutting-edge topic of interest.
Who is involved?
Guest Editors:
- Song-Quan Ong, PhD, Universiti Malaysia Sabah, Malaysia
- Linlin Shen, PhD, Shenzhen University, China
Internal Team:
- In-House Editor: Elizabeth Miller, Scientific Data, UK
- Commissioning Editor: Sophie Gray, Fully OA Brands, Springer Nature, UK
- Managing Editor: Eleanor Smith, Fully OA Brands, Springer Nature, UK
How can I submit my paper?
Visit the Collection page for more information on the Collection, and how to submit your article.