To bridge the gap between traditional laboratory constraints and real-world clinical environments, researchers developed PetsDataLab, a novel, no-code cloud platform that enables veterinary scientists to independently design customized mobile applications for multimodal data gathering. Utilizing this platform, investigators successfully built the*Dog Pain Database app to systematically capture structured demographic profiles, medical histories, validated pain scores, and high-resolution video recordings from 95 canine patients presenting with various discomfort conditions. Although a usability assessment involving 14 veterinary professionals confirmed the app’s strong clarity and user-friendliness, the evaluation also highlighted potential barriers regarding routine integration into busy daily clinic workflows due to time constraints. Ultimately, this proof-of-concept investigation demonstrates how scalable, cloud-based digital tools can streamline complex behavioural data collection in veterinary science while supporting open-science principles and future automated pain assessment technologies.
Prototype-based Explainable Deep Learning for Sex Classification of Mountain Gazelles in the Wild
Standard deep learning tools in automated wildlife monitoring often function
