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Digitalising behavioural data collection through cloud-based technology in veterinary science and beyond.

Michelle Braghetti1, Liat Vichman2, Nareed Farhat2

  • 1Clinical Anesthesiology, Department of Clinical Veterinary Science, Vetsuisse Faculty, University of Bern, Bern, Switzerland.

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|July 4, 2025
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Summary
This summary is machine-generated.

Researchers developed a no-code platform, PetsDataLab, for efficient collection of animal behavior data. This enables creating structured datasets, like the Dog Pain Database, advancing veterinary research and open science.

Keywords:
Dog Pain Databaseclinical data collection tooldigital data collectiondog paindog pain behaviourmobile app for behavioural researchmobile app for researchveterinary pain assessment

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Area of Science:

  • Veterinary science
  • Animal behavior
  • Data science

Background:

  • Field data collection in animal behavior science faces limitations: time, resources, and clinical workflow integration.
  • Need for flexible, efficient, and standardized digital solutions for multimodal behavioral data capture in real-world settings.

Purpose of the Study:

  • To present PetsDataLab, a novel cloud-based, no-code platform for customized app development for behavioral data collection.
  • To demonstrate the platform's utility by creating the Dog Pain Database for research on canine pain indicators.
  • To evaluate the app's usability and potential for broader deployment in veterinary and behavioral science.

Main Methods:

  • Utilized PetsDataLab to develop a custom app for collecting diverse data types (video, images, metadata).
  • Created a structured dataset of dogs with pain-related conditions, including clinical and video data.
  • Conducted a usability test with 14 veterinary professionals using a questionnaire for feedback.

Main Results:

  • The developed app demonstrated strong usability and clarity among veterinary professionals.
  • External testers showed lower agreement for daily clinic integration, indicating potential adoption barriers.
  • The platform successfully enabled scalable, standardized, and clinically compatible behavioral data collection.

Conclusions:

  • Cloud-based platforms like PetsDataLab can bridge research and practice by facilitating efficient data collection.
  • The approach is broadly applicable across behavioral science, supporting open science principles through structured, interoperable data.
  • Further work is needed to address barriers for routine integration into clinical workflows.