VETNET: Assembling Benchmark Datasets for AI-Driven Veterinary Innovation
Authors: Katherine J. Koebel, Renata Ivanek, Parminder Basran, Jennifer Sun, Casey Cazer, Gayatri Anil, Tommaso Banzato, Liz Barton, Dottie Cimino Brown, Leszek Bukowski, Marta Castelhano, Simon Coghlan, Christopher De Sa, Nikki Dutta, Hala Ghali, Julio Giordano, Joshua Glass, David C. Hogg, Nicholas Hollingshead, Adam Kiehl, Ziang Liu, Monica Meduri, Param Mohapatra, Kim Nayyer, Amelia Nelson, Peter-John Mantyla Noble, Christopher Pinard, Beryl Rabindran, Scott Ross, Ron Seccia, Moira Sheehan, Fei Wang, Tracy L. Webb, Kilian Q. Weinberger, Heather M. Whitney, Chen Xin
Organizations: Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA · Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA · Department of Computer Science, Cornell Ann S. Bowers College of Computing and Information Science, Cornell University, Ithaca, NY, USA · Department of Public and Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA · Department of Animal Medicine, Production, and Health, University of Padua, Italy · Vet Validate Ltd., St. John’s Innovation Centre, Cambridge, UK · Mars Veterinary Health, Vancouver, WA, USA · VetApp, Poland · Cornell Veterinary Biobank, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA · School of Computing and Information Systems, The University of Melbourne, Australia · Foundation for Food & Agriculture Research, Washington, DC, USA · Department of Data Science and Research, The Schwarzman Animal Medical Center, New York, USA · Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA · School of Computer Science, University of Leeds, Leeds, UK · Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, USA · Center for Applied Mathematics, Cornell University, Ithaca, NY, USA · Cornell Law School, Cornell University, Ithaca, NY, USA · Small Animal Veterinary Surveillance Network, School of Veterinary Sciences, University of Liverpool, Leahurst Campus, Chester High Road, Neston, Cheshire, UK · Department of Oncology, Toronto Animal Cancer Centre, Toronto, Ontario, Canada · ANI.ML Research, ANI.ML Health Inc., Toronto, Ontario, Canada · Department of Small Animal Clinical Sciences, University of Saskatoon, Saskatoon, Saskatchewan, Canada · Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI), University of Guelph, Guelph, Ontario, Canada · Amazon Web Services (AWS) Open Data, Arlington, VA, USA · Veterinary Medicine Information Technology (VMIT), College of Veterinary Medicine, Cornell University, Ithaca, NY, USA · Breeding Insight, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, FL, USA · Division of Health Informatics and Artificial Intelligence, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA · Department of Radiology, The University of Chicago, Chicago, IL, USA · Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR, China
VETNET represents an ecosystem of live benchmark datasets and evaluation frameworks designed to accelerate AI-driven innovation in animal science and veterinary medicine through standardized, high-quality datasets and multi-institutional collaboration across academic and non-academic sectors. This framework addresses the gap between AI advances in human medicine and the fragmented state of veterinary AI development.
We introduce PetQA, a Korean long-form question-answering (QA) benchmark for evaluating veterinary knowledge and clinical reasoning in large language models (LLMs) and large vision-language models (LVLMs). PetQA contains 10,076 text-only and 8,751 multimodal QA pairs derived from real-world questions about dogs and cats, paired with answers from expert veterinarians. Its test split, PetQA-Bench, further includes annotations for question types and clinical conditions. We evaluate eighteen models using ROUGE, BERTScore, and LLM-as-a-judge metrics for factuality and helpfulness under three settings: zero-shot inference, retrieval-augmented generation (RAG), and supervised fine-tuning (SFT). The benchmarking results provide an overview of the strengths and limitations of current models in addressing veterinary clinical queries and highlight the need for more effective adaptation methods to develop clinically reliable AI systems for veterinary care. To facilitate broader use, we additionally provide translated versions of PetQA-Bench in five languages.
Taegyun Kim, Youngwook Ham, Jungwook Rhim +3
Department of Intelligent Semiconductors, Soongsil University · Kangwon National University · KDI School of Public Policy and Management +1
Animal ecology data collection using drones represents a substantial investment of time, expertise, and financial resources. Yet most existing datasets serve only a single research community, limiting interdisciplinary reuse. We propose a unified drone dataset standard, FAIR^2 Drones, that bridges ecology, robotics, and computer vision by building on existing FAIR and AI-ready data frameworks while adding essential platform metadata and annotation specifications. Our standard enables datasets to simultaneously support ecological analysis, robotics algorithm development, and computer vision benchmarking. We provide open-source validation tools, reference implementations, and multimodal extensions linking drone imagery with complementary sensors such as camera traps, GPS, and acoustics. By standardizing metadata across disciplines, this framework maximizes the scientific return on investment for costly field deployments and accelerates cross-domain collaboration in environmental monitoring.
Jenna Kline, Kilian Meier, Vandita Shukla +14
The Ohio State University, Department of Computer Science and Engineering, Columbus, OH, USA · School of Civil, Aerospace and Design Engineering, University of Bristol, Bristol, United Kingdom · 33D Optical Metrology (3DOM), Fondazione Bruno Kessler (FBK), Trento, Italy +7
Medical imaging AI development is fundamentally dependent on annotated datasets, yet no existing standard provides machine-enforceable validation across dataset structure, annotation provenance, quality documentation, and ML readiness within a single framework. DICOM standardizes image acquisition, storage, and communication at the individual study level. BIDS organizes neuroimaging research datasets with consistent naming conventions. Neither addresses the curation layer, viz., who annotated what, when, with what tool, and to what quality standard. This paper presents VIDS (Verified Imaging Dataset Standard), an open specification that defines folder layout, file naming, annotation provenance schemas, quality documentation, and 21 machine-enforceable validation rules across two compliance profiles. VIDS uses NIfTI as a canonical working format while preserving full DICOM metadata in sidecars for traceability, and supports export to any downstream ML framework (nnU-Net, MONAI, COCO, flat NIfTI) without loss of provenance. Twenty-two compliance dimensions are defined and four major public datasets -- LIDC-IDRI, BraTS, CheXpert, and the Medical Segmentation Decathlon -- are benchmarked against these dimensions. Even widely used datasets satisfy only 20--39% of these dimensions, with provenance and quality documentation as the largest systematic gaps. LIDC-Hybrid-100 is released as a 100-subject VIDS-compliant reference CT dataset with consensus segmentation masks from four radiologist annotations (mean pairwise Dice 0.7765), validating 21/21 on the Full compliance profile. VIDS is fully open source: the specification is CC BY 4.0, all tools are Apache 2.0, the reference validator is available on PyPI (pip install vids-validator), and LIDC-Hybrid-100 is published on Zenodo (https://doi.org/10.5281/zenodo.19582717).