Enhancing reusability of veterinary epidemiological data by creating contextual metadata

Abstract Despite the global adoption of the FAIR guiding principles, significant barriers remain in their practical implementation due to the lack of domain-specific standards for "rich metadata". While generic metadata schemes provide basic information, they often fail to capture the complexity of data quality and the nuances of specialized scientific fields. This gap is particularly evident in veterinary epidemiology, a field characterized by complex, multi-scale data spanning various domains where analytical frameworks trend to be prioritized over raw data description, leading to inconsistent reporting and low data reusability. This paper introduces the first community-developed rich metadata guidelines specifically designed for veterinary epidemiology datasets, accompanied by practical templates and real-world examples. By integrating existing standards with specific guidance on domain-specific attributes and data quality, these guidelines provide a comprehensive, single-source framework accessible to researchers across academic, public, and private sectors. They aim to support researchers and stakeholders in enhancing the reusability of animal health data.