Too big to lose - a FAIR repository for biomedical data derived from home-cage monitoring

Home-Cage Monitoring (HCM) systems have revolutionised the collection of continuous behavioural data in rodent research and offer unprecedented insights into animal behaviour, yet the full potential of these data remains largely untapped. This paper, developed within the European network COST Action TEATIME, argues for a collaborative approach to harness the power of HCM data through interdisciplinary partnerships with machine learning experts and data scientists.We identify challenges that persist in fully capturing and analysing the complete behavioural repertoire of rodents over extended periods and propose the development of FAIR-compliant (Findable, Accessible, Interoperable, and Reusable) repositories for HCM (meta)data.Standardised data formats and comprehensive metadata enable confident predictions and discoveries across diverse scientific domains. By facilitating data sharing and collaborative analysis, we aim to unlock novel insights into health and disease, ultimately enhancing research efficiency and reducing animal use.Establishing this infrastructure will not only advance our understanding of rodent behaviour but also pave the way for innovative applications of machine learning and big data analytics in biomedical research.