Data paper: Cross-species datasets on individual associative learning to regulate access to shared resources in group-housed gestating sows and lactating dairy cows

Individual access to shared resources is a major challenge in group-housed farm animals and has important consequences for welfare, social interactions, and system efficiency. Associative learning using emitted signals (such as sounds and vibrations) represents a promising strategy to regulate access to resources accessible in shared systems (such as feeders or milking robots), yet comparative datasets among and across species remain scarce. This data paper presents two complementary experimental datasets collected in gestating sows and dairy cows, designed to investigate individual associative learning in response to acoustic or vibratory signals predicting access to feed resources. In both experiments, animals were repeatedly exposed to a consistent signal associated with resource availability, and their behavioural responses were recorded at the individual level over time. The datasets include detailed information on response success, latencies, activity patterns, and animals’ characteristics, allowing the characterisation of learning dynamics and inter-individual variability. Data were obtained using a combination of automated recording systems and manual behavioural observations, then curated and structured to enable reuse and cross-species comparisons. These datasets provide a unique resource for researchers interested in animal cognition, precision livestock farming, and welfare-promoted management strategies, and offer opportunities for reanalysis using alternative learning models, behavioural classifications, or data-driven approaches.