Spreadsheet of chromatogram areas for all dairy cows.

The hypothesis of this study is that volatile organic compounds (VOCs) present in bovine cerumen reflect systemic metabolic changes occurring during the transition from late gestation to early lactation and can serve as non-invasive indicators of energy balance and metabolic health in dairy cows. To evaluate this hypothesis, cerumen samples were collected longitudinally from the same animals at different physiological stages and analyzed using headspace gas chromatography–mass spectrometry (HS/GC–MS).
The dataset consists of raw and processed HS/GC–MS chromatograms from which 97 unique VOCs were identified after spectral deconvolution and removal of contaminants, including siloxanes. The detected compounds belong to several chemical classes, such as aldehydes, ketones, alcohols, amides, carboxylic acids, esters, lactones, heterocyclic compounds, and hydrocarbons. Each chromatogram corresponds to a single biological sample, allowing both cross-sectional comparisons between breeds and longitudinal evaluation within individual animals across time.
Data acquisition was performed under standardized HS/GC–MS conditions to ensure analytical reproducibility. Peak detection, alignment, and normalization were applied prior to statistical analysis. An untargeted metabolomics approach was used, meaning that no metabolites were predefined before analysis. Instead, statistical tools including fold-change analysis, non-parametric tests, volcano plots, hierarchical clustering, and heatmap visualization were applied to identify relevant metabolic differences between breeds and physiological stages.
The data reveal clear metabolic differentiation between Jersey and Girolando cows, as well as between pre-partum and early lactation periods. Notable differences were observed in metabolites related to lipid metabolism, oxidative stress, and energy balance, including fatty acids, aldehydes, ketone-related compounds, and alcohols. These patterns are consistent with known physiological adaptations to negative energy balance during the transition period.
Overall, the chromatographic profiles represent metabolic fingerprints that integrate breed-specific characteristics and temporal physiological changes. This dataset can be reused for biomarker discovery, comparative metabolomic studies, validation of alternative statistical approaches, or the development of non-invasive monitoring tools to support health management and decision-making in dairy production systems.