The silence of the labs: Ethical oversight in metabolomics research.

Ethical oversight and transparent reporting are essential for ensuring accountability and participant protection in biomedical research. However, despite the rapid expansion of human metabolomics, the consistency of ethical reporting in this field remains unclear. Here, we introduce MinEth, a benchmark corpus and transformer-based framework for detecting ethics-related statements in metabolomics publications. The corpus comprises 120 full-text research articles annotated for key ethical categories, including approval mentions, institutional review boards, and approval identifiers. Using this resource, we fine-tuned and evaluated several transformer architectures-BERT, RoBERTa, SciBERT, and DeBERTa-v3-for automated classification of ethical statements. Across 20 independent fine-tuning runs using identical data partitions, SciBERT achieved the highest overall ethics macro-F1 (0.614 ± 0.111) and significantly exceeded the other evaluated architectures after Holm correction. DeBERTa-v3 achieved the highest mean F1 for ethics approval identifiers (0.538 ± 0.232), although it did not significantly outperform all competing models. Annotation reliability was substantial (Cohen's κ = 0.84), confirming the robustness of the labeling scheme. Together, these results show that transformer-based language models can identify ethics-related reporting with high sensitivity and strong ranking performance, although F1-score and precision vary across architectures, target categories, and random seeds. MinEth provides an open, reproducible resource for advancing transparency, governance, and machine-readable ethics in metabolomics research.