The HERMES Framework for Experimental Reproducibility

Experimental reproducibility remains a persistent challenge across scientific disciplines despite substantial advances in analytical instrumentation, quality assurance systems, statistical methodologies, and reporting standards. Although reproducibility is frequently examined through the lenses of experimental design, data analysis, and methodological transparency, hidden operational factors embedded within routine laboratory activities often remain underrecognized sources of variability. These factors can introduce cumulative and interacting effects that compromise experimental consistency even when formal protocols are followed. This review examines the influence of operational variability arising from human practices, environmental conditions, reagents and consumables, instrumentation, digital data management systems, and sample-related factors across multidisciplinary laboratory settings. Current mitigation approaches, including standard operating procedures, quality-control programs, electronic laboratory notebooks, and laboratory information management systems, are critically evaluated with emphasis on their strengths and limitations in controlling operational variability. To support a more systematic understanding of reproducibility, the HERMES framework (Human, Environmental, Reagent, Machine, Electronic, and Sample factors) is proposed as a systems-based model for identifying, classifying, and managing hidden sources of experimental variation. By integrating operational influences that are often considered independently, this framework highlights reproducibility as an emergent property of interconnected laboratory systems rather than solely a methodological or statistical outcome. Recognizing and controlling these hidden operational factors may improve experimental reliability, strengthen data quality, and enhance confidence in scientific findings across research disciplines. HERMES is presented as a conceptual framework that integrates principles of operational reproducibility across laboratory disciplines. Future empirical studies are needed to validate its implementation, evaluate its effectiveness, and develop quantitative operational metrics for routine laboratory practice.