devire

Devire is a conceptual framework in data science and research methodology designed to formalize the workflow for deriving testable hypotheses from observational data and validating them through reproducible experiments. The term, used in theoretical and educational contexts, signals an emphasis on both derivation and verification in a unified process.

Core concepts: Hypothesis derivation is guided by domain knowledge and patterns in data. An experimental design phase translates hypotheses into testable protocols with defined data requirements, metrics, and preregistration where possible. The execution phase collects data under controlled or observational conditions, followed by analysis that assesses whether results support or refute the hypotheses. A central aspect of devire is reproducibility: sharing data, code, and documentation to enable independent replication. Some models further require sensitivity analyses and documentation of assumptions.

Lifecycle: Iteration is common; results may lead to refined hypotheses and additional experiments. Variants of devire differ in emphasis on preregistration, collaborative governance, or automation levels in data handling and analysis.

Applications: Used in data science education, research labs, and industries seeking to improve decision making through transparent, hypothesis-driven experimentation.

See also: Hypothesis testing, Experimental design, Reproducibility, Data provenance, Scientific method.