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Creating detailed reference files that document variable units, summary choices, and experimental designs.
Distinct from Variable Context Patterns: Existing candidates refer to programming variable context or AI documentation, not research data code books.
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This project is a research data sharing framework and provenance protocol designed to ensure computational reproducibility. It provides a standardized set of guidelines for transforming raw source data into tidy formats through documented processing scripts and cleaning workflows. The framework distinguishes itself by emphasizing a strict provenance-based packaging system. It requires the organization of raw data, processing recipes, and code books into a single package, ensuring that original unmodified sources are preserved to allow for independent verification of all transformation steps.
Provides a framework for creating detailed code books that define variable units and experimental designs.