analysismake

Analysismake is a conceptual framework and set of tooling ideas for treating data analyses as buildable, reproducible processes. In this approach, a complete analysis is defined by a manifest that lists data sources, transformations, statistical analyses, and outputs, with explicit dependencies. The system automates execution, caches results, and tracks provenance to enable regenerating results from inputs.

Origin and usage: The term Analysismake combines analysis and make, drawing inspiration from build automation tools and modern workflow systems. It appears in discussions of reproducible research and workflow management and is not a single standardized project; rather, it denotes a design pattern or family of tools that encourage modular, auditable pipelines.

How it works: A user writes a configuration file or recipe that declares steps, inputs, parameters, and outputs. A runner resolves dependencies, executes steps in the correct order, and stores intermediate results with metadata. Outputs are produced deterministically when inputs are unchanged, and changes trigger incremental rebuilds to avoid repeating both unchanged steps and heavy computations.

Applications: Analysismake concepts are used to organize data pipelines in science, social science, and digital humanities. They help teams reproduce analyses, share pipelines, and ensure that figures, tables, and reports can be regenerated from raw data without reimplementing the entire workflow.

Limitations: Implementations vary in maturity and scope. The approach requires careful specification of steps and data provenance; non-deterministic analyses or opaque external data can complicate reproducibility. Users may face a learning curve and integration challenges with existing codebases and software environments.

See also: Make (software), Workflow management system, Reproducible research, Data provenance.