Examples
Worked examples
- Is an instance
A renv.lock file pinning every R package version used by an analysis.
- Is an instance
A Dockerfile producing a reproducible Ubuntu 22.04 image with Python 3.11 and pinned dependencies.
Counter-examples
Looks similar, but isn't
- Not an instance
A 'requirements.txt' with unpinned versions (>= or ranges).
- Not an instance
A README listing language versions without pinning libraries.
Editorial commentary
A computational environment is the full software and hardware context in which an analysis actually runs: operating system, language runtime and its version, every library and its version, configuration and environment variables, and hardware-specific dependencies such as GPU drivers and CUDA versions. It is the broadest of this site’s reproducibility-mechanism terms — the thing that needs capturing, as distinct from the specific tool used to capture it.
How this relates to containers and workflow tools
A container image is one mechanism for capturing and distributing a computational environment as a single portable artefact. A workflow tool such as Nextflow or Snakemake often specifies a container image per pipeline step, so that each step’s environment is pinned independently rather than relying on one shared, drift-prone environment for the whole analysis. Package and environment managers (conda, renv, poetry) are a lighter-weight alternative that pins library versions without full OS-level isolation — adequate for many analyses, but not equivalent to a container for capturing OS-level or hardware-driver dependencies.
Why “it worked on my machine” is a documentation failure, not bad luck
An analysis that only reproduces on its original author’s own machine has, by definition, an undocumented computational environment — the fix is recording the environment as explicitly as the code and data, not simply retrying.
References
Also known as
execution environment · research environment
Machine-readable encodings
Use in your systems
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"datePublished": "2026-05-21T02:22:49",
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