smr-software-and-reproducibility

SMR Software and Reproducibility

Use this to meet SMR's defining expectation: a methods paper should ship a **method other people can

run**. SMR readers adopt methods, so released, documented software and a reproducible pipeline are

not optional extras — a beautiful estimator with no usable code is a method no one will use, and

reviewers know it.

What "released software" means at SMR

• A package or well-structured code implementing the method in a mainstream environment (R, Stata,

Python — whatever the audience uses), with a documented interface, not a private analysis script.

• Worked usage: a minimal example that runs the method on toy or example data, so a reader can

confirm it works before adapting it.

• Versioned and citable: deposit in a trusted public repository (e.g., a tagged release with a DOI)

rather than a personal homepage that can disappear.

The reproducibility pipeline

Every exhibit in the paper must be regenerable:

• A README stating software versions, dependencies, expected runtime, and the entry-point command.
• A master script that runs the full pipeline: simulation grid → tables → figures → illustration,

with seeds fixed so Monte Carlo results reproduce exactly.

• One command per exhibit mapping (or a clear table) so a replicator knows which script produces

Table 2, Figure 3, etc.

• For long simulations, provide a smoke-test mode (few replications) that runs quickly and a

full-run mode that reproduces the paper.

The SMR availability statement

SMR requires a data-and-code availability statement (检索于 2026-06；以官网为准):

• Disclose how the study data, code, and materials can be permanently accessed — repository name,

DOI/URL, and any access conditions.

• Code and materials are expected; data should be deposited where possible, and restricted data

must state the reason and the access path/conditions in the statement.

• Use a trusted public repository; reference datasets in DataCite style.
• Write the statement at submission, not at acceptance — it is part of the manuscript's conformance.

Anonymization of the package under double-anonymized review

• The released materials submitted for review must not deanonymize: strip author names, institutional

paths, and account handles from code, README, and repository metadata.

• Use an anonymized repository (e.g., a blinded archive) for the review copy; reveal the public,

citable version at acceptance.

Checklist

• [ ] The method is released as a usable package/structured code, not a private script.
• [ ] A minimal runnable usage example is included.
• [ ] The code is versioned and deposited in a trusted, citable public repository.
• [ ] A README states versions, dependencies, runtime, and the entry command.
• [ ] A master script reproduces every table, figure, and the simulation, with seeds fixed.
• [ ] An exhibit→script map lets a replicator find each result.
• [ ] A smoke-test mode exists for long simulations.
• [ ] The data-and-code availability statement is written, with repository/DOI and any access conditions.
• [ ] The review-copy materials are anonymized.

Anti-patterns

• Private analysis script as "software": code that only the authors can run.
• Unseeded simulation: Monte Carlo results that cannot be reproduced.
• Disappearing host: code on a personal homepage with no archive/DOI.
• Missing exhibit map: a replicator cannot tell which script makes which table.
• Availability statement deferred to acceptance, or vague ("available on request").
• Deanonymizing repo metadata in the review copy.

Output format

[Reproducibility status] release-ready / needs work / not ready
[Software] package/structured code? usage example? citable deposit?
[Pipeline] master script + seeds + exhibit map present? smoke-test mode?
[Availability statement] written? repository/DOI + access conditions stated?
[Anonymization] review-copy materials clean? yes/no
[Next SMR skill] smr-submission