eer-replication-package

GitHub

用于组装欧洲经济评论(EER)期刊强制要求的复制包。在论文实证结果稳定或接受前,整理原始数据、代码及环境配置,生成README和数据可用性声明,确保材料可复现并符合Elsevier政策。

European-Economic-Review-Skills/skills/eer-replication-package/SKILL.md brycewang-stanford/Awesome-Journal-Skills

Trigger Scenarios

论文包含实证、模拟或实验工作 结果稳定需组装提交材料 准备接受时需提供复制材料 审稿人或编辑询问数据/代码可用性

Install

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eer-replication-package -g -y
More Options

Non-standard path

npx skills add https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/European-Economic-Review-Skills/skills/eer-replication-package -g -y

Use without installing

npx skills use brycewang-stanford/Awesome-Journal-Skills@eer-replication-package

指定 Agent (Claude Code)

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eer-replication-package -a claude-code -g -y

安装 repo 全部 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --all -g -y

预览 repo 内 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --list

SKILL.md

Frontmatter
{
    "name": "eer-replication-package",
    "description": "Use when assembling the data and code deposit for a European Economic Review (EER) manuscript under Elsevier's mandatory replication policy. Builds a reproducible package and README; it does not run the analysis or write the paper."
}

Replication Package (eer-replication-package)

When to trigger

  • The paper contains empirical work, simulations, or experiments
  • Results are stabilizing and the deposit should be assembled (not at the last minute)
  • Preparing for acceptance, when the replication materials must be provided
  • A referee or editor asks about data/code availability

The EER replication bar

EER operates a mandatory replication policy: authors of accepted papers with empirical, simulation, or experimental content must provide, prior to publication, the data, programs, and computational details sufficient to permit replication of the reported results (检索于 2026-06;以官网为准). Elsevier's data-and-code availability framework applies; deposits typically go to a repository (e.g., Mendeley Data / Zenodo) and are linked from the article. Re-confirm on the official EER policy page whether there is pre-acceptance verification by a data editor, the exact deposit location, and the handling of restricted data — do not assert a verification step or repository that the current policy does not state. Build the package as you go so the deposit is not a scramble at acceptance.

What to assemble

Data

  • Raw data (or, for restricted data, the access path + a data-availability statement explaining how a replicator obtains it).
  • Intermediate/analysis files with a clear lineage from raw to final.
  • A data dictionary / codebook for constructed variables.
  • A data-availability statement matching what the article claims.

Code

  • One master script (run_all) that regenerates every table and figure from raw data.
  • Scripts ordered and numbered (clean → construct → analyze → exhibits).
  • Pinned environment: package versions recorded (requirements.txt/conda for Python, renv.lock for R, recorded Stata ssc/net versions; Project.toml/Manifest.toml for Julia).
  • Seeds set and reported for simulation / bootstrap / randomization inference.

Documentation (README)

  • Software + versions; hardware/runtime notes; expected outputs mapped to specific tables/figures.
  • Any partial-reproduction scope (e.g., confidential data) stated honestly.
  • License for the code; data-use terms respected.

Checklist

  • run_all master script regenerates every reported table and figure from raw data
  • Raw + intermediate data included, or a clear restricted-data access path given
  • Data dictionary / codebook for constructed variables
  • Environment pinned (language + package versions recorded)
  • Seeds set and reported for all stochastic steps
  • README maps each script/output to specific exhibits; runtime noted
  • Data-availability statement matches the article and the deposit
  • Deposit location confirmed against the current EER/Elsevier policy (检索于 2026-06)
  • Restricted/proprietary data handled per policy; partial-repro scope documented

Anti-patterns

  • Leaving the package to acceptance week — it controls the publication timeline
  • A code dump with no master script and no mapping from output to exhibits
  • Unpinned environments ("it ran on my laptop") that a replicator cannot reproduce
  • Unset seeds making simulation/bootstrap results non-reproducible
  • A data-availability statement that overclaims what is actually deposited
  • Asserting a data-editor verification step or repository not stated in the current policy

Worked vignette (illustrative)

An applied-micro paper ships a single .zip: 00_run_all.do, numbered 01_clean05_tables, a data/raw folder with a public extract plus a documented access path for the confidential employer–employee link, an renv-style version log of Stata packages, seeds fixed in the bootstrap, and a README mapping every do-file to the table/figure it produces and noting the confidential-data partial-reproduction scope. At acceptance the deposit goes to the repository the policy specifies and the article links it.

Output format

【Content】empirical / simulation / experimental (replication policy applies)
【Master script】run_all regenerates all exhibits? [Y/N]
【Data】raw included OR restricted-access path documented? [Y/N]
【Environment】versions pinned + seeds set/reported? [Y/N]
【README】outputs mapped to exhibits + partial-repro scope? [Y/N]
【Deposit】location confirmed vs current policy (检索于 2026-06)? [Y/N]
【Next step】eer-referee-strategy or eer-submission

Version History

  • 1839142 Current 2026-07-05 13:12

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Metadata

Files
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Version
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Hash
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Indexed
2026-07-05 13:12

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