ecj-replication-package

Replication Package (ecj-replication-package)

When to trigger

• The paper is heading toward acceptance and the EJ Data Editor needs a reproducible deposit
• You want the package to pass the EJ Data Editor's reproducibility check on the first pass
• Some data are proprietary or restricted and you must request an exemption and document access
• You are setting up the project early so reproducibility is not a last-minute scramble

> Verify the current policy on the EJ Data Editor site (ejdataeditor.github.io) and the OUP

> Instructions before depositing. EJ runs pre-acceptance reproducibility checks: the paper is

> accepted for final publication only after results have been checked for reproducibility. The

> package is posted to the journal's Zenodo repository or another trusted repository and linked from

> the paper. It is essential to request a data exemption at the point of first submission if you

> face any access restrictions.

What a passing package contains

1. README (the centerpiece) following the DCAS / Social Science Data Editors README template:

• Overview of what the code does and the mapping from code → every exhibit and in-text number.
• Data availability statement: source, terms, whether each dataset is public / restricted / proprietary, and exact access steps (registrations, memberships, monetary and time costs). State clearly if data cannot be shared and why, referencing the exemption requested at first submission.
• Computational requirements: software + versions, packages + versions, OS, memory, and approximate run time.
• Instructions to run: a single master script ordering everything end to end.
• List of every table/figure/in-text number with the script and line that produces it.

1. Data: raw inputs (when license permits) and the code that builds analysis files from them. Provide complete documentation of all variables; if data are in a proprietary format (e.g., Stata .dta), also provide an ASCII/plain-text copy such as .csv. If raw data are restricted, include construction code plus a synthetic/simulated dataset that lets the pipeline run.
2. Code: a master script that reproduces every number, table, and figure from raw inputs, with relative paths and fixed seeds.
3. Output: log files and generated exhibits, so the editor can diff against the paper.

Reproducibility discipline

• One master script; no manual steps, no hard-coded absolute paths, no "run cell 4 then cell 2."
• Set and record random seeds for any simulation, bootstrap, or ML step.
• Pin software and package versions; record them in the README and, where possible, in a lockfile/environment file.
• Every exhibit and in-text number in the paper is regenerated by the code — no hand-edited tables.
• Directory layout is clean: data/ (raw, derived), code/ (build, analysis), output/ (tables, figures, logs).

Restricted / proprietary data (the EJ exemption route)

• Request the exemption at first submission, not at acceptance — the EJ Data Editor stresses this timing.
• You may not need to deposit the data, but you must deposit the code and a precise access path so a third party with the same license can reproduce results.
• Provide a Data Availability Statement and, where feasible, a small simulated dataset matching the schema so the pipeline is executable.
• Confidential-data results may require a verification arrangement with the EJ Data Editor; document it.

Checklist

• [ ] README follows the DCAS template (overview, data availability, requirements, run instructions, exhibit map)
• [ ] Deposit goes to the journal's Zenodo repository (or another trusted repository) with a license allowing replication
• [ ] Package layout matches EJ guidance: 1-paper, 2-appendices, README.pdf, 3-replication-package.zip, and optional 4-confidential-data-not-for-publication.zip
• [ ] Single master script reproduces every table, figure, and in-text number from inputs
• [ ] Software and package versions pinned and recorded
• [ ] Random seeds set and documented
• [ ] Relative paths only; runs on a clean machine in a fresh directory
• [ ] All variables documented; proprietary-format data also provided as ASCII/plain text
• [ ] Data availability statement covers each dataset (public / restricted / proprietary) with access steps and costs
• [ ] Restricted data: exemption requested at first submission
• [ ] Package re-run from scratch and output diffed against the paper, ready for the EJ Data Editor
• [ ] Current EJ/RES data policy (DCAS, Zenodo, EJ Data Editor) verified on the official pages

Anti-patterns

• A zip of scripts with no README and no code → exhibit mapping
• Absolute paths (/Users/me/...) that break on any other machine
• Unset seeds so bootstrap/simulation numbers do not reproduce
• "Data available on request" with no construction code and no access detail
• Requesting a restricted-data exemption only at acceptance instead of at first submission
• Proprietary-only data with no ASCII/plain-text companion and no variable documentation
• Hand-edited tables that the code does not actually generate
• Submitting without re-running the package on a clean environment

Output format

【Policy verified】EJ/RES data policy (DCAS, Zenodo, EJ Data Editor) checked on official pages [y/n]
【README】DCAS template sections present? [y/n each]
【Deposit】Zenodo (or trusted repo) + replication license attached? [y/n]
【Master script】reproduces all exhibits + in-text numbers from raw? [y/n]
【Versions + seeds】pinned/documented? [y/n]
【Data status】public / restricted (exemption at first submission) + access path; ASCII companion? [y/n]
【Clean-machine test】passed, ready for EJ Data Editor? [y/n]
【Next】ecj-submission