# Jet Replication And Data Policy > Use when handling the Journal of Economic Theory (JET) data/code expectations — JET is theorem-proof oriented, but Elsevier Option C applies when research data exist: deposit/cite/link data in a repository or explain why sharing is not possible. Focuses on reproducible computation when a paper has any, plus generative-AI disclosure. Light by design. - Skill: `thedixitjain/jet-replication-and-data-policy` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/jet-replication-and-data-policy` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/jet-replication-and-data-policy/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/thedixitjain/jet-replication-and-data-policy --- # Replication & Data Policy (jet-replication-and-data-policy) ## When to trigger - Your JET paper includes numerical examples, simulations, or computed results and you want to share them well - You are checking what JET requires for data/code at submission or acceptance - You need to get the generative-AI disclosure right ## What JET actually requires - **No journal-run replication archive.** Unlike empirical AEA / Econometric Society journals, JET has no JAE-Data-Archive-style replication archive. Most JET papers are pure theory, so the main reproducibility object is the proof and any supplementary appendix. - **Elsevier Option C applies when research data exist.** Deposit research data in a relevant repository and cite/link it, or state why the data cannot be shared. Data statements are supported in the submission flow and appear with the published article. - **Pure theorem papers still need a clear statement.** If there are no external research data and no computation, say so plainly. If there are numerical examples, simulations, or computer-assisted proof steps, make those artifacts reproducible and link/deposit them where appropriate. - **Generative-AI disclosure is required:** authors must declare any use of generative AI in manuscript preparation **at submission**. Reviewers and editors are **prohibited** from using generative-AI tools during evaluation. ## Reproducible-computation playbook (when the paper has computation) JET's Option C rule is data-focused, but for a theory paper any numerical content should still be reproducible because it strengthens the paper and pre-empts referee doubt: - [ ] One **master script** regenerates every reported number, table, and figure from scratch - [ ] Environment pinned (`requirements.txt`, `Project.toml`/`Manifest.toml`, recorded toolbox versions) - [ ] **Seeds set and reported** for any stochastic illustration - [ ] A short README mapping each script to the theorem/figure it supports - [ ] If shared, choose **one** channel (repo link / Mendeley Data / Data in Brief) and link it in the data statement ## What to package, by content type | Computational content in the paper | Artifact worth sharing | Channel that fits | |---|---|---| | Symbolic verification of closed forms (e.g., checking eq. (7) of a screening model) | one SymPy/Mathematica script per theorem | repo link in the data statement | | Counterexample found by search | the search code plus a certificate script confirming the final example violates the conclusion | repo; the certificate logic also goes in the paper | | Computed equilibria (e.g., a numerical fixed point for a dynamic-contract example) | solver script with tolerances and pinned environment | repo or Mendeley Data | | Experimental/empirical test of the theory (rare at JET) | data, cleaning, and analysis scripts | repository / Mendeley Data / Data in Brief, with Option C statement | | Pure theory, no computation | no archive to manufacture | no-data statement | ## Supplementary-appendix culture (the theory analogue of replication) - At a theorem-proof journal, the unit of "replication" is the **omitted proof**, not a dataset. Long technical arguments go to an online appendix / supplementary file the referee can read. - Make the supplementary appendix **self-contained in notation** and citable by numbered cross-references from the main text (e.g., "Appendix S.2"), so checking it never requires re-deriving the body. - If any proof step is **computer-assisted** — exhaustive finite-case checking, interval arithmetic, symbolic simplification — say so inside the proof and ship the checker; the step is only as credible as a referee's ability to re-run it. - Where the proofs live (in-PDF appendix vs separate supplementary file) varies; confirm against the journal's current author guidelines before splitting files. ## Companion README template ```text README — companion code for "" (JET submission) verify_thm2_bound.py → re-derives eq. (7)–(9); confirms the Theorem 2 bound is attained (Example 1) search_counterexample.jl → finds the Example 3 economy; seed 20250114; runtime < 1 min check_thm4_cases.py → exhaustive check of the 12 finite cases cited in Appendix B, Step 3 env: requirements.txt / Manifest.toml (pinned) Every reported number in the paper appears in the output of exactly one script above. ``` ## Anti-patterns - Assuming JET has a journal-run replication archive — it does not - Treating Option C as optional when the manuscript uses shareable research data - Reporting computed numbers no script can reproduce - Omitting the generative-AI declaration at submission - Treating the optional data statement as a substitute for a checkable proof — the proof carries the paper ## Output format ``` 【Has data/computation?】none / data / computation / both 【Option C】repository citation/link, or no-data/cannot-share statement? [Y/N] 【Reproducible】master script + pinned env + seeds + README? [Y/N] 【AI disclosure】declared at submission? [Y/N] 【Next】jet-submission ``` --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Journal-of-Economic-Theory-Skills/skills/jet-replication-and-data-policy/SKILL.md`