jru-replication-package
GitHub为JRU期刊论文组装可复现的数据、代码和实验材料，撰写数据可用性声明。确保实验流程与结构估计代码可重现，处理人类受试者隐私及专有数据访问问题，构建清晰目录结构供他人运行。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jru-replication-package -g -y
SKILL.md
Frontmatter
{
"name": "jru-replication-package",
"description": "Use when assembling the data, code, and experiment materials for a Journal of Risk and Uncertainty (JRU) manuscript and writing its Data Availability Statement. Builds a transparent, reproducible package; it does not invent evidence or citations."
}
Replication Package (jru-replication-package)
When to trigger
- The paper has experimental or field results and you need a Data Availability Statement for the Springer submission
- z-Tree / oTree / Qualtrics materials and the structural estimation code exist but are not organized for a stranger to run
- A referee or the editor asks whether the elicitation could be reproduced from the materials provided
- Decisions about what data can be shared (human-subjects constraints) versus what must be documented are unsettled
What JRU / Springer expect
JRU requires a Data Availability Statement on original research articles, and Springer strongly encourages sharing the underlying research data (deposit in a recognized repository, with a citable DOI where possible). For this journal the package has two faces that generic econ replication advice misses: the experiment must be reproducible as a procedure (instructions, screens, incentive rules — not just the resulting dataset), and the structural estimation must be re-runnable (code that recovers the reported parameters). Exact policy wording and any mandatory-deposit details are 待核实 — verify on the official submission guidelines.
The two-face package
| Component | Experimental paper | Structural/empirical paper |
|---|---|---|
| Materials | full instructions, decision screens, comprehension checks, the incentive/payment protocol | data source + access terms, construction of the risk-exposure variable |
| Code | z-Tree/oTree/Qualtrics source + analysis scripts | estimation code (MLE/GMM/MSM), from raw data to every reported parameter |
| Data | subject-level choices (de-identified), session metadata, randomization seeds | analysis dataset or a clear access path if proprietary (e.g., admin/insurer data) |
| Reproducibility | a stranger can re-run the experiment AND re-derive the estimates | one script regenerates every table/figure from raw inputs |
Human-subjects and proprietary data
- De-identify subject data; document the IRB/ethics approval. If raw data cannot be shared, share the derived analysis data plus enough documentation to reproduce results.
- For insurer/administrative VSL-type data, state the access terms and provide the code so the pipeline is auditable even if the data are restricted.
- Pre-registration (if the study was pre-registered): link the registry and report deviations.
Layout that a stranger can run
A clean package for a JRU elicitation or estimation paper has a predictable shape:
/instructions— participant-facing text, decision screens, comprehension checks, payment protocol/experiment— z-Tree/oTree/Qualtrics source, with the random-incentive rule visible in code/data— de-identified subject choices, session metadata, randomization seeds, codebook/code— a singlemasterscript that runs raw → cleaned → every table/figure, plus the structural estimationREADME— software versions, run order, expected outputs, and the data-access path for any restricted inputs
The test is blunt: a colleague with the repository and nothing else should be able to (a) re-run the experiment and (b) reproduce every reported parameter.
Writing the Data Availability Statement
- Match the statement to reality: if data are restricted, say so and give the access route, do not over-promise.
- Cite the deposit with its DOI/repository where one exists; a citable archive is stronger than "available on request."
- Cross-check the statement against the actual deposit before submission — a mismatch is a common, avoidable editorial flag.
Checklist
- A Data Availability Statement is drafted and matches what is actually deposited
- Experiment: full instructions, screens, comprehension checks, and incentive rules are included (procedure reproducible)
- Experiment software source (z-Tree/oTree/Qualtrics) and randomization seeds provided
- One master script regenerates every table and figure from raw inputs
- Structural code recovers the reported parameters; environment/versions documented
- Data de-identified; IRB/ethics approval documented; proprietary-data access terms stated
- Pre-registration linked and deviations reported (if applicable)
- Policy specifics verified against official guidelines or marked 待核实
Common reproducibility failures in risk papers
- Seeds not saved, so the randomized lottery sequence cannot be regenerated — the experiment is not reproducible even with the software.
- Hand-edited intermediates between raw choices and the estimation, leaving a gap no script bridges.
- Estimation that does not converge to the reported numbers on a clean machine because tolerances, starting values, or package versions were not pinned.
- Comprehension-check data dropped silently in cleaning, so a stranger cannot reproduce the analysis sample.
A quick self-test: clone the package into a fresh directory, run the master script end to end, and confirm it reproduces the headline parameter without manual intervention.
Anti-patterns
- Depositing the dataset but not the instructions — for an elicitation paper the procedure is the method
- A "results.do" that assumes hand-edited intermediate files no one else has
- Sharing identifiable subject data, or omitting the IRB statement
- A Data Availability Statement that promises data not actually in the package
- Treating Springer's data policy as optional; the statement is required even when data are restricted
Worked vignette (illustrative)
An ambiguity-elicitation paper deposits only the cleaned choice matrix. A referee cannot tell whether the matching-probabilities task was incentive-compatible as run. The JRU-ready package adds the oTree source, the on-screen instructions and comprehension checks, the random-incentive payment rule, the session seeds, and a single script that goes from raw choices to the reported α-MEU estimate — so the elicitation and the estimate are both reproducible.
Output format
【Journal】Journal of Risk and Uncertainty
【Skill】jru-replication-package
【Verdict】ready / complete materials / fix access
【DAS drafted】matches deposit [Y/N]
【Experiment reproducible】instructions+screens+incentives+seeds [Y/N]
【Code reproducible】master script regenerates all exhibits [Y/N]
【Ethics/data】IRB documented; de-identified; access terms stated [Y/N]
【Policy status】verified / 待核实
【Next skill】jru-referee-strategy
Version History
- 1839142 Current 2026-07-05 13:57
