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Pubar transparency and data

Skill brycewang-stanford/Awesome-Journal-Skills/Public-Administration-Review-Skills/skills/pubar-transparency-and-data

Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的 Claude Code/Codex 期刊技能包，从选题、识别策略到表格规范与审稿回复全流程，助你快速发论文。

Install
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill pubar-transparency-and-data

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Use when preparing the transparency / reproducibility materials for a Public Administration Review (PAR) manuscript. PAR is a signatory of the Center for Open Science TOP Guidelines and has adopted transparency standards (data citation, data sharing via Dataverse/QDR, reporting documentation, pre-registration). Covers quantitative and qualitative transparency and the restricted-data path. Prepares the package; it does not waive requirements.

SKILL.md

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Transparency & Data Policy (pubar-transparency-and-data)

PAR endorses the Center for Open Science's Transparency and Openness Promotion (TOP) Guidelines and has adopted transparency standards for authors (检索于 2026-06；以官网为准). Build the materials as you go so a transparency request at review or acceptance does not stall the paper.

When to trigger

  • Building the reproducibility / transparency materials and the data-availability statement
  • A reviewer or editor requested data, code, or documentation
  • Data cannot be fully shared (privacy, FOIA limits, agency restrictions, IRB) and you need the path
  • Deciding whether to pursue pre-registration badges

What PAR's TOP standards cover (verify current wording)

  1. Data & materials citation. Cite all research materials and data sources used, with persistent identifiers where available — treat data as a citable research output.
  2. Data sharing. PAR encourages depositing data in an appropriate repository — Dataverse for quantitative data, the Qualitative Data Repository (QDR) for qualitative data — with a data-availability statement in the manuscript.
  3. Reporting documentation. PAR recommends documenting research design, data preparation, and analysis decisions in a supplementary document, following the relevant reporting standard where applicable (e.g., CONSORT for experiments, STROBE for observational, PRISMA for reviews, COREQ for qualitative).
  4. Pre-registration. Pre-registration of studies and/or analysis plans is supported, and badges may be available — register before data collection/analysis (see pubar-research-design).

Build-as-you-go checklist

  • One master script regenerates every table and figure from raw/constructed data
  • README documents data provenance, construction steps, and how to reproduce each exhibit
  • Seeds set and reported for every stochastic step
  • Software/package versions pinned (renv.lock / requirements.txt / recorded installs)
  • Exhibit numbers in the manuscript match the deposited output exactly
  • Data-availability statement drafted (where data live, or why they cannot be shared)
  • Reporting-standard supplement (CONSORT/STROBE/PRISMA/COREQ) where applicable
  • Pre-registration / pre-analysis plan linked (anonymized) where applicable

Transparency package by evidence type

Build the package around the evidence product, not around a generic folder dump.

Evidence typeMinimum packagePAR-specific risk
Administrative microdataData dictionary, construction log, access conditions, replication code, and an approved restricted-data statement if raw data cannot shipA practitioner-facing claim with no auditable data provenance
Survey / experimentInstrument, sampling frame, recruitment/consent language, randomization code, preregistration or exploratory label, and cleaning scriptsTreatments or outcomes cannot be interpreted by public managers
Qualitative interviews / fieldworkInterview protocol, coding scheme, memo trail, anonymized excerpts or controlled-access deposit, and IRB/consent constraintsOver-disclosure that harms participants, or under-documentation that blocks audit
Case comparison / process tracingCase-selection memo, source inventory, evidence tests, chronology, and rival-explanation log"Illustrative" cases presented as field-wide lessons
Public datasets / dashboardsPersistent links, download dates, version snapshots, transformation scripts, and archived outputsLive web data change after review and no longer reproduce the exhibits

Restricted-data decision tree

  1. Can raw data be public? Deposit the exact analysis data plus code in Dataverse or another persistent repository and cite it in the Data Availability Statement.
  2. Can de-identified analysis data be public? Deposit the de-identified data and document what was removed, masked, top-coded, aggregated, or perturbed.
  3. Can synthetic or toy data run the code? Provide synthetic data plus a validation note that the code path reproduces the tables/figures but not the confidential estimates.
  4. Can only metadata be shared? Provide a data-access route, DUA/IRB constraints, variable list, and a read-only replication log that maps each exhibit to the restricted source.
  5. Is nothing shareable? Treat this as an editor-facing exception request; explain why the claim is still auditable and which independent checks remain possible.

For every restricted path, separate legal/ethical inability from convenience. Convenience is not a transparency rationale.

When data cannot be shared (restricted-data path)

  • Explain why the data are restricted (privacy, IRB, agency/legal restrictions, FOIA limits).
  • Provide instructions on how others can obtain the data (access process, agency contact, DUA).
  • Where feasible, provide synthetic data or de-identified extracts so the code can be run.
  • For qualitative data, QDR supports controlled-access sharing with appropriate protections.

Reproducibility smoke test

Before submission, run a clean-room check:

  • start from a fresh directory or container with only the repository/deposit files;
  • install from the recorded environment file;
  • run the master script end to end;
  • compare every printed table/figure number against the manuscript;
  • record any manual step, proprietary software dependency, or restricted-data substitution.

If the smoke test fails, do not call the package "reproducible"; report the remaining limitation honestly in the Data Availability Statement.

Anti-patterns

  • Treating transparency as a post-acceptance afterthought
  • Depositing code that does not actually reproduce the printed tables/figures
  • A personal URL or dead link instead of a persistent repository (Dataverse/QDR)
  • Claiming data are restricted with no access path or synthetic substitute
  • Undocumented, un-seeded, unpinned code that "works on my machine"

Output format

【Repository】Dataverse (quant) / QDR (qual) — materials staged? [Y/N]
【Evidence type】admin / survey-experiment / qualitative / case-process / public-dataset
【Reproduces tables/figures?】master script verified locally? [Y/N]
【Documentation】README + provenance + seeds + pinned versions + reporting standard? [Y/N]
【Data-availability statement】drafted? [Y/N]
【Restricted data?】public / de-identified / synthetic / metadata-only / exception request
【Smoke test】fresh-run status + remaining manual step
【Pre-registration】badge pursued? [Y/N/NA]
【Next】pubar-review-process

Supplementary resources

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