cps-transparency-and-data

Transparency & Data (cps-transparency-and-data)

CPS enforces political-science replication norms. Papers presenting quantitative analyses will not be granted final acceptance until replication materials (data, code, log files, etc.) are deposited at the CPS Dataverse on Harvard Dataverse, and every paper needs a data availability statement. Build the package as you go so final acceptance does not stall — and follow DA-RT-style transparency for qualitative evidence too.

When to trigger

• Building the replication/reproducibility package and the data availability statement
• A manuscript is near acceptance and the editorial office expects deposited materials
• Data cannot be fully shared (privacy, ethics, provider restrictions) and you need a path
• Preparing a (optional) anonymized pre-analysis plan as supplementary material

What CPS requires (verify current wording — 检索于 2026-06；以官网为准)

1. Deposit to the CPS Dataverse. Quantitative papers deposit replication materials — data, code, log files, and documentation — at the CPS Dataverse on Harvard Dataverse (dataverse.harvard.edu/dataverse/cps). This gates final acceptance, not post-publication.
2. Data availability statement. Every paper includes a statement saying whether data are available, where, and — if available but not shared — why not.
3. Reproducibility. Materials must let an independent researcher regenerate the manuscript's tables and figures: master script + README + pinned versions + seeds.
4. Restricted-data path. If data cannot be shared and sharing is a publication requirement, consult the journal editorial office; explain the restriction and document how others can obtain the data.
5. Preregistration (optional). Authors may submit an anonymized pre-analysis plan as supplementary material; the journal provides it to reviewers on request. Mark registered vs. exploratory analyses.

Qualitative / multi-method transparency (DA-RT spirit)

• Document sources, interviews, and fieldnotes so the evidentiary basis is auditable; use evidence tables or active citation. Where confidentiality requires it, use controlled access (e.g., QDR).
• State clearly which claims rest on which evidence; do not let "the cases show" stand unsupported.

Sharing posture by evidence type

Choose the transparency route before drafting the data availability statement:

Evidence type	Default posture	Documentation to include
Cross-national public datasets	Share constructed data and code; cite original sources	Source versions, merge keys, transformations, and exact download dates
Proprietary/admin data	Share code, synthetic or redacted extracts where allowed, and access instructions	License limits, access route, variable construction, and verification path
Interviews/fieldnotes	Protect identities; share protocols, coding scheme, and evidence table when ethical	Consent limits, anonymization method, and claim-to-evidence map
Text corpus/web data	Share corpus identifiers or permissible text, plus scraping/cleaning code	Collection date, inclusion rules, deduplication, language processing decisions
Pre-analysis plan	Submit anonymized plan as supplement when used	Registered vs. exploratory analyses marked in manuscript and code

The data availability statement should mirror the table: what is shared, where, what is restricted, and how a qualified reader can audit the claim without violating law, ethics, or provider terms.

Build-as-you-go checklist

• One master script regenerates every table and figure from raw/constructed data
• README documents data provenance, construction, 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 package output exactly
• Data availability statement drafted (available where / why not shared)
• Restricted data: explanation + access instructions + editorial-office consultation noted
• Anonymized pre-analysis plan attached where applicable; registered vs. exploratory marked

Anti-patterns

• Treating the deposit as a post-publication afterthought (it gates final acceptance for quant papers)
• Depositing code that does not actually reproduce the printed tables/figures
• A personal URL or generic cloud link instead of the CPS Dataverse
• Omitting the data availability statement, or claiming "available on request" with no plan
• Undocumented, un-seeded, unpinned code that "works on my machine"
• Qualitative claims with no documented evidentiary basis

Output format

【Repository】CPS Dataverse (Harvard) — package staged? [Y/N]
【Reproduces tables/figures?】master script verified locally? [Y/N]
【Data availability statement】drafted? available where / why not?
【Documentation】README + provenance + seeds + pinned versions? [Y/N]
【Sharing posture】public / proprietary-admin / interview-fieldnote / text-corpus / PAP route chosen
【Restricted data?】explanation + access path + editorial-office note?
【Qualitative transparency】sources/evidence documented? [Y/N/NA]
【Pre-analysis plan】anonymized + registered/exploratory marked? [Y/N/NA]
【Next】cps-review-process

Supplementary resources

• ../../resources/official-source-map.md — CPS data policy + CPS Dataverse URL
• ../../resources/external_tools.md — reproducibility tooling and qualitative-transparency options (QDR)