Commres transparency and data

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

CR is a quantitative, social-scientific journal, and its reviewers increasingly expect the materials that let others scrutinize how the numbers were produced. SAGE supports a data-availability statement and open-practices options; build the statement and supporting materials as you write so submission and any open-practice claim go smoothly. Confirm the journal's current wording on the SAGE author page (待核实 on exact policy).

When to trigger

• Drafting the data-availability statement
• Deciding whether to share data, code, and materials, or to preregister
• Preparing a preregistration / pre-analysis plan for a prospective design (note it in the cover letter)
• Data cannot be fully shared (privacy, ethics, platform/legal restrictions) and you need the path forward

What CR / SAGE expects (verify current wording on the policy page)

1. Data-availability statement. State where the data are (repository + identifier), under what conditions they can be accessed, and — if they cannot be shared — why, with instructions for how others might obtain them.
2. Open practices (where pursued).
   • Open data — data and a codebook deposited in a trusted repository with a persistent identifier.
   • Open materials — stimuli, instruments, scales, and code deposited so the study can be reproduced.
   • Preregistration — a time-stamped, registered design/analysis plan; note it in the cover letter.
3. Quantitative materials. Data, code, codebook, scale items, and documentation sufficient to regenerate every reported result; master script + README + pinned versions + seeds.
4. ORCID and ethics. Provide ORCID where requested; state IRB/ethics approval and informed consent; for content analysis, deposit the codebook and intercoder-reliability report.

When data cannot be shared (restricted-data path)

• Explain why in the data-availability statement (ethical/privacy concerns, platform terms of service, or legal restrictions by the provider).
• Provide instructions on how others can obtain the data (access process, application, provider contact).
• Where possible, provide synthetic or de-identified data so the code can be run.

Build-as-you-go checklist

• Data-availability statement drafted (repository, identifier, access conditions, or exemption)
• 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
• Scales/stimuli/codebook deposited (open materials) where claimed
• Content analysis: codebook + intercoder-reliability report included
• Materials anonymized (no author-identifying paths/links) for double-anonymized review

Transparency expectations by study type (decision table)

Match the deposit to the design rather than forcing one template:

Study type	What a CR referee wants deposited	Open practice most relevant
Experiment	data + codebook + stimuli + analysis script	open data + materials + preregistration
Survey / panel	data + scale items + analysis script + measurement model	open data + materials
Content analysis	codebook + coder instructions + reliability subsample + texts	open materials (+ open data)
Computational / text-as-data	corpus or query, model/version, seeds, human-validation set	open materials + open data

For computational measures, the human-validation set is itself the evidence that the automated label means what the paper claims — depositing the classifier without it leaves the construct unverified.

Worked micro-example: a DAS for a copyrighted-news corpus (illustrative)

A computational content analysis of 40,000 news articles (illustrative) hits a familiar wall: the texts are copyrighted and the feed bars redistribution. The path: (1) deposit the codebook, article IDs/URLs, query parameters, and analysis code so a same-license reader reproduces the pipeline; (2) deposit the human-validation sample and shareable derived data; (3) write a data-availability statement naming the restriction, provider, and access route, and offering de-identified derived features (frame proportions per article) so modeling re-runs without raw text.

Anti-patterns

• Treating the data-availability statement as an afterthought rather than a submission element
• Claiming an open-practice credit whose materials are not actually deposited or do not reproduce results
• A personal URL instead of a trusted repository with a persistent identifier
• Claiming data are restricted without giving an access path or synthetic substitute
• De-anonymizing the manuscript via an open-materials link during review

Output format

【Data-availability statement】drafted? repository + identifier or exemption? [Y/N]
【Reproduces tables/figures?】master script verified locally? [Y/N]
【Open practices sought】open data / open materials / preregistration (materials staged?)
【Documentation】README + provenance + seeds + pinned versions? [Y/N]
【Restricted data?】exemption note + access path + synthetic data?
【Ethics/ORCID】IRB + consent stated; ORCID provided? [Y/N]
【Next】commres-review-process

Supplementary resources

• ../../resources/external_tools.md — reproducibility tooling and repositories (OSF, Dataverse, QDR)
• ../../resources/code/ — master-script + seed-discipline skeleton
• ../../resources/official-source-map.md — data-availability and open-practices policy