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.

1. 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.

1. Quantitative materials. Data, code, codebook, scale items, and documentation sufficient to

regenerate every reported result; master script + README + pinned versions + seeds.

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