commres-transparency-and-data — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited commres-transparency-and-data (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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).
conditions they can be accessed, and — if they cannot be shared — why, with instructions for how others might obtain them.
regenerate every reported result; master script + README + pinned versions + seeds.
for content analysis, deposit the codebook and intercoder-reliability report.
service, or legal restrictions by the provider).
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.
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.
【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../../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~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.