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)
- 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.
- 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.
- Quantitative materials. Data, code, codebook, scale items, and documentation sufficient to
regenerate every reported result; master script + README + pinned versions + seeds.
- 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
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
Source: brycewang-stanford/Awesome-Journal-Skills → Communication-Research-Skills/skills/commres-transparency-and-data/SKILL.md
1---2name: commres-transparency-and-data3description: Use when preparing the transparency, open-science, and reproducibility materials for a Communication Research (CR) manuscript — data-availability statement, deposited data/code/materials, preregistration, and the restricted-data path. Prepares the materials; it does not waive requirements.4---5
6
7# Transparency & Data (commres-transparency-and-data)
8
9CR is a quantitative, social-scientific journal, and its reviewers increasingly expect the materials
10that let others scrutinize how the numbers were produced. SAGE supports a **data-availability
11statement** and open-practices options; build the statement and supporting materials **as you write**
12so submission and any open-practice claim go smoothly. Confirm the journal's current wording on the
13SAGE author page (待核实 on exact policy).
14
15## When to trigger
16
17- Drafting the **data-availability statement**
18- Deciding whether to share data, code, and materials, or to preregister
19- Preparing a **preregistration** / pre-analysis plan for a prospective design (note it in the cover letter)
20- Data cannot be fully shared (privacy, ethics, platform/legal restrictions) and you need the path forward
21
22## What CR / SAGE expects (verify current wording on the policy page)
23
241. **Data-availability statement.** State where the data are (repository + identifier), under what
25 conditions they can be accessed, and — if they cannot be shared — **why**, with instructions for how
26 others might obtain them.
272. **Open practices (where pursued).**
28 - **Open data** — data and a codebook deposited in a trusted repository with a persistent identifier.
29 - **Open materials** — stimuli, instruments, scales, and code deposited so the study can be reproduced.
30 - **Preregistration** — a time-stamped, registered design/analysis plan; note it in the cover letter.
313. **Quantitative materials.** Data, code, codebook, scale items, and documentation sufficient to
32 regenerate every reported result; master script + README + pinned versions + seeds.
334. **ORCID and ethics.** Provide ORCID where requested; state IRB/ethics approval and informed consent;
34 for content analysis, deposit the codebook and intercoder-reliability report.
35
36## When data cannot be shared (restricted-data path)
37
38- **Explain why** in the data-availability statement (ethical/privacy concerns, platform terms of
39 service, or legal restrictions by the provider).
40- Provide **instructions on how others can obtain the data** (access process, application, provider contact).
41- Where possible, provide **synthetic or de-identified data** so the code can be run.
42
43## Build-as-you-go checklist
44
45- [ ] **Data-availability statement** drafted (repository, identifier, access conditions, or exemption)
46- [ ] One **master script** regenerates **every** table and figure from raw/constructed data
47- [ ] **README** documents data provenance, construction, and how to reproduce each exhibit
48- [ ] **Seeds** set and reported for every stochastic step; software/package **versions pinned**
49- [ ] Scales/stimuli/codebook deposited (open materials) where claimed
50- [ ] Content analysis: codebook + intercoder-reliability report included
51- [ ] Materials **anonymized** (no author-identifying paths/links) for double-anonymized review
52
53## Transparency expectations by study type (decision table)
54
55Match the deposit to the design rather than forcing one template:
56
57| Study type | What a CR referee wants deposited | Open practice most relevant |
58|------------|-----------------------------------|-----------------------------|
59| Experiment | data + codebook + stimuli + analysis script | open data + materials + preregistration |
60| Survey / panel | data + scale items + analysis script + measurement model | open data + materials |
61| Content analysis | codebook + coder instructions + reliability subsample + texts | open materials (+ open data) |
62| Computational / text-as-data | corpus or query, model/version, seeds, **human-validation set** | open materials + open data |
63
64For computational measures, the **human-validation set is itself the evidence** that the automated
65label means what the paper claims — depositing the classifier without it leaves the construct unverified.
66
67## Worked micro-example: a DAS for a copyrighted-news corpus (illustrative)
68
69A computational content analysis of **40,000 news articles** (illustrative) hits a familiar wall: the
70texts are copyrighted and the feed bars redistribution. The path: (1) deposit the **codebook, article
71IDs/URLs, query parameters, and analysis code** so a same-license reader reproduces the pipeline; (2)
72deposit the **human-validation sample** and shareable derived data; (3) write a data-availability
73statement naming the restriction, provider, and access route, and offering **de-identified derived
74features** (frame proportions per article) so modeling re-runs without raw text.
75
76## Anti-patterns
77
78- Treating the data-availability statement as an afterthought rather than a submission element
79- Claiming an open-practice credit whose materials are not actually deposited or do not reproduce results
80- A personal URL instead of a trusted repository with a persistent identifier
81- Claiming data are restricted without giving an access path or synthetic substitute
82- De-anonymizing the manuscript via an open-materials link during review
83
84## Output format
85
86```
87【Data-availability statement】drafted? repository + identifier or exemption? [Y/N]
88【Reproduces tables/figures?】master script verified locally? [Y/N]
89【Open practices sought】open data / open materials / preregistration (materials staged?)
90【Documentation】README + provenance + seeds + pinned versions? [Y/N]
91【Restricted data?】exemption note + access path + synthetic data?
92【Ethics/ORCID】IRB + consent stated; ORCID provided? [Y/N]
93【Next】commres-review-process
94```
95
96## Supplementary resources
97
98- [`../../resources/external_tools.md`](../../resources/external_tools.md) — reproducibility tooling and repositories (OSF, Dataverse, QDR)
99- [`../../resources/code/`](../../resources/code/) — master-script + seed-discipline skeleton
100- [`../../resources/official-source-map.md`](../../resources/official-source-map.md) — data-availability and open-practices policy
101
102---
103
104**Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Communication-Research-Skills/skills/commres-transparency-and-data/SKILL.md`
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