| name | joc-open-science-and-transparency |
| description | Use when preparing the open-science and transparency materials for a Journal of Communication (JoC) manuscript. JoC requires a Data Availability Statement and supports Open Science Badges for open data, open materials, and preregistration. Covers quantitative, computational, and qualitative transparency and the restricted-data path. Prepares the materials; it does not waive requirements. |
Open Science & Transparency (joc-open-science-and-transparency)
JoC requires a Data Availability Statement on every article and offers Open Science Badges for
open data, open materials, and preregistration. Transparency is not an afterthought — build the
statement and supporting materials as you write so submission and any badge claim go smoothly.
When to trigger
- Drafting the Data Availability Statement (a submission requirement)
- Deciding whether to pursue Open Science Badges (open data / open materials / preregistration)
- Preparing a preregistration 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 JoC expects (verify current wording on the policy page)
- Data Availability Statement (required). 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 Science Badges (optional, earned).
- Open Data — data and a codebook deposited in a trusted repository with a persistent identifier.
- Open Materials — stimuli, instruments, code, and protocols deposited so the study can be reproduced.
- Preregistration — a time-stamped, registered design/analysis plan; note it in the cover letter.
- Quantitative / computational materials. Data, code, codebook, and documentation sufficient to
regenerate every reported result; master script + README + pinned versions + seeds; for automated
text measures, include the human-validation materials.
- Qualitative materials. Share the materials that support the claims (e.g., coding schemes,
evidence tables, de-identified excerpts) with access controls where needed (QDR for controlled access).
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 communication method (decision table)
Because JoC spans communication research broadly, "transparency" differs across traditions. Match the
deposit to the method rather than forcing one template:
| Method | What a JoC referee wants deposited | Badge most relevant |
|---|
| Survey / message experiment | data + codebook + stimuli + analysis script | open data + materials + preregistration |
| 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 |
| Qualitative / critical | coding scheme + evidence tables + de-identified excerpts | open materials where ethics allow |
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. Confirm badge mechanics against the journal's current guidance.
Worked micro-example: a DAS for a copyrighted-news corpus (illustrative)
A computational content analysis of 40,000 news articles (illustrative) on climate coverage hits
a familiar wall: the texts are copyrighted and the feed bars redistribution. The transparency 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, shareable derived data;
(3) write a DAS naming the restriction, provider, and access route, and offering de-identified
derived features (frame proportions per article) so modeling re-runs without raw text — earning
open-materials credit and an honest, hedged DAS.
Anti-patterns
- Submitting without a Data Availability Statement (it is required)
- Claiming a badge whose materials are not actually deposited or do not reproduce the 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]
【Badges sought】open data / open materials / preregistration (materials staged?)
【Documentation】README + provenance + seeds + pinned versions? [Y/N]
【Restricted data?】exemption note + access path + synthetic data?
【Qualitative transparency】coding scheme / evidence documented? [Y/N/NA]
【Next】joc-review-process
Supplementary resources
