| name | lang-data-and-transparency |
| description | Use when preparing the data, annotation, and reproducibility materials for a Language (LSA) manuscript — shared datasets and code, glossed corpora, sound files, and the ethics of working with language consultants and communities. Language values transparent, documented data; over-stating a mandated deposit is as wrong as hiding materials. Documents and shares; it does not run the analysis. |
Data & Transparency (lang-data-and-transparency)
Language increasingly treats documented, checkable data and reproducible analysis as a mark of
serious work: a reader should be able to see the pattern and, where quantitative, re-run the model. But
the requirements differ by subfield and evolve, and linguistic data carry ethical obligations to
consultants and communities that generic "open data" rhetoric ignores. This skill helps you document,
share, and protect your materials appropriately — without over-stating a deposit mandate the journal
may not impose.
When to trigger
- Assembling the data/code/annotation to accompany a submission
- Deciding what can and cannot be shared (consultant confidentiality, community agreements, licensed
corpora)
- A reader asked for the dataset, the glossed corpus, the sound files, or the analysis script
- Writing a data-availability statement
What "transparent" means at Language (by data type)
Quantitative (experiment / corpus)
- Share the analysis-ready data and the script that reproduces the models, tables, and figures;
pin package versions and set seeds. A repository (e.g., OSF) with a readme is the norm.
- If the raw corpus is licensed, share the derived counts + the extraction code so the pipeline is
reproducible even when the source text cannot be redistributed.
Elicited / fieldwork
- Provide numbered, Leipzig-glossed examples with sources; where possible, archive recordings and
annotations in a language archive (e.g., ELAR, PARADISEC, AILLA, TLA) under access terms the
community agreed to.
- Document the elicitation and transcription workflow so another linguist could interpret the data.
Phonetic
- Share measurement scripts (e.g., Praat scripts) and, where consent allows, the sound files or
acoustic measurements; state the alignment/measurement settings.
Ethics of linguistic data (do not skip)
- Consent and community agreements govern what may be archived and how; open sharing is not always
ethical, and "restricted access" is a legitimate, respectful choice.
- Anonymize speakers where required; do not expose identities via metadata or audio.
- Credit consultants and communities per current best practice and any community protocol.
Calibration (do not over- or under-state, hedged)
Language rewards transparency as craft, but the pack does not assert a specific editor-verified
replication mandate — verify the current data-availability policy on the CUP/LSA author pages before
claiming any gate. The honest posture: share what you ethically can, document what you cannot, and never
present restricted community data as if it were freely open. Illustrative: a variationist study shares
the coded token file, the R script, and a codebook on OSF, but keeps the raw interview audio restricted
under the community agreement and says so in the data statement — transparent and ethical at once.
Referee/editor conformance check
| Slip reviewers catch | The Language-appropriate fix |
|---|
| "Results not reproducible from what's shared." | post analysis data + script; pin versions, set seeds |
| "Glosses can't be checked." | numbered Leipzig-glossed examples with per-token sources |
| "Licensed corpus can't be shared." | share derived counts + extraction code + a pointer to the source |
| "Speaker identities exposed." | anonymize; restrict audio per consent |
| "Claims a deposit rule that isn't stated." | describe your sharing; verify the live policy, don't invent one |
Execution bridge (StatsPAI / Stata MCP)
Transparency is a property of the path from data to number, and the cheapest way to keep
that path honest is not to have retyped anything along it. Full map:
execution-with-mcp.
- Run it, then cite it. A number that was computed by a tool call can be recomputed by
a reader with the same script; a number that was remembered cannot. If no server is
connected, adapt
../../resources/code/ and say in the paper that you did.
- Deposit what reproduces the table, not a spreadsheet of the finished table: script,
seed, pinned package versions, and the derived dataset where the licence and the
speakers' consent allow it.
- Where consent or endangerment limits deposit (see the ethics section above), archive
the analysis code anyway — the code is not the speakers' data, and withholding it is the
part reviewers most often read as evasion.
Anti-patterns
- Quantitative results with no shared data or script (reviewers cannot reproduce the model)
- Treating community/consultant data as "open" without consent or agreement
- Exposing speaker identities through metadata, audio, or examples
- Glossed data with no source, so a reader cannot verify the token
- Over-stating a journal replication mandate the live policy does not impose (verify first)
Transparency pass for Language
Treat this skill as an executable review pass, not a prose hint. First lock what evidence underlies each
claim; then judge whether the manuscript answers the venue's real reader: linguists who value checkable
evidence and who also respect the ethics of working with speakers and communities.
- Do the pass: for each claim, name where the supporting data and code live, the access terms, and
any ethical constraint on sharing.
- Return a ledger: give
claim / evidence / where-it-lives / sharing-constraint rows so the next
agent can act.
- Sibling guard: documentation-heavy work may fit Language Documentation & Conservation; if a
sibling owns the contribution, recommend re-routing before polishing.
- Stop condition: do not give submission-ready advice until
resources/official-source-map.md has
been checked for the current data-availability policy.
Output format
【Data types】experiment / corpus / elicited / phonetic / archival
【Shared】analysis data + script posted (repo/OSF)? [Y/N/NA]
【Glossed examples】numbered, Leipzig, sourced? [Y/N]
【Ethics】consent / community agreement / anonymization handled? [Y/N]
【Data statement】accurate, no over-stated mandate? [Y/N]
【Next】lang-tables-figures
Supplementary resources
