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Draft or audit manuscript Data/Code Availability statements, dataset access routes, repository plans, and FAIR metadata. Use for 数据可用性声明、数据共享、数据仓库选择 and dataset citations; not general data cleaning or statistical analysis.

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ToddModica/upstream-skills
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2026년 9월 14일 21:04
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SKILL.md
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nature-data
description
Draft or audit manuscript Data/Code Availability statements, dataset access routes, repository plans, and FAIR metadata. Use for 数据可用性声明、数据共享、数据仓库选择 and dataset citations; not general data cleaning or statistical analysis.
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{"author":"Yuan1z skill, refactored into static/dynamic layers"}
# Nature Data Availability — Router ## Routing protocol For a new task, load the core and matching resources below. Reuse already loaded guidance on follow-ups; load more only when the task needs it. ### 1. Load the manifest and the core layer Read [manifest.yaml](manifest.yaml). Then read every file listed under `always_load`: - `static/core/stance.md` — what the data-availability package is, the default stance, and the source hierarchy. - `static/core/workflow.md` — the eight-step workflow and the output format. ### 2. No content axis — confirm journal and language inline Unlike nature-writing or nature-figure, nature-data has no fragment axis. Its variation is handled at runtime, not by loading different content bodies: - **journal/article type** — if journal-specific instructions conflict with this skill, follow the journal. - **access route** — each dataset is classified into one route (public repository, controlled access, within paper, reused public, third-party restricted, justified request, or not applicable). - **user language** — if the user writes Chinese or requests Chinese guidance, read `static/core/chinese-mode.md` and add the 中文核对 block unless the user requested statement text only. ### 3. Run the workflow For a wording edit or audit of one existing statement, preserve supplied repository identifiers and access conditions and check the affected claims. Report gaps relevant to that statement; do not require a full study-wide dataset inventory or repository redesign. Use the complete workflow below for a new data-sharing plan, full statement, or submission audit. Follow the eight-step workflow in `core/workflow.md`: identify the journal, inventory every supporting dataset, classify each into one access route, choose repository and identifier strategy before drafting, draft the statement with explicit dataset-to-location mapping, add formal dataset citations, run the FAIR/metadata audit, and return ready-to-paste text plus unresolved fields. Do not invent DOIs, accession numbers, repository names, licences, embargo dates, ethics approvals, access committees, or data-use conditions. Flag "available upon request" as weak unless there is a specific legal, ethical, commercial, or third-party restriction. ### 4. Reach for references only when needed The files under `references/` are deep references, not defaults. Open them on demand per the `references.on_demand` table in the manifest — for example `references/policy-principles.md` for the governing rules and edge cases, `references/repository-and-identifiers.md` for repository/accession/DOI choices, `references/statement-patterns.md` for ready-to-adapt statements, `references/fair-metadata-checklist.md` for the FAIR audit, `references/chinese-author-alignment.md` for Chinese wording, and `references/source-basis.md` to justify a rule with its official source. When the target is the flagship journal Nature, also open `references/nature-article-requirements.md` for statement placement, mandatory-deposition routing, central-code review access, materials and structure-file checks. When the target is Nature Machine Intelligence, open `../nature-shared/journal-formats/nature-machine-intelligence.md`. Enforce a Data Availability statement and a separate `Code availability` section after it and before references; check reviewer access, precise restrictions, repository/identifier quality and the Software Submission Checklist for newly developed central code.
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