Nature Data Availability — Router
SkillDatabases & dataThis is a skill for preparing and auditing Nature-ready data availability statements, repository plans, dataset citations, and FAIR metadata checklists for manuscripts. It walks through identifying the journal, listing datasets, classifying access routes, and choosing repositories, then produces ready-to-paste statement text. It is used when a manuscript needs its data sharing section drafted, checked, or revised.
Use Nature Data Availability — Router in Claude, ChatGPT or Ahel Desktop
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Then ask your AI: use the Nature Data Availability skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
No other account needed.
Have the target journal name and the manuscript's dataset list ready.
What your AI can do with it
- Draft a Nature-ready Data Availability statement from a dataset list
- Audit an existing statement against journal requirements
- Classify each dataset by its access route
- Suggest repositories for datasets that need one
- Generate dataset citations and a FAIR metadata checklist
Getting started
- Have the target journal name and the manuscript's dataset list ready.
- Add the nature-data skill to your agent's available skills.
- Ask the agent to prepare or audit the Data Availability section.
- Review the ready-to-paste statement text, dataset citations, and FAIR checklist.
What this skill tells your AI
The instructions your AI receives, as published by yuan1z0825/nature-skills in skills/nature-data/SKILL.md and read by ahel’s review.
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. 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.mdand 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.
Signals
- GitHub stars
- 45k
- Forks
- 2k
- Last commit
- Sep 2026
Questions
- What does nature-data do?
- It prepares, audits, or revises Nature-ready Data Availability statements, repository plans, dataset citations, and FAIR metadata checklists for manuscripts.
- When should I use this skill?
- Use it when you need help with Nature data availability, research data sharing, repository selection, accession numbers, or restricted or sensitive data.
- What workflow does the skill follow?
- It follows an eight-step workflow: identifying the journal, listing datasets, classifying their access routes, choosing repositories, then producing statement text with dataset citations and a FAIR metadata checklist.
- What output can I expect?
- You get ready-to-paste statement text, proper dataset citations, and a FAIR metadata checklist.
Advanced
- Item type
- skill
- Key
nature-data-yuan1z0825- Source
- github.com/yuan1z0825/nature-skills
github.com/yuan1z0825/nature-skills
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