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rseng-data-management-plans

Covers data management plans (DMPs) for research projects: what funders require, drafting a DMP from the project's actual data reality (types, volumes, storage, sharing, preservation, responsibilities, costs), machine-actionable DMPs (RDA common standard, Data Stewardship Wizard, DMPonline funder templates), and keeping the plan synchronized with practice. Use when a proposal or project needs a DMP, when the user mentions data management plans, maDMPs, DS-Wizard or DMPonline, when funder or institutional data policy applies, or when the existing DMP has drifted from what the project actually does with its data. (Day-to-day data practice is rseng-data-management; the software management plan twin is rseng-management-planning.)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills스타 24(GitHub 24 + cccskills 0)

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Data management plans

A DMP answers, before the data exists, the questions that hurt when answered too late: what data will the project produce, where will it live, who may see it, what does that cost, and what survives the grant. Funders require one with most proposals; the useful version is not the compliance document but the honest operating plan - and because it describes practices this pack already engineers, most of a good DMP can be drafted from, and checked against, the project itself. The software counterpart (rseng-management-planning's SMP practice) shares this design; write the two consistently and cross-reference them.

What a plan covers

The standard sections, each answered from project reality:

  1. Data description: types, formats, expected volumes, sources - name open formats deliberately (rseng-scientific-file-formats) and reused third-party datasets with their licenses and terms.
  2. Documentation and metadata: data dictionaries, READMEs, community metadata standards for the domain (rseng-data-management owns the practice; the DMP states which).
  3. Storage and backup during the project: where working data lives, replication, who administers it - institutional storage beats lab improvisation; sensitive data goes where the steward says (rseng-regulatory-compliance).
  4. Access, sharing and legal: who can access what and when, embargo plans, consent and GDPR constraints, anonymization strategy - with honest limits stated (rseng-regulatory-compliance); "as open as possible, as closed as necessary" is the working frame.
  5. Preservation and sharing after the project: which data is deposited, where (domain repository first, Zenodo-class otherwise - rseng-archiving), with what identifiers and licenses (rseng-licensing for data licenses), and what is deliberately discarded (retention has costs; keeping everything is not a plan).
  6. Responsibilities and resources: named roles (who curates, who deposits), storage and curation costs as budget lines - data work is fundable work; say so in the proposal.

Proportionality: a simulation project with regenerable outputs needs a lean DMP centered on code and configs (rseng-reproducibility); a project collecting human-subject data needs the full treatment. Match depth honestly.

Machine-actionable DMPs

DMPs are becoming structured data, not prose PDFs:

  • The RDA DMP Common Standard defines the maDMP schema - a JSON model of datasets, distributions, hosts, licenses and costs that tools exchange.
  • The Data Stewardship Wizard (DS-Wizard) builds DMPs from questionnaire knowledge models and exports funder formats plus maDMP JSON; DMPonline carries the major funder templates. When the user's institution runs one of these, draft THERE (or produce content ready to paste), so the plan lands in the system reviewers and stewards actually use.
  • The agent-friendly consequence: a structured DMP is checkable - datasets listed in the plan can be diffed against datasets the project actually has, licenses in the plan against LICENSE files, deposit promises against archive records (rseng-archiving).

The DMP as a living document

Plans drift: new instruments, bigger volumes, a dataset that cannot be shared after all. Treat the DMP like the SMP:

  • Version it with the project (repository or the DMP platform's versioning); update at milestones, reporting deadlines and whenever data reality changes - a plan contradicted by practice is a liability at review and audit time (rseng-management-planning owns the cadence).
  • Run a drift check when revisiting: promised repositories vs actual deposits, promised metadata vs delivered, promised retention vs disk reality. Report gaps as actions with owners.
  • Record AI assistance in drafting or revising the plan in aidecl.yaml (rseng-ai-declaration).

Working with this skill

This skill is source-independent: its authority is the RDA common standard, the platform documentation and the RDMkit guidance linked below. It is the data twin of the SMP practice in rseng-management-planning; rseng-data-management holds the underlying practice.

Learn more (verified):

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Related skills

Check whether any of these applies before moving on:

  • rseng-archiving - deposit and preservation promises
  • rseng-data-management - the practice the plan describes
  • rseng-licensing - data license choices in the plan
  • rseng-management-planning - SMP twin, shared drafting cadence
  • rseng-regulatory-compliance - GDPR, consent and anonymization sections
  • rseng-scientific-file-formats - naming open formats deliberately
<!-- related-skills:end -->

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같은 저장소의 스킬

rseng-agent-security

무료

Covers operating AI coding agents securely: auditing whether the agent runs sandboxed, permission and dangerous-command configuration, containerized environments, secrets kept out of context, network egress and token privileges, prompt-injection risk, and the privacy of agent use itself. Use PROACTIVELY when permission gating is broadly disabled (skip-permissions modes), when secrets are visible to the agent's shell, or when the agent processes untrusted code, issues or web content; when a project holds personal or confidential data while a cloud agent is active (what leaves the machine, retention, training use), or when the user asks how to run coding agents safely or privately. (Supply-chain security: rseng-security; disclosing agent contributions: rseng-ai-declaration.)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

rseng-ai-declaration

무료

Covers declaring AI involvement with the AI Declaration Format (https://ai-declaration.org): creating and maintaining an aidecl.yaml that records which AI tools and agents were used, what each contributed, when and how much, plus the README disclosure footnote, JSON/JSON-LD export and CI validation. Machine-readable disclosure is the practice; this format is one implementation. Use PROACTIVELY when an AI assistant or agent creates or modifies a project's content - generate aidecl.yaml if missing, update it after AI-assisted changes. Also use when the user asks about AI transparency, provenance, disclosure or EU AI Act disclosure, wants to state that NO AI was used, or mentions aidecl. For concealment requests see rseng-honesty; for the human review duty see rseng-human-verification.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

rseng-archiving

무료

Covers long-term archiving of research software and data: Software Heritage save requests and SWHID persistent identifiers, Zenodo deposits with versioned DOIs and forge integration, choosing domain and institutional repositories, deciding what to archive (code, data, environments, documentation) and when, and archiving at project retirement. Use when the user wants software or data preserved beyond the life of a forge account, grant or lab, mentions Software Heritage, SWHIDs, Zenodo deposits or archiving, retires or hands over a project, prepares artifacts that must stay resolvable for a paper, or when funder policy requires long-term preservation. (Cutting the release itself is rseng-publishing-releasing; day-to-day data storage and repository choice is rseng-data-management.)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

rseng-big-data-processing

무료

Covers processing research data that outgrows one machine's memory: out-of-core and chunked computation, Dask for scaling the scientific Python stack, Spark for distributed tabular pipelines, lazy evaluation, partitioning strategies, idempotent and restartable batch jobs, and knowing when NOT to distribute. Use when datasets no longer fit in memory, when the user mentions Dask, Spark, out-of-core or larger-than-memory data, when a pandas/NumPy workflow hits memory limits, or when designing batch pipelines over many files. (Cluster job submission and job arrays are rseng-hpc-computing; pipeline orchestration engines are rseng-workflows; profile first with rseng-performance-profiling.)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

rseng-ci-cd

무료

Covers continuous integration and delivery for research software: CI/CD concepts, automating builds and tests with GitHub Actions and GitLab CI/CD, and wiring an organization's GitLab CI infrastructure to a GitHub-hosted project. Use when the user asks to set up CI, write a pipeline or workflow file (.github/workflows, .gitlab-ci.yml), add automated builds/tests on push or pull request, publish a package or deploy docs when a tag or release is cut, cache dependencies to speed up CI runs, choose between GitHub Actions and GitLab CI, use self-hosted or GPU runners, mirror a repo, or report external CI status back to GitHub. For what to test and shrinking a test matrix see rseng-testing; for linter and pre-commit policy see rseng-code-quality.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

rseng-citation-hygiene

무료

Covers verifying that every citation is real, correct and current: checking references in manuscripts, READMEs, references files and code metadata against Crossref and OpenAlex, screening cited DOIs against the Retraction Watch database, catching fabricated or mis-attributed citations (a documented AI failure mode), and keeping bibliographies and CITATION.cff files resolvable. Use PROACTIVELY before any bibliography, reference list or citation metadata is finalized or published, whenever the agent itself has produced citations, and when the user asks to check references, mentions broken DOIs, retracted papers or citation verification, or prepares a manuscript, README or software paper with references. (Making your own software citable is rseng-citation-metadata.)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

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