# General Fair Data Review > Review a manuscript or code repository for FAIR data compliance (Findable, Accessible, Interoperable, Reusable), producing a structured report with pass/fail per principle and actionable remediation steps. - Skill: `learningmatter-mit/general-fair-data-review` (Agent Skill) - Install (CLI): `npx skillmds@latest add learningmatter-mit/general-fair-data-review` - Raw SKILL.md: https://api.skillmd.com/api/skills/learningmatter-mit/general-fair-data-review/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: learningmatter-mit (https://skillmd.com/u/learningmatter-mit) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/learningmatter-mit/general-fair-data-review --- # General FAIR Data Review ## Goal Assess whether a manuscript submission or standalone code/data repository satisfies the FAIR Guiding Principles (Wilkinson et al., *Sci. Data* 2016). The output is a structured reviewer report — analogous to a peer-review report — that scores each FAIR sub-principle, identifies gaps, and provides concrete remediation steps the authors can act on before publication. This skill is complementary to [general-peer-review](../general-peer-review/SKILL.md), which focuses on scientific methodology. Run both in sequence for a complete review. --- ## Prerequisites - A manuscript (PDF or markdown) **and/or** a link to a code/data repository (GitHub, Zenodo, Figshare, etc.) - Read access to any supplementary files, Data Availability Statements (DAS), or README files provided by the authors --- ## Instructions ### 1. Identify Review Scope Determine what artifacts are under review. Three modes exist: | Mode | Input | Focus | |------|-------|-------| | **Manuscript + data/code** | PDF + repo URL | Full FAIR review | | **Manuscript only** | PDF | DAS quality, metadata richness, identifier presence | | **Code/data repo only** | Repo URL / directory | Repository-level FAIR compliance | State the mode explicitly at the start of the review report. --- ### 2. Read the Manuscript and Data Availability Statement Load the manuscript. Locate and extract: - The **Data Availability Statement** (DAS) — usually a dedicated section near the end. - Any **Code Availability Statement**. - All **data/code repository URLs** or DOIs mentioned. If no DAS exists, flag immediately as a **Critical Finding** (fails F4, A1, R1.1). --- ### 3. Inspect the Data/Code Repository For each repository URL found, check the following. If no repository exists, mark all sub-principles below as **Fail**. ``` Repository inspection checklist: - Does a persistent identifier (DOI, Handle) exist? → F1 - Is metadata present and rich (title, authors, description, keywords, license)? → F2, R1 - Does the metadata explicitly reference the dataset/code identifier? → F3 - Is the repository indexed in a searchable resource (Zenodo, Figshare, OSF, etc.)? → F4 - Can the data/code be accessed via a standard protocol (HTTP/HTTPS, FTP)? → A1 - Is the protocol open and free (no proprietary portal login required)? → A1.1 - If restricted, is there a documented access procedure? → A1.2 - Does metadata remain accessible even if data is removed? → A2 - Are standard, community-recognized formats used (CIF, JSON, CSV, HDF5, not .xlsx or proprietary)? → I1 - Are domain ontologies or controlled vocabularies used for metadata fields? → I2 - Are cross-references to related datasets or publications included? → I3 - Is a clear, machine-readable license present (CC-BY, MIT, Apache 2.0, etc.)? → R1.1 - Is provenance documented (how data was generated, software versions, parameters)? → R1.2 - Do files conform to domain community standards (e.g., CIF for crystal structures, SMILES for molecules, HDF5 for trajectories)? → R1.3 ``` --- ### 4. Score Each FAIR Sub-Principle For every sub-principle (F1–F4, A1–A2, I1–I3, R1–R1.3) assign: - **Pass** — requirement fully met - **Partial** — requirement partially met; improvement needed - **Fail** — requirement not met or artifact absent - **N/A** — not applicable to this submission type --- ### 5. Atomistic/Computational Science Specific Checks In addition to the generic FAIR checklist, evaluate the following domain-specific criteria: **Structures & Trajectories** - Crystal structures deposited as `.cif` (not as images or in supplementary PDF tables) - MD trajectories deposited in open formats (`.xyz`, `.extxyz`, `.h5md`, `.lammpsdump`) with a `README` specifying units, timestep, ensemble - Force field / MLIP checkpoints deposited with version, training set provenance, and validation metrics **Computational Parameters** - DFT: INCAR/POTCAR/KPOINTS or equivalent included or described with exact values (not "similar to ref. X") - MLIP: model architecture, training hyperparameters, and train/val/test split recorded - MD: timestep, thermostat/barostat settings, equilibration protocol documented **Software Environment** - `environment.yml` or `requirements.txt` present with pinned versions - Scripts runnable from the deposited repository without undocumented external dependencies --- ### 6. Generate the Structured Review Report Produce a report in the following format: ```markdown # FAIR Data Review Report **Manuscript title:** [title] **Review date:** [date] **Reviewer:** AI FAIR Data Reviewer (general-fair-data-review skill) **Review mode:** [Manuscript + data/code | Manuscript only | Code/data repo only] --- ## Summary [2–4 sentences: overall FAIRness level, most critical gaps, overall recommendation: Ready / Minor Revisions / Major Revisions / Not Acceptable] --- ## FAIR Scorecard | Principle | Sub-principle | Status | Evidence / Gap | |-----------|--------------|--------|----------------| | **Findable** | F1: Persistent identifier | Pass/Partial/Fail | ... | | | F2: Rich metadata | | | | | F3: Metadata references data ID | | | | | F4: Indexed in searchable resource | | | | **Accessible** | A1: Retrievable via standard protocol | | | | | A1.1: Protocol open and free | | | | | A1.2: Auth procedure documented | | | | | A2: Metadata accessible if data removed | | | | **Interoperable** | I1: Formal/shared knowledge representation | | | | | I2: FAIR vocabularies used | | | | | I3: Qualified references to other data | | | | **Reusable** | R1: Rich, accurate, relevant attributes | | | | | R1.1: Clear data usage license | | | | | R1.2: Detailed provenance | | | | | R1.3: Domain community standards met | | | --- ## Major Concerns [Number sequentially. For each: state issue → why problematic → actionable fix.] 1. **[Issue title]** - *Problem:* ... - *Impact:* ... - *Fix:* ... --- ## Minor Concerns - ... --- ## Atomistic/Computational Specific Findings [Report on structure formats, trajectory deposits, software environments, parameter completeness.] --- ## Questions for Authors 1. ... --- ## Recommended Repositories (if none provided) If no repository was deposited, suggest domain-appropriate options: | Data type | Recommended repository | |-----------|----------------------| | Crystal structures | CCDC, ICSD, Materials Cloud, Zenodo | | Molecular dynamics trajectories | Materials Cloud, Zenodo, NOMAD | | ML models / checkpoints | Hugging Face, Zenodo, MACE-Models | | General datasets | Zenodo, Figshare, Dryad | | Code | GitHub + Zenodo DOI via Zenodo GitHub integration | ``` --- ## Document-Specific Workflows ### Manuscript Review (PDF) > [!WARNING] > Read the PDF text directly. Do **not** assume data exists unless a DOI or repository URL is explicitly present in the manuscript body or supplement. Steps: 1. Extract DAS and Code Availability Statement verbatim. 2. Resolve any DOIs or URLs found. 3. If DOIs resolve to a live repository, proceed with repository inspection (Step 3). 4. If only "data available upon request" is stated: flag as **Fail** for F1, F4, A1, R1.1 — this does not meet FAIR standards. ### Code/Data Repository Review Only Steps: 1. Read top-level `README.md`. 2. Check for `LICENSE`, `environment.yml`/`requirements.txt`, `CITATION.cff`. 3. Inspect directory structure for data files and their formats. 4. Check metadata on the repository platform (Zenodo record, GitHub About section, etc.). --- ## Constraints - **Scope**: Focus strictly on data/code FAIRness. Scientific methodology critique belongs in [general-peer-review](../general-peer-review/SKILL.md). - **Tone**: Objective and constructive. Every Fail must include a concrete, actionable fix. - **"Data available upon request"**: Always flag as non-FAIR. Not acceptable per RSC Digital Discovery and FAIR principles. - **Proprietary formats**: `.xlsx`, `.mat`, Gaussian `.chk`, VASP `WAVECAR` without open alternatives are I1/R1.3 failures. - **License absence**: Unlicensed ≠ open. Always flag missing licenses as R1.1 Fail. --- ## References - Wilkinson, M. D. et al., "The FAIR Guiding Principles for scientific data management and stewardship", *Sci. Data* **3**, 160018 (2016). [DOI: 10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.2016.18) - RSC Digital Discovery Data Review Guidelines. [rsc.org/publishing](https://www.rsc.org/publishing/publish-with-us/publish-a-journal-article/digital-discovery) ## See Also - [general-peer-review](../general-peer-review/SKILL.md) - [general-deep-research](../general-deep-research/SKILL.md) --- **Author:** Magdalena Lederbauer **Contact:** [GitHub @mlederbauer](https://github.com/mlederbauer)