fair-data-principles

Files (2)

• SKILL.md
• validate_fair.py · scripts/

SKILL.md (first lines)

# FAIR Data Principles

Core principle: Scientific data and computational pipelines must be first-class citizens — machine-actionable, uniquely identifiable, formally described with open ontologies, and fully reproducible. Every dataset earns a rich metadata schema; every entity earns a persistent identifier.

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## What I do

- **Audit & Structure Datasets to FAIR Standards:** Findable (F1–F4), Accessible (A1–A2), Interoperable (I1–I3), and Reusable (R1.1–R1.3).
- **Author Machine-Readable Metadata Schemas:** Frictionless Data Package (`datapackage.json`), GSC MIxS environmental packages, and domain data dictionaries.
- **Enforce Biological & Scientific Ontologies:** GTDB taxonomy, KEGG Orthology, IUBMB Enzyme Commission (EC), Pfam protein domains, NCBI TaxIDs, and GO terms.
- **Ensure Computational Provenance:** Software environment locks (`environment.yml`, `requirements.txt`), deterministic POSIX execution scripts (`set -euo pipefail`), and formal scientific citations (`CITATIONS.md`).

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## The 4 Pillars & Implementation Protocols

### 1. Findable (F) — Data & Metadata Can Be Discovered
* **F1. Globally Unique & Persistent Identifiers:**
  - Never rely on transient or system-dependent file paths as keys.
  - Implement deterministic, hierarchical naming schemes:
    - *Samples:* Standardized sample codes (e.g., `ST1`, `T1`).
    - *Contigs:* Sample-prefixed identifiers (e.g., `{Sample}_{ContigID}`).
    - *Genes / CDS:* Hierarchical compound keys (e.g., `{Sample}_{ContigID}_{CDS#}`).
    - *Genomes / MAGs:* Deterministic bin accessions (e.g., `{Sample}_bin.{ID}.fa`).
  - Prefix external ontology terms with their standard namespace (`KEGG:K00635`, `EC:2.3.1.20`, `Pfam:PF03007`).
* **F2. Rich Descriptive Metadata:**
  - Provide a dedicated sample metadata table (`metadata/samples_metadata.tsv`) specifying library layout, environmental ontology terms (ENVO/MIxS), sequencing platform, and sequencing yield.
* **F3. Metadata Explicitly Includes Identifiers:**
  - Every row in every tabular matrix must preserve the compound primary key (`sample`, `contig_id`, `query`, `bin_id`).
* **F4. Machine-Readable Registration:**
  - Provide a machine-readable schema (e.g. `metadata/datapackage.json` following the Frictionless Data standard) describing field types, formats, constraints, and descriptions.

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### 2. Accessible (A) — Data & Metadata Can Be Retrieved
* **A1. Retrievable via Open, Standardized Protocols:**
  - All files, code, and configurations must be accessible via open, non-proprietary protocols (Git, HTTPS, standard POSIX CLI).
* **A1.1 / A1.2. Free & Transparent Access:**
  - Zero proprietary paywalls or closed binary dependencies required to read or process metadata and analysis tables.
* **A2. Independent Metadata Persistence:**
  - High-value metadata matrices, quality metrics, and schema definitions must persist directly in the repository, remaining accessible even if multi-gigabyte raw sequence files (BAM / FASTQ) are archived or moved to cold storage.

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### 3. Interoperable (I) — Data Can Be Integrated with Other Workflows
* **I1. Formal, Shared, Open Language:**
  - Store tabular data in UTF-8 encoded Tab-Separated Values (`.tsv`) with standard UNIX line endings (`\n`). Avoid opaque binary formats (e.g., proprietary Excel sheets or unexported database dumps) for primary results.
* **I2. Standardized Community Ontologies:**
  - Integrate authoritative, versioned ontologies:
    - **Taxonomy:** Genome Taxonomy Database ([GTDB](https://gtdb.ecogenomic.org/)) 7-rank prefix format (`d__`, `p__`, `c__`, `o__`, `f__`, `g__`, `s__`).
    - **Enzymes:** IUBMB Enzyme Commission numbers (`EC:x.x.x.x`).
    - **Functional Orthology:** KEGG Orthology (`ko:Kxxxxx`), Modules (`Mxxxxx`), Pathways (`ko00061`).
    - **Protein Domains:** EMBL-EBI Pfam accessions (`PFxxxxx`).
    - **Functional Categories:** NCBI COG single-letter categories.
* **I3. Qualified Cross-References:**
  - Create traceable semantic linkages: physical coordinates $\rightarrow$ gene annotation $\rightarrow$ sequencing depth $\rightarrow$ MAG bin $\rightarrow$ taxonomic classification.

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### 4. Reusable (R) — Data & Methods Can Be Replicated and Built Upon
* **R1. Rich Scientific Attributes:**
  - Provide comprehensive metrics (e.g., per-base mean depth, fractional pathway completeness, CheckM2 completeness and contamination, MIMAG draft quality tiers).
* **R1.1. Clear Open-Source License:**
  - The repository must include an explicit, permissive open-source license file (`LICENSE`), such as MIT or Apache-2.0 for code, and CC-BY 4.0 for data.
* **R1.2. Detailed Provenance & Reproducibility:**
  - Document all underlying tools, algorithm versions, and reference database dates in [`CITATIONS.md`](CITATIONS.md).
  - Provide locked environment specifications: Conda/Mamba [`environment.yml`](environment.yml) and Python [`requirements.txt`](requirements.txt).
  - Provide executable bash scripts (`command.sh`) with strict error flags (`set -euo pipefail`).
* **R1.3. Domain-Relevant Standards Compliance:**
  - **Genomics & MAGs:** Follow the **MIMAG standards** ([Bowers et al., 2017 *Nat. Biotechnol.*](https://doi.org/10.1038/nbt.3893)) for reporting High-Quality (HQ), Medium-Quality (MQ), and Low-Quality (LQ) MAGs.
  - **Environmental Sampling:** Follow the **GSC MIxS standards** (water, sediment, soil, host-associated packages).

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## Bioinformatics & Metagenomics Overlay

1. **MIMAG Genome Quality Reporting:**
   - Always report `Completeness`, `Contamination`, `Heterogeneity`, and `Marker Lineage`.
   - Classify MAGs explicitly:
     - **HQ:** Completeness $\ge 90\%$, Contamination $< 5\%$.
     - **MQ:** Completeness $\ge 50\%$, Contamination $< 10\%$.
     - **LQ:** Completeness $< 50\%$ or Contamination $\ge 10\%$.
2. **Abundance Normalization & Verification:**
   - Clearly state depth units (raw per-base mean depth from `mosdepth`, TPM, or SCG copies-per-cell).
   - Enforce depth conservation assertions: $\sum \text{stratified depths} = \text{unstratified total depth}$.
3. **Compound Primary Keys:**
   - Metagenomic assemblies across multiple samples can duplicate contig IDs (e.g., `contig_1`). Always join tables on compound keys `(sample, contig_id)`.

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## Anti-Patterns

- **Ad-hoc column names:** `col_1`, `val`, `res` without units or definitions.
- **Local absolute paths in primary keys:** Keys tied to a specific workstation directory.
- **Proprietary binary storage:** Trapping primary analytical tables in closed formats.
- **Unversioned taxonomy:** Free-text taxonomic names without GTDB or NCBI taxonomy version anchors.
- **Missing LICENSE file:** Leaving reuse rights ambiguous.
- **Unpinned software environments:** Omitting tool versions or relying on `@latest`.

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## When to use me

- Structuring new bioinformatics or metagenomics project repositories.
- Preparing analytical tables and metadata for publication or public deposition.
- Creating machine-readable data dictionaries (`datapackage.json`).
- Auditing existing codebases and data packages for FAIR compliance.

From bioassembly/antigravity-sage, MIT.

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