fair-data-model-assessment

name

fair-data-model-assessment

description

Assess data models against FAIR principles using RDA-FDMM indicators. Use when: (1) Evaluating vendor-delivered data models for FAIR compliance, (2) Reviewing schemas, ontologies, or data dictionaries before integration, (3) Creating FAIR assessment reports for data governance reviews, (4) Preparing data model documentation for enterprise or regulatory standards, (5) Auditing existing data assets for FAIRness gaps. Covers 41 RDA indicators across Findable, Accessible, Interoperable, Reusable dimensions with maturity scoring (0-4 scale).

FAIR Data Model Assessment

Assess data models, schemas, and data dictionaries against FAIR principles using the RDA FAIR Data Maturity Model framework.

Quick Reference

Task	Approach
Full assessment	Follow Assessment Workflow below
Quick check	Use Essential Indicators only (see references/rda-indicators.md)
Generate report	Run scripts/generate_report.py after assessment
Calculate scores	Run scripts/score_calculator.py with assessment JSON

Context: Data Models vs Published Datasets

The RDA-FDMM was designed for published datasets with DOIs. Internal data models require adapted assessment:

RDA Focus	Data Model Adaptation
DOI/PID resolution	Schema defines unique entity identifiers
Registry indexing	Model documented in enterprise catalog
HTTP retrieval	Schema accessible via standard formats
License metadata	Usage rights documented

See references/assessment-questions.md for the full adapted question set.

Assessment Workflow

Step 1: Gather Artifacts

Collect all available documentation:

• Schema files (JSON Schema, XSD, DDL, etc.)
• Data dictionaries or field definitions
• Entity-relationship diagrams
• Metadata specifications
• Provenance documentation
• Usage/license documentation

Step 2: Determine Scope

Not all 41 indicators apply to pre-publication data models. Classify the assessment:

Pre-publication internal model: Focus on indicators F2, F3, I1, I2, I3, R1, R1.2, R1.3 Model with planned publication: Include F1, F4, A1, A2, R1.1 Published/registered model: Full indicator set applies

Step 3: Conduct Assessment

For each applicable indicator in references/rda-indicators.md:

1. Read the indicator definition and priority level
2. Answer the assessment questions in references/assessment-questions.md
3. Assign maturity level (0-4):
   • 0: Not applicable or not addressed
   • 1: Initial/ad-hoc implementation
   • 2: Basic/partial implementation
   • 3: Defined/consistent implementation
   • 4: Managed/optimized implementation

Record responses in this structure:

{
  "model_name": "Vendor Data Model X",
  "assessment_date": "2025-01-06",
  "assessor": "Name",
  "scope": "pre-publication",
  "indicators": {
    "F1": { "maturity": 2, "notes": "UUIDs defined but not globally resolvable" },
    "F2": { "maturity": 3, "notes": "Rich metadata in data dictionary" }
  }
}

Step 4: Calculate Scores

Run the score calculator:

python scripts/score_calculator.py assessment.json

This produces:

• Per-principle scores (F, A, I, R)
• Overall FAIRness percentage
• Priority-weighted score (Essential > Important > Useful)

Step 5: Generate Report

python scripts/generate_report.py assessment.json --output report.md

The report includes:

• Executive summary with overall score
• Per-principle breakdown with findings
• Gap analysis highlighting low-maturity indicators
• Specific recommendations for improvement

Interpreting Results

Score Range	Interpretation
80-100%	Excellent FAIR compliance
60-79%	Good compliance, minor gaps
40-59%	Moderate compliance, improvement needed
20-39%	Significant gaps, prioritize remediation
0-19%	Major FAIR deficiencies

Domain-Specific Standards

For life sciences data models, see references/domain-standards.md for:

• CDISC standards (CDASH, SDTM, ADaM)
• HL7 FHIR resources
• ISA framework
• Allotrope Foundation schemas

Common Findings and Remediation

Low F scores: Add persistent identifiers, improve metadata richness, register in catalog Low A scores: Document access protocols, ensure format longevity Low I scores: Map to standard vocabularies, use formal schemas, add qualified references Low R scores: Add license info, document provenance, align with community standards

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Interactive Assessment Mode

For guided assessments, Claude can interactively walk through each indicator, ask questions, and build the assessment JSON.

Starting an Interactive Assessment

Say: "Start an interactive FAIR assessment for [model name]"

Claude will guide you through:

1. Collecting basic information (model name, assessor, scope)
2. Walking through applicable indicators based on scope
3. For each indicator:
   • Explaining the indicator purpose
   • Asking relevant assessment questions
   • Suggesting maturity level based on responses
   • Capturing notes/evidence
4. Generating complete assessment JSON
5. Calculating and displaying scores

Scope-Based Indicator Sets

Not all 41 indicators apply to every context. Choose your scope:

Pre-publication internal model (8 indicator groups):

• Focus on: F2, F3, I1, I2, I3, R1, R1.2, R1.3
• Skip: External identifiers, catalog registration, access protocols

Planned-publication model (14 indicator groups):

• Add to above: F1, F4, A1, A1.1, A2, R1.1

Published/registered model (16 indicator groups, 41 total indicators):

• Full indicator set applies

Interactive Question Flow

For each indicator group, Claude will:

1. Explain the indicator purpose and relevance
2. Ask the assessment questions from references/assessment-questions.md
3. Suggest a maturity level based on your answers:
   • All yes → Maturity 3-4
   • Some yes → Maturity 2
   • Awareness only → Maturity 1
   • None → Maturity 0
4. Confirm the score with you (you can override)
5. Capture notes and evidence

Example for F2 (Rich Metadata):

━━━ Assessing F2: Rich Metadata for Discovery ━━━

This indicator checks whether your model has sufficient
descriptive information for discovery by humans and machines.

Questions:
1. Does a data dictionary exist with field-level documentation? [y/n]
2. Are data types specified for all fields? [y/n]
3. Are constraints documented (nullable, length, format)? [y/n]
4. Are business definitions provided (not just technical names)? [y/n]
5. Are valid value sets/enumerations documented? [y/n]
6. Is the purpose/context of the model documented? [y/n]
7. Are relationships between entities documented? [y/n]

Based on your answers (5/7 yes), I suggest maturity level 3:
"Comprehensive data dictionary with business definitions"

Accept maturity 3? [Enter to accept, or type 0-4 to override]
Notes for this indicator: ___

Assessment Commands

During interactive mode, you can use these commands:

Command	Action
skip	Skip current indicator
back	Return to previous indicator
summary	Show current assessment progress
save	Save current progress to JSON file
calculate	Calculate scores with current responses
help	Show available commands
quit	Exit assessment (progress is lost unless saved)

Quick Assessment Mode

For rapid initial screening, ask for: "Quick FAIR assessment for [model name]"

This uses the 10-question quick checklist:

1. Does every entity/table have a unique identifier? (F1)
2. Is there a complete data dictionary? (F2)
3. Is the model registered in a searchable catalog? (F4)
4. Can the schema be accessed without proprietary tools? (A1)
5. Is the schema in a formal, machine-readable format? (I1)
6. Are fields mapped to standard vocabularies? (I2)
7. Are relationships explicitly documented? (I3)
8. Are all fields fully documented with business definitions? (R1)
9. Are usage rights/license documented? (R1.1)
10. Does the model align with domain community standards? (R1.3)

Quick scoring: 8-10 = Strong, 5-7 = Moderate, 0-4 = Significant gaps

Validation

The skill includes JSON validation for assessment files:

# Validate an assessment file
python scripts/score_calculator.py assessment.json --validate-only

# Calculate scores with validation
python scripts/score_calculator.py assessment.json

# Skip validation (not recommended)
python scripts/score_calculator.py assessment.json --no-validate

# Treat warnings as errors
python scripts/score_calculator.py assessment.json --strict

Calibration

For consistent scoring across assessors, see references/calibration-guide.md:

• Maturity level definitions with evidence requirements
• Scoring examples for each indicator
• Inter-rater reliability process
• Reference assessments for training