{
  "methodology_version": "1.0.0",
  "released_on": "2026-09-15",
  "publisher": "Strategic Signal",
  "qualification_rule": "Include only an observable transaction or governed arrangement in which a proprietary corporate corpus, operating history, institutional memory, or controlled rights to that corpus are strategically material to AI training, post-training, customization, validation, evaluation, or improvement. Ordinary enterprise AI deployments and retrieval-only connectors are excluded unless the AI counterparty receives separately material data rights.",
  "publication_rule": "AI-discovered, human-reviewed. No candidate is published automatically.",
  "primary_transaction_taxonomy": [
    "Data Asset Acquisition",
    "Proprietary Corpus License",
    "Training Rights License",
    "Governed Live Data Access",
    "Federated / Controlled Learning Rights",
    "Sovereign / On-Prem Learning",
    "Strategic Model Co-Development",
    "Proprietary Data + Equity Partnership"
  ],
  "orthogonal_facets": {
    "asset_disposition": ["Stranded / Separable", "Embedded / Continuing"],
    "corpus_renewal": ["Finite Corpus", "Renewable Corpus"],
    "rights": ["training", "post_training", "inference_retrieval", "ownership_transfer", "derived_model", "sublicensing", "retention"]
  },
  "taxonomy_note": "Stranded institutional memory and source-code/software are asset characteristics, not transaction forms. Keeping them as orthogonal facets prevents overlapping primary categories and makes the ontology durable across many observations.",
  "score_scale": {
    "factor_rating": "0–5 in 0.5-point increments",
    "calculation": "Sum of factor weight × rating ÷ 5, rounded to the nearest integer.",
    "bands": {
      "80-100": "Exceptional",
      "65-79": "High",
      "50-64": "Material",
      "35-49": "Moderate",
      "0-34": "Limited"
    },
    "unknowns": "A factor is null when evidence is insufficient. A total is published only when scored factors cover at least 70% of possible weight; observed weights are normalized to 100. Coverage is published beside each score. A zero means evidence of absence or weakness, not missing evidence."
  },
  "asset_score": {
    "name": "AI Data Asset Score",
    "purpose": "Measures the intrinsic strategic usefulness of the underlying data asset, not the quality of price discovery.",
    "minimum_coverage_pct": 70,
    "factors": [
      {"key": "uniqueness_scarcity", "label": "Uniqueness / scarcity", "weight": 12},
      {"key": "historical_depth", "label": "Historical depth", "weight": 8},
      {"key": "decision_outcome_richness", "label": "Decision → outcome richness", "weight": 13},
      {"key": "decision_value_density", "label": "Decision Value Density", "weight": 13, "definition": "The economic consequence represented by the decisions in the corpus: for example drilling, clinical, underwriting, yield, fraud, or safety interventions."},
      {"key": "real_world_grounding", "label": "Real-world grounding", "weight": 10},
      {"key": "domain_value", "label": "Domain economic value", "weight": 9},
      {"key": "refreshability", "label": "Refreshability", "weight": 8},
      {"key": "proprietary_advantage", "label": "Proprietary advantage", "weight": 9},
      {"key": "model_learning_usefulness", "label": "Model-learning usefulness", "weight": 10},
      {"key": "non_replicability", "label": "Non-replicability", "weight": 4},
      {"key": "rights_usability", "label": "Rights usability", "weight": 4}
    ]
  },
  "transaction_signal_score": {
    "name": "Transaction Signal Score",
    "purpose": "Measures how strongly the transaction evidences standalone economic value for proprietary corporate data.",
    "minimum_coverage_pct": 70,
    "factors": [
      {"key": "disclosed_economics", "label": "Disclosed economics", "weight": 16},
      {"key": "clean_price_discovery", "label": "Clean price discovery", "weight": 16},
      {"key": "data_consideration_separability", "label": "Separability of data consideration", "weight": 15},
      {"key": "explicit_model_use", "label": "Explicit training / model-improvement use", "weight": 14},
      {"key": "rights_clarity", "label": "Clarity of rights purchased", "weight": 12},
      {"key": "competing_bids", "label": "Competing bids", "weight": 10},
      {"key": "strategic_buyer_quality", "label": "Strategic buyer quality", "weight": 6},
      {"key": "precedent_value", "label": "Repeatability / precedent value", "weight": 6},
      {"key": "independent_model_uplift", "label": "Independent model-uplift evidence", "weight": 5}
    ]
  },
  "evidence_rules": [
    "Prefer company announcements, regulatory filings, court documents, and investor materials.",
    "Use secondary reporting to corroborate, clarify, or document facts not available in a primary source.",
    "Label analysis and estimates; never present them as disclosed terms.",
    "Do not infer ownership transfer from access, training rights from retrieval, or sublicensing from partnership language.",
    "Use explicit unknown or not-disclosed states and record the date last reviewed."
  ],
  "version_policy": "Methodology versions are immutable. Corrections to observations are recorded in release metadata; scoring-rule changes require a new methodology version and a versioned restatement."
}