DATASETS.md

Proprietary Knowledge Protocol — enabling agents to access specialized datasets, behavioral models, and domain expertise for intelligent decision-making.

Part of the protocols.md network

📊 Draft v0.1 - Knowledge exchange layer for autonomous intelligence. Exploratory specification for proprietary dataset markets. RFC stage

Problem

Agents need more than raw feeds — they need curated intelligence:

Domain expertise — Years of specialized knowledge can't be streamed.
Behavioral patterns — Complex models built from millions of interactions.
Cultural context — Nuanced understanding that prevents costly mistakes.
Safety boundaries — Hard-learned edge cases and failure modes.
Relationship graphs — Proprietary networks that reveal hidden connections.

Raw data is commodity. Processed intelligence is competitive advantage.

Solution – Intelligence Markets

GET https://datasets.md/discover

datasets.md creates markets for proprietary knowledge — curated datasets that embody expertise, patterns, and contextual understanding that agents need to operate intelligently.

Core APIs

Knowledge Discovery

GET /discover?domain=medical_diagnosis&specificity=rare_diseases

Find specialized knowledge bases, behavioral models, and domain expertise.

Contextual Intelligence

{
  "dataset_id": "competitor_strategies",
  "type": "market_intelligence",
  "coverage": {
    "pricing_patterns": 8400,
    "product_launches": 12300,
    "market_positioning": 9700,
    "response_times": 3200
  },
  "validation": {
    "expert_curated": true,
    "data_points": "2.3M observations",
    "accuracy_rate": 0.94,
    "last_updated": "2025-09-20"
  },
  "access_models": {
    "full_license": "$4,700",
    "query_based": "$0.50/lookup",
    "embedding_access": "$1,200/month"
  }
}

Behavioral Models

POST /models/query
{
  "dataset": "user_intent_patterns",
  "context": {
    "sequence": ["search", "compare", "hesitate", "exit"],
    "time_gaps": [2.3, 45.1, 12.7],
    "metadata": {"device": "mobile", "time": "evening"}
  }
}

// Returns learned behavioral insight
{
  "pattern_match": "consideration_fatigue",
  "probability": 0.78,
  "recommendation": "simplify_choices",
  "similar_patterns": 47291,
  "confidence": 0.91,
  "micropayment": "$0.15"
}

Domain Expertise Access

POST /expertise/consult
{
  "knowledge_base": "materials_engineering",
  "query": {
    "application": "high_stress_joint",
    "conditions": ["temperature: 800C", "cycles: 1M", "load: 450MPa"],
    "constraints": ["weight_critical", "cost_sensitive"]
  }
}

{
  "recommendation": "titanium_alloy_grade_5",
  "properties": {
    "yield_strength": "880MPa",
    "fatigue_life": "1.4M cycles",
    "weight_savings": "45%"
  },
  "alternatives": [
    {"material": "inconel_718", "tradeoff": "cost +230%"},
    {"material": "steel_4340", "tradeoff": "weight +67%"}
  ],
  "similar_applications": 847,
  "confidence": 0.92,
  "fee": "$12.50"
}

Relationship Graphs

// Query proprietary network graphs
POST /graphs/traverse
{
  "dataset": "supply_chain_dependencies",
  "start_node": "component_8K4",
  "depth": 3,
  "filters": ["critical_path", "single_source"]
}

{
  "vulnerabilities": [
    {
      "node": "supplier_X72",
      "risk_score": 0.82,
      "alternatives": 2,
      "lead_time": "14 weeks"
    }
  ],
  "hidden_dependencies": 7,
  "graph_complexity": 0.73,
  "insight_value": "$4,200"
}

Proprietary Dataset Markets

Dataset Type	Curation Years	Access Cost	Uniqueness	Value Score
Engineering Specifications	40+	$8,000/yr	Irreplaceable	0.98
Market Microstructure	15+	$12,000/yr	Exchange-specific	0.96
Behavioral Patterns	10+	$0.10/query	Platform-specific	0.87
Logistics Networks	25+	$3,500/yr	Route-critical	0.95
Competitor Intelligence	12+	$2,000/month	Market-specific	0.89

Intelligence Applications

Market Dynamics Intelligence

# Access years of market microstructure patterns
dataset = datasets.connect("liquidity_dynamics")

# Unusual market condition
conditions = {
  "spread_widening": 3.2,
  "volume_profile": "inverted",
  "time_of_day": "14:47",
  "correlated_assets": ["retreating"]
}

insight = dataset.analyze(conditions)
# Returns: {"pattern": "pre_announcement_positioning", 
#           "probability": 0.84,
#           "typical_duration": "12-15min",
#           "historical_matches": 472}

Supply Chain Intelligence

# Navigate complex vendor relationships
POST /knowledge/apply
{
  "dataset": "supplier_reliability_matrix",
  "scenario": {
    "component": "high_precision_sensor",
    "quantity_needed": 50000,
    "timeline": "Q2_2025",
    "risk_tolerance": "low"
  }
}

{
  "recommendations": {
    "primary_supplier": "vendor_A47",
    "backup_strategy": "dual_source",
    "lead_time": "12_weeks",
    "price_variance": "±7%"
  },
  "risk_factors": ["geopolitical", "capacity_constraints"],
  "similar_procurements": 234,
  "success_rate": 0.91
}

Operational Optimization

# Leverage fleet optimization patterns
logistics_db = datasets.license("urban_delivery_patterns")

# Complex routing scenario
scenario = {
  "deliveries": 847,
  "time_windows": "mixed",
  "traffic": "event_congestion",
  "fleet_available": 42
}

strategy = logistics_db.optimize(scenario)
# Returns: {"routing": "hub_and_spoke_modified",
#           "efficiency_gain": "34%",
#           "similar_days": 89,
#           "fuel_saved": "$1,240"}

Trust & Provenance

{
  "curation_proof": {
    "expert_hours": 12000,
    "source_diversity": 847,
    "peer_review": true,
    "field_validation": "3 years",
    "update_frequency": "quarterly"
  },
  "quality_metrics": {
    "coverage": 0.94,
    "accuracy": 0.97,
    "recency": "30 days",
    "edge_cases": 4700
  },
  "attribution": {
    "contributor_reputation": true,
    "citation_chain": "preserved",
    "modification_log": "immutable",
    "licensing": "smart_contract"
  }
}

Why This Matters

Raw data is everywhere. But the difference between a naive agent and an intelligent one is access to processed expertise — the kind that takes years to build and can't be replicated from public feeds.

With datasets.md, agents gain:

Institutional knowledge — Decades of expertise, instantly accessible
Behavioral intelligence — Patterns learned from millions of interactions
Market positioning — Competitive dynamics and strategies
Operational wisdom — Learn from others' costly mistakes
Competitive edge — Proprietary insights that can't be scraped

Knowledge Economics

Proprietary datasets command premium prices because they represent:

Curation investment — Years of expert filtering and structuring
Exclusivity value — Competitive advantages from unique access
Liability reduction — Preventing costly mistakes worth millions
Time compression — Centuries of collective experience, instantly applied

{
  "value_models": {
    "expertise_licensing": "$1K-50K/year",
    "query_micropayments": "$0.01-10/query",
    "exclusive_access": "$100K-1M deals",
    "revenue_sharing": "1-10% of value created"
  }
}

Technical Architecture

storage: IPFS + Encrypted shards
indexing: Knowledge graphs + Vector embeddings
query: GraphQL + Semantic search
verification: Expert signatures + Usage attestations
payments: Subscription contracts + Escrow markets
access: OAuth + Capability tokens
updates: Delta sync + Versioning
privacy: Differential privacy + Secure enclaves

Network Effects

As the knowledge market matures:

Quality emergence — Best datasets command highest prices
Specialization rewards — Niche expertise becomes valuable
Composite intelligence — Combining datasets creates new insights
Trust accumulation — Proven datasets build reputation moats
Knowledge liquidity — Expertise flows to highest-value applications

spec_version: 0.1.0-draft

published: 2025-09-21T10:47:00Z

content_hash: sha256:b8d5f9c3e2a1d7b6c4f8e9a2d5c7f3b9e1a8d4c2

status: experimental

contact: proofmdorg@gmail.com

datasets.md