> ## Documentation Index
> Fetch the complete documentation index at: https://docs.axiomancer.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Data catalog

> Browse the nine Codex datasets — civic proceedings, port governance, maritime activity, permits, labor flows, POIs, OSHA safety, and a unified event timeline.

Axiom Codex publishes nine datasets covering civic proceedings, port authority governance, maritime activity, permits, labor flows, points of interest, workplace safety, and a unified event timeline that ties them all together. Every dataset follows the [normalization standard](/codex/normalization-standard), ships with pre-computed AI labels, and joins to any other Codex dataset without custom wrangling.

## Datasets

<Columns cols={2}>
  <Card title="Civic Intelligence" icon="landmark" href="/codex/schemas/civic-intelligence">
    **517K records** — Council votes, permits, and zoning decisions with entity extraction, sentiment scores, upzoning probability, and DAG-mapped approval sequences.

    Tags: NLP-ready, Labeled, Temporal
    Formats: Parquet, JSON Lines
  </Card>

  <Card title="Port Authority Governance" icon="building-2" href="/codex/schemas/port-authority-governance">
    **1,800+ records** — Board meetings, agenda items, claims, facts, minutes, and tariffs from five major US port authorities with claim/fact separation and cross-port normalization.

    Tags: Governance, Structured, Multi-port
    Formats: Parquet, JSON Lines
  </Card>

  <Card title="AIS Maritime Positions" icon="ship">
    **1.4M positions** — Decoded vessel tracks, port calls, and anchor events enriched with Equasis vessel metadata, flag state, DWT, and kinematic fingerprints.

    Tags: Time-series, Enriched, Geospatial
    Formats: Parquet
  </Card>

  <Card title="Urban Signal Grid" icon="grid-2x2">
    **454K H3 cells** — Cell-level ESGI composite scores and 8 signal-group subscores across 22 US metros at H3 resolution 8.

    Tags: Geospatial, Scored, Multi-signal
    Formats: GeoParquet, CSV
  </Card>

  <Card title="Events Timeline" icon="calendar-clock" href="/codex/schemas/events-timeline">
    **1.7M+ events** — Unified temporal intelligence spanning permits, council decisions, AIS anomalies, OSHA violations, and business openings normalized to a single event schema.

    Tags: Temporal, Multi-source, Cross-domain
    Formats: JSON Lines, Parquet
  </Card>

  <Card title="LEHD Commuter Flows" icon="route">
    **454K OD pairs** — Census LEHD worker origin-destination pairs normalized to H3 cells with income bands, job sector, and Huff gravity index pre-computed.

    Tags: Geospatial, Demographics, Transport
    Formats: CSV, Parquet
  </Card>

  <Card title="POI Intelligence" icon="map-pin">
    **89K locations** — Points of interest enriched with category taxonomy, NAICS codes, pioneer business flags, walk/transit scores, and a reviews sample.

    Tags: Enriched, Categorized, Labeled
    Formats: JSON
  </Card>

  <Card title="Permit Signals" icon="file-text">
    **2.1M permits** — Building permit activity across 22 metros with LLM-extracted scope type, building type, unit count, and estimated cost tier.

    Tags: NLP-extracted, Temporal, Development
    Formats: Parquet, CSV
  </Card>

  <Card title="OSHA Safety Index" icon="triangle-alert">
    **500K+ inspections** — OSHA inspection records with NLP-classified hazard categories, violation severity tiers, and inflation-adjusted penalty normalization by H3 cell.

    Tags: NLP-classified, Safety, Industrial
    Formats: CSV, Parquet
  </Card>
</Columns>

## What makes Codex datasets different

Every record in every dataset satisfies the [Axiom Portable Record Standard (APRS)](/codex/normalization-standard). In practice, that means:

* **Zero-wrangling joins.** A fixed set of [shared keys](/codex/join-keys) (`h3_index`, `event_id`, `jurisdiction_slug`, `mmsi`, `imo`, and more) lets you join any two datasets with a single SQL `JOIN`.
* **Pre-computed AI labels.** Entity types, categories, sentiment, and risk scores are computed at normalization time — not at query time.
* **LLM-ready formatting.** Every dataset ships a Markdown-KV view (`llm_text`) optimized for RAG and LLM reasoning, benchmarked at 60.7% accuracy versus 44.3% for raw CSV.
* **Bitemporal timestamps.** Every record separates event time (`occurred_at`) from system time (`ingested_at`), publication time (`published_at`), and legal effective dates (`effective_from` / `effective_to`).
* **Versioned monthly snapshots.** Immutable monthly releases you can pin to for reproducible research.
* **Full provenance.** Every record carries a `provenance` chain documenting each transformation stage, and a `confidence_score` with per-dataset methodology.

## Pricing and delivery

| Tier           | Records                                            | Formats                              | Price                                                               |
| -------------- | -------------------------------------------------- | ------------------------------------ | ------------------------------------------------------------------- |
| **Research**   | 100K-record stratified sample                      | Parquet                              | Free on [Hugging Face](https://huggingface.co/axiom-ai) (CC-BY-4.0) |
| **Commercial** | Full dataset                                       | Parquet, CSV, JSON Lines, GeoParquet | \$299/dataset/month                                                 |
| **Enterprise** | Full dataset + [entity graph](/codex/entity-graph) | All formats + Markdown-KV            | Contact sales                                                       |

All paid tiers deliver monthly immutable Parquet snapshots via signed Cloudflare R2 download URLs. See [getting started](/codex/getting-started) for setup instructions.

## Standards compliance

Every dataset ships with a DCAT-US v3.0 `catalog.jsonld` sidecar for discoverability by data.gov-compatible crawlers. Per-dataset ontology crosswalks map Codex fields to the relevant domain standards:

| Dataset                   | Target standards                                     |
| ------------------------- | ---------------------------------------------------- |
| Civic Intelligence        | DCAT-US v3.0, PROV-DM, schema.org/GovernmentService  |
| Port Authority Governance | DCAT-US v3.0, PROV-DM, schema.org/GovernmentService  |
| AIS Maritime Positions    | IHO S-100, IMO A.600(15), ITU-R M.1371               |
| Urban Signal Grid         | OSCRE IDM 2.0, OGC API — Features, DCAT-US v3.0      |
| Events Timeline           | W3C PROV-DM, schema.org/Event, DCAT-US v3.0          |
| LEHD Commuter Flows       | Census TIGER/FIPS, OGC GeoJSON, DDI-Lifecycle        |
| POI Intelligence          | schema.org/LocalBusiness, GS1 GLN, OGC GeoJSON       |
| Permit Signals            | DCAT-US v3.0, schema.org/ConstructionPermit, PROV-DM |
| OSHA Safety Index         | schema.org/GovernmentPermit, NAICS, FRS, DCAT-US     |

Full crosswalks are available at [axiomcodex.io/standards](https://axiomcodex.io/standards).

## RAG and vector search

Every dataset ships with a `_chunks` Parquet file containing structure-aware chunks designed for retrieval-augmented generation. Chunks follow source boundaries (agenda items, speaker turns, permit scopes) rather than fixed token windows, and are compatible with LangChain and LlamaIndex `ParentDocumentRetriever` patterns.

Each chunk carries:

* A deterministic `chunk_id` (SHA-256-derived) for stable vector index keys
* A `parent_chunk_id` for parent-child retrieval
* An `evidence_anchor` for tracing back to the source document
* Token counts and embedding metadata

<Note>
  AIS Maritime Positions are point-in-time records and are not chunked. The Urban Signal Grid, LEHD Flows, and POI Intelligence datasets use their compact Markdown-KV views as single-record chunks.
</Note>

## Next steps

<Columns cols={2}>
  <Card title="Normalization standard" icon="shield" href="/codex/normalization-standard">
    The contract every record satisfies — field definitions, versioning rules, and conformance requirements.
  </Card>

  <Card title="Join keys" icon="link" href="/codex/join-keys">
    The registry of shared keys that make cross-dataset joins work without wrangling.
  </Card>

  <Card title="Chunking policy" icon="scissors" href="/codex/chunking-policy">
    How records are chunked for RAG — structure-aware boundaries, deterministic IDs, and parent-child retrieval.
  </Card>

  <Card title="Entity graph export" icon="share-2" href="/codex/entity-graph">
    A Parquet edge-list for multi-hop reasoning over entities, vessels, permits, and jurisdictions (Enterprise tier).
  </Card>

  <Card title="Bitemporal fields" icon="clock" href="/codex/bitemporal-fields">
    Per-dataset reference for every temporal field, so you always know which clock a timestamp is on.
  </Card>

  <Card title="Claim/fact separation" icon="layers" href="/codex/claim-fact-layer">
    How source assertions are separated from resolved facts, with full provenance.
  </Card>

  <Card title="Reference notebooks" icon="notebook-text" href="/codex/notebooks">
    Jupyter notebooks demonstrating upzoning classification and civic risk mapping with research-tier data.
  </Card>
</Columns>