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

# Data & Validation: verify the numbers yourself

> Two transparency features a live SQL editor backed by DuckDB-Wasm and a per-race pipeline quality audit so you can interrogate the data directly rather than taking the charts on faith.

## What this pillar does

Data & Validation provides the tools to answer: **should I trust this number, and can I
check it myself?**

* **Query Lab** gives you a full SQL editor running against every registered gold mart in
  your browser the same Parquet files every other feature reads. If a chart looks wrong,
  you can write the query and compare.
* **Data Quality Audit** shows the per-race breakdown of every lap the pipeline excluded
  and why: neutralisation flags, rain laps, out/in laps, and anomalies. The same invariants
  run automatically in CI; this page surfaces the per-race detail the gate summarises.

The pipeline exclusion rules are documented in the [Quality & Coverage](/data/data-quality)
data-tab page. The ML models have their own trust surface in
[The Machine](/app/families/the-machine) pillar above.

## Every feature in this pillar

<CardGroup cols={2}>
  <Card title="Query Lab" icon="terminal" href="/app/query-lab">
    A full SQL editor backed by DuckDB-Wasm. Write raw queries against every registered gold
    mart no credentials, no server, results in milliseconds.
  </Card>

  <Card title="Data Quality Audit" icon="shield" href="/app/data-quality-audit">
    Per-race usable-lap counts and a breakdown of every excluded lap type neutralisation,
    rain, out/in laps, and anomalies for a season.
  </Card>
</CardGroup>