# Data Model

## Layers

### 0 — Source
Untouched Danish/English SMK snapshots plus provenance. No rewriting in place.

### 1 — Canonical
Stable internal representation with consistent units, explicit nulls and language-aware textual fields.

### 2 — Features
Deterministic values derived solely from one artwork and its source/enrichment data.

### 3 — Normalized
Values calculated relative to a named, frozen corpus/profile.

## Main artifacts

### `source-manifest.json`
Identifies exact input snapshots by filename, size, SHA-256, observed JSON structure and record counts.

### `artworks.parquet`
One canonical row per artwork identity.

Suggested fields include:

- `object_number`
- `public_domain`
- localized title/object-name fields when useful (`*_da`, `*_en`)
- stable classification/object type fields
- `image_width_px`, `image_height_px`, `image_aspect_ratio`
- `physical_width_cm`, `physical_height_cm`, `physical_depth_cm`
- `physical_area_cm2`, `physical_aspect_ratio`
- source/enrichment provenance
- source-presence flags such as `present_in_da`, `present_in_en`

### `palette.parquet`
One row per artwork + canonical palette colour.

Suggested fields:

- `object_number`
- `rank`
- `hex`
- `weight`
- `weight_source`
- `srgb_r`, `srgb_g`, `srgb_b`
- `oklab_l`, `oklab_a`, `oklab_b`
- `oklch_l`, `oklch_c`, `oklch_h_deg`
- palette provenance/source/version

### `features.parquet`
One row per artwork with Feature Specification v1 values and quality flags.

### `normalized-features.parquet`
One row per artwork per normalization profile/version, or a wide equivalent if simpler to query.

### `quality-report.json`
Corpus-wide counts, missingness, exclusions, warnings and distribution diagnostics.

## Identity

Prefer `object_number` as the stable artwork key where present. Detect and report missing/duplicate IDs before silently deduplicating.

If duplicate IDs exist, inspect them and implement an explicit deterministic policy with provenance; do not keep an arbitrary “first row”.

## Null semantics

Null means unavailable/not safely derivable.

Zero is a real value and must never stand in for missing data.

## Corpus membership

Canonical ingestion and feature extraction may include records that are not eligible for `smk-public-visual-v1`.

Normalization membership is a separate, explicit decision. Store inclusion/exclusion reasons rather than deleting records merely because a downstream profile cannot use them.