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README.md

@@ -20,132 +20,15 @@ size_categories:
 configs:
   - config_name: tracks
     data_files: "tracks/train-*.parquet"
-    description: "18.3M electronic music tracks with artist, genre, label, year"
   - config_name: artists
     data_files: "artists/train-*.parquet"
-    description: "1.4M electronic music artists with genres, labels, country"
   - config_name: labels
     data_files: "labels/train-*.parquet"
-    description: "353K record labels with genres and country"
   - config_name: genres
     data_files: "genres/train-*.parquet"
-    description: "832 electronic music genres from Ishkur + Discogs"
   - config_name: genre_graph
     data_files: "genre_graph/train-*.parquet"
-    description: "352 genre evolution relationships with time ranges"
 default_config_name: tracks
-dataset_info:
-  - config_name: tracks
-    features:
-      - name: id
-        dtype: string
-      - name: title
-        dtype: string
-      - name: artist_name
-        dtype: string
-      - name: discogs_artist_id
-        dtype: string
-      - name: discogs_release_id
-        dtype: string
-      - name: subgenre
-        dtype: string
-      - name: styles_json
-        dtype: string
-      - name: label
-        dtype: string
-      - name: discogs_label_id
-        dtype: string
-      - name: country
-        dtype: string
-      - name: year
-        dtype: int32
-      - name: search_query
-        dtype: string
-      - name: source
-        dtype: string
-    splits:
-      - name: train
-        num_examples: 18315675
-  - config_name: artists
-    features:
-      - name: id
-        dtype: string
-      - name: name
-        dtype: string
-      - name: discogs_artist_id
-        dtype: string
-      - name: primary_genres
-        dtype: string
-      - name: labels
-        dtype: string
-      - name: country
-        dtype: string
-      - name: active_since
-        dtype: int32
-      - name: track_count
-        dtype: int32
-      - name: source
-        dtype: string
-    splits:
-      - name: train
-        num_examples: 1424582
-  - config_name: labels
-    features:
-      - name: id
-        dtype: string
-      - name: name
-        dtype: string
-      - name: discogs_label_id
-        dtype: string
-      - name: primary_genres
-        dtype: string
-      - name: country
-        dtype: string
-      - name: founded_year
-        dtype: int32
-      - name: source
-        dtype: string
-    splits:
-      - name: train
-        num_examples: 352984
-  - config_name: genres
-    features:
-      - name: id
-        dtype: string
-      - name: name
-        dtype: string
-      - name: scene
-        dtype: string
-      - name: emerged_era
-        dtype: string
-      - name: bpm_low
-        dtype: int32
-      - name: bpm_high
-        dtype: int32
-      - name: energy_typical
-        dtype: int32
-      - name: aliases
-        dtype: string
-      - name: source
-        dtype: string
-    splits:
-      - name: train
-        num_examples: 832
-  - config_name: genre_graph
-    features:
-      - name: source_genre
-        dtype: string
-      - name: target_genre
-        dtype: string
-      - name: start_year
-        dtype: int32
-      - name: end_year
-        dtype: int32
-      - name: source
-        dtype: string
-    splits:
-      - name: train
-        num_examples: 352
 ---
 
 # Electronic Music Knowledge
@@ -164,61 +47,43 @@ Built for [DJ Treta](https://dj.treta.life) — an autonomous AI DJ — but usef
 | `genres` | 832 | Electronic genre taxonomy from Ishkur's Guide + Discogs (166 with BPM ranges) |
 | `genre_graph` | 352 | Genre evolution relationships with time ranges |
 
-## Usage
+## Quick Start
 
 ```python
 from datasets import load_dataset
 
-# Load tracks (default)
-tracks = load_dataset("NaturNestAI/electronic-music-knowledge", "tracks")
-
-# Load artists
-artists = load_dataset("NaturNestAI/electronic-music-knowledge", "artists")
-
-# Load genre taxonomy
-genres = load_dataset("NaturNestAI/electronic-music-knowledge", "genres")
-
-# Load genre evolution graph
-graph = load_dataset("NaturNestAI/electronic-music-knowledge", "genre_graph")
+# Load tracks (default config)
+tracks = load_dataset("NaturNestAI/electronic-music-knowledge", "tracks", split="train")
 
-# Load labels
-labels = load_dataset("NaturNestAI/electronic-music-knowledge", "labels")
+# Load other configs
+artists = load_dataset("NaturNestAI/electronic-music-knowledge", "artists", split="train")
+genres = load_dataset("NaturNestAI/electronic-music-knowledge", "genres", split="train")
+labels = load_dataset("NaturNestAI/electronic-music-knowledge", "labels", split="train")
+graph = load_dataset("NaturNestAI/electronic-music-knowledge", "genre_graph", split="train")
 ```
 
-### Example: Find melodic techno tracks
+## Examples
 
+### Find melodic techno tracks
 ```python
 tracks = load_dataset("NaturNestAI/electronic-music-knowledge", "tracks", split="train")
 melodic = tracks.filter(lambda x: x["subgenre"] == "Melodic House & Techno")
-print(f"Found {len(melodic)} melodic techno tracks")
-print(melodic[0])  # {'title': '...', 'artist_name': '...', 'label': '...', ...}
+print(f"{len(melodic)} melodic techno tracks")
 ```
 
-### Example: Find artists on Drumcode label
-
+### Find artists on a label
 ```python
 artists = load_dataset("NaturNestAI/electronic-music-knowledge", "artists", split="train")
-drumcode = artists.filter(lambda x: x["labels"] and "Drumcode" in x["labels"])
-print(f"Drumcode artists: {len(drumcode)}")
+drumcode = artists.filter(lambda x: x["labels"] and "Drumcode" in str(x["labels"]))
 ```
 
-### Example: Genre evolution graph
-
+### Genre evolution graph
 ```python
 graph = load_dataset("NaturNestAI/electronic-music-knowledge", "genre_graph", split="train")
-# What influenced Melodic Techno?
+# What influenced a genre?
 influences = graph.filter(lambda x: x["target_genre"] == "melodictechno")
-for row in influences:
-    print(f"{row['source_genre']} → melodictechno ({row['start_year']}-{row['end_year']})")
 ```
 
-## Data Sources
-
-| Source | License | What it provides |
-|--------|---------|-----------------|
-| [Discogs Data Dump](https://data.discogs.com/) (April 2026) | CC0 1.0 | 4.9M electronic releases → tracks, artists, labels, 666 styles |
-| [Ishkur's Guide to Electronic Music v3](https://github.com/igorbrigadir/ishkurs-guide-dataset) | Open | 166 genre taxonomy with BPM ranges, 11K representative tracks, evolution graph |
-
 ## Schema
 
 ### tracks
@@ -230,21 +95,20 @@ for row in influences:
 | `discogs_artist_id` | string | Discogs artist ID |
 | `discogs_release_id` | string | Discogs release ID |
 | `subgenre` | string | Primary Discogs style (e.g., "Melodic House & Techno") |
-| `styles_json` | string | JSON array of all styles |
+| `styles_json` | string | JSON array of all Discogs styles |
 | `label` | string | Record label name |
-| `discogs_label_id` | string | Discogs label ID |
 | `country` | string | Release country |
 | `year` | int | Release year |
-| `search_query` | string | Pre-computed "Artist - Title" for music search |
+| `search_query` | string | Pre-computed "Artist - Title" for YouTube/music search |
 | `source` | string | Data source ("discogs" or "ishkur") |
 
 ### artists
 | Column | Type | Description |
 |--------|------|-------------|
-| `id` | string | Unique artist ID ("discogs:12345") |
+| `id` | string | "discogs:{artist_id}" |
 | `name` | string | Artist name |
 | `primary_genres` | string | Primary genre/style |
-| `labels` | string | First known label |
+| `labels` | string | Known label |
 | `country` | string | Country of origin |
 | `active_since` | int | Year of earliest release |
 | `track_count` | int | Number of tracks in dataset |
@@ -255,34 +119,51 @@ for row in influences:
 | `id` | string | Genre slug |
 | `name` | string | Genre name |
 | `scene` | string | Ishkur scene grouping (House, Techno, Trance, etc.) |
-| `bpm_low` | int | Typical BPM range - low end |
-| `bpm_high` | int | Typical BPM range - high end |
+| `bpm_low` / `bpm_high` | int | Typical BPM range |
 | `energy_typical` | int | Typical energy level (1-10) |
 | `aliases` | string | Alternative genre names |
 
+### labels
+| Column | Type | Description |
+|--------|------|-------------|
+| `id` | string | "discogs:{label_id}" |
+| `name` | string | Label name |
+| `primary_genres` | string | Primary genre |
+| `country` | string | Country |
+| `founded_year` | int | Year of earliest release |
+
+### genre_graph
+| Column | Type | Description |
+|--------|------|-------------|
+| `source_genre` | string | Genre that influenced |
+| `target_genre` | string | Genre that was influenced |
+| `start_year` / `end_year` | int | Time range of influence |
+
+## Data Sources
+
+| Source | License | Contribution |
+|--------|---------|-------------|
+| [Discogs Data Dump](https://data.discogs.com/) (April 2026) | CC0 1.0 | 4.9M electronic releases, 1.4M artists, 353K labels, 666 styles |
+| [Ishkur's Guide to Electronic Music v3](https://github.com/igorbrigadir/ishkurs-guide-dataset) | Open | 166 genre taxonomy with BPM ranges, 11K tracks, evolution graph |
+
 ## Planned Enrichment (v2)
 
-- BPM and key data from AcousticBrainz (29.5M pre-computed audio features, CC0)
-- Artist similarity graph (label co-occurrence + genre overlap)
+- BPM and musical key from AcousticBrainz (29.5M tracks, CC0)
+- Artist similarity graph
 - MusicBrainz cross-reference IDs
-- Beatport Top 100 ground truth (BPM, key, energy)
-- GiantSteps ground truth (BPM, key for 1,268 EDM tracks)
-- DJ set transition data from mir-aidj
+- DJ set transition data
 
-## Built With
+## Pipeline
 
-- Pipeline: [VeltriaAI/music-intelligence](https://github.com/VeltriaAI/music-intelligence)
-- Tools: Python, Polars, lxml (streaming XML parser)
-- Extensible adapter architecture — add new data sources by dropping a file
+Built with [VeltriaAI/music-intelligence](https://github.com/VeltriaAI/music-intelligence) — extensible source adapter architecture. Add new data sources by dropping a Python file.
 
 ## Citation
 
 ```bibtex
 @dataset{electronic_music_knowledge_2026,
-  title={Electronic Music Knowledge: A Comprehensive Metadata Dataset for Electronic Music},
+  title={Electronic Music Knowledge},
   author={NaturNest AI},
   year={2026},
-  publisher={Hugging Face},
   url={https://huggingface.co/datasets/NaturNestAI/electronic-music-knowledge},
   license={CC0-1.0}
 }
@@ -290,4 +171,4 @@ for row in influences:
 
 ## License
 
-CC0 1.0 Universal — No Rights Reserved. All source data is CC0 (Discogs) or openly licensed (Ishkur).
+CC0 1.0 Universal — No Rights Reserved.