Dataset Management: Quality Scoring and License Validation

The DatasetManager provides comprehensive music dataset management with intelligent quality scoring, license validation, and metadata consistency checking. It ensures datasets meet quality standards and comply with legal requirements before usage.

Overview

The DatasetManager enables:

• Quality Scoring: Multi-factor scoring system based on 5 key metrics
• License Validation: Automatic verification of license compatibility
• Metadata Analysis: Comprehensive metadata completeness and consistency checks
• Dataset Organization: Efficient management of large dataset collections
• Content Diversity: Measurement of genre, artist, and style diversity
• Statistical Insights: Detailed analytics about dataset characteristics

Quality Assurance

The quality scoring system uses a weighted multi-factor approach to objectively assess dataset quality. This ensures your recommendation engine trains on high-quality data.

Architecture

Core Components

The DatasetManager consists of three main components:

from qfzz.datasets.manager import DatasetManager
from qfzz.datasets.models import Dataset, DatasetLicense, LicenseType

# Initialize with allowed licenses
manager = DatasetManager(
    allowed_licenses=['CC-BY', 'CC-BY-SA', 'CC0']
)

Key Classes:

Component	Purpose	Role
DatasetManager	Orchestrator	Manages datasets, scoring, validation
Dataset	Data container	Holds tracks and metadata
DatasetLicense	License info	Validates license compatibility

Dataset Models

Creating Datasets

Create a dataset with tracks and metadata:

from datetime import datetime

# Create dataset with license
license = DatasetLicense(
    license_type='CC-BY',
    license_url='https://creativecommons.org/licenses/by/4.0/',
    attribution_required=True,
    commercial_use=True,
    derivative_works=True,
    share_alike=False
)

dataset = Dataset(
    dataset_id='dataset_electronic_2024',
    name='Electronic Music Collection',
    description='High-quality electronic and dance tracks',
    version='1.0.0',
    license=license,
    creator_id='curator_001',
    tracks=[
        {
            'track_id': 'track_001',
            'title': 'Neon Dreams',
            'artist': 'SynthWave Artist',
            'genre': 'electronic',
            'mood': 'energetic',
            'energy': 0.8,
            'tempo': 120,
            'duration': 240,
            'album': 'Digital Horizons',
            'year': 2024
        },
        # ... more tracks
    ]
)

# Add to manager
if manager.add_dataset(dataset):
    print("✓ Dataset added successfully")
    print(f"Quality Score: {dataset.quality_score:.3f}")
else:
    print("✗ Dataset rejected due to license incompatibility")

License Compatibility

The manager validates that dataset licenses are compatible with the allowed licenses list before acceptance. Incompatible licenses are automatically rejected.

Track Structure

Each track in a dataset should include:

track = {
    # Required fields
    'track_id': 'unique_id',
    'title': 'Track Title',
    'artist': 'Artist Name',
    'genre': 'electronic',
    'duration': 300,  # seconds

    # Recommended fields
    'mood': 'energetic',
    'energy': 0.7,  # 0.0-1.0
    'tempo': 128,   # BPM
    'album': 'Album Name',
    'year': 2024,

    # Optional fields
    'isrc': 'USRC17607839',
    'composer': 'Composer Name',
    'key': 'C',
    'key_confidence': 0.95
}

Quality Scoring System

Understanding Quality Factors

The quality score is calculated from 5 weighted factors:

def calculate_quality_score(dataset):
    """
    Quality Score = (
        metadata_completeness * 0.30 +
        data_consistency * 0.25 +
        dataset_size * 0.20 +
        diversity * 0.15 +
        license_permissiveness * 0.10
    )
    """
    pass

Quality Factor Weights:

Factor	Weight	Description	Impact
Metadata Completeness	30%	Richness of track metadata	Highest
Data Consistency	25%	Uniformity and validity of data	High
Dataset Size	20%	Number and duration of tracks	Moderate
Diversity	15%	Genre/artist variety	Moderate
License Permissiveness	10%	Freedom of use and modification	Low

1. Metadata Completeness (30% weight)

Measures the completeness and richness of track metadata:

required_fields = ['title', 'artist', 'genre', 'duration']
optional_fields = ['album', 'year', 'mood', 'energy', 'tempo']

# Scoring formula:
# Required fields: 70% of completeness score
# Optional fields: 30% of completeness score

def analyze_metadata_completeness(dataset):
    """Analyze metadata completeness of a dataset."""

    if not dataset.tracks:
        return 0.0

    scores = []
    for track in dataset.tracks:
        # Count required fields
        required_present = sum(1 for field in required_fields
                              if field in track and track[field])
        required_score = (required_present / len(required_fields)) * 0.7

        # Count optional fields
        optional_present = sum(1 for field in optional_fields
                              if field in track and track[field])
        optional_score = (optional_present / len(optional_fields)) * 0.3

        track_score = required_score + optional_score
        scores.append(track_score)

    return sum(scores) / len(scores)

completeness = analyze_metadata_completeness(dataset)
print(f"Metadata Completeness: {completeness:.1%}")

Improve Completeness

• Add all required fields to every track
• Include optional fields like mood, energy, and tempo
• Use consistent field names and formats

2. Data Consistency (25% weight)

Measures uniformity and validity of data across tracks:

def analyze_data_consistency(dataset):
    """Analyze data consistency of a dataset."""

    if not dataset.tracks:
        return 0.0

    # Field consistency
    sample_fields = set(dataset.tracks[0].keys())
    field_scores = []

    for track in dataset.tracks:
        track_fields = set(track.keys())
        overlap = len(sample_fields & track_fields) / len(sample_fields)
        field_scores.append(overlap)

    field_consistency = sum(field_scores) / len(field_scores)

    # Value validity
    validity_scores = []
    for track in dataset.tracks:
        score = 1.0

        # Validate duration
        if 'duration' in track and track['duration'] <= 0:
            score -= 0.2

        # Validate energy
        if 'energy' in track and not 0.0 <= track['energy'] <= 1.0:
            score -= 0.2

        # Validate tempo
        if 'tempo' in track and not 40 <= track['tempo'] <= 300:
            score -= 0.2

        validity_scores.append(max(0.0, score))

    value_validity = sum(validity_scores) / len(validity_scores)

    return (field_consistency * 0.5 + value_validity * 0.5)

consistency = analyze_data_consistency(dataset)
print(f"Data Consistency: {consistency:.1%}")

Consistency Checks:

Check	Impact	Resolution
Missing required fields	-20% per field	Add missing fields
Invalid duration (≤0)	-20%	Ensure positive duration
Invalid energy (not 0-1)	-20%	Normalize energy to 0-1
Inconsistent field structure	-percentage	Standardize track structure

3. Dataset Size (20% weight)

Evaluates the scale and scope of the dataset:

def analyze_dataset_size(dataset):
    """Analyze size scoring for a dataset."""

    track_count = len(dataset.tracks)

    # Logarithmic scoring
    if track_count == 0:
        return 0.0
    elif track_count < 10:
        return 0.2   # Very small
    elif track_count < 50:
        return 0.4   # Small
    elif track_count < 100:
        return 0.6   # Medium
    elif track_count < 500:
        return 0.8   # Large
    else:
        return 1.0   # Very large

size_score = analyze_dataset_size(dataset)
print(f"Dataset Size Score: {size_score:.1%}")

Size Tiers:

Tracks	Score	Category	Notes
0-10	0.2	Tiny	Too small for reliable recommendations
10-50	0.4	Small	Adequate for focused use
50-100	0.6	Medium	Good for most applications
100-500	0.8	Large	Very useful, diverse
500+	1.0	Very Large	Excellent for training

4. Diversity (15% weight)

Measures variety of genres, artists, and styles:

def analyze_diversity(dataset):
    """Analyze diversity of a dataset."""

    if not dataset.tracks:
        return 0.0

    # Genre diversity
    genres = set(track.get('genre') for track in dataset.tracks if 'genre' in track)

    # Artist diversity
    artists = set(track.get('artist') for track in dataset.tracks if 'artist' in track)

    track_count = len(dataset.tracks)

    # Scores
    genre_diversity = min(1.0, len(genres) / 10.0)
    artist_diversity = min(1.0, len(artists) / max(1, track_count / 5))

    return (genre_diversity * 0.5 + artist_diversity * 0.5)

diversity = analyze_diversity(dataset)
print(f"Diversity Score: {diversity:.1%}")

Improve Diversity

• Include tracks from multiple genres
• Include tracks from many different artists
• Aim for 10+ genres and artists with even distribution
• Avoid over-representation of single genres or artists

5. License Permissiveness (10% weight)

Evaluates freedom of use and modification:

def analyze_license_permissiveness(license):
    """Analyze license permissiveness scoring."""

    score = 0.5  # Base score

    # Commercial use allowed: +0.2
    if license.commercial_use:
        score += 0.2

    # Derivative works allowed: +0.2
    if license.derivative_works:
        score += 0.2

    # No share-alike requirement: +0.1
    if not license.share_alike:
        score += 0.1

    return min(1.0, score)

license_score = analyze_license_permissiveness(dataset.license)
print(f"License Permissiveness: {license_score:.1%}")

License Scoring:

License Type	Commercial	Derivatives	Share-Alike	Score
CC0	Yes	Yes	No	0.9
CC-BY	Yes	Yes	No	0.9
CC-BY-SA	Yes	Yes	Yes	0.7
CC-BY-NC	No	Yes	No	0.5
Proprietary	No	No	No	0.1

License Validation

Supported Licenses

from qfzz.datasets.models import LicenseType

supported_licenses = [
    LicenseType.CC0,                    # Public domain
    LicenseType.CC_BY,                  # Attribution required
    LicenseType.CC_BY_SA,               # Attribution + Share-Alike
    LicenseType.CC_BY_NC,               # Attribution + Non-Commercial
    LicenseType.CC_BY_NC_SA,            # All restrictions
    LicenseType.MIT,                    # Permissive code license
    LicenseType.APACHE_2,               # Permissive code license
    LicenseType.GPL_3,                  # Copyleft code license
    LicenseType.PUBLIC_DOMAIN           # No restrictions
]

License Compatibility

# Define allowed licenses for your use case
allowed_licenses_commercial = ['CC-BY', 'CC0', 'MIT', 'Apache-2.0']
allowed_licenses_research = ['CC-BY', 'CC-BY-SA', 'CC0', 'GPL-3.0']

manager_commercial = DatasetManager(allowed_licenses=allowed_licenses_commercial)
manager_research = DatasetManager(allowed_licenses=allowed_licenses_research)

# Validate license
license = DatasetLicense(
    license_type='CC-BY',
    license_url='https://creativecommons.org/licenses/by/4.0/',
    attribution_required=True,
    commercial_use=True,
    derivative_works=True,
    share_alike=False
)

is_valid = manager_commercial.validate_license(license)
print(f"License valid for commercial use: {is_valid}")

License Compliance

Always ensure datasets comply with license requirements: - Provide attribution when required - Respect non-commercial use restrictions - Include license text with distributed datasets - Track derivative works for share-alike licenses

Managing Datasets

Add Datasets

manager = DatasetManager()

# Create and add a dataset
dataset = Dataset(
    dataset_id='jazz_collection_2024',
    name='Jazz Standards Collection',
    description='Curated jazz standards for music analysis',
    version='2.0.0',
    license=DatasetLicense(
        license_type='CC-BY',
        license_url='https://creativecommons.org/licenses/by/4.0/',
        attribution_required=True,
        commercial_use=True,
        derivative_works=True,
        share_alike=False
    ),
    creator_id='jazz_curator'
)

# Add tracks
for i in range(100):
    dataset.add_track({
        'track_id': f'jazz_{i:03d}',
        'title': f'Jazz Track {i}',
        'artist': f'Jazz Artist {i % 10}',
        'genre': 'jazz',
        'mood': 'mellow' if i % 2 else 'upbeat',
        'energy': 0.3 + (i % 7) * 0.1,
        'tempo': 80 + (i % 60),
        'duration': 180 + (i % 120),
        'album': f'Album {i // 20}'
    })

# Add to manager
success = manager.add_dataset(dataset)
print(f"Dataset added: {success}")
print(f"Quality Score: {dataset.quality_score:.3f}")

Remove Datasets

# Remove a dataset
removed = manager.remove_dataset('jazz_collection_2024')

if removed:
    print("✓ Dataset removed")
else:
    print("✗ Dataset not found")

Retrieve Datasets

# Get specific dataset
dataset = manager.get_dataset('jazz_collection_2024')

if dataset:
    print(f"Dataset: {dataset.name}")
    print(f"Tracks: {dataset.get_track_count()}")
    print(f"Quality Score: {dataset.quality_score:.3f}")
else:
    print("Dataset not found")

Querying Datasets

List All Datasets

# Get all datasets
all_datasets = manager.list_datasets()

# Get high-quality datasets only
high_quality = manager.list_datasets(min_quality=0.7)

# Print summary
for dataset in high_quality:
    print(f"{dataset.name}: {dataset.quality_score:.3f} "
          f"({dataset.get_track_count()} tracks)")

Filter by Quality

# Get datasets with specific quality thresholds
premium_datasets = manager.list_datasets(min_quality=0.8)
acceptable_datasets = manager.list_datasets(min_quality=0.6)
all_datasets = manager.list_datasets(min_quality=0.0)

print(f"Premium datasets: {len(premium_datasets)}")
print(f"Acceptable datasets: {len(acceptable_datasets)}")
print(f"All datasets: {len(all_datasets)}")

Dataset Analysis

Get Statistics

stats = manager.get_statistics()

print("Dataset Manager Statistics:")
print(f"  Total Datasets: {stats['total_datasets']}")
print(f"  Total Tracks: {stats['total_tracks']}")
print(f"  Average Quality: {stats['average_quality_score']:.3f}")
print(f"  Unique Genres: {stats['unique_genres']}")
print(f"  Unique Artists: {stats['unique_artists']}")
print(f"  Allowed Licenses: {', '.join(stats['allowed_licenses'])}")

Detailed Dataset Analysis

def analyze_dataset(dataset):
    """Perform comprehensive dataset analysis."""

    return {
        'name': dataset.name,
        'track_count': dataset.get_track_count(),
        'total_duration_hours': dataset.get_total_duration() / 3600,
        'quality_score': dataset.quality_score,
        'genres': dataset.get_genres(),
        'artists': dataset.get_artists(),
        'unique_genres': len(dataset.get_genres()),
        'unique_artists': len(dataset.get_artists()),
        'license': dataset.license.license_type,
        'created': dataset.created_at,
        'updated': dataset.updated_at
    }

analysis = analyze_dataset(dataset)
print(f"Dataset Analysis: {analysis['name']}")
print(f"  Tracks: {analysis['track_count']}")
print(f"  Duration: {analysis['total_duration_hours']:.1f} hours")
print(f"  Quality: {analysis['quality_score']:.3f}")
print(f"  Genres: {analysis['unique_genres']}")
print(f"  Artists: {analysis['unique_artists']}")

Track Management

Adding Tracks

# Add individual tracks
dataset.add_track({
    'track_id': 'new_track_001',
    'title': 'New Jazz Standard',
    'artist': 'New Artist',
    'genre': 'jazz',
    'energy': 0.5,
    'mood': 'contemplative',
    'tempo': 90,
    'duration': 240,
    'album': 'New Album',
    'year': 2024
})

# Bulk add tracks
new_tracks = [
    {'track_id': f'track_{i}', 'title': f'Track {i}', ...}
    for i in range(100)
]

for track in new_tracks:
    dataset.add_track(track)

print(f"Dataset now has {dataset.get_track_count()} tracks")

Removing Tracks

# Remove a track
removed = dataset.remove_track('track_001')

if removed:
    print("✓ Track removed")
    # Re-calculate quality score
    quality = manager.calculate_quality_score(dataset)
    dataset.quality_score = quality
else:
    print("✗ Track not found")

Track Validation

def validate_track(track):
    """Validate track data integrity."""

    required = ['track_id', 'title', 'artist', 'genre', 'duration']
    issues = []

    # Check required fields
    for field in required:
        if field not in track or not track[field]:
            issues.append(f"Missing required field: {field}")

    # Check value ranges
    if 'energy' in track and not 0.0 <= track['energy'] <= 1.0:
        issues.append("Energy must be between 0.0 and 1.0")

    if 'duration' in track and track['duration'] <= 0:
        issues.append("Duration must be positive")

    if 'tempo' in track and not (40 <= track['tempo'] <= 300):
        issues.append("Tempo should be between 40 and 300 BPM")

    return {
        'valid': len(issues) == 0,
        'issues': issues
    }

# Validate a track
result = validate_track(dataset.tracks[0])
if result['valid']:
    print("✓ Track is valid")
else:
    for issue in result['issues']:
        print(f"✗ {issue}")

Advanced Patterns

Dataset Merging

def merge_datasets(manager, dataset1_id, dataset2_id, new_id):
    """Merge two datasets into one."""

    ds1 = manager.get_dataset(dataset1_id)
    ds2 = manager.get_dataset(dataset2_id)

    if not ds1 or not ds2:
        return None

    # Create merged dataset
    merged = Dataset(
        dataset_id=new_id,
        name=f"{ds1.name} + {ds2.name}",
        description=f"Merged from {dataset1_id} and {dataset2_id}",
        version='1.0.0',
        license=ds1.license,  # Use first dataset's license
        creator_id=ds1.creator_id,
        tracks=ds1.tracks + ds2.tracks
    )

    # Calculate quality for merged dataset
    quality = manager.calculate_quality_score(merged)
    merged.quality_score = quality

    return merged

# Merge datasets
merged = merge_datasets(manager, 'dataset1', 'dataset2', 'merged_dataset')
if merged:
    manager.add_dataset(merged)
    print(f"✓ Merged dataset quality: {merged.quality_score:.3f}")

Quality Improvement Recommendations

def get_quality_recommendations(dataset):
    """Generate recommendations to improve dataset quality."""

    recommendations = []

    # Check size
    if dataset.get_track_count() < 50:
        recommendations.append("Add more tracks (currently < 50)")

    # Check metadata completeness
    completeness_score = 0
    required_fields = ['title', 'artist', 'genre', 'duration']
    optional_fields = ['album', 'year', 'mood', 'energy', 'tempo']

    for track in dataset.tracks:
        required_present = sum(1 for f in required_fields if f in track and track[f])
        if required_present < len(required_fields):
            recommendations.append(f"Track {track.get('track_id')} missing required fields")
            break

    for track in dataset.tracks:
        optional_present = sum(1 for f in optional_fields if f in track and track[f])
        if optional_present < 2:
            recommendations.append("Add more optional fields (mood, energy, tempo) to tracks")
            break

    # Check diversity
    genres = dataset.get_genres()
    if len(genres) < 5:
        recommendations.append(f"Increase genre diversity (currently {len(genres)} genres)")

    artists = dataset.get_artists()
    if len(artists) < dataset.get_track_count() / 5:
        recommendations.append("Increase artist diversity")

    return recommendations

# Get recommendations
recs = get_quality_recommendations(dataset)
for rec in recs:
    print(f"💡 {rec}")

Dataset Versioning

def version_dataset(manager, dataset_id, new_version):
    """Create a new version of a dataset."""

    original = manager.get_dataset(dataset_id)
    if not original:
        return None

    # Create versioned copy
    versioned = Dataset(
        dataset_id=f"{dataset_id}_v{new_version}",
        name=f"{original.name} (v{new_version})",
        description=original.description,
        version=new_version,
        license=original.license,
        creator_id=original.creator_id,
        tracks=original.tracks.copy(),
        metadata={**original.metadata, 'parent_version': dataset_id}
    )

    quality = manager.calculate_quality_score(versioned)
    versioned.quality_score = quality

    return versioned

# Create new version
v2 = version_dataset(manager, 'dataset_001', '2.0.0')
if v2:
    manager.add_dataset(v2)
    print(f"✓ Created version {v2.version}")

Integration with Other Features

With Blockchain Trust Network

from qfzz.blockchain.trust_network import BlockchainTrustNetwork

network = BlockchainTrustNetwork()

# Record dataset quality on blockchain
for dataset in manager.list_datasets(min_quality=0.7):
    network.add_trust_record(
        content_id=dataset.dataset_id,
        creator_id=dataset.creator_id,
        initial_score=dataset.quality_score,
        metadata={
            'dataset_name': dataset.name,
            'tracks': dataset.get_track_count(),
            'genres': len(dataset.get_genres())
        }
    )

# Mine records
network.mine_pending_records()

With Edge Optimization

from qfzz.edge.optimizer import EdgeOptimizer

optimizer = EdgeOptimizer()

def get_dataset_for_device(manager, device_id):
    """Select appropriate dataset for device capabilities."""

    device = optimizer.get_device_config(device_id)
    if not device:
        return None

    # Select dataset based on device
    if device.device_type.value == 'smartphone':
        # Smaller, high-quality datasets
        datasets = manager.list_datasets(min_quality=0.8)
    else:
        # All datasets
        datasets = manager.list_datasets(min_quality=0.6)

    # Prefer smaller datasets for bandwidth-constrained devices
    if device.bandwidth_mbps < 5.0:
        datasets = sorted(datasets, key=lambda d: d.get_track_count())

    return datasets[0] if datasets else None

Best Practices

1. Ensure Complete Metadata

# Good: Rich metadata
track = {
    'track_id': 'track_001',
    'title': 'Song Name',
    'artist': 'Artist Name',
    'genre': 'electronic',
    'mood': 'energetic',
    'energy': 0.8,
    'tempo': 128,
    'duration': 240,
    'album': 'Album Name',
    'year': 2024
}

# Suboptimal: Minimal metadata
track = {
    'track_id': 'track_001',
    'title': 'Song',
    'artist': 'Artist'
}

2. Maintain Data Consistency

# Ensure all tracks have consistent structure
required_structure = ['track_id', 'title', 'artist', 'genre', 'duration']

for track in dataset.tracks:
    for field in required_structure:
        if field not in track:
            print(f"⚠️ Track missing {field}")

3. Validate Licenses Upfront

# Always validate license before adding dataset
license = DatasetLicense(...)
if manager.validate_license(license):
    manager.add_dataset(dataset)
else:
    print("✗ License not compatible")

4. Monitor Quality Scores

# Regularly check quality scores
stats = manager.get_statistics()

if stats['average_quality_score'] < 0.6:
    print("⚠️ Average quality is low - consider dataset review")

Performance Optimization

Efficient Querying

# Cached queries
high_quality_cache = {}

def get_high_quality_datasets(manager, threshold=0.8):
    """Get high-quality datasets with caching."""

    if threshold not in high_quality_cache:
        high_quality_cache[threshold] = manager.list_datasets(min_quality=threshold)

    return high_quality_cache[threshold]

Batch Operations

# Batch dataset operations
def batch_add_datasets(manager, dataset_list):
    """Add multiple datasets efficiently."""

    added = 0
    rejected = 0

    for dataset in dataset_list:
        if manager.add_dataset(dataset):
            added += 1
        else:
            rejected += 1

    return {'added': added, 'rejected': rejected}

Testing Datasets

def test_dataset_quality():
    """Test dataset quality scoring."""

    manager = DatasetManager()

    # Create test dataset
    license = DatasetLicense(
        license_type='CC-BY',
        license_url='https://creativecommons.org/licenses/by/4.0/',
        attribution_required=True,
        commercial_use=True,
        derivative_works=True,
        share_alike=False
    )

    dataset = Dataset(
        dataset_id='test_dataset',
        name='Test Dataset',
        description='For testing',
        version='1.0.0',
        license=license,
        creator_id='test_creator'
    )

    # Add quality tracks
    for i in range(100):
        dataset.add_track({
            'track_id': f'test_track_{i:03d}',
            'title': f'Test Track {i}',
            'artist': f'Artist {i % 10}',
            'genre': ['pop', 'rock', 'jazz', 'electronic'][i % 4],
            'mood': ['energetic', 'calm'][i % 2],
            'energy': 0.5 + (i % 10) * 0.05,
            'tempo': 90 + (i % 60),
            'duration': 180 + (i % 120),
            'album': f'Album {i // 20}',
            'year': 2024
        })

    # Test adding dataset
    assert manager.add_dataset(dataset)
    assert dataset.quality_score > 0.7

    # Test retrieval
    retrieved = manager.get_dataset('test_dataset')
    assert retrieved is not None
    assert retrieved.get_track_count() == 100

    # Test statistics
    stats = manager.get_statistics()
    assert stats['total_datasets'] >= 1

    print("✓ All dataset tests passed")

test_dataset_quality()

Troubleshooting

Issue: Low Quality Score

# Diagnose quality issues
def diagnose_low_quality(manager, dataset_id):
    """Diagnose why a dataset has low quality."""

    dataset = manager.get_dataset(dataset_id)
    if not dataset:
        return None

    metadata = manager._score_metadata_completeness(dataset)
    consistency = manager._score_data_consistency(dataset)
    size = manager._score_dataset_size(dataset)
    diversity = manager._score_diversity(dataset)
    license = manager._score_license(dataset.license)

    print(f"Quality Breakdown:")
    print(f"  Metadata: {metadata:.1%} (target: 100%)")
    print(f"  Consistency: {consistency:.1%} (target: 100%)")
    print(f"  Size: {size:.1%} (target: 100%)")
    print(f"  Diversity: {diversity:.1%} (target: 100%)")
    print(f"  License: {license:.1%} (target: 100%)")

    return {
        'metadata': metadata,
        'consistency': consistency,
        'size': size,
        'diversity': diversity,
        'license': license
    }

Roadmap

See Roadmap for planned enhancements:

• [ ] Automatic metadata enrichment
• [ ] Audio fingerprint analysis
• [ ] Audio feature extraction (MIR)
• [ ] Duplicate detection
• [ ] Genre auto-classification
• [ ] Mood detection via machine learning
• [ ] Multi-language metadata support
• [ ] Dataset version control

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