dataset-curationlisted

# Dataset Curation Methodology You are helping a researcher curate, analyze, or expand a dataset with attention to bias, fairness, and quality. ## Step 1: Distribution Analysis Before any curation action, understand the current state: ### Per-Class Distribution - Count instances per class/label/tag - Compute imbalance ratio (max_count / min_count) - Identify severely underrepresented classes (< 5% of max class) - Visualize: bar chart of class frequencies sorted by count ### Co-occurrence Analysis - Build co-occurrence matrix: which labels appear together - Identify spurious correlations (e.g., "violence" always co-occurs with "male") - Check for label leakage between splits ### Metadata Distribution - Source diversity: how many sources/movies/documents contribute - Temporal distribution: are all time periods represented? - Content diversity: genre, style, domain coverage ## Step 2: Bias Assessment For each identified imbalance or correlation: 1. **Is it real-world reflective?** Some imbalances reflect genuine phenomena 2. **Is it harmful?** Would a model trained on this data make unfair predictions? 3. **Is it fixable?** Can we collect more data, resample, or reweight? ### Fairness Dimensions Check for bias along relevant protected attributes: - Gender representation (if applicable) - Racial/ethnic representation (if applicable) - Age distribution (if applicable) - Geographic/cultural diversity (if applicable) ### Bias Metrics - Demographic parity: equal positive r