# Dataset Nutrition Label Framework Notes

## Overview
The Dataset Nutrition Label is a diagnostic framework for assessing dataset health and quality, inspired by nutritional labels on food products. It provides standardized "nutrition facts" about datasets.

## Core Concept
Just as food nutrition labels help consumers make informed decisions, Dataset Nutrition Labels help researchers and practitioners understand:
- What data is in the dataset
- How the data was collected
- What biases or limitations exist
- Whether the dataset is appropriate for their use case

## Modular Structure

The framework is organized into modules that assess different aspects of dataset quality:

### Metadata Module
- Basic dataset identification
- Creator/maintainer information
- Versioning and update history

### Provenance Module
- Data source documentation
- Collection methodology
- Temporal and geographic coverage

### Composition Module
- Population representation
- Class distribution
- Missing data patterns

### Quality Module
- Data completeness metrics
- Accuracy assessments
- Consistency checks
- Outlier detection

### Bias Module
- Representation bias assessment
- Measurement bias identification
- Historical bias documentation

## Diagnostic Elements

The label flags potential issues or characteristics:
- **Red flags**: Serious concerns (e.g., severe class imbalance)
- **Yellow flags**: Cautions (e.g., moderate missing data)
- **Green indicators**: Strengths (e.g., comprehensive documentation)

## Reference
- Project homepage: https://datanutrition.org/
- Prototype implementation: https://ahmedhosny.github.io/datanutrition/
