kumbh/neurolab-health-nutrition

NeuroLab Health & Nutrition Knowledge Base

A curated instruction-tuning dataset for health and nutrition AI assistants, with a focus on Indian dietary guidelines, food safety, and packaged food analysis.

Dataset Description

This dataset provides question-answer pairs formatted for supervised fine-tuning (SFT) of large language models. It covers:

• E-numbers / Food Additives — Safety profiles, origins, regulatory status
• Glycaemic Index (GI) — GI values and glycaemic load for 40+ common foods including Indian staples
• ICMR Dietary Guidelines — Recommended Dietary Allowances for Indian population groups (ICMR-NIN 2020)
• NOVA Classification — Ultra-processed food identification (Groups 1-4)
• Nutrient Deficiency Guide — Symptoms, at-risk groups, food sources, and absorption tips for 8 key nutrients
• Packaged Food Analysis — Products from Open Food Facts with nutritional breakdowns
• USDA Nutritional Composition — Per-100g nutritional data for common whole foods

Data Splits

Split	Rows
Train	2,781
Validation	309
Total	3,090

Data Fields

Each example contains:

{
  "conversations": [
    {"from": "system", "value": "You are NeuroLab AI..."},
    {"from": "human", "value": "What is the GI of brown rice?"},
    {"from": "gpt", "value": "The glycaemic index of brown rice is 50..."}
  ]
}

The Parquet split additionally includes question, answer, source, and quality fields for easy filtering.

Sources

• ICMR-NIN 2020 Dietary Guidelines (India)
• Glycaemic Index Database (Atkinson et al., 2021)
• E-number / Food Additives Reference (EU Regulation 1333/2008)
• NOVA Food Processing Classification (Monteiro et al.)
• Open Food Facts (openfoodfacts.org)
• USDA FoodData Central
• Expert-curated Q&A pairs

Intended Use

• Fine-tuning language models for health and nutrition question-answering
• Building RAG pipelines for food safety and dietary guidance
• Research on Indian dietary patterns and food labelling

Limitations

• Nutritional values are reference averages; individual food products vary.
• ICMR RDAs are specific to the Indian population and may differ from WHO or USDA recommendations.
• Packaged food data from Open Food Facts may be incomplete or user-contributed.
• This dataset is for educational purposes and should not replace personalised medical advice.

License

Creative Commons Attribution 4.0 (CC BY 4.0)

Citation

@dataset{neurolab_nutrition_2024,
  title     = {NeuroLab Health \& Nutrition Knowledge Base},
  author    = {NeuroLab},
  year      = {2024},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/datasets/kumbh/neurolab-health-nutrition}
}