Several USDA food composition databases, including the Food and Nutrient Database for Dietary Studies (FNDDS), Standard Reference (SR) Legacy, and the USDA Branded Food Products Database, have transitioned to FoodData Central, a new and harmonized USDA food and nutrient data system. FoodData Central also includes expanded nutrient content information as well as links to diverse data sources that offer related agricultural, environmental, food, health, dietary supplement, and other information. The new system is designed to strengthen the capacity for rigorous research and policy applications through its search capabilities, downloadable datasets, and detailed documentation. Application developers can incorporate the information into their applications and web sites through the application programming interface (API) REST access. The constantly changing and expanding food supply is a challenge to those who are interested in using food and nutrient data. Including diverse types of data in one data system gives researchers, policymakers, and other audiences a key resource for addressing vital nutrition and health issues. FoodData Central: Includes five distinct types of data containing information on food and nutrient profiles, each with a unique purpose: Foundation Foods; Experimental Foods; Standard Reference; Food and Nutrient Database for Dietary Studies; USDA Global Branded Food Products Database. Provides a broad snapshot in time of the nutrients and other components found in a wide variety of foods and food products. Presents data that come from a variety of sources and are updated as new information becomes available. Includes values that are derived through a variety of analytic and computational approaches, using state-of-the-art methodologies and transparent presentation. FoodData Central is managed by the Agricultural Research Service and hosted by the National Agricultural Library. Resources in this dataset: Resource Title: Website Pointer for FoodData Central. File Name: Web Page, url: https://fdc.nal.usda.gov/index.html Includes Search, Download data, API Guide, Data Type Documentation, and Help pages.

The USDA FoodData Central API provides access to nutrient data for over 300,000 foods including Foundation Foods, branded products, SR Legacy, and survey foods (FNDDS). Each food record includes macronutrients, micronutrients, serving sizes, and data source information. Rate limited to 1,000 requests per hour with a free data.gov API key. Data is public domain under CC0 license.

DATASET OVERVIEW

Comprehensive ingredient-level data for 10,000+ food products from USDA FoodData Central. Each entry contains detailed ingredient lists showing complete product formulations including base ingredients, additives, preservatives, vitamins, and minerals.

This dataset captures the full complexity of modern food formulations - ideal for ingredient text mining, allergen detection, additive analysis, and food chemistry research. Covers 58 food categories including crops, livestock, dairy, seafood, and processed foods.

Key Stats: 10,000+ products | 98.1% ingredient coverage | 58 categories | USDA verified

DATA SCIENCE APPLICATIONS

• Ingredient Text Mining & NLP - Extract unique ingredients, build co-occurrence networks, topic modeling
• Allergen Detection - Identify wheat (467), milk (417), soy (458), egg (141) mentions automatically
• Food Additive Analysis - Track preservatives, artificial flavors, clean label trends
• Product Formulation - Reverse engineer recipes, compare branded vs generic
• Nutritional Fortification - Map vitamin/mineral enrichment patterns
• Machine Learning - Category prediction, brand identification, healthiness scoring

COLUMN DESCRIPTORS

1. fdc_id (integer)

   • Unique identifier from USDA FoodData Central database
   • Use for linking to additional USDA data sources
   • Range: 6-7 digit numerical codes

2. description (string)

   • Full product name and description
   • Includes brand variations and product specifications
   • Examples: "WHEAT SANDWICH BREAD, WHEAT", "ORGANIC MILK, WHOLE"

3. brand_owner (string)

   • Company or manufacturer name
   • Present for branded/packaged foods
   • Null for generic/unbranded commodities
   • Coverage: ~60% of products

4. brand_name (string)

   • Specific brand or product line name
   • Sub-brand within brand_owner portfolio
   • Coverage: ~45% of products

5. ingredients (string)

   • Complete ingredient list as labeled on product
   • Ordered by quantity (descending)
   • Includes sub-ingredients in parentheses
   • Contains: vitamins, minerals, preservatives, additives, processing agents
   • Coverage: 98.1% (9,810+ products)
   • Average length: 220 characters
   • Max length: 1,327 characters

6. serving_size (float)

   • Standardized serving size amount
   • Paired with serving_size_unit for complete measurement
   • Null for commodities without standard servings

7. serving_size_unit (string)

   • Unit of measurement for serving size
   • Common values: "g" (grams), "ml" (milliliters), "oz" (ounces), "cup", "piece"

8. food_category (string)

   • USDA standard food category classification
   • High-level groupings: "Baked Products", "Dairy and Egg Products", etc.
   • Used for regulatory and nutritional database organization

9. search_category (string)

   • Agricultural/botanical search term used for data collection
   • Granular categories: wheat, rice, beans, chicken, milk, etc.
   • 58 unique categories spanning crops, livestock, seafood, processed foods
   • Useful for agricultural and supply chain analysis

ETHICALLY MINED DATA

Data Source: USDA FoodData Central API (https://fdc.nal.usda.gov/) - Official U.S. government public domain database - Free API access for research and commercial use - All ingredient data already publicly disclosed on FDA-required product labels

Data Collection: - Respectful API scraping at 3.5 requests/second (well below limits) - Full compliance with USDA API terms of service - No proprietary or confidential information - Reproducible methodology with documented approach

This dataset respects government open data policies, FDA labeling regulations, and consumer right to ingredient information.

DATA COMPLETENESS

Overall Quality: 99.1% Complete - Total cells: 90,000 (10,000 rows × 9 columns) - Non-null: 89,220 cells - Completeness: 99.13%

Key Field Coverage: - Ingredients: 98.1% (9,810/10,000 products) - Descriptions: 100% (all products named) - Categories: 100% (all categorized) - Brand info: ~60% (branded products only) - Serving sizes: ~85% (packaged foods)

Missing data primarily affects raw commodities (no ingredient lists needed) and generic products (no brand info). Exceptional quality for text mining and allergen detection with minimal preprocessing required.

ACKNOWLEDGEMENTS

Data Source: U.S. Department of Agriculture, Agricultural Research Service. FoodData Central, 2024. https://fdc.nal.usda.gov/

Creator: Kanchana Karunarathna

License: CC BY 4.0 (Free for academic, research, and commercial use with attribution.)

Special thanks to USDA for maintaining open food composition data and FDA for standardized labeling requirements.

Understanding the nutritional composition of everyday foods is essential for diet planning, health analysis, and building intelligent food-related applications. This dataset provides clean, structured, and easy-to-use nutritional information for more than 200 commonly consumed foods, including fruits, vegetables, grains, dairy, beverages, snacks, and cooked dishes.

The data has been sourced from the USDA FoodData Central API, which is one of the most trusted open food-nutrition sources globally. Only normal, everyday foods were selected—no supplements, no powdered mixes, no infant formulas, and no obscure scientific items.

This dataset is ideal for:

• Health monitoring projects
• Nutrition recommendation systems
• Calorie estimation models
• Research studies
• Machine learning unsupervised Learning tasks
• Food comparison dashboards
• Fitness and diet apps

The dataset is curated to be clean, practical, and ready for ML.

Dataset Summary

• Total rows: 205
• Total columns: 9
• Data type: Numerical + categorical
• Missing values: Minimal
• Food types included: fruits, vegetables, grains, snacks, beverages, protein sources, Indian foods, Western foods

Feature Description

A professionally curated nutrition dataset of 7000+ foods with complete Spanish translations, including 900+ original Costa Rican and Latin American specialties not found in other databases. Provides detailed nutritional information (calories, protein, carbohydrates, fat, vitamins, minerals, amino acids, fatty acids - up to 400+ data points when available) for any serving size via API, with 100g as the default. Built from 14+ years of manual curation, translation, and quality control. Integrates USDA FoodData Central with extensive original regional research.

[Note: Integrated as part of FoodData Central, April 2019.] The USDA Branded Food Products Database is the result of a Public-Private Partnership, whose goal is to enhance public health and the sharing of open data by complementing USDA Food Composition Databases with nutrient composition of branded foods and private label data provided by the food industry. Members of the Public-Private Partnership include: Agricultural Research Service (ARS), USDA (www.ars.usda.gov) Institute for the Advancement of Food and Nutrition Sciences (IAFNS) (www.iafns.org) GS1 US (www.gs1us.org/) 1WorldSync (www.1worldsync.com) Label Insight (www.labelinsight.com) University of Maryland, Joint Institute for Food Safety and Applied Nutrition (jifsan.umd.edu) The BFPDB includes: product name and generic descriptor, serving size in grams or milliliters, nutrients on the Nutrition Facts Panel per serving size and 100 gram-basis, 100 ml-basis, or fluid oz-basis, ingredient list, (never before captured by USDA), and date stamp associated with most current product formulation. All data will be archived, allowing for dietary trends tracking. The BFPDB allows: dietitians to provide specific dietary guidance; researchers to better link dietary intakes to disease measures; and policy makers to develop guidance which promotes public health. New in this August 2018 release are downloadable database files (ASCII .csv and MS Access), Application Programming Interface (API), and Documentation and Download User Guide.

Protein and Nutrient Content of 5000+ Food Items

This dataset contains nutritional information for over 5000 food items extracted from the USDA FoodData Central API.

📊 Columns Included

• Food_Item: Name of the food item
• Category: General food group
• Protein_g_per_100g
• Calories_per_100g
• Fat_g_per_100g
• Carbs_g_per_100g
• Fiber_g_per_100g
• Sugar_g_per_100g
• Calories_per_gram_Protein: Efficiency of protein
• Vitamins: A, C, D, B12
• Minerals: Calcium, Iron, Magnesium, Potassium, Zinc

💡 How It Was Created

Fetched from the USDA FoodData Central API using a custom Python script, filtering for common foods across categories like meat, legumes, dairy, grains, vegetables, and fruits.

🔍 Potential Use-Cases

• Nutrition & diet planning
• Fitness & bodybuilding apps
• Machine learning models for meal planning or calorie estimation
• Data analysis of nutrient density

🔓 License

This dataset is released under CC BY 4.0

Central Plains Experimental Range Study for Long-Term Agroecosystem Research in Nunn, Colorado The Central Plains Experimental Range (CPER) is a site with the The Long-Term Agroecosystem Research (LTAR) Network, which consists of 18 sites across the continental United States (US) sponsored by the US Department of Agriculture, Agricultural Research Service, universities and non-governmental organizations. LTAR scientists seek to determine ways to ensure sustainability and enhance food production (and quality) and ecosystem services at broad regional scales. They are conducting common experiments across the LTAR network to compare traditional production strategies (“business as usual or BAU) with aspirational strategies, which include novel technologies and collaborations with farmers and ranchers. Within- and cross-site network success towards achieving the desired outcomes of enhancing quality food production and reducing environmental impact requires that LTAR scientists and collaborators have well-timed access to various data. We are striving to create opportunities to package and share long-term legacy observations from each site, with new data and metadata in useable, well documented and consistent formats for them.