The main target users of the food nutrition composition dataset are: the public, nutritionists, businesses, etc., who can refer to the data in this dataset to understand the nutrient content of ingredients, and also use it as a reference for dietary guidelines.

[Note: Integrated as part of FoodData Central, April 2019.] The USDA National Nutrient Database for Standard Reference (SR) is the major source of food composition data in the United States and provides the foundation for most food composition databases in the public and private sectors. This is the last release of the database in its current format. SR-Legacy will continue its preeminent role as a stand-alone food composition resource and will be available in the new modernized system currently under development. SR-Legacy contains data on 7,793 food items and up to 150 food components that were reported in SR28 (2015), with selected corrections and updates. This release supersedes all previous releases. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_DB.zipResource Description: Locally stored copy - The USDA National Nutrient Database for Standard Reference as a relational database using AcessResource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: Locally stored copy - ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.

Dataset Name: Food Nutrition Dataset

Overview:

The Comprehensive Nutritional Food Database provides detailed nutritional information for a wide range of food items commonly consumed around the world. This dataset aims to support dietary planning, nutritional analysis, and educational purposes by providing extensive data on the macro and micronutrient content of foods.

Data Format:

The dataset is structured as a CSV (Comma-Separated Values) file, which can easily be imported into most data analysis tools and software for further processing and analysis.

Column Descriptions

1. Food: The name or type of the food item.

2. Caloric Value: Total energy provided by the food, typically measured in kilocalories (kcal) per 100 grams.

3. Fat( in g): Total amount of fats in grams per 100 grams, including the breakdowns that follow.

4. Saturated Fats( in g): Amount of saturated fats (fats that typically raise the level of cholesterol in the blood) in grams per 100 grams.

5. Monounsaturated Fats( in g): Amount of monounsaturated fats (considered heart-healthy fats) in grams per 100 grams.

6. Polyunsaturated Fats( in g): Amount of polyunsaturated fats (include essential fats your body needs but can't produce itself) in grams per 100 grams.

7. Carbohydrates( in g): Total carbohydrates in grams per 100 grams, including sugars.

8. Sugars( in g): Total sugars in grams per 100 grams, a subset of carbohydrates.

9. Protein( in g): Total proteins in grams per 100 grams, essential for body repair and growth.

10. Dietary Fiber( in g): Fiber content in grams per 100 grams, important for digestive health.

11. Cholesterol( in mg): Cholesterol content in milligrams per 100 grams, pertinent for cardiovascular health.

12. Sodium( in g): Sodium content in milligrams per 100 grams, crucial for fluid balance and nerve function.

13. Water( in g): Water content in grams per 100 grams, which affects the food’s energy density.

14. Vitamin A( in mg): Amount of Vitamin A in micrograms per 100 grams, important for vision and immune functioning.

15. Vitamin B1 (Thiamine)( in mg): Essential for glucose metabolism.

16. Vitamin B11 (Folic Acid)( in mg): Crucial for cell function and tissue growth, particularly important in pregnancy.

17. Vitamin B12( in mg): Important for brain function and blood formation.

18. Vitamin B2 (Riboflavin)( in mg): Necessary for energy production, cell function, and fat metabolism.

19. Vitamin B3 (Niacin)( in mg): Supports digestive system, skin, and nerves health.

20. Vitamin B5 (Pantothenic Acid)( in mg): Necessary for making blood cells, and helps convert food into energy.

21. Vitamin B6( in mg): Important for normal brain development and keeping the nervous and immune systems healthy.

22. Vitamin C( in mg): Important for the repair of all body tissues.

23. Vitamin D( in mg): Crucial for the absorption of calcium, promoting bone growth and health.

24. Vitamin E( in mg): Acts as an antioxidant, helping to protect cells from the damage caused by free radicals.

25. Vitamin K( in mg): Necessary for blood clotting and bone health.

26. Calcium( in mg): Vital for building and maintaining strong bones and teeth.

27. Copper( in mg): Helps with the formation of collagen, increases the absorption of iron and plays a role in energy production.

28. Iron( in mg): Essential for the creation of red blood cells.

29. Magnesium( in mg): Important for many processes in the body including regulation of muscle and nerve function, blood sugar levels, and blood pressure and making protein, bone, and DNA.

30. Manganese( in mg): Involved in the formation of bones, blood clotting factors, and enzymes that play a role in fat and carbohydrate metabolism, calcium absorption, and blood sugar regulation.

31. Phosphorus( in mg): Helps with the formation of bones and teeth and is necessary for the body to make protein for the growth, maintenance, and repair of cells and tissues.

32. Potassium( in mg): Helps regulate fluid balance, muscle contractions, and nerve signals.

33. Selenium( in mg): Important for reproduction, thyroid gland function, DNA production, and protecting the body from damage caused by free radicals and from infection.

34. Zinc( in mg): Necessary for the immune system to properly function and plays a role in cell division, cell growth, wound healing, and the breakdown of carbohydrates.

35. Nutrition Density: A metric indicating the nutrient richness of the food per calorie.

Each of these columns provides critical data that can help in understanding the nutritional content of various foods, supporting a wide range of dietary, health, and medical research applications.

Use Cases:

This dataset is invaluable for researchers in nutritional ...

The USDA (United States Department of Agriculture) National Nutrient Database for Standard Reference (SR) is the major source of food composition data in the United States. It provides the foundation for most food composition databases in the public and private sectors.

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 Daily Food & Nutrition Dataset provides a detailed record of everyday food consumption paired with essential nutritional values. It is designed to support data analysis, health monitoring, and machine-learning applications related to diet, wellness, and personalized nutrition.

This dataset captures a variety of food items along with their macronutrient and micronutrient composition, enabling users to explore dietary patterns, build predictive health models, and perform nutritional optimization. It is suitable for projects involving calorie tracking, nutrient recommendation systems, diet classification, or exploratory data analysis within the field of nutrition science.

Key Features

• Food Item & Category Identifies each food entry and its general classification (e.g., fruit, vegetable, grain, beverage, snack, etc.).

• Nutritional Components Includes major nutrients that influence health and energy intake:

  • Calories (kcal)
  • Protein (g)
  • Carbohydrates (g)
  • Fat (g)
  • Fiber (g)
  • Sugars (g)
  • Sodium (mg)
  • Cholesterol (mg)

• Meal Context The Meal_Type column specifies whether the food was consumed during breakfast, lunch, dinner, or as a snack — useful for temporal or behavioral pattern analysis.

• Hydration Tracking Water_Intake (ml) allows hydration monitoring alongside nutritional consumption, enabling more holistic dietary assessments.

Potential Use Cases

• Building ML models for calorie prediction or nutrient estimation
• Designing personalized diet recommendations
• Meal-type classification and consumption pattern analysis
• Nutritional health dashboards or tracking applications
• Correlation studies between nutrient intake and hydration patterns

This dataset aims to serve health researchers, data scientists, nutritionists, and enthusiasts who want to analyze or model dietary behavior in a structured, meaningful way.

This dataset is not to be taken seriously. It has been synthetically generated to simulate real-world dietary records and reflects diverse food intake patterns through a randomized data generation process. It includes food categories, meal types, and nutritional values based on general nutritional guidelines and publicly available food databases.

[Note: Integrated as part of FoodData Central, April 2019.] The database consists of several sets of data: food descriptions, nutrients, weights and measures, footnotes, and sources of data. The Nutrient Data file contains mean nutrient values per 100 g of the edible portion of food, along with fields to further describe the mean value. Information is provided on household measures for food items. Weights are given for edible material without refuse. Footnotes are provided for a few items where information about food description, weights and measures, or nutrient values could not be accommodated in existing fields. Data have been compiled from published and unpublished sources. Published data sources include the scientific literature. Unpublished data include those obtained from the food industry, other government agencies, and research conducted under contracts initiated by USDA’s Agricultural Research Service (ARS). Updated data have been published electronically on the USDA Nutrient Data Laboratory (NDL) web site since 1992. Standard Reference (SR) 28 includes composition data for all the food groups and nutrients published in the 21 volumes of "Agriculture Handbook 8" (US Department of Agriculture 1976-92), and its four supplements (US Department of Agriculture 1990-93), which superseded the 1963 edition (Watt and Merrill, 1963). SR28 supersedes all previous releases, including the printed versions, in the event of any differences. Attribution for photos: Photo 1: k7246-9 Copyright free, public domain photo by Scott Bauer Photo 2: k8234-2 Copyright free, public domain photo by Scott Bauer Resources in this dataset:Resource Title: READ ME - Documentation and User Guide - Composition of Foods Raw, Processed, Prepared - USDA National Nutrient Database for Standard Reference, Release 28. File Name: sr28_doc.pdfResource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: ASCII (6.0Mb; ISO/IEC 8859-1). File Name: sr28asc.zipResource Description: Delimited file suitable for importing into many programs. The tables are organized in a relational format, and can be used with a relational database management system (RDBMS), which will allow you to form your own queries and generate custom reports.Resource Title: ACCESS (25.2Mb). File Name: sr28db.zipResource Description: This file contains the SR28 data imported into a Microsoft Access (2007 or later) database. It includes relationships between files and a few sample queries and reports.Resource Title: ASCII (Abbreviated; 1.1Mb; ISO/IEC 8859-1). File Name: sr28abbr.zipResource Description: Delimited file suitable for importing into many programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Title: Excel (Abbreviated; 2.9Mb). File Name: sr28abxl.zipResource Description: For use with Microsoft Excel (2007 or later), but can also be used by many other spreadsheet programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/ Resource Title: ASCII (Update Files; 1.1Mb; ISO/IEC 8859-1). File Name: sr28upd.zipResource Description: Update Files - Contains updates for those users who have loaded Release 27 into their own programs and wish to do their own updates. These files contain the updates between SR27 and SR28. Delimited file suitable for import into many programs.

This data package shows the information on Condiments Required in Food, Food Group Display, Global Health Nutrition Data, Condiment Food for USDA Kids and USDA Nutrient Retention Factors

The nutrition values of fish and other aquatic foods have recently gained global recognition for their potential to alleviate ‘hidden hunger’ in many contexts and for many nutritional vulnerable people. Yet, data for most fish, aquatic species, and forms of aquatic foods (particularly those of lower commercial value) are unavailable and unattainable due to the prohibitive cost of high-quality nutrient analysis. This means the databases that house the data that do exist are simultaneously incredibly valuable and riddled with gaps. Many initiatives have risen to address this challenge of compiling the best quality, to all available, data on the nutrient qualities of fish and other aquatic foods. There are multiple databases that now exist through which a researcher or policy maker might locate or contribute data. These include (1) Analytical Food Composition Database; (2) Food Composition Database for Biodiversity Global food composition database for fish and shellfish (3) Seafood Data (4) FishNutrients (5) Aquatic Food Composition Database (6) FoodEXplorer (7) the many different National Food Composition Databases. With input from experts from the fields of food sciences, nutrition and fisheries, and with a rapid review process by database curators, we compiled the metadata for seven different databases that contain large data set on nutrient qualities of fish and other aquatic foods. By summarising metadata, and generating a comparison between databases, we envisage that this tool will help researchers navigate these different tools, and better understand their different strengths and limitations.

[Note: Integrated as part of FoodData Central, April 2019.] USDA's Food and Nutrient Database for Dietary Studies (FNDDS) is a database that is used to convert food and beverages consumed in What We Eat In America (WWEIA), National Health and Nutrition Examination Survey (NHANES) into gram amounts and to determine their nutrient values. Because FNDDS is used to generate the nutrient intake data files for WWEIA, NHANES, it is not required to estimate nutrient intakes from the survey. FNDDS is made available for researchers using WWEIA, NHANES to review the nutrient profiles for specific foods and beverages as well as their associated portions and recipes. Such detailed information makes it possible for researchers to conduct enhanced analysis of dietary intakes. FNDDS can also be used in other dietary studies to code foods/beverages and amounts eaten and to calculate the amounts of nutrients/food components in those items.
FNDDS is released every two-years in conjunction with the WWEIA, NHANES dietary data release. The FNDDS is available for free download from the FSRG website. Resources in this dataset:Resource Title: Website Pointer to Food and Nutrient Database for Dietary Studies. File Name: Web Page, url: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fndds/ USDA's Food and Nutrient Database for Dietary Studies (FNDDS) is a database that is used to convert food and beverages consumed in What We Eat In America (WWEIA), National Health and Nutrition Examination Survey (NHANES) into gram amounts and to determine their nutrient values.

This dataset was developed to understand the nutrient content of the commonly consumed foods in New Zealand.

• The linkages between two prominent New Zealand studies (or databases) were evaluated, in order to determine the matching between the foods in those studies.
• The first study was the 2008/09 New Zealand Adult Nutrition Survey (NZANS) for the total New Zealand population, which presents the commonly consumed foods (a total of 346) in New Zealand.
• The second study was the New Zealand FOODfiles™ 2018 Version 01, which is a database that provides nutrition information (both essential nutrients and essential amino acids) for 2,767 foods.
• Some of the foods in the 2008/09 NZANS are dishes (or menus); the nutrient content of each of those dishes was constructed based on the work by Drew et al. (2020).

References

1. Drew J, Cleghorn C, Macmillan A, Mizdrak A. 2020. Healthy and Climate-Friendly Eating Patterns in the New Zealand Context. Environ Health Perspect. 128(1): 017007.
2. Plant and Food Research, Ministry of Health. 2019. New Zealand Food Composition Database. The New Zealand Institute for Plant and Food Research Limited. [accessed 12 Feb 2020]. https://www.foodcomposition.co.nz/
3. University of Otago, Ministry of Health. 2011. A Focus on Nutrition: Key Findings of the 2008/09 New Zealand Adult Nutrition Survey. Wellington, New Zealand: Ministry of Health.

Last update: 12 September 2020

The dataset, Survey-SR, provides the nutrient data for assessing dietary intakes from the national survey What We Eat In America, National Health and Nutrition Examination Survey (WWEIA, NHANES). Historically, USDA databases have been used for national nutrition monitoring (1). Currently, the Food and Nutrient Database for Dietary Studies (FNDDS) (2), is used by Food Surveys Research Group, ARS, to process dietary intake data from WWEIA, NHANES. Nutrient values for FNDDS are based on Survey-SR. Survey-SR was referred to as the "Primary Data Set" in older publications. Early versions of the dataset were composed mainly of commodity-type items such as wheat flour, sugar, milk, etc. However, with increased consumption of commercial processed and restaurant foods and changes in how national nutrition monitoring data are used (1), many commercial processed and restaurant items have been added to Survey-SR. The current version, Survey-SR 2013-2014, is mainly based on the USDA National Nutrient Database for Standard Reference (SR) 28 (2) and contains sixty-six nutrientseach for 3,404 foods. These nutrient data will be used for assessing intake data from WWEIA, NHANES 2013-2014. Nutrient profiles were added for 265 new foods and updated for about 500 foods from the version used for the previous survey (WWEIA, NHANES 2011-12). New foods added include mainly commercially processed foods such as several gluten-free products, milk substitutes, sauces and condiments such as sriracha, pesto and wasabi, Greek yogurt, breakfast cereals, low-sodium meat products, whole grain pastas and baked products, and several beverages including bottled tea and coffee, coconut water, malt beverages, hard cider, fruit-flavored drinks, fortified fruit juices and fruit and/or vegetable smoothies. Several school lunch pizzas and chicken products, fast-food sandwiches, and new beef cuts were also added, as they are now reported more frequently by survey respondents. Nutrient profiles were updated for several commonly consumed foods such as cheddar, mozzarella and American cheese, ground beef, butter, and catsup. The changes in nutrient values may be due to reformulations in products, changes in the market shares of brands, or more accurate data. Examples of more accurate data include analytical data, market share data, and data from a nationally representative sample. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES 2013-14 (Survey SR 2013-14). File Name: SurveySR_2013_14 (1).zipResource Description: Access database downloaded on November 16, 2017. US Department of Agriculture, Agricultural Research Service, Nutrient Data Laboratory. USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES (Survey-SR), October 2015. Resource Title: Data Dictionary. File Name: SurveySR_DD.pdf

This dataset contains detailed nutritional and portion information for 1,000 different food items. It provides a comprehensive view of various nutritional components, including calorie content, macronutrients, and specific food categories. This data is essential for analyzing dietary patterns, making nutritional comparisons, and developing health recommendations.

Usage:

This dataset is suitable for a variety of analyses, including nutritional content evaluations, dietary pattern assessments, and health impact studies. It can be used to compare different foods, identify nutritional trends, and make informed dietary recommendations.

Open Food Facts: The World's Largest Open Database of Food Products

Ready to tackle real-world data science challenges that impact global food transparency? Explore, analyze, and contribute to a dataset of over 4 million food products from across the globe.

This is more than just a dataset; it's a living, breathing project with tangible applications in health, nutrition, and sustainability. Whether you're interested in nutrition, supply chains, or the environmental impact of food, you have the power to make a difference.

• Contribute to AI with real-world impact: We have a list of AI and machine learning tasks that directly contribute to food transparency. Your skills can help millions of consumers make more informed choices.
• Join us for Google Summer of Code 2025: We have exciting machine learning and data science projects lined up. We are actively seeking students and additional mentors. This is a fantastic opportunity to work on meaningful projects with a passionate community.
• Support a global cause: Open Food Facts is a volunteer-run non-profit organization. Every contribution helps us maintain our servers and continue our mission. Donate here to support our work (we do not accept funding from the food industry to ensure our independence).

About Open Food Facts

Open Food Facts is a free, open, and collaborative database of food products from around the world. It contains detailed information on ingredients, allergens, nutritional content, and other data found on product labels.

By the Community, For the Community: * Massive Scale: The database includes over 4 million products from 150 countries. * Crowdsourced Data: More than 5,000 volunteers have contributed by scanning barcodes and uploading product images using our Android and iPhone apps. * Completely Open: The entire database is published as open data, available for anyone to use for any purpose. Discover existing projects or build your own!

Dataset Structure

The dataset is provided as a single table, FoodFacts, available in both CSV (FoodFacts.csv) and SQLite (database.sqlite) formats. With over 150 columns, this rich dataset offers a vast playground for analysis and modeling.

Key Column Groups:

The columns can be broadly categorized as follows:

• Product Identification:

  • code: The barcode of the product (EAN-13 or UPC).
  • product_name: Name of the product.
  • generic_name: A more generic description of the product.
  • brands, brands_tags: The brand or brands associated with the product.
  • url, image_url, image_small_url: Links to the product page and images on Open Food Facts.

• Product Characteristics:

  • quantity: The amount of the product (e.g., "500 g", "2 L").
  • packaging, packaging_tags: Information about the product's packaging.
  • categories, categories_tags, categories_en: The product's food categories.
  • labels, labels_tags, labels_en: Certifications and labels (e.g., "Organic", "Gluten-Free").

• Origin and Sourcing:

  • origins, origins_tags: Where the ingredients come from.
  • manufacturing_places, manufacturing_places_tags: Where the product was manufactured.
  • countries, countries_tags, countries_en: Countries where the product is sold.

• Ingredients and Allergens:

  • ingredients_text: The full list of ingredients.
  • allergens, allergens_en: Declared allergens.
  • traces, traces_tags, traces_en: Potential traces of allergens.
  • additives_n, additives_tags, additives_en: Number and list of food additives.
  • Information on palm oil content (ingredients_from_palm_oil_n, etc.).

• Nutritional Information (per 100g):

  • Macronutrients: energy_100g, fat_100g, saturated_fat_100g, carbohydrates_100g, sugars_100g, proteins_100g, fiber_100g, salt_100g.
  • Vitamins: vitamin_a_100g, vitamin_c_100g, vitamin_d_100g, vitamin_b1_100g, etc.
  • Minerals: calcium_100g, iron_100g, magnesium_100g, potassium_100g, etc.
  • Fatty Acids: Detailed breakdown of fatty acids (omega_3_fat_100g, linoleic_acid_100g, etc.).

• Scores and Classifications:

  • nutrition_grade_fr: The Nutri-Score, a five-letter nutrition grade (A to E).
  • pnns_groups_1, pnns_groups_2: Food categories used to compute the Nutri-Score.
  • carbon_footprint_100g: The carbon ...

This dataset provides comprehensive nutritional information for a variety of foods. It includes calorie counts, macronutrient breakdowns (carbohydrates, proteins, and fats), and other relevant nutritional details. This dataset can be valuable for individuals seeking to manage their diet, track calorie intake, or explore dietary trends.

Potential Use Cases:

Dietary Analysis: Analyze dietary trends, identify popular food choices, and understand nutritional patterns. Calorie Tracking Apps: Develop mobile applications to help users monitor their calorie intake and make informed food choices. Nutrition Research: Conduct research on the nutritional content of foods, the impact of diet on health, and the development of dietary guidelines. Machine Learning Models: Train machine learning models to predict calorie content based on food ingredients or descriptions. Data Dictionary:

food_item: Name of the food item. calories: Caloric content of the food item in calories. protein: Protein content of the food item in grams. fat: Fat content of the food item in grams. carbohydrates: Carbohydrate content of the food item in grams. sugar: Sugar content of the food item in grams. fiber: Fiber content of the food item in grams. [Additional columns as relevant, e.g., sodium, cholesterol, vitamins, minerals] Data Quality and Source: The data was sourced from [Specify your data source, e.g., USDA FoodData Central, nutrition databases, or original data collection]. It has been cleaned and preprocessed to ensure accuracy and consistency.

This dataset includes 345 indicators, such as immunization rates, malnutrition prevalence, and vitamin A supplementation rates across 263 countries around the world. Data were collected on a yearly basis from 1960-2016.

Following a request from the European Commission for a review of European dietary reference values (DRVs), the EFSA’s Panel on Dietetic Products, Nutrition and Allergies (NDA) has prepared a number of Scientific Opinions on DRVs for micronutrients. The DATA Unit supported this activity by estimating the nutrient intake of a number of micronutrients in nine selected European countries and different age groups. In addition, the DATA Unit also provided information on average content of food sources of the respective nutrients per country based on the composition database, as well as main food group contributors to nutrient intakes and assessed the comparability of the provided data with pertinent published intake data.

Intake estimates have been assessed using food consumption data from the EFSA Comprehensive Food Consumption Database (EFSA, 2011a) and the EFSA Nutrient composition database. Food composition data used to populate the Nutrient composition database were provided to EFSA through the EFSA procurement project ‘Updated food composition database for nutrient intake’ (Roe at al., 2013). Data were provided following the EFSA specification for standard sample description for food and feed and were classified according to the FoodEx2 classification system of EFSA (EFSA, 2011b).

The food composition data used in these assessments and here published cover the following vitamins and minerals: calcium (Ca); copper (Cu); cobalamin (vitamin B12); magnesium (Mg); niacin; phosphorus (P); potassium (K); riboflavin; thiamin; iron (Fe); selenium (Se); vitamin B6; vitamin K, zinc (Zn), and vitamin E1. The food composition dataset contains data from seven2 countries: Finland, France, Germany, Italy, Netherlands, Sweden, and United Kingdom. This dataset version has been checked for outliers but is prior to data completion for missing foods and nutrient values.

1 Vitamin E is defined as alpha-tocopherol (AT) only, however as most food composition databases in the EU contain values as alpha-tocopherol equivalents (TE), data on TE are also provided

2 For the nutrient intake estimates of Ireland and Latvia present in the opinions of the EFSA Panel on Dietetic Products, Nutrition and Allergies (NDA), food composition data from UK and Germany were respectively used

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This dataset compiles information on 100 of the world's healthiest foods, providing a detailed nutritional breakdown and historical context for each item. It aims to be a valuable resource for nutritionists, data scientists, and health enthusiasts interested in exploring the relationships between various foods, their nutritional content, and their cultural significance.

Each entry includes the food's name, key nutritional highlight, serving size, place of origin, and detailed nutritional information per 100g serving. The nutritional data covers essential metrics such as calories, protein content, fiber content, vitamin C levels, and an antioxidant score. This comprehensive approach allows for in-depth analysis and comparison of different foods across various health parameters.

Columns Description:

Food: Name of the healthy food item

Nutrition Value: Key nutritional highlight or benefit of the food

Quantity: Typical serving size of the food

Originated From: Geographical origin or region where the food is traditionally from

Calories: Number of calories per 100g serving

Protein (g): Grams of protein per 100g serving

Fiber (g): Grams of dietary fiber per 100g serving

Vitamin C (mg): Milligrams of Vitamin C per 100g serving

Antioxidant Score: A measure of the food's antioxidant properties (scale and methodology to be specified)

Branded foods databases are becoming very valuable not only in nutrition research but also for clinical practice, policymakers, businesses, and general population. In contrast to generic foods, branded foods are marked by rapid changes in the food supply because of reformulations, the introduction of new foods, and the removal of existing ones from the market. Also, different branded foods are available in different countries. This not only complicates the compilation of branded foods datasets but also causes such datasets to become out of date quickly. In this review, we present different approaches to the compilation of branded foods datasets, describe the history and progress of building and updating such datasets in Slovenia, and present data to support nutrition research and monitoring of the food supply. Manufacturers are key sources of information for the compilation of branded foods databases, most commonly through food labels. In Slovenia, the branded food dataset is compiled using standard food monitoring studies conducted at all major retailers. Cross-sectional studies are conducted every few years, in which the food labels of all available branded foods are photographed. Studies are conducted using the Composition and Labeling Information System (CLAS) infrastructure, composed of a smartphone application for data collection and online data extraction and management tool. We reviewed various uses of branded foods datasets. Datasets can be used to assess the nutritional composition of food in the food supply (i.e., salt, sugar content), the use of specific ingredients, for example, food additives, for nutrient profiling, and assessment of marketing techniques on food labels. Such datasets are also valuable for other studies, for example, assessing nutrient intakes in dietary surveys. Additional approaches are also being tested to keep datasets updated between food monitoring studies. A promising approach is the exploitation of crowdsourcing through the mobile application VešKajJeš, which was launched in Slovenia to support consumers in making healthier dietary choices.

Comprehensive database of food nutritional information with detailed analysis of macronutrients, micronutrients, and dietary components.