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The state of food composition databases: data attributes and FAIR data harmonization in the era of digital innovation

  • Sarah Brinkley
  • , Jenny J. Gallo-Franco
  • , Natalia Vázquez-Manjarrez
  • , Juliana Chaura
  • , Naa K.A. Quartey
  • , Sahar B. Toulabi
  • , Melanie T. Odenkirk
  • , Eva Jermendi
  • , Marie Angélique Laporte
  • , Herman E. Lutterodt
  • , Reginald A. Annan
  • , Mariana Barboza
  • , Endale Amare
  • , Warangkana Srichamnong
  • , Andres Jaramillo-Botero
  • , Gina Kennedy
  • , Jaclyn Bertoldo
  • , Jessica E. Prenni
  • , Maya Rajasekharan
  • , John de la Parra
  • Selena Ahmed
  • Bioversity International
  • Centro Internacional de Agricultura Tropical
  • Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran
  • Universidad Javeriana
  • Kwame Nkrumah University of Science and Technology
  • Colorado State University
  • Wageningen University & Research
  • University of California at Davis
  • Ethiopia Public Health Institute
  • Mahidol University
  • California Institute of Technology
  • American Heart Association
  • Rockefeller Foundation

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Introduction: Food composition databases (FCDBs) are essential resources for characterizing, documenting, and advancing scientific understanding of food quality across the entire spectrum of edible biodiversity. This knowledge supports a wide range of applications with societal impact spanning the global food system. To maximize the utility of food composition data, FCDBs must adhere to criteria such as validated analytical methods, high-resolution metadata, and FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable). However, complexity and variability in food data pose significant challenges to meeting these standards. Methods: In this study, we conducted an integrative review of 35 data attributes across 101 FCDBs from 110 countries. The data attributes were categorized into three groups: general database information, foods and components, and FAIRness. Results: Our findings reveal evaluated databases show substantial variability in scope and content, with the number of foods and components ranging from few to thousands. FCDBs with the highest numbers of food samples (≥1,102) and components (≥244) tend to rely on secondary data sourced from scientific articles or other FCDBs. In contrast, databases with fewer food samples and components predominantly feature primary analytical data generated in-house. Notably, only one-third of FCDBs reported data on more than 100 food components. FCDBs were infrequently updated, with web-based interfaces being updated more frequently than static tables. When assessed for FAIR compliance, all FCDBs met the criteria for Findability. However, aggregated scores for Accessibility, Interoperability, and Reusability for the reviewed FCDBs were 30, 69, and 43%, respectively. Discussion: These scores reflect limitations in inadequate metadata, lack of scientific naming, and unclear data reuse notices. Notably, these results are associated with country economic classification, as databases from high-income countries showed greater inclusion of primary data, web-based interfaces, more regular updates, and strong adherence to FAIR principles. Our integrative review presents the current state of FCDBs highlighting emerging opportunities and recommendations. By fostering a deeper understanding of food composition, diverse stakeholders across food systems will be better equipped to address societal challenges, leveraging data-driven solutions to support human and planetary health.

Original languageEnglish
Article number1552367
JournalFrontiers in Nutrition
Volume12
DOIs
StatePublished - 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • FAIR data
  • food components
  • food composition data
  • food composition data management
  • food composition database
  • food quality
  • metadata
  • nutritional database

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