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

Creators: Brinkley, Sarah¹; Gallo-Franco, Jenny J.²; Vázquez-Manjarrez, Natalia³; Chaura, Juliana⁴; Quartey, Naa K. A.⁵; Toulabi, Sahar B.⁶; Odenkirk, Melanie T.⁶; Jermendi, Eva⁷; Laporte, Marie-Angélique⁸; Lutterodt, Herman E.⁵; Annan, Reginald A.⁵; Barboza, Mariana⁹; Amare, Endale¹⁰; Srichamnong, Warangkana¹¹; Jaramillo-Botero, Andres^{4, 12}; Kennedy, Gina¹; Bertoldo, Jaclyn¹³; Prenni, Jessica E.⁶; Rajasekharan, Maya²; de la Parra, John¹⁴; Ahmed, Selena¹³

1. Bioversity International
2. Centro Internacional de Agricultura Tropical
3. Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán
4. Pontificia Universidad Javeriana
5. Kwame Nkrumah University of Science and Technology
6. Colorado State University
7. Wageningen University & Research
8. The Periodic Table of Food Initiative, Bioversity International, Montpellier, France
9. University of California, Davis
10. Ethiopian Public Health Institute
11. Mahidol University
12. California Institute of Technology
13. American Heart Association
14. Rockefeller Foundation

Style

An error occurred while generating the citation.

Abstract

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.MethodsIn 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.ResultsOur 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.DiscussionThese 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.

Copyright and License

© 2025 Brinkley, Gallo-Franco, Vázquez-Manjarrez, Chaura, Quartey, Toulabi, Odenkirk, Jermendi, Laporte, Lutterodt, Annan, Barboza, Amare, Srichamnong, Jaramillo-Botero, Kennedy, Bertoldo, Prenni, Rajasekharan, de la Parra and Ahmed. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. We are grateful for the support of The Periodic Table of Food Initiative (PTFI) by The Rockefeller Foundation, The Foundation for Food and Agriculture Research, Seerave Foundation, Fourfold Foundation, Atria Health Collaborative, and the Bill & Melinda Gates Foundation. The content, findings and conclusions presented are those of the authors and do not necessarily reflect the official views, positions or policies of the funders or the institutions with which the funders or the authors are affiliated.

Contributions

SB: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing. JG-F: Conceptualization, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. NV-M: Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing – review & editing. JC: Data curation, Investigation, Methodology, Supervision, Validation, Visualization, Writing – review & editing. NQ: Writing – review & editing, Data curation, Investigation, Methodology, Validation. ST: Data curation, Investigation, Methodology, Writing – review & editing. MO: Formal analysis, Methodology, Visualization, Writing – review & editing. EJ: Data curation, Investigation, Validation, Writing – review & editing. M-AL: Methodology, Supervision, Writing – review & editing. HL: Data curation, Methodology, Supervision, Writing – review & editing. RA: Data curation, Methodology, Supervision, Writing – review & editing. MB: Data curation, Writing – review & editing. EA: Data curation, Methodology, Writing – review & editing. WS: Data curation, Methodology, Supervision, Writing – review & editing. AJ-B: Supervision, Writing – review & editing. GK: Writing – review & editing. JB: Data curation, Formal analysis, Investigation, Writing – review & editing. JEP: Funding acquisition, Supervision, Writing – review & editing. MR: Conceptualization, Funding acquisition, Resources, Supervision, Writing – review & editing. JP: Conceptualization, Funding acquisition, Supervision, Writing – review & editing. SA: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing.

Data Availability

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.

Additional Information

This article is part of the Research TopicDatabases and Nutrition, volume III. View all 7 articles

Files

Name	Size	Download all
fnut-4-1552367.pdf md5:3c92223c2106920eb3b9518e20dcf01f	2.6 MB	Preview Download
Table 1.xlsx md5:a227700610b1a6de924e46bab2d3f7b3	602.1 kB	Download

	All versions	This version
Views	0	0
Downloads	0	0
Data volume	0 Bytes	0 Bytes