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
{"title":"食品成分数据库现状:数字创新时代的数据属性与FAIR数据协调。","authors":"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","doi":"10.3389/fnut.2025.1552367","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Discussion: </strong>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.</p>","PeriodicalId":12473,"journal":{"name":"Frontiers in Nutrition","volume":"12 ","pages":"1552367"},"PeriodicalIF":4.0000,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11974508/pdf/","citationCount":"0","resultStr":"{\"title\":\"The state of food composition databases: data attributes and FAIR data harmonization in the era of digital innovation.\",\"authors\":\"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\",\"doi\":\"10.3389/fnut.2025.1552367\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Discussion: </strong>These scores reflect limitations in inadequate metadata, lack of scientific naming, and unclear data reuse notices. 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The state of food composition databases: data attributes and FAIR data harmonization in the era of digital innovation.
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.
期刊介绍:
No subject pertains more to human life than nutrition. The aim of Frontiers in Nutrition is to integrate major scientific disciplines in this vast field in order to address the most relevant and pertinent questions and developments. Our ambition is to create an integrated podium based on original research, clinical trials, and contemporary reviews to build a reputable knowledge forum in the domains of human health, dietary behaviors, agronomy & 21st century food science. Through the recognized open-access Frontiers platform we welcome manuscripts to our dedicated sections relating to different areas in the field of nutrition with a focus on human health.
Specialty sections in Frontiers in Nutrition include, for example, Clinical Nutrition, Nutrition & Sustainable Diets, Nutrition and Food Science Technology, Nutrition Methodology, Sport & Exercise Nutrition, Food Chemistry, and Nutritional Immunology. Based on the publication of rigorous scientific research, we thrive to achieve a visible impact on the global nutrition agenda addressing the grand challenges of our time, including obesity, malnutrition, hunger, food waste, sustainability and consumer health.