食品中的病原体(PIF):一个开放获取的欧洲食品中生物危害发生数据数据库

IF 4 4区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Microbial Risk Analysis Pub Date : 2025-02-27 DOI:10.1016/j.mran.2025.100342
Ursula Gonzales-Barron , Ana Sofia Faria , Anne Thebault , Laurent Guillier , Lucas Ribeiro Mendes , Lucas Ribeiro Silva , Winy Messens , Pauline Kooh , Vasco Cadavez
{"title":"食品中的病原体(PIF):一个开放获取的欧洲食品中生物危害发生数据数据库","authors":"Ursula Gonzales-Barron ,&nbsp;Ana Sofia Faria ,&nbsp;Anne Thebault ,&nbsp;Laurent Guillier ,&nbsp;Lucas Ribeiro Mendes ,&nbsp;Lucas Ribeiro Silva ,&nbsp;Winy Messens ,&nbsp;Pauline Kooh ,&nbsp;Vasco Cadavez","doi":"10.1016/j.mran.2025.100342","DOIUrl":null,"url":null,"abstract":"<div><div>The collection of occurrence data of foodborne pathogens in foods faces the hindrances of dispersion of information, lack of standardisation and harmonisation, and ultimately, high expenditure in time and resources. The Pathogens-in-Foods (PIF) database was conceived as a solution to centralise published data on prevalence and concentration of pathogenic bacteria, viruses and parasites occurring in foods, obtained through systematic review (SR), and categorised in harmonised data structures under controlled terminologies. The present article outlines how PIF was constructed to adhere to the FAIR (findability, accessibility, interoperability and reusability) principles for scientific data management; and proceeds with a description of the PIF concept, which entails two phases: the SR process and the population of PIF. The protocolled SR process is supported by a well-defined search strategy, inclusion criteria, and rules for internal validation assessment; whereas the population of PIF with new data relies in data extraction, validation and release. The article then introduces a novel data quality approach, named as the CCC approach (data consistency, conformity and completeness), which ensures proper interpretation of data, richness of data, and flawless transcription of data. After a brief explanation of the three PIF components – database, back-end and front-end – the article proceeds with the exposition of the data model, as well as the capabilities of the front-end, including data search, insertion and curation. The future of PIF lies in expanding its capabilities, addressing emerging challenges, and leveraging technological advancements to maintain its relevance and utility in the evolving landscape of food safety.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"29 ","pages":"Article 100342"},"PeriodicalIF":4.0000,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pathogens-in-Foods (PIF): An open-access European database of occurrence data of biological hazards in foods\",\"authors\":\"Ursula Gonzales-Barron ,&nbsp;Ana Sofia Faria ,&nbsp;Anne Thebault ,&nbsp;Laurent Guillier ,&nbsp;Lucas Ribeiro Mendes ,&nbsp;Lucas Ribeiro Silva ,&nbsp;Winy Messens ,&nbsp;Pauline Kooh ,&nbsp;Vasco Cadavez\",\"doi\":\"10.1016/j.mran.2025.100342\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The collection of occurrence data of foodborne pathogens in foods faces the hindrances of dispersion of information, lack of standardisation and harmonisation, and ultimately, high expenditure in time and resources. The Pathogens-in-Foods (PIF) database was conceived as a solution to centralise published data on prevalence and concentration of pathogenic bacteria, viruses and parasites occurring in foods, obtained through systematic review (SR), and categorised in harmonised data structures under controlled terminologies. The present article outlines how PIF was constructed to adhere to the FAIR (findability, accessibility, interoperability and reusability) principles for scientific data management; and proceeds with a description of the PIF concept, which entails two phases: the SR process and the population of PIF. The protocolled SR process is supported by a well-defined search strategy, inclusion criteria, and rules for internal validation assessment; whereas the population of PIF with new data relies in data extraction, validation and release. The article then introduces a novel data quality approach, named as the CCC approach (data consistency, conformity and completeness), which ensures proper interpretation of data, richness of data, and flawless transcription of data. After a brief explanation of the three PIF components – database, back-end and front-end – the article proceeds with the exposition of the data model, as well as the capabilities of the front-end, including data search, insertion and curation. The future of PIF lies in expanding its capabilities, addressing emerging challenges, and leveraging technological advancements to maintain its relevance and utility in the evolving landscape of food safety.</div></div>\",\"PeriodicalId\":48593,\"journal\":{\"name\":\"Microbial Risk Analysis\",\"volume\":\"29 \",\"pages\":\"Article 100342\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2025-02-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Microbial Risk Analysis\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352352225000027\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microbial Risk Analysis","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352352225000027","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

食品中食源性病原体发生数据的收集面临着信息分散、缺乏标准化和协调以及最终花费大量时间和资源的障碍。食品中病原体(PIF)数据库被设想为一种解决方案,用于集中通过系统审查(SR)获得的关于食品中致病菌、病毒和寄生虫的流行率和浓度的已发表数据,并在受控术语下按统一数据结构进行分类。本文概述了如何构建PIF以坚持科学数据管理的FAIR(可查找性,可访问性,互操作性和可重用性)原则;并开始描述PIF概念,其中包括两个阶段:SR过程和PIF的人口。协议化的SR过程由定义良好的搜索策略、包含标准和内部验证评估规则支持;而具有新数据的PIF的填充依赖于数据的提取、验证和发布。然后,本文介绍了一种新的数据质量方法,称为CCC方法(数据一致性、一致性和完整性),它确保了对数据的正确解释、数据的丰富性和数据的完美转录。在简要介绍了三个PIF组件(数据库、后端和前端)之后,本文继续阐述数据模型,以及前端的功能,包括数据搜索、插入和管理。PIF的未来在于扩大其能力,应对新出现的挑战,并利用技术进步来保持其在不断变化的食品安全领域的相关性和实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Pathogens-in-Foods (PIF): An open-access European database of occurrence data of biological hazards in foods
The collection of occurrence data of foodborne pathogens in foods faces the hindrances of dispersion of information, lack of standardisation and harmonisation, and ultimately, high expenditure in time and resources. The Pathogens-in-Foods (PIF) database was conceived as a solution to centralise published data on prevalence and concentration of pathogenic bacteria, viruses and parasites occurring in foods, obtained through systematic review (SR), and categorised in harmonised data structures under controlled terminologies. The present article outlines how PIF was constructed to adhere to the FAIR (findability, accessibility, interoperability and reusability) principles for scientific data management; and proceeds with a description of the PIF concept, which entails two phases: the SR process and the population of PIF. The protocolled SR process is supported by a well-defined search strategy, inclusion criteria, and rules for internal validation assessment; whereas the population of PIF with new data relies in data extraction, validation and release. The article then introduces a novel data quality approach, named as the CCC approach (data consistency, conformity and completeness), which ensures proper interpretation of data, richness of data, and flawless transcription of data. After a brief explanation of the three PIF components – database, back-end and front-end – the article proceeds with the exposition of the data model, as well as the capabilities of the front-end, including data search, insertion and curation. The future of PIF lies in expanding its capabilities, addressing emerging challenges, and leveraging technological advancements to maintain its relevance and utility in the evolving landscape of food safety.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Microbial Risk Analysis
Microbial Risk Analysis Medicine-Microbiology (medical)
CiteScore
5.70
自引率
7.10%
发文量
28
审稿时长
52 days
期刊介绍: The journal Microbial Risk Analysis accepts articles dealing with the study of risk analysis applied to microbial hazards. Manuscripts should at least cover any of the components of risk assessment (risk characterization, exposure assessment, etc.), risk management and/or risk communication in any microbiology field (clinical, environmental, food, veterinary, etc.). This journal also accepts article dealing with predictive microbiology, quantitative microbial ecology, mathematical modeling, risk studies applied to microbial ecology, quantitative microbiology for epidemiological studies, statistical methods applied to microbiology, and laws and regulatory policies aimed at lessening the risk of microbial hazards. Work focusing on risk studies of viruses, parasites, microbial toxins, antimicrobial resistant organisms, genetically modified organisms (GMOs), and recombinant DNA products are also acceptable.
期刊最新文献
Optimizing vaccination strategies for mpox control in endemic areas: Modeling insights from the Democratic Republic of Congo Development and implementation of a rapid risk assessment tool to enhance and standardise aquatic animal health risk management Editorial Board Microbial risk and health burden associated with the domestic preparation of lentils in France and Hungary Incorporating genomic virulence in a quantitative microbial risk assessment to assess the public health impact of alternative microbiological criteria for Salmonella
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1