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 welldefined
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.
