Establishment of a prototypic Quantitative Microbial Risk Assessment (QMRA) food and feed safety model repository

Quantitative Microbial Risk Assessment (QMRA), model repository, information exchange standard, Knowledge Junction (KJ), FSK-Lab, KNIME
First published in EFSA Supporting Publications
13 September 2019
31 July 2019
External Scientific Report

The present document has been produced and adopted by the bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.


The transfer and re‐use of existing food safety knowledge (ranging from experimental data, published mathematical models to risk assessment software code) is currently a major bottleneck in the area of food safety risk assessment. Such knowledge transfer and knowledge re‐use would be possible if curated, community‐driven risk assessment knowledge repositories would exist, that provide mathematical models/modules in a machine‐readable format.

This project therefore aims at the development of necessary resources (standards, ontology‐based controlled vocabularies, software tools and services) facilitating the establishment of open, community‐driven, curated knowledge repositories for scientists and risk assessors. The main goal of this project is the provisioning of a proof‐of‐principle implementation of a food and feed safety model repository on the following basis:

  • Development of a generic model exchange format (FSK‐ML) to encode food and feed safety models, data and simulation scenarios.
  • Development of open technical resources (converter, software libraries, data processing workflows) as well as domain‐specific ontologies facilitating the broad adoption of FSK‐ML in the area of Quantitative Microbial Risk Assessment (QMRA)
  • Development and extension of software tools for de‐novo generation, export, import, validation and execution of FSK‐ML files.
  • Establishment of a prototypic web‐based knowledge repository for QMRA with seamless interoperation with EFSA's Knowledge Junction (KJ), which allows access to the collected resources as well as the provision of digital object identifiers (DOI).
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