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LOGO-RAKIP

RAKIP VRE

The food safety community is generating a variety of scientific knowledge (e.g. scientific publications, experimental data and mathematical models) and resources (databases and software tools for model generation and application). However, the access to this knowledge and the exchange of information between databases and software tools are currently difficult and time consuming. Therefore, three European institutions specialized in food safety risk assessment (ANSES, BfR and DTU Food) initiated a joint project to establish new community resources facilitating the efficient knowledge integration and exchange into and between IT-based applications and resources. The envisaged �Risk Assessment Modelling and Knowledge Integration Platforms� (RAKIP) will be based on harmonized data formats and consistent rules for knowledge annotation. The feasibility of this concept will be exemplified through an RAKIP Web Portal allowing users to access and download risk assessment models, modules thereof and related data in a harmonized file format. These files can then be imported and executed by software tools supporting the proposed harmonized file format. The RAKIP Web Portal therefore also contains supporting resources needed for the harmonized description and exchange of knowledge.

 

Keywords: Food Safety, Modelling, Virtual Research Environments, Risk Assessment, Model Repository, Diseases

SYSTEM-RAKIP

Targeted end users

The RAKIP solutions are designed to support the whole risk assessment community including national and international risk assessment agencies, business operators and academic institutions. Modellers, risk assessors and risk managers will benefit most from the RAKIP portal. However other users like research scientists can also make use of the RAKIP resources, e.g. share their experimental data or apply improved modelling tools in the future. In this sense, the end-users will find in the RAKIP Web Portal a web-based model repository that can be freely assessed and searched, and where models can be shared, searched, downloaded and executed. Modellers can contribute their models to the repository by uploading their models in the harmonized format. The RAKIP Web Portal is however not primarily intended to be a new �press-the-button� model simulation software. It will be improved continuously such that experts and non-experts can make use of it.

SHARING

Benefits

The benefits of the RAKIP Webportal is to provide a new web-based resource for the Emerging Risk Identification community that can serve as an �Emerging Risk and Knowledge Exchange Portal� in the future. To accomplish this, the generated resource has to go beyond an infrastructure for sharing text documents (as it is now). Specifically, it will be demonstrated that the generated AGINFRA+ VRE infrastructure can be used to share information, data and data-analysis pipelines developed by different EFSA member states within the EREN network. Furthermore, new opportunities to visualize results of calculations performed by these data mining and data analysis operations will be provided. Finally, it will be explored how the VRE infrastructure might overcome limits of existing desktop tools with respect to usability, as unified web-based graphical user-interfaces become available.



Tools


RAKIP Model Repository

The RAKIP Model Repository & Web Services Demonstrators contain: The RAKIP Model Repository � a web-based data and model repository that can be freely assessed by any user. It will contain risk assessment data and models provided by the food safety community. The RAKIP Model Repository will be maintained and extended in a collaborative fashion. The Web Services allow users to create, execute and visualize models and also upload them into the RAKIP Model Repository. It will become possible to create and share fully annotated experimental data sets. It is foreseen, that in the future new data/models entries will undergo a curation and quality control process. Efficient knowledge integration and exchange strongly depends on the support generated by the food safety community itself. It is necessary to emphasize, that the desired increased acceptance of microbial modelling and risk assessment technologies by end users will only be achieved if software engineers, (risk) modellers, lab scientists, project managers and end users from food industry and governmental agencies jointly promote the use of open Model Repositories.

FSK-Lab

Food Safety Knowledge Lab (FSK-Lab) is an open-source extension plugin to the Konstanz Information Miner (KNIME). FSK-Lab enables KNIME users to work with FSK models within KNIME.

Source code: FSKLab
Food Safety Knowledge Markup Language (FSK-ML) Software Developer Guide: Download
Under development software developer guide: Download

RAKIP Harmonization Resources

Models and data need to be annotated in a harmonized way. For this, we have defined a structured list of metadata, called �Metadata Master Table�, that are relevant for describing data or models in the risk assessment and predictive microbial modelling domains. For each metadata concept it has also been defined if this concept is considered mandatory or not and what cardinality each metadata concept has. The �Generic Metadata Schema� contains the complete list of metadata concepts that allow to describe in detail all models or data. Apart from the �Generic Metadata Schema�, we have defined dedicated metadata schema for specific model / data classes, using relevant subset of the generic metadata schema. The proposed metadata schema can be accessed via: https://goo.gl/PE4ys

To support harmonized annotation of food safety knowledge we established an online resource with controlled vocabularies for those metadata concepts that are not free text. Some of them are specific for the different model classes. These controlled vocabularies are based on the terms used by other sources like ontologies, standards and tools (SSD-CODE, FOODON, MIME, PMM-Lab, OpenFSMR, Bibliographic Ontology Specification, etc.) The lists of proposed controlled vocabularies for food safety knowledge annotation can be accessed via: https://goo.gl/wbFoZU

The terms describing the steps and entities in the risk assessment model generation process including PM and QMRA modelling have been discussed within the RAKIP project until consensus has been reached. All these terms have been detailed in an online glossary that can be freely accessed. The proposed glossary with terms describing the risk assessment model generation process can be accessed via: https://goo.gl/b4ADho

In order to facilitate the provisioning of metadata from the �Metadata Master Table� schema compliant to FSK-ML, a Google Sheet called �Metadata Master Table (MIRROR)� has been created. It meets all the requirements for importing metadata required by FSK-ML and includes the lists of controlled vocabularies as dropdown lists. The �Metadata Master Table (MIRROR)� will be updated automatically and instantly in case the �Metadata Master Table� or the lists of controlled vocabularies are modified. A guide for �How to annotate a model with the Excel template� will be released soon. The Excel template for model metadata provisioning compliant to FSK-ML can be accessed and downloaded via: https://goo.gl/Mp6ekq

All the RAKIP Harmonization Resources can be improved and updated by the community. Any proposal for improving those resources can be made via an online form. So far, the RAKIP partners are the curators of these resources. The online form for making proposals for improving RAKIP Harmonization Resources can be accessed via: https://goo.gl/9g7zUF

The RAKIP community supports the Food Modeling Journal (FMJ) which is an innovative open access journal. FMJ facilitates the publication of mathematical models and data sets in the area of food science. The journal is focussed on submissions documenting the following outcomes of the research cycle: data, models, software, data analytics pipelines and visualisation methods relevant for modelling in food science. The journal will consider manuscripts for publication related (but not limited) to the following topics: food safety, food quality, food control, food defense, food design. FMJLogo

FSK-ML (Food Safety Knowledge Markup Language)

Food safety risk assessments, control of food production processes as well as the development of new food products are nowadays supported by application of mathematical modelling and data analysis techniques. This creates an increasing demand for resources facilitating the efficient, transparent and quality proven exchange of relevant information, e.g. analytical data, mathematical models, simulation setting as well as simulated data. For example, new parameterized microbial models are frequently made publicly available only in written mode via scientific publications. However, in order to apply these models to a given practical decision support question (e.g. on the growth/no-growth of a microorganism in a specific food matrix under given processing conditions) the interested end-user would have to re-implement the model based on information provided in a publication. Here it would be more efficient if those who create parameterized models could provide their model additionally as a file complying with a standardized file format that is also capable of transferring all relevant meta data. Such a file could e.g. be provided as a supplement to the publication and could be read-in by the end user�s software tools (thus overcoming an error-prone re-implementation process). A first standardized file format has been proposed in the �Predictive Modelling in Food Markup Language (PMF-ML) Software Developer Guide�. This document describes in detail how experimental data and mathematical models from the domain of predictive microbial modelling (and beyond) can be saved and encoded in a software independent manner. With the Food Safety Knowledge Markup Language (FSK-ML) we now extend the PMF-ML format to enable the exchange of knowledge / information that is embedded in specific script-based programming languages (e.g. �R�, Matlab, Python). I.e. the FSK-ML guidance document primarily aims at harmonizing the exchange of food safety knowledge (e.g. predictive models) including the associated meta data where this knowledge is only available in a software dependent format. The FSK-ML format therefore relaxes and adapts certain specifications of the PMF-ML format while at the same time maintaining the highest possible synergies between both formats. This will also help to make sure that food safety models encoded in a software independent manner (using PMF-ML) can easily be interpreted by FSK-ML import and export software functions in the future.

The FSK Guidance Document:
FSK_guidance_document_20170823
Current version under review:
FSK_guidance_document_under_review