SYSTEMS

The 'Food Safety Knowledge Markup Language (FSK-ML)' format allows us to uniformly document mathematical models and model-based simulation results, and make these available to other researchers for supercomputer-based forecasts or further optimization of models. With FSK-ML, even models that were developed in different ways programming languages can be exchanged in a harmonized format. For the first time, it is possible to integrate suitable models from other scientists into in-house calculations, simulations, and assessments at the push of a button. Also, simulation results are transparent to others, as the used software code and all model parameters are visible to everyone and thus, results can be recalculated.

The FSK-ML information exchange format, which was extended and tested by the BfR under the AGINFRA+ project (2017-2019), allows us o better and more quickly assess human health risks in the future. This means that previously developed predictive models can now quickly be calculated with different simulation scenarios and adapted to fit the issue at hand - whether it concerns the risk of salmonella in fresh eggs or possible transmission of Campylobacter germs from raw chicken breast fillet to green salad in the kitchen. {module INSIDE STORY}

The new FSK-ML data standard also makes it easier for researchers to make their results available in accordance with the FAIR data principles (findability, accessibility, interoperability, and reusability). In particular, the support of the FAIR data principles means that data and information can be found, accessed, and used by different software solutions in a long-term manner.

With the development of the FSK-ML information exchange format, the BfR provides the basis for the future initialization risk assessment. With FSK-ML, software developers in the food safety domain can now easily expand their current and future tools to include new functions for importing and exporting models. FSK-ML also represents the basis for the development of web-based model databases, where researchers from different disciplines can search for established models or even share their own models. One example of such a model database is the 'RAKIP_portal' (https://aginfra.d4science.org/web/rakip_portal/catalogue), developed in the AGINFRA+ project. Models, which can be made available and downloaded via this online platform, can then be used in different software tools on in-house computers or on other online platforms.

The use of FSK-ML models on one's own computer is for example possible by the open-source software named "FSK-Lab" (https://foodrisklabs.bfr.bund.de/fsk-lab/) that was also developed by the BfR. In-house and external models can be imported, exported, edited, joined, and even run with this intuitive software. In this way, each user can set up their own predictions or simulation calculations. There is also an extension named "FSK2R" for the open-source scripting language R, which was previously presented at an international conference (esa.ipb.pt/icpmf11/welcome) in 2019.

Moreover, there are already scientific journals, such as the Food Modelling Journal (FMJ) (https://fmj.pensoft.net/), which enable FSK-ML compliant models to be imported with all relevant metadata. For example, an 'executable model paper' can be automatically generated in the FMJ in this way. The presented model is not only downloaded but is also calculated online with user-defined input parameters. Such innovative digital solutions make a significant contribution to increasing the transparency and reproducibility of scientific work, as the results presented in the article, e.g. in the review process, can be tested effectively. Moreover, the models contain all relevant metadata, such as the range of applicability.