In the last decades a large number of models have been developed in the quantitative microbial risk assessment (QMRA) and predictive microbiology (PM) domains. These models were generated with different scripting languages (e.g. R, MATLAB), commercial tools (e.g. @Risk) or even proprietary software tools (e.g. FSSP, FDA-iRISK). The heterogeneity in software tools used to generate models together with the lack of a harmonized model exchange format has made the (re)-use of existing models in different software tools or simulation environments very difficult. The adoption of a harmonized information exchange format called Food Safety Knowledge Markup Language (FSK-ML) would be a solution to this challenge. FSK-ML defines a framework for encoding all relevant data, metadata and model scripts in a machine-readable format. A specific feature of FSK-ML is that it allows the user to provide model scripts in different scripting languages (e.g. R, Perl, Python or MATLAB). Model metadata can be provided in accordance with the metadata schema and controlled vocabularies proposed from the Risk Assessment Knowledge Integration Platform (RAKIP) community.

In order to achieve a broad adoption of FSK-ML by the scientific community it is however of extraordinary importance to provide support in terms of easy to use software solutions. In fact, it has to be as simple as possible for the end-user to create, export, import or modify standard-compliant files. Food Safety Knowledge Lab (FSK-Lab) represents such a user-friendly software tool that can create, read (import), write (export), execute and combine FSK-ML compliant objects. All metadata needed to annotate a model can also be entered and edited through FSK-Lab. It also allows generating (export) files that comply with FSK-ML and that carry the file extension “.fskx”. This ensures that all information on a model is contained in this information exchange file and that the user does not have to write and compile FSK-ML files “by hand”.

FSK-Lab extends the open source Konstanz Information Miner (KNIME) data analytics platform (URL: www.knime.org), which is a graphical programming framework allowing users to create data analysis workflows from building blocks (so called nodes). Within FSK-Lab, each node has a specific task e.g. model creation can be performed with the “FSK Creator” node. As a KNIME extension, FSK-Lab allows the user to execute and integrate code from several programming languages, like Java, R, Python. All FSK-Lab software code is freely available under the GNU public license version 3.

在过去的几十年中，在定量微生物风险评估（QMRA）和预测微生物学（PM）领域开发了许多模型。这些模型是使用不同的脚本语言（例如R，MATLAB），商业工具（例如@Risk）甚至专有软件工具（例如FSSP，FDA-iRISK）生成的。用于生成模型的软件工具的异构性以及缺乏统一的模型交换格式，使得在不同的软件工具或仿真环境中重新使用现有模型非常困难。采用称为食品安全知识标记语言（FSK-ML）的统一信息交换格式将是应对这一挑战的解决方案。FSK-ML定义了一种框架，用于以机器可读格式对所有相关数据，元数据和模型脚本进行编码。FSK-ML的一个特殊功能是它允许用户以不同的脚本语言（例如R，Perl，Python或MATLAB）提供模型脚本。可以根据风险评估知识集成平台（RAKIP）社区提出的元数据模式和受控词汇表提供模型元数据。

为了使科学界广泛采用FSK-ML，在易于使用的软件解决方案方面提供支持非常重要。实际上，最终用户创建，导出，导入或修改符合标准的文件必须尽可能地简单。食品安全知识实验室（FSK-Lab）代表了这样一种用户友好的软件工具，该工具可以创建，读取（导入），写入（导出），执行和组合FSK-ML兼容对象。注释模型所需的所有元数据也可以通过FSK-Lab输入和编辑。它还允许生成（导出）符合FSK-ML且带有文件扩展名“ .fskx”的文件。这样可确保有关模型的所有信息都包含在此信息交换文件中，并且用户不必“手动”编写和编译FSK-ML文件。

FSK-Lab扩展了开源的Konstanz Information Miner（KNIME）数据分析平台（URL：www.knime.org），该平台是一个图形编程框架，允许用户从构件（所谓的节点）创建数据分析工作流。在FSK-Lab中，每个节点都有特定的任务，例如，可以使用“ FSK Creator”节点执行模型创建。作为KNIME扩展，FSK-Lab允许用户执行和集成来自多种编程语言（如Java，R，Python）的代码。所有FSK-Lab软件代码均可在GNU公共许可证版本3下免费获得。