In the last decades the microbial food safety community has developed a variety of valuable knowledge (e.g., mathematical models and data) and resources (e.g., databases and software tools) in the areas of quantitative microbial risk assessment (QMRA) and predictive microbiology. However, the reusability of this knowledge and the exchange of information between resources are currently difficult and time consuming. This problem has increased over time due to the lack of harmonized data format and rules for knowledge annotation. It includes the lack of a common understanding of basic terms and concepts and of a harmonized information exchange format to describe and annotate knowledge. The existence of ambiguities and inconsistencies in the use of terms and concepts in the QMRA and predictive microbial (PM) modelling necessitates a consensus on their refinement, which will allow a harmonized exchange of information within these areas. Therefore, this work aims to harmonize terms and concepts used in QMRA and PM modelling spanning from high level concepts as defined by Codex Alimentarius, Food and Agriculture Organization (FAO) and World Health Organization (WHO), up to terms generally used in statistics or data and software science. As a result, a harmonized schema for metadata that allows consistent annotation of data and models from these two domains is proposed. This metadata schema is also a key component of the Food Safety Knowledge Markup Language (FSK-ML), a harmonized format for information exchange between resources in the QMRA and PM modelling domain. This work is carried out within a research project that aims to establish a new community resource called Risk Assessment Modelling and Knowledge Integration Platform (RAKIP). This platform will facilitate the sharing and execution of curated QMRA and PM models using the foundation of the proposed harmonized metadata schema and information exchange format. Furthermore, it will also provide access to related open source software libraries, converter tools and software-specific import and export functions that promote the adoption of FSK-ML by the microbial food safety community. In the future, these resources will hopefully promote both the knowledge reusability and the high-quality information exchange between stakeholders within the areas of QMRA and PM modelling worldwide.

在过去的几十年中，微生物食品安全界已经在定量微生物风险评估（QMRA）和预测微生物学领域开发了各种有价值的知识（例如，数学模型和数据）和资源（例如，数据库和软件工具）。但是，这种知识的可重用性以及资源之间的信息交换目前困难且耗时。由于缺乏统一的数据格式和知识注释规则，随着时间的流逝，此问题日益严重。它包括缺乏对基本术语和概念的共同理解，以及缺乏用于描述和注释知识的统一信息交换格式。在QMRA和预测性微生物（PM）模型中，术语和概念的使用存在歧义和不一致之处，因此有必要就其改进达成共识，这将使这些领域内的信息交流得以协调。因此，这项工作旨在协调QMRA和PM建模中使用的术语和概念，涵盖了由食品法典粮食及农业组织（FAO）和世界卫生组织（WHO），最多使用统计，数据和软件科学中常用的术语。结果，提出了一种元数据的统一模式，该模式允许对来自这两个域的数据和模型进行一致的注释。此元数据模式也是食品安全知识标记语言（FSK-ML）的关键组件，该语言是QMRA和PM建模域中资源之间信息交换的统一格式。这项工作是在一个研究项目中进行的，该项目旨在建立一个称为风险评估建模和知识集成平台（RAKIP）的新社区资源。该平台将使用提议的统一元数据架构和信息交换格式的基础，促进共享和执行策展的QMRA和PM模型。此外，它还将提供对相关开源软件库，转换器工具和特定于软件的导入和导出功能的访问权限，这些功能可促进微生物食品安全界采用FSK-ML。将来，这些资源有望在全球QMRA和PM建模领域促进利益相关者之间的知识可重用性和高质量的信息交换。