COST Action CA24145
Working Group 4
Data infrastructure & management
The overall goal of WG4 is to design a robust, open-access data infrastructure that enables the storage, management, and reuse of standardised techno-functionality data generated within INFOTECH-DATA. WG4 describes the digital and data-science backbone of the Action by translating experimental and conceptual outcomes from other Working Groups into interoperable, FAIR-compliant data assets for analysis, comparison, and predictive modelling. By developing database architectures, metadata schemas, data ingestion pipelines, and quality-control mechanisms, WG4 ensures that techno-functionality data becomes findable, accessible, interoperable, and reusable across the European food science community and beyond. The infrastructure developed within WG4 will not only support the immediate objectives of INFOTECH-DATA, but will also provide a sustainable foundation for long-term data-driven research, cross-domain integration, and predictive decision-making in food innovation.
SUB-GOALS
01- State-of-the-art analysis
WG4 will systematically analyse the state of the art in data infrastructures relevant to food techno-functionality, including existing data repositories, database architectures, ontologies, metadata standards, and data governance approaches used within and beyond food science. This analysis will identify best practices, reusable components, and existing standards that can be adopted or adapted within INFOTECH-DATA, while also revealing gaps and limitations that necessitate new solutions. WG4 aims to avoid redundant development, maximise interoperability with external data resources, and ensure that the INFOTECH-DATA infrastructure is compatible with current and future data ecosystems in food science, data science, and related domains.
02-Metamodel
The second sub-goal of WG4 is to define and formalise a metadata model that captures the essential contextual information required to ensure the reusability, comparability, and interpretability of techno-functionality data. This includes translating domain knowledge from food science into structured metadata elements that reflect ingredient properties, processing conditions, and measurement settings, while maintaining sufficient generalizability.
03- Data infrastructure
WG4 will implement an open-access, FAIR-compliant data infrastructure to enable the structured storage of techno-functional data and associated metadata. This infrastructure supports interoperability, methodology versioning, data quality control, and scalable data growth suitable for academic data science.
04- Case studies
The last sub-goal is to describe case studies for demonstrating the scientific and technological value of the data infrastructure by enabling proof-of-concept predictive modelling and data-driven analysis of techno-functional properties. By linking curated datasets to basic predictive models, WG4 will illustrate how standardised data can support informed decision-making in food formulation and lay the groundwork for more advanced data science and machine learning approaches beyond the duration of the Action.