Innovation Intelligence FVG - A FAIRification Process

Innovation Intelligence FVG is a project developed by Area Science Park that integrates heterogeneous datasets, differing in both type and source, in order to describe the innovation capabilities of companies operating in the Friuli Venezia Giulia region. The system consists of data processing pipelines, an SQL database, and a dashboard interface. Despite the overall quality and value of the platform, the current infrastructure presents several limitations related to system complexity, fragmentation of data processing workflows, and lack of structured documentation. These issues represent the main challenge addressed in this work, which aims to improve the system while preserving the know-how and operational logic developed over time. This thesis presents the development of a FAIR-oriented proof of concept (PoC) designed to address these limitations through the application of the FAIR principles — Findability, Accessibility, Interoperability, and Reusabil ity. The proposed PoC includes modular Python-based data pipelines, au tomated DCAT-compliant metadata generation, provenance tracking mech anisms, semantic crosswalks based on external ontologies, and a lightweight web architecture developed using Django, SQLite, and Docker containeriza tion. The resulting mockup demonstrates how a FAIR-oriented framework can improve data management processes by enhancing organization, interop erability, transparency, reproducibility, and long-term maintainability, while preserving compatibility with the existing infrastructure. Although the im plementation does not achieve full FAIR compliance, the work highlights both the potential and the practical challenges of FAIRification processes in complex data systems.