In response to the escalating global conflicts, predictive models have become extremely important for peacekeeping initiatives. The proliferation of “Hybrid Threats,” including terrorism and unconventional warfare, necessitates innovative strategies for enhancing peace and security. This paper outlines a collaborative effort with the United Nations Global Service Center (UNGSC) to develop a predictive tool for domestic conflicts in Africa, using diverse open-source datasets. The study employs data engineering, visualization, and integration techniques to explore commonalities and discrepancies among the datasets. Notably, data inconsistencies emerge, underscoring the significance of verifying information sources. While horizontal and vertical data integration possibilities are identified, challenges related to data anomalies and miscommunication are highlighted. To build a reliable predictive model, rigorous data analysis, expert insights, and a multidimensional approach are indispensable, ultimately contributing to conflict prevention and sustainable peacekeeping.

Over the last decade, the importance of FAIR principles as a reference for data reusability and openness has increased constantly. Various tools for FAIR data assessment exist, including manual approaches like the FAIR Data Self-Assessment Tool (SAT) and automated tools as the FAIR Evaluation Services and Generic Automatic Tool (GAT). However, subjectivity in manual assessment and limited guidance in automated tools represent significant drawbacks. In such a context, in 2020, the European Commission introduced the “European Data Strategy” to create a unified data market, preserving European competitiveness and data sovereignty. Common European data spaces facilitate access to data in various sectors and promote convergence of data infrastructures and regulations, with a focus on interconnection and interoperability, complying to international standards, such as INSPIRE and FAIR principles, to achieve a unified EU data space. In this work, we propose a tool called CkanFAIR for the automatic assessment of dataset FAIRness in PA portals. The tool incorporates the European Data Quality Guidelines for a more comprehensive dataset evaluation and implements the euFAIR metrics. With this tool, we aim to improve the quality and sharing of data in a reliable environment that can pave the way to the creation of a data space fully compliant with FAIR principles, as well as conforming to community standards in PAs portals. From a wider perspective, it could also signify the initial phase in embracing FAIR Digital Twins (FDT). To validate our results, we compared CkanFAIR with corresponding SAT and GAT, highlighting significant differences.