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Common European Data Spaces and Robust AI for Transparent Public Governance

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The world is currently confronting unprecedented challenges, encompassing a global pandemic, ongoing conflicts, and environmental and energy crises. In response to these crises, there has been a significant surge in emergency public spending, accompanied by a relaxation of due diligence checks and oversight.
Consequently, this has given rise to patterns of corruption and fraudulent activities, exerting profound and detrimental impacts on the European economy, society, environment, and politics.

CEDAR sees these challenges as catalysts for research, innovation, and collaboration.

GOAL 1 - IDENTIFY

GOAL 1 - IDENTIFY

Collect, generate, harmonise, synchronise, protect, and share new large-scale, high-quality, high-value datasets.

GOAL 2 - BUILD

GOAL 2 - BUILD

Improve methods and tools for effective data management and machine learning (ML) operations (DataOps, MLOps).

GOAL 3 - DEVELOP

GOAL 3 - DEVELOP

New data analytics and machine learning methods for robust, data-efficient, human-centric, and well-informed decision making.

GOAL 4 - VALIDATE

GOAL 4 - VALIDATE

Promote CEDAR results with relevant public and private stakeholders and generate direct and tangible impacts on the European economy, society, and environment.

Articles

Cedar now featured in the latest HiPEAC magazine (June 2026 Data & AI special issue) with the article "Transforming EU governance The CEDAR project and the dawn of a data-driven public procurement era"
This code of practice supports compliance with the AI Act transparency obligations related to marking and labelling of AI-generated content.
Enter the Data Lifecycle Cluster, a collaborative initiative dedicated to automating the full data lifecycle to enable trusted and interoperable European Data Spaces.
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