Research Projects

individualised care from early risk of cardiovascular disease to established heart failure

Facilitating Mutual Recognition: Analytics and Capacity Building Information Legal EXplainable Tool to Strengthen Cooperation in the Criminal Matter


Main goals
Judicial cooperation in criminal matters and mutual recognition models in the EU still need to be fully harmonized across Member States. There is a gap between EU law and national law, as protection standards may not have the same substantial meaning in EU law and domestic jurisdictions. Other factors also hamper the effectiveness of mutual recognition instruments, such as diverging legal traditions, linguistic barriers, or scarce access to other States’ domestic case law. Thanks to a multidisciplinary research team in criminal law, IT, and legal informatics, FACILEX aims to fill this gap by tailoring legal knowledge to achieve accurate decision-making. It provides a multilevel online platform grounded on a comprehensive comparative legal analysis, focusing on the implementation of EU mutual recognition instruments in different Member States.

Our contribution
The scientific team at Maastricht University consists of Rohan Nanda and Gijs van Dijck.
Our team will develop multilingual natural language processing tools and resources to harmonize the EU mutual recognition instruments with the National legislation and case law. We will also be involved in the legal research and analysis of the EU Cooperation Mechanisms to deliver a national report for the Netherlands.

Status of Support: Ongoing

Source of Support: European Union (Justice Programme 2022)

Project page
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Real-world-data Enabled Assessment for heaLth regulatory decision-Making


Main goals
The REALM project consortium is composed of 13 European partners and two associated partners. Together they will develop a collaborative framework through which regulatory authorities, software developers, healthcare professionals and policy offers can jointly create and evaluate innovative medical device software – for the direct benefit of patients and healthcare practitioners.
The consortium sets out to develop an innovative and inclusive platform that leads to a transparent ecosystem for the evaluation and certification of software in healthcare where developers as well as regulatory and health technology assessment bodies have access to a standardised set of technology stack and data.

Our contribution
With Distinguished Professor Michel Dumontier as the project coordinator, the team at Maastricht University aims to create a collaborative framework for regulatory authorities, application developers, healthcare professionals, and policy officers. Together they will co-create and evaluate the software for medical and healthcare use. Their work package will focus on Real-World Data and Synthetic Data Repositories for REALM infrastructure.

Status of Support: Ongoing

Source of Support: European Union

Project/Proposal Start and End Date: 01/01/2023 - 31/12/2026

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AI-powered DAta curation & publishing Virtual Assistant


AIDAVA (AI-powered DAta curation & publishing Virtual Assistant) aims to create an AI-powered virtual assistant to maximize the quality of health data for clinical research and care. AIDAVA will develop new AI technologies (knowledge graphs, natural language processing, graph embeddings, explainable AI) to coordinate data work with humans in the loop and apply to personal data in 3 languages with applications to cancer and cardiovascular health.

Our contribution
Michel Dumontier is the PI, and Remzi Celebi is the project coördinator of the AIDAVA project at Maastricht University. 

Status of Support: Ongoing

Project Number: 101057062

PI: Michel Dumontier

Source of Support: Horizon Europe

Project/Proposal Start and End Date: 09/2022-8/2026

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DIgital TEChnologies as an enabler for a conTinuous transformation of food safety system


Major Goals
The aim of the DiTECT proposal is to provide quantifiable evidence, using the latest advances in software technologies and high throughput, rapid, non-invasive sensors for detecting, assessing, and mitigating biological and chemical hazards and environmental contaminants. 

Our contribution
Our team will develop a set of data management tools, including a large-scale data repository to enable the collection, integration, storage, and access to the large quantities of data collected by the analytical and high throughput processes. The scientific team for DiTECT at Maastricht University consists of Christopher Brewster, Michel Dumontier and Remzi Celebi.

Status of Support: Ongoing

Project Number: 861915

Name of PD/PI: George-John Nychas

Source of Support: Horizon 2020

Project/Proposal Start and End Date: 01/2020-12/2024

Project page
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Previous projects

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