Research Projects

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 standards of protection may not have the same substantial meaning in EU law and in 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 for the harmonization of 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 with the aim of delivering a national report for the Netherlands.
 

Status of Support: Ongoing

Source of Support: European Union (Justice Programme 2022)

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
The project is coordinated by Distinguished Professor Michel Dumontier. The team at Maastricht University t aims to create a collaborative framework for regulatory authorities, application developers, healthcare professionals, and policy officers to 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

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 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

Biomedical Data Translator

Major Goals
The Biomedical Data Translator is a multi-year NIH/NCATS-supported project for the development of a comprehensive Biomedical Data Translator that integrates multiple types of existing data sources, including objective signs and symptoms of disease, drug effects, and intervening types of biological data relevant to understanding pathophysiology and developing treatments. 

Our contribution
Our team (Remzi Celebi, Michel Dumontier, Vincent Emonet and Arif Yilmaz) creates a machine-learning-based drug repositioning tool and a tool to author and contribute structured facts to the Translator ecosystem as well contributing to the core architecture.  

Status of Support: Ongoing

Project Number: OT2TR003434-01S2

Name of PD/PI: Chunhua Weng, Casey Ta, Michel Dumontier

Source of Support: NIH/NCATS

Project/Proposal Start and End Date: 01/2022-11/2023

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

Knowledge Graphs at Scale

Major Goals
KnowGraphs is an H2020 Marie Skłodowska-Curie Innovative Training Network to tackle the problem of scaling up the use of knowledge graphs. 15 Early Stage Researchers (ESRs) will address challenges pertaining to the representation, creation, management, and utilization of knowledge graphs. 7 beneficiaries and 7 associated partners will facilitate the career development of the ESRs through mentoring, workshops, winter schools, and dedicated training. We will train 2 ESRs in the network on KG metadata and reuse.

Our contribution
Michel Dumontier is the PI for the project and Remzi Celebi, Christopher Brewster, Özge Erten, Maryam Mohammadi (ESR's) and Vincent Emonet are the project's team members at Maastricht University.

Status of Support: Ongoing

Project Number: 860801

Name of PD/PI: Axel Ngonga Ngomo

Source of Support: Horizon 2020

Project/Proposal Start and End Date: 01/2020-12/2023

European Open Science Cloud for the Life Sciences

EOSC-Life is an H2020/EOSC-funded initiative that brings together the 13 Life Science ‘ESFRI’ research infrastructures (LS RIs) to create an open, digital and collaborative space for biological and medical research.

The project will publish ‘FAIR’ data and a catalogue of services provided by participating RIs for the management, storage and reuse of data in the European Open Science Cloud (EOSC). This space will be accessible to European research communities.

Our contribution
Michel Dumontier is involved in WP6 Task 1: developing a wizard-like tool to inform different kinds of users on the FAIR principles.

Status of Support: Ongoing

Project Number: 824087

Name of PD/PI: Elixir

Source of Support: Horizon 2020

Project/Proposal Start and End Date: 03/2019-06/2023

Open Data Infrastructure for Social Science and Economic Innovations

Main goals
The main goal of the project is to develop a synthetic data generator framework using artificial intelligence technologies while concurrently exploring ethical-legal perspectives in the trade-off between data privacy and the potential utilization of synthetic representations. We will study 1) the quality of synthetically generated data to real world data as a function of privacy cost, 2) the quality of preservation of multi-attribute relations in the face of increased individual variation, and 3) the utility of synthetic data in certain kinds of social science research.

Our contribution
ODISSEI's project team at Maastricht University consists of Michel Dumontier (task leader), Chang Sun, Birgit Wouters and Carlos Utrilla Guerrero.