FAIR Data Science

We study how medical data conform to Open Science's FAIR guidelines: Findable, Accessible, Interoperable, and Reusable. We also examine the use of patient data and sensitive data from vulnerable populations.

Aim of the research

The research aims to enhance decision-making in healthcare by creating federated AI-ready, privacy-preserving data pipelines. The focus is on antenatal care (ANC), infectious diseases, and neglected diseases, with an emphasis on data from the humanitarian sector and vulnerable communities. The project involves technical investigations into data engineering, access-controls and meaningful permission on the data reuse with full privacy preservation and secure data analytics, utilizing FAIR Data Points and Stations. The research is carried out within the Value-driven Ownership of Data Accountability Network (VODAN), using linked data repositories. The research focuses on data-handling across countries and continents, and has a special focus on collaboration with Africa, Asia and the Global South. The goal is to empower individuals and organizations to control their data, fostering trust in data-driven practices.

Group members

  • Miriam van Reisen (Group leader)
  • Ruduan Plug
  • Aliya Aktau
  • Putu Hadi Purnama Jati
  • Samson Yohannes Amare
  • Tesfit Gebremeskil
  • Bereket Godifay
  • Kokob Gebru Kidanu
  • Jimmy Ben Forry
  • Rens Kievit

Partners / collaborators

  • Philips
  • GAIC Research Network
  • VODAN
  • EEPA
  • Africa University Network on FAIR Open Science

Selected publications