EEPA offers FAIR data stewardship trainings, specifically focused on the practical implementation of FAIR data architectures for sensitive health and humanitarian data. The training is organised in collaboration with VODAN, the African University Network of FAIR Open Science (AUNFOS) and the Africa Health Data Space. 

What is FAIR data stewardship?

Our programme follows the FAIR-OLR principles, which stand for Findable, Accessible, Interoperable and Reusable, while taking into account data Ownership, Localisation and Regulatory Compliance. This framework is designed to improve the sharing and usability of research data, enhancing collaboration and innovation in various fields, whilst promoting ethical data use and data sovereignty, which practices that safeguard sensitive data.

Who is the training meant for?

The training is intended for those working with sensitive or humanitarian data and have some experience in data management, data science, computer science, ICT, archive, or informatics. Whilst the training is relatively basic, some of the concepts might be difficult to understand for those who have no knowledge in data science.

Duration and certification

The training comprises a three-months weekly training of two-hours. Participants are expected to spend an additional 2 hours each week working on assignments. The training provides a certificate for 6 ECT issued by AUN-FOS.

Contents of the training

The training will cover the following topics:

  • FAIR principles and ethics: Understanding the foundational concepts of FAIR data and the ethical implications of data stewardship
  • Setting up Common Data Models: Techniques for establishing standardised data models to ensure consistency across datasets
  • Machine-actionable data capture: Methods for collecting and inputting data in machine-actionable format for easy access, analysis and reuse
  • Ontologies and the creation of new concepts: Introduction to ontologies and how to develop new concepts for improved data representation
  • Data transformation: Strategies for converting data into usable formats that align with FAIR principles
  • Access conditions: Exploring various protocols and conditions governing data accessibility
  • RDF and triple stores: Understanding the Resource Description Framework (RDF) and how to use triple stores for managing linked data
  • Dashboard creation: Techniques for building interactive dashboards to visualize and analyze data effectively
  • Linked analysis in Linked Data Store: Methods for performing linked data analysis within a linked data environment
  • Setting up your FAIR Data Point: Guidelines for establishing an accessible and compliant FAIR data point for hosting and sharing datasets
  • Use cases
Click here to register for our 2026 training

Additional information

For additional information on previous training sessions that we have hosted, please visit the following pages: