Research Data Management in (Bioscience) Engineering and Natural Sciences – a practical training course


Research & Valorization

Target Group

Doctoral students of the Doctoral School of (Bioscience) Engineering and Natural Sciences, no foreknowledge required.


This in-depth course will help doctoral students to develop their knowledge and practical skills in handling and managing the research data they collect. Having these skills becomes increasingly important to researchers seeking to advance their careers. The lecturer will guide the attendees through the key aspects of how to manage, document, store and safeguard research data well and how to plan and implement good data management in research projects.

Learning outcomes of the course

Upon completion of this course, students should have an understanding of what Research Data Management is, what it all comprises, and why it is important in academic research.
They should have an understanding of the FAIR data principles, and how they can make data more FAIR. They should be able to successfully manage all types of research data and to document both the research itself, as well as the data in a comprehensive way.

Students should be able to comply to the UGent and funders’ policies with regard to RDM- and DMP (Data Management Plan) requirements. They should also be fully aware how to use UGent infrastructure for RDM related tasks, and able to work with data in a secure way (both in terms of physical storage as in methods to safeguard sensitive/personal data).

Topic of the course

Essential key-concepts and skills in Research Data Management(RDM) will tackled. This hands-on workshop will focus on all kinds of data (both qualitative and quantitative) and cover the following aspects:

  • Introduction: Why and how to manage research data?
  • What is FAIR data? (Findable, Accessible, Interoperable, Reusable)
  • Planning: How to plan your research data management and write a data management plan?
  • Documenting: How to make research data and data processing understandable and reusable?
  • Storage: Strategies for storing data during and after the project.
  • Security: How to safeguard your data?
  • Organisation & structure: Strategies for naming, organising and structuring your data files.
  • Data Sharing & Open Science: How to share research data? Introduction to open science.
  • Ethical and legal issues in data sharing and handling confidential information.

Organizing Committee & Lecturers

Thomas Van de Velde, Myriam Mertens, Jan Lammertyn, Laura Standaert, Paula Oset, Stefanie De Bodt (Data Stewards @ Boekentoren - DOZA)

Time schedule

Research Data Management in BSE and NS

Lecturer: Stefanie De Bodt and Paula Oset Garcia 


21/04/2022 13:30-17:30 + 22/04/2022 09:00-13:00


Registration fee

Free of charge for Doctoral School members. The no show policy applies.


Members of the Doctoral School of (Bioscience) Engineering, follow this link
Members of the Doctoral School of Natural Sciences, follow this link

Teaching and learning material

Lecture combined with practical exercises. Presentation slides.

Number of participants

Maximum 25



Evaluation methods and criteria (doctoral training programme)

100 % participation