Research Data Management 101

This course provides PhD candidates with the essential knowledge and the core skills to manage research data according to best practice. Learners will be able to integrate good data management practices within their workflows from the beginning of their projects. The application of this knowledge to their research will allow them to reflect on how to work efficiently and in a reproducible manner with their research data, while complying with funders and institutional requirements.

This course is particularly useful for PhD Candidates in their first year who require a hands-on introduction to Research Data Management (RDM) and Data Management Plans (DMPs). You do not need any prior knowledge to take this course.

Please find a detailed description of the course on the Graduate School website.


Courses academic year 2023 - 2024

Friday 3 November until Thursday (incl.) 23 November 2023.

In person classes (mandatory):

Monday 06.11. 2023 14:00 – 15:30 - TBC 

Tuesday 14.11.2023 14:00-16:00 - blue room

Thursday 23.11.2023 14:00 - 15:30 - blue room


Relevant information

  • This is a three weeks blended course, meaning that is a mix of self-studying and online/face-to-face class meetings (which are mandatory).  
  • The time to be spent for the course is approximately 16 hours in total, which is equivalent  to 1.5 GS credits in the category of Research skills of the GS Education Programme, if the requirements are fulfilled (more information here).
  • The online learning environment is Brightspace. Participants will get instructions on how to access Brightspace four days before the starting date of the course.

The registration to the course is via Coachview, the course registration application of the Graduate School Doctoral Education (GS DE) programme.


Looking for a self-learning option? 

If you prefer a self-learning option, or if you are a 3rd or 4th year PhD candidate and you are interested in learning about RDM, we suggest you to learn about it through the following resource:  

Important: no GS credits are provided for PhD candidates following this self-learning resource.