R4.A1 Research Data management 101
In preparation, you need to go through Modules 0 (introduction) and 1 before the first virtual class.
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 reproducibly with research data, while complying with funder and institutional requirements.
Mandatory activities in the course:
- Complete the data flow map assignments
- Attend the three class sessions (remote)
- Engage in the weekly discussions
- Complete all topics of the course
After this course, the students are able to:
- Realize the important role that good data management plays in research
- Identify different types of research data and recognize the regulations, policies and/or legal requirements associated with them.
- List the main components of the FAIR data principles and connect them to their own research workflows.
- Employ the acquired knowledge to design an efficient research data management strategy for their projects according to best practices.
There are six self-paced modules and three remote class sessions (on Zoom).
Module 1: The importance of RDM (Self-study online and virtual class session)
Module 2: Essentials for research data (Self-study online)
Module 3: FAIR data principles and their main elements (Self-study online)
Module 4: Realizing FAIR data (Self-study online and virtual class session)
Module 5: How to plan for research data management (Self-study online)
Module 6: Wrap-up (Virtual class session)
Course registration of Graduate School Doctoral Education (GS DE) courses is via Coachview.
For questions and information please contact TU Delft Graduate School via: