Responsible research data management (RDM)

Please note:
In the academic year 2023-2024 the RDM course will take place in this setting for the last time in October/November 2023. As from 2024, RDM will be integrated in the course 'Open science and research transparency'.

Increasingly, researchers are expected to make their research data available, in such a way that it is “as open as possible and as closed as necessary”. Additionally, having a data management plan is required by many stakeholders like funding agencies, ethical review boards, or journals.

But what does this mean, and how can you put this into practice? What are the practical consequences of research data management (RDM) and how do you handle personal/sensitive data and take care of long-term preservation, access, and reuse of your data? These and related questions about RDM are the topic of this workshop.

We will discuss best practices for documenting, organizing, sharing, and archiving your research data, including personal data and working in international collaboration settings. You will practice with writing a README and learn how to write or improve the data management plans for your own research projects.

Effectively managing data can help to optimise research outputs, increase the impact of your research, and support open scientific inquiry. After completing this workshop, PhD candidates will be better equipped to manage their data throughout the entire research data lifecycle, from the stage of project planning to the end.

After completion of this workshop, you will be able to:

• identify legal and privacy issues to be addressed in your research and data management plans;
• apply best practices to document, organize, share, and archive your research data;
• explain the FAIR principles and the importance of metadata for research;
• complete data management plans for your research;
• and find available information and support for research data management.

Besides having some prior experience with research data (could also be in the Bachelor/Master phase), there are no specific entry requirements for this course. For PhD candidates, we recommend that you follow this course in the first year of their project, if possible. This will help implement the best practices in RDM from the start of your project and to avoid overlap with other RDM trainings that you might follow during your PhD.

Before the first session:

1. Read the following article: Briney, K., Coates, H., & Goben, A. (2020). Foundational Practices of Research Data Management. Research Ideas and Outcomes, 6, e56508. https://doi.org/10.3897/rio.6.e56508 (Open Access)

2. Suggested reading for researchers working with qualitative data: DuBois, J. M., Strait, M., & Walsh, H. (2018). Is it time to share qualitative research data?. Qualitative Psychology, 5(3), 380. https://doi.org/10.1037/qup0000076 (Open Access)

Before the second session (can already be done before the first session, if you like):

3. Try to reflect on possible research data management, privacy, and legal issues that you might encounter in your PhD research projects (or which you already encountered). Having examined potential issues before the second session will help you make the most out of this session and to implement the best practices being taught

4. Take the short training module on Privacy in Research: https://maken.wikiwijs.nl/125518/Privacy_in__Research