Number of sessions: 2
Hours per session: 4 hours (session 1), 3 hours (session 2)
- free for PhD candidates of the Graduate School
- €400,- for non-members
- Consult our enrolment policy for more information.
In the academic year 2022-2023 this course will take place online
October 4 2022
October 11 2022
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 participants should have done the following:
- Take the short training module on Privacy in Research: https://maken.wikiwijs.nl/125518/Privacy_in__Research
- 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)
- Respond to a short survey about data management in your project. The survey will be sent to you by email shortly after the enrolment deadline.
- 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.apa.org/fulltext/2017-12030-001.html (Open Access)
- Try to reflect on possible privacy, legal and research data management issues that you might encounter in your PhD research projects (or which you already encountered). Having examined potential issues before starting the course will help you make the most out of this course and to implement the best practices being taught.
The Erasmus University Library has developed several e-learning modules on, among other subjects, responsible research data management. These modules consist of short videos, activating exercises and explanatory texts. The e-modules are complementary to our existing courses on information skills:
- Doing the literature review
- Searching and managing your literature
- Visual exploration of scientific literature with VOSviewer
Eduard Klapwijk is a Research Data Steward at Erasmus University as well as a postdoc in the university’s SYNC lab. He advises and supports researchers on how to organise their research data effectively in accordance with international standards and develops and implements research data management guidelines with the aim to make research more open and reproducible. His main focus as a postdoc at Erasmus University is to determine the optimal sample size in developmental studies for a range of fMRI tasks.
Nils Arlinghaus is the Data Steward Coordinator at Erasmus Research Services (ERS). His primary task is the coordination of the data steward team at EUR, as well as working on the EUR-wide efforts to move research practice towards FAIR data and open & responsible science. As a part of the pool of data stewards, he also advises and supports researchers directly.