Spotlight on: Eduard Klapwijk

Every other week, the Thematic DCCs and the Data Steward Interest Group (DSIG) put the spotlight on one research data steward working in the Netherlands, to stimulate knowledge exchange and peer-to-peer learning.

What drew you towards the research data management field?

During my PhD in the early 2010s, I became increasingly disillusioned by the focus on papers, papers, and more papers. I wished we could focus instead on producing less but more meaningful, methodologically robust research results. But at the time, I did not manage to change my own research practices for the better. I then left academia rather disillusioned, and tried my luck as a management consultant for one and a half year (while finishing my dissertation). Yet I missed doing research, and I was lucky enough to get a postdoc position in the lab of Eveline Crone. She has been a great mentor who gave me the opportunity to develop many new skills needed for open and reproducible science. Helping colleagues within the lab to work more reproducible was something I really enjoyed, as well as producing tools for other researchers. I realized I might enjoy a supporting role in which I could be more effective in improving research practices. That is why I tried my luck as a research data steward early 2021, and I do not regret it!

What is an activity/task of your role that you find yourself looking forward to?

The part I enjoy the most about my job is when I can create something useful and practical for researchers. This can range from a practical piece of advice for an individual researcher to making a short presentation for one of our EUR Data Coffee Breaks, and from creating and sharing a short script for automating data handling to producing materials to make research data policies digestible (such as this infographic about publication packages).

What is something unexpected that you can offer help with, if a colleague reaches out to you?

In addition to supporting data management, I can also offer support for writing reproducible analysis code. Currently, I am developing a workflow for reproducibility checks at my faculty, which focuses on computational reproducibility of both data and code. This is greatly supported by a fellowship from the Netherlands eScience Center.

What do you think your community of research data professionals is missing?

Many colleagues in the spotlight have mentioned this before, but I think a more formal and extensive training curriculum for data stewards would be very helpful. As part of this, I would especially welcome more training focused on applying controlled vocabularies and variable-level metadata in practice.

What is a topic you would want to collaborate on with others?

I am definitely open to team up on more training activities. Luckily, I am already collaborating with a group of great colleagues on training a Data Carpentry for Social Sciences across institutions. With a colleague at EUR, I am currently developing a workshop about publication packages for social sciences (see the online materials in progress). If you are interested in collaborating, please let me know!

Could you point us to a resource, learning platform, tool or similar which you find useful or inspirational?

I love the Brain Imaging Data Structure (BIDS). It is a great example of a community driven project: many people worked together to create a specification that transformed the way to handle neuroimaging data. Before the BIDS specification I worked with a lot of datasets that were messy in their own peculiar way. Thanks to BIDS, all those complex datasets can now be structured very neatly in a uniform way. It gives me peace of mind just to look at such well-organized datasets. But more importantly, it has made data processing and analysing in the neuroimaging field much easier, more reproducible and less error-prone because analysis software is now build with the BIDS specification in mind.

Get in touch with Eduard: LinkedIn, Orcid, and Github

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