How do you apply data science to your projects?
I work on quite diverse topics - from human behaviour simulations, to study movement patterns, to investigating the complex interplay between insomnia and depression. While the topics are very diverse, I often apply similar data science techniques. I often work on one topic in a particular discipline and learn about a new technique or data science approach that inspires me in different projects as well.
Is there a project from this past year that you are most proud of?
Last year I set-up the human behaviour simulation lab. With this lab we aim to measure, explain, and model human behaviour to find substantiated and practical solutions to societal questions. Last year we ran a study at the NEMO Science Museum where we collected movement data and ran an experiment on how to promote sustainable behaviour. The study was unique because of its multidisciplinary character and was made into a documentary!
What do you like most about being a DSC member?
I was surprised how much I share with other data scientists, even though we work on vastly different topics. In multidisciplinary research it is often challenging to communicate with people from different departments, even when you are working on the same topic.
Here, it is the other way around: I often understand very little about the topics everyone is working on, but we can still brainstorm about the data problems at hand through our common language of data science. These conversations are generally full of possibilities and new ways forward. That feels very exciting!
What is your favourite data science method?
I really enjoy conceptualising a research question and matching it to an optimal data science method. Over the last years I have often worked with network analysis across various topics - from social dynamics to psychological networks. I find it interesting to learn more on how a similar technique is applied in different disciplines, which can be very inspiring and result in new research ideas.
Are you camp Python/R/or something else?
I have done things in Python that would have taken me ages (if at all possible) in R, and vice versa. I am more proficient in R, but hope to become a full member of both “camps” some day!