I would hazard a guess that lying in bed at night you do not spend much time thinking about how you can make your research more multi-disciplinary? But should you?
What is multi-disciplinary research?
First, let's define what it means for your research to be multi-disciplinary. EU consortium project proposals always require you to show how your consortium is multi-disciplinary and how that is important to the project you are proposing. It often seems too straightforward of a question to know how to answer that in a project proposal. Yes, it is because we have multiple disciplines as partners in a consortium. At one point, I was concerned about the difference between multi-disciplinary and interdisciplinary. If you look at these two words up online, you will get pretty much the same definition. The way I understood the difference at the time was that multi-disciplinary refers to the composition of a team or a group while interdisciplinary is about doing work that requires input from multiple disciplines. When you read lots of call topic texts and spend time designing projects and writing proposals, the difference between these two words can begin to seem like inconsequential semantics.
Emergence of multiple disciplines
In the video below, Nature plots the network defined by their publications over their long publishing history.
What is interesting is that at the start there were no disciplines. Over time more and more disciplines emerged.
When you look at individual papers such as Watson and Crick's original manuscript on the structure of DNA, you see that papers from multiple disciplines are cited in that paper and multiple disciplines cite that paper. Is the network of citations a sufficient amount of multi-disciplinarity?
Or as suggested in the EU grant proposal guidance, should we be working in multi-disciplinary projects?
Or should it be interdisciplinary, hinting at the need to work together on the same problems?
Interdisciplinary projects are difficult. Different disciplines speak a different language. This is apparent in projects that revolve around data, which these days are almost every project.
How much about data science should a translational researcher understand and how much about translational research should a data scientist understand?
You only have to read a few email threads between data scientists and translational researchers to appreciate that there is a substantial gap in understanding, and no amount of email is going to bridge that gap. When translational researchers interact with data sciences and can 'see' what they mean, they gain an appreciation of what is possible.
Is how can you interact more with other disciplines to solve problems together a better question to ask?