Because good research needs good data

The data management plan – what is the purpose with it?

Rosa Lönneborg, Research data coordinator, KTH Royal Institute of Technology | 12 November 2021

A few years ago I became research data coordinator at KTH.  One of the first projects for the research data team was to find out how to provide KTH with a tool for writing data management plans (DMPs). The incentive for this was mainly due to increasing requirements from funding agencies on having a DMP in funded projects. In a survey sent out on a national level to researchers at Swedish universities, a majority of the respondents’ thought a DMP could be useful whereas about 20% saw a DMP as useless and/or unnecessary administrative task.

One free-text comment from the national survey that stuck with me was the comment that all type of documentation should have a clear purpose. So, what is the purpose of having a data management plan?

It seemed to me from discussions with different stakeholders that there were different perspectives on what the purpose of it was. In the research data team, we had some discussions on how a DMP could be helpful as a way to collect information that made it possible for the research support to improve the current support and services.

To fulfil a requirement to get funded is also a kind of purpose, but one that will seem rather pointless and hardly motivating. However, the requirements from the funding agencies on DMPs is probably the primary reason that gets most people to actually put down the time to draft a plan (while swearing over the unwanted administration).  But behind these requirements lies a reasoning (I hope!) that making a plan actually could be useful for a deeper purpose; that of improving data management by planning for it early on. Improved data management then results in more well-documented research, enabling good practices for reproducible, high-quality science in those areas that are data-driven (not all research relies that heavy on “data” though, so the usefulness varies). 

I don’t believe drafting a DMP does any magic in itself for improving data management, and if done quickly while swearing probably have very little, if any, desired effect. But I believe that drafting a DMP in combination with a) understanding the purpose of it and b) actually using the DMP during the research project will have an actual positive effect in improving data management.

To explain why a DMP could be useful and how to draft one, we arrange workshops for researchers that provides an opportunity of having a dialogue on what is important for data management and encourage people to actually use a DMP. If you are actually using the DMP, there is often a need to update it during the active research process, since plans tend to change along the way in such a complex activity as scientific research. This is why I now see versioning as a key functionality for a DMP tool.

So at least in my point of view, explaining why and provide good support on how to make a DMP is important for making a DMP useful for real. And the DMP is just a piece of the puzzle to obtain good research data management.  This means there is still a lot of potential for improvement – which is the major reason why the work I do still feels meaningful and fun now a few years later :)

We would like to say thanks to Rosa for sharing with us this blog post for DMPonline knowledge exchange series!

As always, we are keen to hear from you about how you use the tool, how RDM works at your institution and fits within your workflows and also how we can improve it, so please feel free to contact us at the details below: 

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