Why plan research data management in advance?

Every aspect of research data management (RDM) is equally important and all parts of the process need to be properly discussed and planned. Poor data management planning (DMP) is a risk that can potentially cause problems for the research project. This fourth section of the ”Know your data” article series highlights the importance of planning ahead.

(Tämä artikkeli on saatavilla myös suomeksi.)

Text: Tanja Lindholm & Liisa Siipilehto

In the first section of the article series, we mentioned that RDM covers the mechanical, managerial, and technical handling of research data, and problems in any part of the RDM life cycle can significantly harm the research. Therefore, a lack of forward planning can lead to serious trouble along the way. Data is a researcher’s most valuable asset – if you lose it, you can say goodbye to your research. There are also several other aspects to how a failure to plan ahead can affect the whole research process.

Data is a researcher’s most valuable asset – if you lose it, you can say goodbye to your research.

It is not enough to just think about the kind of data you are going to use, especially in situations where you have collaborators and/or you are using existing non-published data. It is crucial that you consider the different aspects of data management early on in the application stage.

The importance of planning ahead – an example

Think of a situation where you and a collaborator from a different institution (both with valuable long-term data) have received a positive funding decision. As you have mentioned in the application, you plan to make a written agreement about the data. You then realise that there is much more to consider than just the length of time each party can use the data. You then need to consider, for example, what part of the data is going to be published, as well as where, when, and under what license. You also realise that you both see things differently and there are several aspects of the project that you don’t fully understand. After six months, you have finally come up with some kind of mutual agreement, and although it doesn’t completely fulfill the funder’s open policy demands, it is something you can live with. You are ready to sign the agreement, but then there are some extra demands from one of the institutions. Hence, the agreement is sent from one lawyer to another and this takes time, as they are busy. After almost a year, everything is agreed and you can finally share the data with each other. Now, this situation that has delayed your research has been caused by a fairly simple thing that could have been thought about during the application process.

A lack of forward planning may have a more severe effect on your data and  these problems generally only appear at the end of the project.

This is just one example of what might happen – and actually has happened. A lack of forward planning may have a more severe effect on your data and  these problems generally only appear at the end of the project. For example, issues can arise when data is opened with no agreements in place. Not having proper agreements may lead to various conflicts during the project.

Link DMP into the research planning

Each part of RDM is equally important and they all need to be fully discussed and prepared, preferably at the earliest stage of planning. You should link data management planning (DMP) into the research planning, covering every stage from the experimental phase to publication. Viewing it from this perspective, you should not wait for the positive funding decision to begin your planning. A proper plan prepared in advance will ensure that your most valuable asset – research data – is safe during and after the project.

   Research Data Management – know your data!

Research data management (RDM) is a crucial part of any research. First and foremost, the aim of RDM is to make the research process as efficient as possible. Second, RDM will help you meet the expectations and requirements of your organisation and research funders. RDM skills are fundamental skills for researchers and they apply to everyone who handles data for research projects. By learning RDM, you get to KNOW YOUR DATA!

In this series, the University of Helsinki Data Support team introduces the key components of RDM and data management planning (DMP): what they are, why they are important, and where to look for more help with RDMP (research data management planning) issues. The series comprises six parts:

1) What is research data management (RDM)? (3.9.2020)
2) The components of research data management (17.9.2020)
3) Why research data management? (30.9.2020)
4) Why plan research data management in advance? (22.10.2020)
5) Effective research data management? – DMP to the rescue! (19.11.2020)
6) Where is the help and support for research data management? (8.12.2020)