Why plan research data management in advance?

All parts of RDM are equally important and all of them needs to be properly discussed and planned. Poor data management planning involves risks that can cause problems for the research project. The fourth part of the Know your data article series highlights the importance of planning ahead.

(Tämä artikkeli on saatavilla myös suomeksi.)

Text: Tanja Lindholm & Liisa Siipilehto

As mentioned already in the first part of the article series, RDM (research data management) covers mechanical, managerial and technical handling of research data and problems in any part of the RDM life cycle can cause serious damage to the research. Thus, not planning ahead might lead into serious trouble along the way, since research data is researchers most valuable asset – by losing it, you can say goodbye to your research. There are several other aspects how not planning ahead might affect the whole research process.

Just thinking about what kind of data you are going to use is just not enough, especially in cases where you have collaborators and/or you use existing non published data. It is crucial to consider different aspects of data management already at the application stage.

Think about a case where you and your collaborator from different institutes (both with valuable long-term data) have received positive funding decision. As you have mentioned in the application, you are going to make written agreements about the data. You realize that there is much more to consider than just how long each party can use the data. You need to consider for example what part of the data is going to be published, where, when and under what license. You also realize that you see things differently and there are several aspects you really do not know anything about. After six months, you have finally come up with some kind of mutual agreement that does not fulfill the funders’ open policy demands completely, but this is something you can live with. You are ready to sign the agreement, but there are some extra demands from one of the institutes. Hence, the agreement is send from one lawyer to another and since they are busy, this takes time as well. After almost a year, everything is agreed and you can finally share the data with each other. Now, this fairly simple thing that could have been thought already while applying, has delayed your research by almost a year.

This is just one example of what might happen – and what actually has happened. Not planning ahead might have much more severe effects on your data and they usually appear at the end of the project, for example, while opening the data, when there are no agreements. Not having proper agreements, might cause all kind of friction during the project.

All parts of RDM are equally important and all of them needs to be properly discussed and planned, preferably at the planning stage. You should link data management planning into research planning, from an experimental phase into publication. When thinking from this perspective, planning after the positive funding decision is already too late. With proper planning ahead, you make sure 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 – secondly, it will help you to meet the expectations and requirements of your organization and research funders. RDM skills are basic researchers skills and they apply to everyone who handles research data in a research project. By learning RDM you get to KNOW YOUR DATA!

In this series, the University of Helsinki Data Support team introduces all key components related to RDM and DMP; what they are, why they are important and where to look for further help in RDMP 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?


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