Why research data management (RDM)?

In the first parts of the blog series we covered, what is RDM (research data management). In this post, we will trace the details behind the changes in the research process related to RDM and explain why it is increasingly important to understand the significance of RDM.

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Text: Tanja Lindholm

A lot has changed during the past decades, and by no doubt the major changes have been the spread of digital tools facilitating the research environments, the development of the world-wide web and the growth of data storage and computing – this has been the beginning of the so-called digital revolution (Fig. 1).

The digital era has brought many new opportunities for research and researchers in the form of new tools, large storing capacities as well as easy sharing. On the other hand, the change has brought many new challenges and responsibilities. Therefore, the importance of research data management has grown.

Let’s take a brief look at what has changed and what the changes mean, using a rough comparison for a field work at present time and early 2000s.

In the early 2000s, our fieldwork tools were e.g. pen, paper, analogue or digital cameras. While pen and paper still exist today, our main tools in many places are mobile phones and related apps. Fifteen to twenty years ago, our data was stored in memory cards, USB sticks, external hard drives and whatever cheap and large enough systems were available at that time to store our data. At present, we can hold the data in phones or transfer it to a different system or a colleague almost anytime and anywhere in the world.

In both cases, it is easy to think of different kinds of worst-case scenarios. In the early 2000s, data was easily lost if it existed only, for example, on external hard drives. At the time, commercial and self-made storing and sharing solutions were employed because institutes could not follow the rapid development of safe storing solutions. Today, when an online connection is available almost everywhere you are, sensitive data collected or kept in phones, can spread around the world in minutes. Nowadays, most institutes provide good and safe solutions! There is no excuse to use unsafe or homemade solutions anymore.

New policies and laws follow the digital era and for a good reason. At the same time, our research data needs to be FAIR (findable, accessible, interoperable, re-usable), open and traceable. Thinking of the digital changes and the demands of responsible science,  research is not what it used to be. We produce vast amount of data, but a minor topic in public discussion as well as researcher training is, what do we do with it and how to handle it, what to keep and what to delete and so on, has remained a minor topic. Therefore, we need comprehensive research data management – not only to keep up with the legal and ethical issues, but also to better manage large amounts of data. The systematic practices resulting from proper planning also save time, money and effort, when everyone working in a project are following the same plan.

In the next part of this blog series, we will take a closer look at why it is important to plan RDM ahead.

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? 5) How to manage (your) research data? 6) Where is the help and support for research data management?