 What is research data management? Research data management, or RDM, is a broad term encompassing all practices and actions to ensure that research data are secure, sustainable, easy to find, understand and reuse. But what does that actually mean? Let's dissect research data management. It consists of two concepts, research data and management. So, what are research data? It is hard to come up with one definition for research data, because it is highly domain and context specific. Therefore, we refer to research data as any information collected or generated for the purpose of analysis, in order to generate or validate scientific claims. There is a huge variety of data types. Research data can be classified in different ways, for example based on their content. Numerical, textual, multimedia, etc. Based on their format. Spreadsheets, databases, images, maps, audio files. Or based on the collection mode, such as experimental data, observational, simulation, or derived or compiled from other sources. Or, for example, its digital or non-digital nature. Or its primary or secondary character. Has the data been generated by the researchers for a specific purpose? Or was it originally created by someone else for other purpose? Finally, is the data raw or processed? Keep in mind that besides the research data itself, RDM also extends to managing documentation needed to make those data understandable. Now what is the management of research data? Management refers to activities or actions such as planning, collecting and organizing data, documenting and describing. Storing and backing up and preserving, sharing and controlling access to research data. These actions take place at different phases of what we call the research data life cycle. So RDM is about taking proper care of data, not only during, but also after research, so that data is preserved and can be used in the longer term. Research data are not just a byproduct of scientific research, nor a simple means to article publication. On the contrary, research data should be cared for as first class research objects. And RDM is about exactly that. Two concepts related to research data management are fair and open. Fair stands for findable, accessible, interoperable and reusable. With good RDM practices, we aim to make data fair, and as open as possible but as closed as necessary. Implementing good RDM practices can initially take some effort and time. But it also yields significant benefits for yourself, the research community, and society at large. No wonder RDM is increasingly being considered an essential part of good research practice. Good reasons for properly managing and sharing research data range from more selfish, pragmatic reasons, to more altruistic reasons. Think for instance of minimizing the risk of losing valuable data. Or increasing your research efficiency and the impact and visibility of your research. But also, accelerating scientific discovery and living up to the principle that publicly funded research is a public good. Do you want to know more? Why not have a look at our web pages?