Research Data Management (RDM)

Research data management (RDM) includes all activities with digital research data that ensure their permanent availability and comprehensibility. It refers to measures regarding proper data handling throughout the whole research data lifecyle – from planninig a research project, data collection and documentation, to storage, sharing and publication of research data. Good RDM practices should contribute to reusable research data that enables new, innovative research based on already existing information and should support traceability and verification of previous research results. When it comes to RDM, a so called Data Management Plan (DMP) is the method of choice. A DMP is a structured document that describes how research data will be handled during and after the project end.

Duration:  3:50 mins

Content: The video gives a short introduction to the concept of “research data management”. 

Ghent University Data Stewards (2020). Knowledge clip: What is Research Data Management (RDM)? Available at:

License:  CC BY 4.0

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 dataManagement 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 lifecycle. So our DM 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 in 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 webpages (


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Further Information

Comprehensive information about research data management on the website

  • Event Series “Research Data Management in Auastria”

The event series “Research Data Management in Austria” is aimed at researchers and / or people involved in research support and serves to promote networking and exchange on the topic of research data management, writing a data management plan and similar related topics. Below you find domain specific material (slides and videos) regarding Research Data Management:

  • Hönegger, Lisa (9th March 2021). Research Data Management in the Social Sciences: RDM basics, challenges and support. [Presentation. Webinar for the event Research Data Management in Social Sciences]. Handle: 11353/10.1168878
  • Medical University of Graz und BioTechMed-Graz (8th June 2021). Research Data Management (RDM) in the Life Sciences: From Writing DMPs to RDM Practices and RDM Support. [Slides. Research Data Management in the Life Sciences]. Handle: 11353/10.1206156
  • Medical University of Graz und BioTechMed-Graz (8th June 2021). Research Data Management (RDM) in the Life Sciences: From Writing DMPs to RDM Practices and RDM Support. [Video. Research Data Management in the Life Sciences]. Handle: 11353/10.1202593
  • Train-the-Trainer Concept on Research Data Management provided by FDMentor

The train-the-trainer program on research data management (RDM) comprises the aspects of research data management as well as didactic units on learning concepts, workshop design, and a range of didactic methods

Biernacka, Katarzyna, Bierwirth, Maik, Buchholz, Petra, Dolzycka, Dominika, Helbig, Kerstin, Neumann, Janna, … Wuttke, Ulrike. (2020). Train-the-Trainer Concept on Research Data Management (Version 3.0). Zenodo. (from p. 36 on)

  • Research Data Management and Science Europe

  • Practical Guide to the International Alignment of Research Data Management – Extended Edition provided by Science Europe

This resource offers targeted guidance for organisations, scientific communities, as well as individual researchers, to organise research data and preserve it appropriately. Originally released in 2019, and following its successful uptake by many organisations, the extended edition features a brand-new rubric to facilitate the evaluation of a data management plan (DMP). The guide also presents core requirements for DMPs, criteria for the selection of trustworthy repositories, and guidance for researchers to comply with organisational requirements.


FAIR Data Austria (2021). “Research data management (RDM)“. In: Research Data Management Open Educational Resources Collection. (

License: CC BY 4.0 unless otherwise stated.