Research data management

The amount of research data is growing exponentially and the ability to administer and exploit research data efficiently is a requirement to meet DTU’s ambition of being among the top five technical universities in Europe.

Furthermore, research institutions are facing more and more external demands for storage and sharing of research data regarding accessibility, data security and research integrity.

Good Research Data Management is one prerequisite for ensuring that the outcomes of research are reliable, credible and reproducible. Data management should be accounted for in all stages of a research project - in the planning phase, during the active research and while publishing results, and even after the project is finished.

Research Data Management spans many different areas, including:

Data Collection

Research data can be many different things, depending on the type of research they are used for – e.g. observational data, experimental data, simulation data or processed data – and come in a variety of different types and formats – such as tables, documents, audio and video recordings, methods, algorithms and software, real-time data, big data, smart data and many, many more. Some data might also be sensitive or confidential and require special care.

Data Storage

Most researchers use the IT infrastructure provided by their institution for storing and sharing their data with collaborators. However, in some cases special solutions might be needed, in particular when the amounts of data are very large or when additional security requirements need to be fulfilled. Frequent and reliable backups are crucial at any stage of the research avoid data loss.

Data Sharing

Research data is very valuable and of high interest for others in the scientific community and the society at large. When research is funded by public money, the methods should be transparent and the outcomes should be made available for everyone. Sharing data will enable reuse and stimulate new research projects. Data sets can be published in special journals and repositories and can – in the same way as regular articles – be acknowledged and cited and thereby increase the visibility of your work.


Documentation of data is often considered time-consuming and costly. However, documentation also means adding value to the data and making it usable in a broader sense. Any file or data set should contain metadata describing its origin, that is when, where, how and by whom it was created. Good documentation is a prerequisite for making research reproducible and complying with the Danish Code of Conduct for Research Integrity.

Long-term Preservation

There is a high risk of data getting lost when a project finishes or the researcher who collected the data leaves the institution. This would mean a big waste of time, money and knowledge. Choosing which data should be preserved for a longer time and making sure that it is readable and understandable is a major challenge but also a rewarding investment.


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