Guide to data management

To collect and manage research data is more than just saving data on a hard drive. Data management is relevant trough out the project - planning, collecting data, publishing and archiving in a repository.

Data management is relevant in all phases of a research project and it is important to consider the management and organization of research data from the very beginning as this will save time later on. In addition, good research data management is an essential part of research integrity as defined in the Danish Code of Conduct for Research Integrity.

DTU Library offers support in the planning, organizing and sharing of research data. This guide will provide information on most aspects related to data management and cover the entire Data Life Cycle.

Data Life Cycle

The "Data Life Cycle" is a simple model that illustrates the different aspects of research data management and their relation to the stages of a typical research project:

  • Planning and start-up and
    In the start-up and planning phase of a research project, you need to consider aspects such as data management plans, funder requirements, copyright, ethics and use of data.
     
  • Research and analysis
    During your research project and in the analysis phase, you need to consider aspects in relation to documentation, storage, back up, safety issues and access to data.
     
  • Preservation and sharing
    Towards the end of the project, you need to consider issues such as long-term preservation, data repositories and data publication.

In reality, research processes are rarely as linear as illustrated here but the model summarizes the most important aspects of data management that researchers are faced with at different times.

Contact

E-mail:
datamanagement@dtu.dk

Phone:
45 25 72 50

Research data at DTU

Research at DTU is very diverse, often even within the same research group. Nonetheless, common practices for research data management exist across different departments - depending on the type of research that is being conducted.

See the following examples for an illustration of how different types of research data are typically being handled across the Data Life Cycle: