Data Management

How and when to plan your data handling. The guide contains a list of key elements to consider when you create a data management plan.

Last modified: 04.04.2022

Data management is about optimal handling of research data through its entire life cycle. From initial planning to collection and processing of data, to final publication, archiving, or destruction of data.

The typical research data life cycle is as follows:

  • Planning: During the planning phase, the focus is on identifying any requirements for data management, and documenting how it should be handled. At the same time, it covers the process of screening if there are any data policies on behalf of collaborating partners that need clarification - e.g. demands for Open Access, clarification of data rights, relevant legislation etc.
  • Collecting, analysing, and processing: Focus on facilities for working with data, security, and documentation of data by means of meta data, etc.
  • Archiving: Long-term storage of data is not only important to you as researcher, but also to archives, universities and others, who might have interest in your data.
  • Publishing: Data can be made available on many different platforms (repositories), with varying terms regarding access, rights etc. The possibility of assigning DOI, or other types of persistent identifiers for data sets for use in articles etc., is also crucial to ensure retrieval of the data.

The research process is to a large degree an iterative process, wherefore the above points can move around in circles several times. Do not despair if your data handling does not fit into this process model.

Video introduction - what is Research data management

Vlachos, E., Larsen, A.V., Zurcher, S., Hansen, A.F. (2019). ‘Introduction’. In: Holmstrand, K.F., den Boer, S.P.A., Vlachos, E., Martínez-Lavanchy, P.M., Hansen, K.K. (Eds.),  Research Data Management (eLearning course). doi: 10.11581/dtu:00000048




In addition to the above life cycle, data management generally focuses on a number of themes that cut across the research data life cycle:

  • Backup and recovery: What are the possibilities for data backup, and how can data be recovered in the event of data loss?
  • Ethics and privacy: There is a significant focus on how to secure personal data, when working with personally sensitive data, and on which ethical and legal requirements that can be applied. In connection with publication, there is special focus on how to secure the individual through depersonalization of data.
  • Referencing and citation: The need to be able to refer to your own as well as others' data is particularly important in connection with publication of data and articles. It requires both that your data are available on a platform that enables referencing, and that the data are stored in a format that makes referencing possible.
  • Security: Protection of data concerns setting up correct mechanisms to ensure that only the right people have access to the data, and that any requirements for logging of access are respected.
  • Responsibilities and roles: Who is responsible for securing good data practice and entering into agreements with suppliers of service etc.? This involves considerations on who is to take charge of the administration of data once a project is concluded etc.?
  • Economy: Planning, costs for converting data, space for archiving etc. are all expenses that may strain the budget of a research project. In order to illustrate the expenses for data management, it is important to have an overview of the data life cycle.
  • IPR and licensing: Who has the rights to the data, which license applies to the data, and who has the authority to give others access to your data?
  • File formats: In connection with publication and archiving, it is recommended that data are stored in open non-proprietary formats. For example, storing tabular data in CSV, pictures in JPG, etc.
  • Integrity and quality assurance: What type of data documentation is available that may illustrate the process of data management, and how is it available? Which conventions for naming, folder structures, etc. are currently used?
  • Meta data: Which meta data (data about data) accompany the research data to make it understandable and retrievable?

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