Why is planning of research data management useful?

Data management planning is not only for meeting expectations and policies of institutions and funding agencies. You should consider the Data Management Plan as a help to plan things, that will come up later in the research process and  as a checklist, that helps you not to forget something concerning research data. While planning, keep the whole Research Data Lifecycle in mind.

Data Management Plan (DMP)

Nearly all granting agencies make good practice in research data management a condition of their funding programs. This means that all project proposals should include a data management section, that briefly outlines how research data will be handled during the project. But a data management plan is useful for purposes beyond merely satisfying the requirements of the research funder. It also helps individual researchers, institutes and research groups to manage data effectively as well as to reduce the risk of data loss or other threats that could render the data illegible or unusable (e.g. the obsolescence of software).

A DMP typically provides project-specific information on the following topics:

  • Data collection and organisation
  • Ethical, legal, security issue
  •  Data exchange and re-use
  • Data storage, preservation

You will find detailed recommendations and best practices on all of the above topics on this website. For quick guidance on writing a DMP, see below for our checklists and other recommended documents, websites and examples.

On the following subpages you will find more information on the requirements of research funders as well as information on the main principles and concepts and a short introduction to data organisation and documentation.


Research-data-life-cycle

The research data life cycle describes different stages of research data management along the research cycle. Since this can vary depending on the subject area, there are also different versions of the research data life cycle, where the exact terms used for the stages vary. For most disciplines, the stages of the life cycle can be summarised as follows:

  • Plan and fund
  • Collect and analyse
  • preserve and store
  • publish and share
  • discover and reuse

This subdivision serves as a guide to describe where certain data management tasks occur.