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Data Management Plan

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Data Management Plan (DMP)

Writing a data management plan (DMP) when you start your research can help to ensure you achieve a standard of transparency and integrity in your research. In a DMP you describe the data you plan to collect, generate and use for your research, whether it is data you create yourself of existing data created by someone else. You describe in a structured way how you will manage those data during and after your research. You write about:

  • how the data were created and what they mean
  • safe storage of data so they can’t go missing or be tampered with
  • data security so that only people allowed to access the data can access them
  • publishing the data as evidence for your published papers (unless there are restrictions to do so), so that your findings can be verified and reproduced
  • individual and institutional responsibilities to look after your data
  • safeguards for ethical and privacy reasons if research involves human participants

Good data management does not end with planning. It is important that your research data are then managed according to this plan. You can review and update the plan according to the progress of the research (it’s a living document). After your research has ended your DMP will form the permanent track record for the data your research produced.

Key issues to find out when you start writing a data management plan are:

  • know your institution’s policies and services, such as storage and backup strategy, intellectual property rights policy, data management policy and any data sharing facilities like an institutional repository
  • ownership of your data
  • your legal, ethical and other obligations regarding research data, towards research participants, colleagues, research funders and your institution

Here are some examples where good data management plans can help achieve research integrity:

  • Documenting in detail how your data were created and processed provides clear evidence, for example:
    • a lab book describes the experimental set up and all parameters that define your data, show the processes used to collect the data and gives and overview of all data collected
    • an interview schedule and question list describe the collection of information via interviews, with a referenced codebook showing your interpretation of the interview content during your analysis
    • commentary lines in computer code describe the logic of what your code does step by step.
  • The licence agreement and use conditions of third party data explain how you can or cannot use third party data in your research, for example you may be allowed to use data for analysis but not copy and publish them ad you should cite the data you use (like you would cite a publication) whereby it is important to check the ownership of those data.
  • In research with human participants, documenting the informed consent procedures used in your research and the personal data you may collect helps to plan which level of security is needed when storing and handling the data and how data need to be anonymised to respect the privacy of people.
  • Publishing your research data in a FAIR way gives transparency about how you reached your research findings bases on those data.
  • If other researchers want to reproduce your results, they need to be able to access the data and any documentation that clearly explains how the data were generated and how to interpret them. Data can for example be made available openly in a data repository with a clear use licence.
Local, interdisciplinary and international collaborations

In collaborative research, it is important to describe the planned data management practices at each partner organisation, and to have a dedicated person at each site responsible for data management. In international collaborations, it can be that there are differences in the ethical and legal framework for research, or the expectation of institutions or funders for data management. Developing a data management plan can help to ensure that all these aspects are addressed before data collection starts.