General information on data management plans

A data management plan (DMP) describes the handling of research data within a research project. Typically, a DMP contains information on

  • which data is generated in the project and how it is collected, generated or reused,
  • how data is described,
  • how data is processed and stored,
  • how data is archived and reused,
  • and which resources are required for this.

It makes sense to prepare a DMP in parallel to project planning or application submission. Funding bodies increasingly request DMPs.

The creation of a DMP does not have to be time-consuming and helps you to maintain an overview and to avoid risks such as data loss, accusations of scientific misconduct, data protection violations or problems with exploitation rights.

We have compiled a short list of recommendations and further material below to support you in creating your own data management plan. If you still have questions or need further support, please do not hesitate to contact us.

Recommendation for DMP creation

  • Plan your RDM early, i.e. before collection to be able to align with legislation and policies. Think about which regulations and policies affect your RDM. Do you need an ethics vote or a consent of participants?
  • Think about a data collection strategy. What data is needed in which quality for answering the research questions? List the different types of data you expect for the project. For each type of data, will it be reused, collected or created? And how? Which processing steps are required? How can you ensure adequate data quality and monitor it?
  • Plan tools/services to use per datatype. Also consider how data will be stored, secured and preserved during the project and long-term. As a minimum consider: Does any type of data have a volume that can’t be handled reasonably with your regular available IT systems? If something fails, will you have a backup? Who requires access to the data? Does access need authorization procedures? How can we realize long-term storage for relevant data? Your institution may have regulations where to store your data and may provide adequate services.
  • Work on a comprehensive documentation as metadata. Write down all information a researcher like you would need to discover, understand, access and use your data. Complete this metadata over time in the project. At the end, there should be at least a written explanation of the data, i.e. what does it mean? An information on the original collection purpose. If applicable, information on methods for creation. Information about the creators, about the date/period of creation, the data’s structure and, if applicable, needed software/tools to work with the data.
  • Think about which standards and open file formats for data and metadata are sensible in your project. Are you confident, that you could use your data in this format in 10 years? If you use standard tools provided by your organization, this point may be easy. Also community standards or recommendations by data repositories where you could publish a certain type of data are good choices.
  • If applicable, i.e. if you expect (shared) working directories that can get messy, think about strategies to keep them clean (e.g. folder and naming structures, rules for removing deprecated data etc.)
  • Define under which conditions do you plan to share which data. Can these conditions be expressed with common licenses? If there is data that can’t be shared, why? If applicable, does the consent form for study participants address data sharing? Do you prefer to wait with sharing/publishing data for your own publication or for a graduate’s degree?
  • Define roles, responsibilities and resources. Think about who is responsible for which data-related tasks, e.g., data collection, quality control, publication and which resources are needed for these tasks (e.g. money, time, tools)? What happens if one of these responsible persons leaves your institution, will there be problems?
  • Write your plans regarding the above aspects down. This is your data management plan. Keep this information up to date. Maybe even set regular dates for self-monitoring, i.e. for checking and improving your RDM processes and for updating your DMP.
  • If you are required to provide a DMP for somebody, use the appropriate DMP-template.

Material

You can find further information on the website forschungsdaten.info.

 

Question catalogs

Question catalogs can help to think of typical important questions or to specifically address all the questions specified by a funding body. The following are recommended as compact question catalogs

 

Tools

There are tools that can help with the creation of DMPs. They allow you to work your way through structured input masks and sometimes provide customized questionnaires and templates for specific funding bodies. The use of such tools is a matter of taste and probably makes more sense for more extensive DMP templates, e.g. for some EU funding lines. You can find a good overview at forschungsdaten.info. With your MHH ID, you can use the RDMO tool (named GRO Plan) via the Academic Cloud.