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Research Data Management: Hei Tīmata | Getting Started

Information about research data management plans and how to create one

What is research data management?

Research Data Management is the handling of research data throughout the research lifecycle, from conception, collection and ethical considerations through to storage, access and sharing, publication and potential reuse. All research uses data, and research data includes a wide range of information types such as:

  • Spreadsheets
  • Laboratory notebooks or field notes
  • Questionnaires, transcripts, codebooks
  • Audio or video files
  • Protein or genetic sequences
  • Slides, artifacts, specimens or samples
  • Models, algorithms, code or scripts
  • Manuscripts, sheet music, stage notes, researcher notes

Management of research data usually takes on the form of a Data Management Plan (DMP). There are some online tools that can help you to create your own DMP, but remember that your research, and your data, is unique to your research project. Practically, this means that not every question in these tools is going to have an answer from you about your data. It is also important to remember that your data may change, include some new sources, data types and that this means it is in your best interest to think through the possibilities when creating your DMP even if you don't believe they have bearing on your current intended use of data. A few typical tools for creating a DMP include:

It is important to note that these tools do not have questions about Indigenous Data Sovereignty and as such you will need to consult the section in our guide for help with those aspects.


Proper research data management is a requirement for many funders. The Ministry of Business Innovation and Employment (MBIE), for example, is one of the largest research funders in New Zealand and requires a DMP as part of the funding process. You can find more information about MBIE and other funder policies here:

Apart from being a requirement for many funders, having a DMP can solve many issues before they become problems. Lack of data management planning can result in the loss of data or even the violation of people's privacy. 

Until recently, data management has often been done last minute or using the first method found without subject, data types, access and sharing and many other factors taken into consideration. This can cause the process to be time-consuming and prone to errors. Working through a DMP at the start of the research lifecycle, and then amending it as data management or research needs change, can save time and help you anticipate and mitigate problems.


Apart from mitigating potential issues and saving you time by being a useful tool to think ahead, DMPs can have other benefits. These include, but are not limited to:

  • Making research project management easier
  • Allows budgets to take data storage into account
  • Transparency and accountability
  • Making your data more usable and shareable by others
  • Allows your data to more easily be used as a research output to boost your researcher profile

Need Help?

For assistance, reach out to the Open Research Team at