Data management planning
The University's Research Data Policy requires researchers to submit a plan as part of the proposal for all research projects.
What should a data management plan cover?
Writing a data management plan typically involves answering a series of questions about how you plan to create, describe, secure, retain and share your data. There are tools and templates available to help you structure your plan, with questions or topics specific to your funder's requirements.
Your plan should be concise and appropriate to the nature of your research, with more detailed plans for larger projects. You should justify the decisions you make and be prepared to implement your plan. You can also update your plan once your project has started to reflect changes in your research.
If you've not written a data management plan before, it can be helpful to look at what a good example should look like. The Digital Curation Centre maintains a list of example data management plans or you can contact RDM@napier.ac.uk for some Edinburgh Napier examples.
Details on what a DMP should include can be obtained from several sources, including, but not limited to:
In summary, a data management plan should typically consider the following topics:
What data will you create or re-use?
Are you reusing data?
What types of new data will you create?
What format and why? Recommended formatting from UK data service.
Estimate the size of the data you'll create.
What methods will you use to capture data and how will these ensure that the data are high quality?
How will you document and describe your data?
What contextual information is needed for you or someone else to understand your data?
Are there any standards that you will use? The Digital Curation Centre maintains a list of metadata standards for different disciplines.
How will you protect your data and those associated with your research?
Where will you store your data?
How will you secure your data?
How will you protect your research participants?
Will you obtain informed consent for data retention and sharing?
How will you anonymise data to safeguard the privacy of your participants?
Which data will you retain and preserve after your project ends?
Which subsets of your data will you keep at the end of your project?
Will you retain all of the raw data or is a processed version more suitable to preserve?
How will you prepare your data for long-term preservation?
What contextual information do you need to retain so that your data remain understandable and usable?
Where will you archive your data to ensure that they are preserved and sustained for several years after your project ends?
How big will your final dataset be and will there be any costs associated with archiving them, such as data deposit charges?
What are your plans for data sharing?
Can you demonstrate that you'll plan ahead to maximise data sharing?
Are there any reasons why you would not be able to share some of your data?
- Would they be covered by the Data Protection Act, licence restrictions or contractual confidentiality clauses?
- Are there ethical reasons why data should not be released?
When will you share your data?
How will you share your data?
How will you disseminate your research? Will you include a data access statement in published articles?
Does your chosen method of data preservation provide a persistent URL such as a Digital Object Identifier?
What licences will you assign to your data for sharing?