IS research support



How to manage your research data

You plan! Engaging in data planning early on in your project can save you time and effort later. The decisions you make at this stage will affect how you collect, use, share and publish your data, and can prove highly beneficial in the longer term. The plan you make should stay with you throughout your project and form the basis of the documentation you'll need when you submit your data to a repository.

What to include

What you want to include in your data management plan may vary, depending on your research discipline. It would usually cover:

  • the type of data you expect to create or use
  • how you will record your data, and where you will keep it
  • how you will organise your data and keep it safe
  • what data you will keep after your project
  • how you will share your data

Writing your plan

Once you have thought through your project, these tools and templates can help you to capture the information and keep it up to date throughout your project.

  • Tools and templates from the Digital Curation Centre (DCC)
  • DMPonline: a tool to help you write an effective data management plan, from bid-preparation to completion.

Plan to organise your data

Organising your data in a structured and practical way makes it easier to find and keep track of. Whether they contain data, recordings, interview responses, or drafts of papers, lots of files can easily get out of control.

A key part of a data management plan is making it a useful document, not just other piece of paper. Consider what files you are you going to have, and how you will manage them. Try thinking about:

File names

Meaningful file names help you know the content and status of a file.

  • Use terms like project acronyms, researcher’s initials, or information that describes the type of file
  • Add version numbers, file status, or a date
  • Keep file names short
  • Don't use spaces or special characters

File structure

Think about the best hierarchy for files. Should they be organised by:

  • type of data: text, dataset, images
  • research activities: interviews, surveys
  • or type of material: documentation, publications, data?

Making an effective hierarchy and sticking to it will help you reliably find files in the future, and know exactly where files should go as your project progresses.

Distinguishing between different versions of your files

Version control helps you distinguish between different iterations of your work, so that you can find correct versions as needed. You should decide:

  • how many copies of a file you need to keep
  • how long you need to keep them
  • how you will tell each version apart, for example by using a consistent naming convention (see above)

If you store files in various places, you'll need to remember to synchronise the copies regularly. You can get tools that automatically do this for you, such as Microsoft OneDrive. Make sure you maintain single master, or milestone, versions of files in a suitable format and store them in a single location.

Look after your non-digital data

Hard copy data, like laboratory notebooks, journals or consent forms, are at risk if not managed properly, so you should include them in your plan. Depending on the scale of materials, consider digitising them by scanning or taking a digital photo.

If that's not practical, you can protect material in other ways, such as using a fireproof filing cabinet.

Manage access to your data

You may want to restrict access to your data during your project - for example if the data is commercially sensitive or you are concerned about Intellectual Property (IP).

If so, you have to consider carefully how to store your data to prevent unauthorised access.

Keep your data safe

Data can be lost in many different ways: through human error, hardware failure, software or media faults, or malicious hacking and virus infection. Digital data files can also be corrupted in storage or through file transfer.

How you can protect your data:

Give your data context

Keep information to help you interpret your data and give it context in the short and long term.

Record when the data was collected and by whom. Keep this information with the data files wherever possible. This will also help when you come to store or share your data.

This documentation may be:

  • study-level information, such as technical reports, working papers, or laboratory notebooks
  • data level information, which may be embedded in the data
  • metadata, which you can apply to your data when entering it on to a web platform, such as a catalogue or institutional repository.

Create a ReadMe file

When you come to the end of a project, it's important to remember how you got there. Whether you decide to upload your data to a repository or not, it's useful to take the time to create a ReadMe file.

ReadMe files explained: read this page for an example ReadMe file and outline.

A ReadMe file is so-called because in the process of reusing your data, you should look at this file first. Its intent is to describe everything someone would need to know to replicate the data, or to use it and understand it properly. In the context of a data repository, a ReadMe file will also:

  • give people viewing your data context and information to understand it
  • help viewers to understand how and why the data was created, and under what circumstances
  • ensure other researchers can use your data in the correct context
  • verify your data collection methodology

Writing a ReadMe file isn't difficult, especially if you have an up-to-date data management plan, as both files contain the same key elements.


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Last Updated: 18/03/2019