Write your data management plan

A research data management plan helps you keep control and be ready for every stage. Most funders expect to see a plan when you apply for a grant. Major funders specifiy templates for the plan but, where there isn't a template or you are not applying for external funding, there are general principles to follow.

Data management plans online

DMP Online has been developed by the Digital Curation Centre to help you write data management plans. Our University of Kent membership provides a customised service for our researchers.  

Guidance for writing your data management plan

The topics listed below are the typical questions you may be asked, how to respond to them and why.  

Data summary

Example questions:

  • What existing data sources will the research project use? Provide references.
  • What types of data will the research create? Why did you decide to use these data types?

Existing data sources

Funders expect you to have considered existing data for reuse.

  • Describe any data you will be reusing and any intellectual property, copyright or general reuse conditions and preparations you need to make. 
  • Include references to the data sources. 
  • If not reusing data, you may need to explain why, and demonstrate any searches you have done to establish that useful data doesn't already exist.

Data types and formats

What sort of data will you create, what formats will you be working with and why did you choose them?

  • Describe the content, coverage and data type (for example tabular data, survey data, experimental measurements, models, software, audiovisual data, physical samples) for all the data developed during your project. 
  • This does not include literature search strategies or references.

For each type of data, describe the data and the proposed file formats.

During the project, while you're collecting and processing the data:

  • These may be the formats created by software (for example Excel – xls; Word - docx, or digital image formats - raw), or analogue formats (for example handwritten notebooks, physical objects). 
  • Will you move the data during the project and will you change the format?

After the project, when the data are archived:

  • Consider access for and sharing within the project team and by external users who will reuse the archived data.
  • Physical or analogue data can be recorded or photographed or otherwise converted to a digital format - see formats for optimum long-term preservation and sharing.

Why these types and formats?

  • Describe how you chose data types and formats in the context of how suitable they are for answering the research question. 
  • How will new data be compatible with existing data?
  • For each data type outline the open or standard format you will use to archive the data and describe when you will convert the data to that format.

Non-digital data:

If your data is created in a physical or non-digital format describe how you will convert it to a digital format for processing, storage and online sharing?

Data collection

Example questions:

  • What methodologies will you use to create the data?
  • How will the selected project team be suitable for the data and digital aspects of the work?
  • Include how the University’s data support teams may need to support the project.

Data gathering

For each data type, describe the software or processes you will use to gather the data, for example:

  • questionnaires: use Online Surveys - you need to get an account first
  • structured interviews: describe how you will ensure consistent interview technique, record the responses and create transcriptions
  • workshops: explain how these will be organised and what activities you will use to generate data from them. How will this be recorded?

Organisation and analysis

Describe the processes and software you will use to analyse and organize the data:

  • How will you process the data? What software will you use?
  • What standards will you use to make sure the data is accurate? How will you control and document the consistency and quality of the data during collection and processing?  Consider codes of practice for your discipline as well as technical solutions like CheckSums (used while transferring digital files).
  • How will the data be managed during the project. What naming conventions and folder structure will you use to arrange the data in a logical and understandable way?
  • What documentation will you create to make sure the project team follow the standards and protocols you describe?

Project team

  • If the collection of data is carried out by many different people or is removed from the place it will be analysed, explain how the data will be transported and collated.
  • Explain how the project team are qualified to carry out the data collection.

Institutional support

Explain what support you will have from the University. This may include support for software or for handling very large or complex data sets.

The Information Services research support team can help with any technical requirements, for example:

  • new or upgraded software and hardware
  • a website or new online functionality
  • data gathering or analysis.

Short-term data storage

Example questions:

  • How will the data be stored in the short term?
  • What backup will you have during the project to make sure no data is lost?

Kent network

Describe where and how you will be caring for your data during the project. 

High Performance Computing

If you are processing computational data and algorithms or working with 'Big Data' or 'machine learning', you may need to use the High Performance Computing service

Email helpdesk@kent.ac.uk if you need help getting started.

Data collected in the field

Describe how you will store data that is collected in the field:

Follow this advice about saving, accessing and backing up your work at Kent.


  • What measures will you take to back up your data?
  • What processes can you rely on using University of Kent networked facilities?
  • Who will have responsibility for making sure backups happen?

Long-term data storage

Example questions:

  • How will the data be stored in the long term?
  • Where have you decided to store it? Why is this appropriate?
  • How long will the data be stored and why?
  • Costs of storage – why are these appropriate?


UKRI funders support the Common Principles on Data Policy and expect that research data created from their funding is available for reuse in the long term. Other funders like Wellcome, Nuffield and EU grant sources also expect research data to be preserved and accessible on a FAIR basis

In practice, this means that the data are curated and archived in a digital format in line with recognized digital preservation standards and stored in a reliable data repository. 

Your research data management plan needs to reflect that you support those principles and know how to put them into practice.

Choosing a repository

Explain where you have decided to store your data and why that is appropriate.

OpenDOAR (from Jisc) and Re3Data (from DataCite) list repositories that meet the minimum standards for data repositories. These minimum standards include:

  • an explicit mission to preserve and provide access to data, supported by appropriate policies and governance relating to ethical and legal compliance, data quality, integrity and preservation
  • funding and institutional credibility and stability to ensure long-term sustainability with commitment to ongoing data access and preservation through robust technical infrastructure and security procedures
  • metadata schema in line with known community standards (for example OAI-PMH) so the data and metadata records can be understood widely and in the long-term:
    • files are accompanied by documentation describing how the data were created and how they can be reused, as well as available licences to enable confidence in reuse.
    • procedures and systems are in place to ensure data and metadata are validated and to record any changes to the files and record. 
    • each data record has citation information and unique identifiers, validated by a central body, for example DOI.org.
  • appropriate support:
    • staff with expertise and knowledge to support researchers in depositing and managing their data
    • guidance, procedures and policy documentation available to researchers and end users.

The most appropriate data repository for your data may be one that is devoted to particular funding sources, communities of practice or file types (which may have technical applications suitable to preserve or render specialist file types). 

Kent Data Repository

The University's Kent Data Repository (KDR) is a general facility that aims to make sure all of Kent's data is preserved, managed and, where possible, shared.

It is there for researchers if a specialist archive does not exist or is not available. If you chose to deposit your data in another repository, KDR can hold a record and link to the data for internal record keeping and external reporting.


Assume that your data will be stored in perpetuity, unless there's a reason for limiting the time the data is available. If that is the case, explain why.

Storage costs

Explain why any storage costs are appropriate. Costs related to long term storage will be permitted if they're fully justified and relate to the project. You must provide full justification in Justification of Resources (JoR).

  • Storage of research data on KDR is part of the University of Kent standard service and is free.
  • Zenodo and UK Data Archive ReShare also offer a free service at the point of delivery if you don't have a more suitable alternative.

Data sharing

Example questions:

  • How will the data will be shared? What value will it have to others?
  • How will the data enhance the area? How could it be used in future?
  • When will you release the data? How will you let audiences know?
  • Will the data need to be updated?
  • Will the data be open or will you charge for it?
  • Is there a cost associated with sharing your data?

As open as possible

The UKRI Common Principles on Data Policy are based on the idea that:

"Publicly funded research data are a public good, produced in the public interest, which should be made openly available with as few restrictions as possible in a timely and responsible manner."

This means that you need to prepare your data and manage your projects with the assumption that it will be openly available. It does not mean that the interest and rights of participants should be set aside, but that you must put processes and contingencies in place to make sure your data can be shared and the rights of others are protected.

The best way to make sure your data are shared responsibly is to manage them according to the FAIR data principles: findable, accessible, interoperable, reusable.

Value of your data

  • Indicate the potential value of your data to:
    • peer reviewers and students seeking to understand and explore your project
    • immediate colleagues working in a similar area to you
    • colleagues in the broader disciplinary area
    • scholars in other areas
    • big data projects, economic interests and the general public
    • policy makers
    • anyone else?
  • Consider the value of the data itself and of the methodology to other scholars and projects.
  • Describe the potential value of each type of data and how you will ensure maximum reuse.

Releasing the data

Say when you will release the data. If you are not releasing them in line with AHRC guidelines of a minimum of three years, you must justify this.

If the data will have value to different audiences, how will they be informed?

It's acceptable to restrict access to the data to project team members only while the project is running and while publications are prepared. But you should release it for general sharing as soon as possible and definitely within any deadlines in funder guidelines.

  • Describe how you can make this possible without compromising participant or third party interests.

Future updates

If the data will continue to build up at the end of the project, you may need plans to prepare and archive these additional datasets. You can add these to the original record as extra files or archive them as completely new records linked to the original data.

Charging and costs

To meet FAIR Data and UKRI principles the data should be free at the point of access. If you plan to charge people to access the data you must justify this.

If there is a cost associated with sharing the data (for example long-term storage) you must include full justification in the Justification of Resources (JoR).

Example questions:

  • What are the legal and ethical considerations around collecting the data?
  • What are the legal and ethical considerations around releasing and storing the data?

Data collection

Describe the intellectual property and data requirements around the data. If there are none describe why.

General Data Protection Regulations (GDPR) will apply to any project that involves human participants. The new GDPR regulations don't require any action beyond that previously covered by data protection legislation and good research practice.

The University of Kent GDPR guidelines explain how you can protect your participants effectively while making sure you can use, archive and share your data to an optimum extent.

In general, the collection and processing of data for the purposes of University research is allowable as it is a 'task carried out in the public interest'. This means that although you may need to get consent for collecting and processing personal data for ethical reasons, GDPR does not require consent to collect or process data.

However, GDPR does require that research does meet other standards. Data collection must be:

  • transparent: make sure the participants know why you are collecting their data and what you will do with it. Using a privacy notice will outline all this information.
  • explicit: the privacy notice needs to describe all the processing you will conduct. Preparation of data for preservation and sharing is considered a part of the initial purpose of the research.
  • adequate: only collect data that you need for the purposes of your project. If you don't need to know the names, or ages of participants, for instance, don't retain that information.
  • accurate: make sure any data you do collect is accurate – check back with the participants.
  • limited: only keep personal data as long as you need to. As soon as possible complete any processes to anonymise the data and prepare it for further processing and archiving. Anonymous data is not restricted by GDPR as long as all information that can be used to identify the participants directly, or indirectly, is removed.
  • secure: follow the University of Kent guidelines surrounding the use of mobile digital devices and use password-protected network facilities; this will safeguard the data against loss, destruction or damage.

Sensitive data

If your data involves particularly sensitive data you need extra safeguards, but these already exist at the University. They include:

  • Research Ethics Committee review and approval
  • governance checks (including Health Research Authority assessment for health research)
  • peer review from public funders
  • data minimisation and minimisation of recruitment numbers
  • pseudonymisation and other technical safeguards against accidental disclosure and loss or corruption of research data.

If you have any particular circumstances contact University of Kent Research Services and check your school research ethics procedures. Check any specific disciplinary guidance; for example, The Association of Social Anthropologists publish guidelines for conducting research with people in other countries.

Release and storage

You need to think about the anonymity of participants if this was promised, as well as commercial interests and intellectual property.

  • Make sure you have anonymized the data as part of the data preparation for preservation and sharing.
  • Consider other legal and ethical implications for sharing and storing your data.
  • Make sure you have included information about the long-term management of your data in the privacy statement you share with participants.
  • If using secondary data, you will need to take original intellectual property (IP) into account.
  • Where commercial partners have an interest in the data, this needs to be married with the requirements of AHRC: you may need to restrict access for a period or redact the data.
  • Any patents or IP developed by the project will impact on data sharing and reuse: discuss these with Kent Innovation & Enterprise.


Need help with research data management? Email researchsupport@kent.ac.uk

Find out all the ways you can get in touch:

Research support links

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