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Research Data Management

A comprehensive guide to the best practices for planning, collecting, working with, sharing and reusing research data

What to cover in a DMP?

An effective DMP should be concise and easy-to-follow. Click on below topics to explore the essential elements that should be covered in a core DMP:

Project name & ID


Project description

Give a brief description of your research to help others understand the purposes for which the data are being collected or created, which should include:

  • nature of the project
  • aims and purposes
  • research question

Project duration


Funding body(ies)

If applicable.

Grant number

If applicable.

Principal researcher(s) / investigator(s)

Provide the name, digital identifier (e.g. ORCID), and contact details of the main researchers involved in the project.

Related policies

List any relevant funder, institutional, departmental or group policies that apply to the research data, e.g. institutional data policy or departmental guidelines. Details of or links to the policies should be given, if available.

Date written & Date of last update

A DMP is a live document that needs to be reviewed & updated regularly throughout the research project. Recording date information is important for version control and placing the DMP in context.

Data description

  • Type
  • Format
  • Estimated Volume

Describe the provenance, type and format of the data to be collected in the project. Justify the choice of format and consider the implications of data format and data volumes in terms of storage, backup and access. You should consider:

  • What type of data will be collected? Is it observational, experimental, simulation, or modeling data?
  • In what file formats? Do your chosen formats and software enable sharing and long-term access to the data?
  • What is the estimated size of the data?

Data collection method(s)

  • Capture Methods
  • Standards

Explain the data capture process, and outline any approaches to ensure the quality of data being created. You should think about:

  • What standards or methodologies will be used?
  • What kind of equipment or software will be required?
  • How will you structure and name your folders and files and why?
  • How will you handle versioning / file naming conventions?
  • What quality assurance processes will you adopt?

Existing dataset(s) to be reused

  • Will you re-use any existing data and how?

Documentation and Metadata

Describe the documentation accompanying the data to that helps to users including yourself to find and reuse it in the future. Whenever possible, the data documentation should follow relevant community standards to facilitate the interoperability of data.

  • What information is needed for reusing the data in the future?
  • How will you capture or create the documentation and metadata?
  • What metadata standards will you use and why?

Ethical issues

Explain your plan to address any ethical and privacy issues that may affect your data.

  • Have you gained consent for data preservation and sharing?
  • How will you protect the identity of participants if required? e.g. via anonymization
  • How will sensitive data be handled to ensure it is stored and transferred securely?

Copyright and Intellectual Property Rights (IPR) issues

Explain the copyright / IPR and data licensing issues on either reuse or sharing of data.

  • Who owns the data?
  • How will the data be licensed for reuse?
  • Are there any restrictions on the reuse of third-party data?
  • Will data sharing be postponed / restricted? e.g. to publish or seek patents

Storage and backup

Describe the data storage and backup arrangement to avoid data loss.

  • Do you have sufficient storage or will you need to include charges for additional services?
  • How will the data be backed up?
  • Who will be responsible for backup and recovery?
  • How will the data be recovered in the event of an incident?

Data security

Describe the security measures and outline any formal standards that will be used to protect the security and privacy of sensitive or valuable data.

  • What are the risks to data security and how will these be managed?
  • How will you control access to keep the data secure?
  • How will you ensure that collaborators can access your data securely?
  • If creating or collecting data in the field how will you ensure its safe transfer into your main secured systems?

Selection and Retention

Indicate which data and the length of time the data to be kept beyond the project period. When creating the selection criteria, you should consider the following aspects:

  • relevant contractual, legal or regulatory requirements on data preservation
  • potential reuse value of the data
  • length of time data to be preserved

Preservation plan

Explain the preservation plan for data with long-term value, including the plan for preparation and documentation of data for sharing and archiving, resources and tools needed for effective data curation, etc. You should consider:

  • data repository to be used and its association charges
  • time, effort and costs needed for data preservation


Specify your plan for data sharing in addressing the following questions:

  • What data will be shared?
  • Who will have access to the data?
  • Where will the data to be shared be located?
  • When will the data be shared?
  • How will researchers locate and access the data?


Outline any restrictions on data sharing imposed by e.g. funders, publishers, or relevant laws and regulations. You should consider:

  • What action will you take to overcome restrictions?
  • For how long do you need exclusive use of the data and why?
  • Will a data sharing agreement (or equivalent) be required?


Outline the roles and responsibilities for each data management activity as well as the DMP implementation. You should consider: 

  • Who is responsible for implementing the DMP, and ensuring it is reviewed and revised?
  • Who will be responsible for each data management activity?


List out any resources needed for carrying out the DMP. These costs can usually be written into grant applications but need to be clearly outlined and justified, such as:

  • payments to service providers within institutions
  • payments to external data centres for hosting data
  • income derived from licensing data, etc.

Source: DCC. (2013). Checklist for a Data Management Plan. v.4.0. Edinburgh: Digital Curation Centre. Available online:

lightbulbPlease note that data management planning is an ongoing process. A DMP is a live document that should be reviewed and updated regularly throughout the course of the project when necessary.

Example DMPs

See below example plans to help you get started with data plans for different research funders: