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Research Data Management

Good research starts with good data. This guide will help you understand how to organize, store, protect, and share your data throughout your research journey.

What is Data Planing?

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What is a DMP?

A strategic document outlining how research data will be collected, organized, stored, shared, and preserved throughout a project's lifecycle.

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Why is it Important?

Ensures data integrity, enables reproducibility, meets funding requirements, and maximizes research impact through proper data stewardship.

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When to Create?

Ideally before data collection begins, often required at grant application stage, and updated throughout the research lifecycle.

Where to start: Tools for DMP

Recognizing the importance of data planning but not sure where to begin? Fortunately, free, established online platforms can guide you every step of the way. These tools walk you through the process of crafting a Data Management Plan—whether you need to meet specific funder requirements or prefer a flexible, generic template—making them especially valuable for researchers new to DMP preparation. Two of the most popular and user-friendly options are DMPTool and DMPonline, both of which offer intuitive interfaces, customizable templates, and clear, step-by-step instructions to help you get started with confidence.

What should be included: Data Management Checklist

This checklist provides comprehensive guidance for creating a Data Management Plan (DMP). Each section contains essential questions to consider and detailed guidance to help you develop a robust data management strategy for your research project.

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Administrative Data

ID

A pertinent ID as determined by the funder and/or institution.

Funder

State research funder if relevant.

Grant Reference Number

Enter grant reference number if applicable [POST-AWARD DMPs ONLY].

Project Name

If applying for funding, state the name exactly as in the grant proposal.

Project Description

Questions to consider:
  • What is the nature of your research project?
  • What research questions are you addressing?
  • For what purpose are the data being collected or created?
Guidance:

Briefly summarise the type of study (or studies) to help others understand the purposes for which the data are being collected or created.

Name of Principal Investigator(s) or main researcher(s)

Name of Principal Investigator(s) or main researcher(s) on the project.

ID of Principal Investigator(s) or main researcher(s) ID

E.g. ORCID: https://orcid.org/0000-0002-1825-0097

Project Data Contact

Name (if different to above), telephone and email contact details.

Related Policies

Questions to consider:
  • Are there any existing procedures that you will base your approach on?
  • Does your department/group have data management guidelines?
  • Does your institution have a data protection or security policy that you will follow?
  • Does your institution have a Research Data Management (RDM) policy?
  • Does your funder have a Research Data Management policy?
  • Are there any formal standards that you will adopt?
Guidance:

List any other relevant funder, institutional, departmental or group policies on data management, data sharing and data security. Some of the information you give in the remainder of the DMP will be determined by the content of other policies. If so, point/link to them here.

Data Collection

What data will you collect or create?

Questions to consider:
  • What type, format and volume of data?
  • Do your chosen formats and software enable sharing and long-term access to the data?
  • Are there any existing data that you can reuse?
Guidance:

Give a brief description of the data, including any existing data or third-party sources that will be used, in each case noting its content, type and coverage. Outline and justify your choice of format and consider the implications of data format and data volumes in terms of storage, backup and access.

How will the data be collected or created?

Questions to consider:
  • What standards or methodologies will you use?
  • How will you structure and name your folders and files?
  • How will you handle versioning?
  • What quality assurance processes will you adopt?
Guidance:

Outline how the data will be collected/created and which community data standards (if any) will be used. Consider how the data will be organised during the project, mentioning naming conventions, version control and folder structures. Explain how consistency and quality will be controlled and documented. This may include calibration, repeat samples, standardised capture, entry validation, peer review, or controlled vocabularies.

Documentation & Metadata

What documentation and metadata will accompany the data?

Questions to consider:
  • What information is needed for the data to be read and interpreted in the future?
  • How will you capture / create this documentation and metadata?
  • What metadata standards will you use and why?
Guidance:

Describe the documentation that will accompany the data to help secondary users understand and reuse it. Include details such as creator, title, date, conditions of access.

Documentation may also cover methodology, procedural info, variable definitions, vocabularies, units, assumptions, file formats. Identify how and where this will be recorded, using community standards where possible.

Ethics & Legal Compliance

How will you manage any ethical issues?

Questions to consider:
  • Have you gained consent for data preservation and sharing?
  • How will you protect participant identity (anonymisation)?
  • How will sensitive data be stored and transferred securely?
Guidance:

Address ethical issues: anonymisation, ethics committee referrals, consent agreements. Show awareness and planning. For human participant research, ensure consent for sharing and reuse.

How will you manage copyright & IPR?

Questions to consider:
  • Who owns the data?
  • How will data be licensed for reuse?
  • Restrictions on third-party data reuse?
  • Any publication/patent embargo?
Guidance:

State IPR ownership and licences. For multi-partner projects, cover IPR in consortium agreements. Consider funder/institutional policies and permissions for third-party data.

Storage & Backup

How will data be stored & backed up during research?

Questions to consider:
  • Do you have sufficient storage or need extra charges?
  • How will data be backed up?
  • Who is responsible for backup & recovery?
  • How will data be recovered after an incident?
Guidance:

State backup frequency and locations. How many copies? Avoid only local drives. Prefer managed storage and automated backups. If third-party, ensure compliance with policies and data jurisdiction.

How will you manage access & security?

Questions to consider:
  • What are security risks & how manage them?
  • How will you control secure access?
  • How ensure collaborators access data securely?
  • How transfer field data safely into secured systems?
Guidance:

For confidential data (personal, trade secrets), outline security measures and standards (e.g. ISO 27001).

Selection & Preservation

Which data should be retained, shared, and/or preserved?

Questions to consider:
  • What data must be retained/destroyed by contract, law, or regulation?
  • How decide what additional data to keep?
  • Foreseeable research uses?
  • Retention & preservation durations?
Guidance:

Consider reuse value, validation, new studies, teaching. Balance obligations, value, cost, and preparation effort (e.g. format conversion).

What is the long-term preservation plan?

Questions to consider:
  • Which repository/archive will hold the data?
  • Repository/archive charges?
  • Costs/time for data preparation/preservation?
Guidance:

Outline preservation beyond grant lifetime: curation, documentation, and resources if not using an established repository.

Data Sharing

How will you share the data?

Questions to consider:
  • How will users discover your data?
  • With whom and under what conditions?
  • Repository vs. direct requests?
  • Availability timing?
  • Persistent identifier?
Guidance:

Choose sharing method based on data type, size, complexity, sensitivity. Cite past sharing examples. Consider citation and acknowledgement.

Are restrictions required?

Questions to consider:
  • How minimise or overcome restrictions?
  • Exclusive use period?
  • Need for data sharing agreement?
Guidance:

Explain expected sharing difficulties (confidentiality, consent, IPR) and mitigation (e.g. NDA for confidentiality).

Responsibilities & Resources

Who will be responsible for data management?

Questions to consider:
  • Who implements and reviews the DMP?
  • Who handles each activity?
  • Responsibilities across partner sites?
  • Consortium agreement coverage?
Guidance:

Define roles for data capture, metadata, quality, storage, backup, archiving, sharing. Name individuals where possible and note policy compliance roles.

What resources are required?

Questions to consider:
  • Specialist expertise or training needed?
  • Hardware/software beyond institutional provision?
  • Repository charges?
Guidance:

List software, hardware, technical expertise needed. Outline and justify dedicated resources.

Quick Actions

Source: DCC. (2013). Checklist for a Data Management Plan. v.4.0. Edinburgh: Digital Curation Centre. Available online: http://www.dcc.ac.uk/resources/data-management-plans