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Research Data Management: Writing a Data Management Plan (DMP)

Data Management Plans (DMP's) and policy at NUI Galway

The Research Data Management Policy at NUI Galway defines a Data Management Plan (DMP) as "A formal document that outlines how data are to be handled both during a research project and after the project is completed. The goal of a data management plan is to consider the many aspects of data management: metadata generation, data preservation, and analysis before the project begins; ensuring that data are well managed in the present, and prepared for preservation in the future."

The policy requires that:

  • All research proposals should include a Data Management Plan (DMP) with effect from the date of approval of this policy. Where a funder has stipulated specific requirements the DMP must comply with these.
  • Any costs/resources associated with data curation/management must be incorporated in the DMP and provision for long-term retention identified (if required).  
  • The DMP must be adhered to and the commitments to store/publish data form part of the execution of the research project.
  • PI’s and research staff/students are required to complete training in the development of DMP and appropriate Data Protection, IT Security Training.

Reasons to create a Data Management Plan (DMP) include:

  • Saves time
  • Minimises reorganisation later
  • Avoids duplication
  • Increases research efficiency
  • Provides guidelines for everyone on the research team
  • Avoids the risk of data loss
  • Ensures adequate preparation for data preservation
  • Helps to quantify the resources required
  • Ensures that others can understand and re-use your research data in the future

NOTE: a data management plan is a living document that grows with the project.

Components of a general Data Management Plan

Information about data and data format

  • Description of data to be produced e.g.experimental, observational, physical collections, models and their outputs, simulation outputs, curriculum materials, software, images, interviews, surveys, etc
  • How data will be acquired e.g. when and where
  • How data will be processed e.g. software used, algorithms, workflows
  • File formats e.g. justification, naming convention
  • Quality assurance and control during sample  collection, analysis, and processing
  • Existing data e.g. if existing data are used, what are their origins,will your data be combined with existing data and what is the relationship between your data and existing data?
  • How data will be managed in short-term e.g. version control, backing up, security and protection, who will be responsible?

Metadata content and format

  • Documentation and reporting of data
  • Contextual details: critical information about the dataset
  • Information important for using the data
  • Descriptions of temporal and spatial details, instruments, parameters, units, files, etc. 
  • What metadata are needed e.g. any details that make data meaningful 
  • How metadata will be created and/or captured e.g. lab notebooks, GPS units, auto-saved on instrument?
  • What format will be used for the metadata e.g. standards for community, justification for format chosen

Policies for access, sharing and re-use

  • Obligations for sharing e.g funding agency, institution, other organization or legal
  • Details of data sharing e.g how long, when, how access can be gained, data collector rights
  • Ethical/privacy issues with data sharing
  • Intellectual property and copyright issues e.g. who owns the copyright, related institutional policies, funding agency policies, embargos for political/commercial reasons
  • Intended future uses/users for data
  • Citation e.g. how should data be cited when used, persistent citation?

Long-term storage and data management

  • What data will be preserved?
  • Where will it be archived e.g. most appropriate archive for data, community standards?
  • Data transformations/formats needed e.g. consider archive policies
  • Who will be responsible e.g. contact person for archive?


  • Anticipated costs e.g. time for data preparation and documentation, hardware/software for data preparation and documentation, personnel, archive costs
  • Funding for costs

Read about costing data management at UK Data Service



DMPonline a tool to help you write your data management plan. It contains templates for most of the major funding bodies. Make sure the template you use is compatible with the requirements set out by your funder. There is a short tutorial at the link but the tool is straightforward and easy to use. Just sign In to create an account and get started. Some funders mandate the use of DMPonline, while others point to it as a useful option. You can download funder templates without logging in, but the tool provides tailored guidance and example answers from the DCC and many research organisations.

Data Stewardship Wizard from the GoFAIR organisation provides a smart questionnaire to guide you through the creation of a data stewardship plan. It includes hints multimedia content, external resources and help.

DMP checklists

There are many data management checklists available that explain and guide the process of creating a data management plan. The Digital Curation Centre provides a checklist as well as guidance and some examples.


Examples of Data Management Plans

A summary of example plans organised by research funders is provided by the Digital Curation Centre.

LIBER the Association of European Research Libraries provides a Data Management Plan Catalogue which is a central hub for DMPs from different disciplines. It also includes quality reviews of the DMPs.