Guide to preparing a data management plan

After years of consultation and development, SSHRC, the Natural Sciences and Engineering Research Council (NSERC), and the Canadian Institutes of Health Research (CIHR) launched the Tri-Agency Research Data Management Policy in 2021 to promote excellence in data management practices within the Canadian research community. As outlined in the policy, certain funding opportunities will now require data management plans to be submitted as part of an application. The Research Data Management page on outlines the funding opportunities at SSHRC, NSERC and CIHR that require data management plans.

SSHRC welcomes feedback at on how to improve this guide.

Research data management

Research data management supports the effective and responsible conduct of research, including the collection, documentation, storage, sharing and preservation of research data within and beyond the lifecycle of a given project.

Research data can be:

  • primary sources to support technical or scientific enquiry, research, scholarship or artistic activity;
  • evidence in the research process; and/or
  • evidence commonly accepted in the research community as necessary to validate research findings and results.

All digital and nondigital content have the potential to become research data.

Research data may be experimental, observational, operational, third-party, public sector, monitoring, processed or repurposed data.

Data management principles

Grant recipients are not required to openly share their project’s research data. SSHRC supports the principle that research data collected through the use of public funds should be responsibly and securely managed and, where ethical, legal and commercial obligations allow, be available for reuse by others. Data should, when possible and appropriate, be managed using the FAIR Principles. The FAIR Principles provide guidelines to improve the findability, accessibility, interoperability and reuse of digital assets as follows:

  • Findable: Data and supplementary materials are described with sufficiently rich metadata and assigned a unique and persistent identifier.
  • Accessible: Metadata and data are understandable to humans and machines. Data is deposited in a trusted repository.
  • Interoperable: Metadata use formal, accessible, shared and broadly applicable language to represent knowledge.
  • Reusable: Data and collections have clear usage licences and provide accurate information about their provenance.

When conducting research in collaboration with specific populations, researchers should consult with impacted individuals and groups to co-develop data management principles and be particularly mindful of sensitive and/or high risk data, adhering to the principle of “do not harm.” Researchers working with sensitive and/or high risk data should also conduct risk assessments of data collection and management processes to ensure confidentiality and security of data, including after the end of the grant.

Data related to research by and with First Nations, Métis or Inuit communities must be managed according to data management principles developed and approved by these communities. This includes, but is not limited to, considerations of Indigenous data sovereignty, as well as data collection, ownership, protection, use and sharing. 

Consult, where applicable, the First Nations Principles of Ownership, Control, Access and Possession (OCAP®), as well as the CARE Principles for Indigenous Data Governance—CARE stands for Collective benefit, Authority for control, Responsibility and Ethics. These two sets of principles regarding Indigenous data governance may be helpful but do not necessarily respond to the needs and values of distinct First Nations, Métis and Inuit communities, collectives and organizations. SSHRC recognizes that a distinctions-based approach is needed to ensure that the unique rights, interests and circumstances of the First Nations, Inuit and Métis Peoples are acknowledged, affirmed and implemented.

Data management plans

Data management plans (DMPs) are living documents that outline a project’s plans for research data management. The content, format and length of DMPs depend on the nature of the given research project. DMPs can be developed to guide a single research project or span a multi-project research initiative or longer-term program of research. The DMP submitted at the application stage could, depending on the complexity and duration of the project, be presented as a more high-level outline and then be expanded on throughout the life of the project.

DMPs in the context of research by and with First Nations, Métis and Inuit communities, collectives and organizations should recognize Indigenous data sovereignty and include options for revising how data will be managed.

Throughout the course of a research project, from conception to planning to implementation and conclusion, DMPs can be developed or expanded for different purposes or modified to accommodate changes.

Benefits of DMPs

Research data management is increasingly recognized as a component of research excellence and many funders around the world are implementing data management requirements that include DMPs. Preparing DMPs will help contribute to Canadian research excellence and better position researchers to take part in international partnerships and collaborations because they offer the following benefits:

  • DMPs prompt researchers to consider aspects of data management that they might not otherwise consider in advance.
  • DMPs can be useful in preparing for ethics approval.
  • DMPs help to reduce work and minimize data-related problems throughout the course of a research project.
  • The process of preparing a DMP can lead to improvements in research plans and methodologies.

DMP formats and content

Researchers are encouraged to familiarize themselves with the DMP resources available at their institutions, and to work with representatives from their institutions as they develop their DMPs (e.g., data management experts, representatives from the research office and research ethics board office). Templates, tools and supports are available online in a variety of formats and lengths, some specific to disciplines or domains, that can assist researchers in preparing DMPs. For example, the DMP Assistant is a national, online, bilingual data management planning tool supported by the Digital Research Alliance of Canada that applicants may choose to use. The DMP Assistant provides guidance and examples to help prepare a DMP, and it allows for sharing, revising and exporting the plan.

In explaining how they plan to manage a project’s research data, applicants can use the following guiding sections as applicable (not all of these will be relevant to every research project):

  1. Data collection
    • what data will be collected, created, linked to or acquired
    • how data will be collected
    • how existing datasets will be used, and what new data will be created over the course of the research project
      • Tip: Address data collection issues such as data types, file formats, naming conventions and data organization–factors that will improve the usability of your data and contribute to the success of your project.
  2. Documentation and metadata
    • how data will be documented and formatted
      • Tip: Because data are rarely self-explanatory, all research data should be accompanied by metadata (information that describes the data according to community best practices).
      • Tip: Implement measures to ensure the accessibility of data. In the DMP, clarify the formats in which data will be stored to ensure that they are compatible with community infrastructure (including access to the internet and other technologies), and accessibility supports. For example, documents should be stored in formats that allow for text-to-speech software (i.e., not as images) to support access for low-visioned users.
  3. Storage and backup
    • how and where data will be stored during the research project
      • Tip: Plan how research data will be stored and backed up throughout and beyond the research project. Appropriate storage and backup not only helps protect research data from catastrophic losses (due to hardware and software failures, viruses, hackers, natural disasters, human error, etc.), but also facilitates appropriate access by current and future researchers.
  4. Preservation
    • where data will be deposited for long-term preservation (see requirement in section 3.3 of the Tri-Agency Research Data Management Policy)
      • Tip: Data preservation will depend on potential reuse value, whether there are obligations to either retain or destroy data, and the resources required to properly curate the data and ensure that they remain usable in the future. In some circumstances, it may be desirable to preserve all versions of the data (e.g., raw, processed, analyzed, final), but in others, it may be preferable to keep only selected or final data (e.g., transcripts instead of audio interviews).
  5. Sharing and reuse
    • whether and how data will be shared (if appropriate) and the potential for data to be reused
    • what data access procedures will be implemented (if applicable)
      • Tip: Consider the types of data that will be used in the project and the risk level associated to determine if the data are sensitive and/or high risk. Visit the Sensitive Data Guidance section of the Digital Research Alliance’s training resources on research data management for more information.
      • Tip: Provide a clear explanation if data cannot be responsibly and ethically shared; do not simply state that they will not be shared. For example, some community partner requirements may not allow the sharing of data (e.g., Indigenous traditional or sacred knowledge). Explain the context and outline the plan.
  6. Responsibilities and resources
    • the research team’s data-related roles and responsibilities (e.g., who is responsible for data management tasks, maintenance of the project’s data repository, succession planning, and roles and responsibilities of other team members, where appropriate)
      • Tip: Establish procedures/policies for ongoing training of project participants on the DMP, including technical elements and repository maintenance.
      • Tip: A large project will involve multiple data stewards. The principal investigator should identify at the beginning of a project all the people who will have responsibilities for data management tasks during and after the project.
  7. Ethics and legal compliance
    • ethical, legal and commercial considerations for the data (e.g., how the project will comply with laws and ethical guidelines that apply to the data)
      • Tip: Build in consultations with impacted communities throughout the life of a project and consider data sovereignty and community ownership, where applicable. Co-develop the DMP with these communities. Indicate if these communities have agreed to the data management strategies and how they will be involved in the management of the data.
      • Tip: Consider the following for projects working with remote and/or international communities:
        • data legislation compliance (the DMP should comply with the strictest relevant jurisdiction)
        • policies on crossing international borders with data on devices
        • location of the server
        • how researchers in remote locations will be able to upload data (e.g., access and cost)
        • access to data for researchers in remote locations, including internet access/reliability, instruments, or data stored on a server


Institutions are a good starting point for accessing support for research data management and preparing DMPs. The tri-agency policy requires that all postsecondary institutions and research hospitals eligible to administer agency funds create an institutional research data management strategy that outlines how the institution will provide its researchers with an environment that enables and supports research data management.

The Digital Research Alliance of Canada, a federally funded not-for-profit organization mandated to transform how research across all academic disciplines is organized, managed, stored and used in Canada, has a suite of research data management tools and services available on its website. SSHRC would like to acknowledge the Alliance’s Network of Experts for their contributions in developing these resources.

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