Research Data Management Planning Guide Ask Us

A data management plan (DMP) is a strategic roadmap that ensures your research data are organized, documented, accessed, and preserved both during and after the project. In simple terms, it is a living document that outlines how research will be handled throughout the research lifecycle. 

DMP can be created with or without DMP Assistant. The advantage of using DMP Assistant is that it follows guidelines created by Canadian RDM experts in partnership with the Tri-Agency.  

Why you need a DMP?

• protect research investment
• research integrity and reproducibility
• meet Tri-Agency and Institutional requirements
• maximize impact through data sharing

Funder requirements for research data

In March 2021 the Tri-Agency - CIHR, NSERC, SSHRC - launched the Tri-Agency Research Data Management (RDM) Policy along with accompanying FAQ. The RDM Policy applies to all research funded by the Tri-Agency. As of August 2025, the following funding opportunities require DMPs:

CIHR (Canadian Institutes of Health Research):

• Network Grants in Skin Health, Bone Health and Muscular Dystrophy (October 2022)
• Team Grants: Strengthening the Health Workforce for System Transformation (June 2023)
• Team Grants: Lung Health (July 2023)
• Operating Grant: Clinical Trials Projects (July 2023)
• Team Grants: Improving Health and Administrative Data and Monitoring for Rare Diseases (August 2023)
• Operating Grant: Addressing Infectious and Congenital Syphilis in Canada (September 2023)
• Canadian Consortium on Neurodegeneration in Aging (September 2023)
• Team Grants: Embracing Diversity to Achieve Precision and Health Equity (October 2023)
• Canadian Immunization Research Network (CIRN) renewal (November 2023)
• Team Grant: HIV/AIDS and STBBI Community-Based Research (December 2023)
• Team Grant: Biomedical Research for HIV/AIDS and STBBI (April 2024)
• Canadian Consortium on Neurodegeneration in Aging Phase III: Research Teams (August 2024)
• Operating Grant: HCRI—Data Analysis Using Existing Databases and Cohorts (May 2024)
• Team Grant: Bringing Biology to Cancer Prevention (forthcoming)
• Team Grant: Pan-Canadian Network: Emerging Learning Health System in Perinatal Mental Health 

* CIHR continues to identify funding opportunities to pilot DMPs, but is no longer posting the funding opportunities. Consult funding opportunities on ResearchNet to learn whether they require DMPs.

NSERC (Natural Sciences and Engineering Research Council):

• Subatomic Physics Discovery Grants - Individual and Project (November 2023)
• NSERC - ECCC Call for Proposals on Plastics Science and Innovation for a Cleaner and More Sustainable Future (May 2025)
• Alliance Society (September 2025)

SSHRC (Social Sciences and Humanities Research Council):

• Partnership Grants Stage 2 (October 2023)
• Policy Innovation Partnership Grants (September 2025)

** By the end of 2026, a DMP is expected to become a requirement for all Tri-Agency grants and programs.

How to incorporate security into RDM planning?

(from the Tri-Agency RDM Policy FAQ)

When conducting research that involves sensitive data or has potential for dual use, researchers may need to take additional measures to balance the need for data-sharing and access with that for protection from threats. To ensure that the integrity of their research is not compromised and research results (e.g., data sets, publications, patents) are secure and protected until they choose to disseminate them, researchers should put in place good physical and cyber security practices and infrastructure. These practices should be agreed to by all research team members and partners.

Datasets that are de-identified may be deposited into:

• UNB Dataverse or
• FRDR.

Both repositories preserve datasets for long-term access and reuse, and assign a DOI to each dataset. Datasets with a sensitive nature may be subject to restricted access, as established by the REB's protocol and/or determined by the researcher or Indigenous community. Depending on the data, access may be open or restricted.

 For more information on safeguarding research, consult Sensitive Data Management guide and Indigenous research guide. 

Journal or Publisher requirements for research data 

In accordance with journal standards for transparency and reproducibility, all raw and processed data supporting the findings of a study submitted for peer-review should be made available in a recognizable research data repository and assigned a persistent identifier (DOI). The complete dataset, including data, any associated code, metadata and documentation should be accessible through the DOI to facilitate peer-review verification and future re-use. Reviewers and future readers may request access to additional relevant materials by contacting the corresponding author. 

The accepted phrase is typically found within the Data Availability Statement or under the "Data Availability" section of a research paper. 

Check the 'Instructions for Authors' section of your target journal to determine whether there are any recommendations or requirements regarding research data. The most common requirements are:

1. deposit research data in a recognizable data repository 
2. include a data availability statement (DAS) in the manuscript

RDM Strategy (2023-2026)

The University of New Brunswick RDM Strategy (2023-2026) aims to support researchers in meeting Tri-Agency requirements by providing infrastructure, services, and training. Contact  Research Data Management Services @ UNB Libraries (rdm.services@unb.ca) with any further questions.

Quick checklist: Does your project need a DMP?

If you answer yes to any of the following, you should complete a DMP:

• Your Tri-Agency grant requires one (check the list above)
• Your research involves human participants or sensitive data (TCPS 2 applies)
• You will generate, collect, or repurpose digital data
• You plan to deposit data in a repository such as UNB Dataverse or FRDR
• Multiple researchers or institutions are involved
• Your research involves Indigenous data (OCAP® principles apply)

What happens if you don't plan?

• Data loss due to no backup strategy
• Issues with publishing manuscripts due to missing data availability statements
• Ethics violations from mishandling participant data (TCPS 2 non-compliance)
• Wasted effort re-collecting or re-analyzing data
• Lost data citations because data is not discoverable or citable

The simplified DMP template below (funding application stage) was developed by the Data Management Planning Expert Group and adapted to reflect local services for UNB users. You can find the complete document on Zenodo https://doi.org/10.5281/zenodo.15490375 .

If you use DMP Assistance, the template is available directly within the tool. 

DMP template (pre-funding stage)

1. What considerations will you take into account with respect to ethical, legal, or commercial issues?

Describe any applicable ethical, legal, or commercial considerations related to your project and data. This includes research involving Indigenous communities and knowledges, human subjects, legal and commercial considerations/agreements, partnerships or data with a high level of risk associated with it. Depending upon the nature of your project, you may want to consider how parts will be shared through knowledge translation/mobilization or through technology transfer if you develop new technology.

• Guidance:
  Consider how you will manage data throughout the lifecycle of the project, including how you will safeguard information, protect potentially sensitive data, support potential long-term data stewardship, and meet requirements, including those set forth by funders, research ethics boards, or other obligations, as applicable. If needed, contact RDM Services @ UNB Libraries for guidance and to find out what resources and support are available. Some notable key considerations include:
  - How will you support long-term data stewardship, including data deposit and appropriate sharing? Note - research involving human participants will very often require both informed consent and ethics approval with respect to data sharing.
  - How will data deemed as being sensitive be effectively safeguarded and protected across the lifecycle of your research project?

2. What data will you collect or otherwise bring into your project under this plan?

Describe the data that will be collected, generated, and/or acquired.

• Guidance:
  Research data refers to any information that is collected, observed, generated, or acquired to validate your research findings. Notable examples of research data may include data files, questionnaires, transcripts, samples, physical collections, software, models, algorithms, lab notebook, codebooks, methodologies, workflows, and other materials to be produced during the course of the project. When describing your data be sure to:

• - Describe the data types (e.g., image data, textual data, numerical data, audiovisual, etc)
  - Briefly explain how the data will be collected or generated - if already existing data will be acquired, describe the source of the data (e.g., citations, URL, persistent identifiers linked to the data, acquisition requests) and specify what data will be used.
  - Indicate if your data is, or may possibly be, considered as sensitive (e.g., health, administrative and/or clinical records, participant interviews, involving sensitive topics, etc).
  - Indicate if your data involves Indigenous communities and/or knowledges and information.
  - Describe if your data are in proprietary vs. non-proprietary formats. 
  - As best able, provide an estimate of the size of the data, taking into account multiple versions of data needed (e.g., raw, master, analytic). This will additionally help when considering storage data storage needs for the project.

3. How will you document data for future re-use or validation?

Describe how you will document your data to ensure that it is easily read and interpreted correctly throughout the research process. If applicable, specify any data and/or metadata standards that are being used to support your research project.

• Guidance:
  - Structured documentation allows for your research and data to be understood, reproduced, and, potentially, reused by others. Notable examples of data documentation may include such things as data dictionaries, codebooks, readme files, lab and/or field notes, code and syntax, user guides, etc.
  - Metadata standards provide a set list of descriptive fields (similar to a vocabulary) for providing relevant contextual information to describe data. Most metadata standards use open and machine-readable formats (e.g., JSON; XML), which facilitates opportunities for interoperability, including future exchange and reuse of metadata between systems and software used for analysis, as well as for supporting data sharing and indexing in a repository or database for search and discovery purposes.
  - Consider at the funding application stage how you will ensure that your data is efficiently documented and captured through out your project (e.g. scripting, tool-generated, user-documented), including by whom, can help to identify needs, including staffing resources and expertise, that may possibly be built into your application and supported through grant funds.
  

4. How will data be stored, accessed and worked with?

Describe both where and how data will be stored, accessed, and worked with during the active phases of your research including as applicable:

• all versions of data (e.g., raw, master, analytic)
• all activities (e.g., data collection, processing, analysis, dissemination)
• all software and platforms
• who requires access, including security measures (e.g., Investigators, research staff, collaborators, partners)
• how data will be backed up to prevent data loss

Guidance:
Consider and identify both where and how data will be stored, accessed, and worked with across the active phases of your research. Projects involving multiple people (e.g., co-investigators, research staff, trainees, partners) need to consider who requires access, as well as how they will work with data.
If you are working with sensitive data, you will require secure storage. Contact RDM Services @ UNB Libraries to discuss options. If you are working with data from human participants, you will need to follow any directions from your local research ethics board.
Many server and cloud based environments, such as the Digital Research Alliance of Canada’s freely available Rapid Access Service, can support projects’ active data storage and access needs, including for geographically dispersed teams, greatly reducing the need for data transferring and storing data on personal computers and mobile devices.

Notable things to consider and describe as able at the funding application stage include:

• - Where and how all versions of data will be stored and worked with - this includes data that are unprocessed and identifiable (raw data), those that are processed and de-identified (master data), as well as analytic files.
  - Who needs access and to what versions of data - Consider if, for instance, co-investigators, trainees, and/or research staff may require access to data. Should your project move forward, in some instances, individuals may require ethics approval in order to access raw or sensitive data.
  - How people will access and work with data - will you utilize a server or cloud based environment and, if so, will all team members be able to access

If for any reason data are being worked with or stored on personal computers or mobile devices, describe how you will ensure that data are backed up to prevent data loss, including if to an external hard drive, a server, and/or a cloud solution. You may also want to contact your institution's data library or IT divisions to ensure you are compliant with local policies.

5. How will data be managed, discoverable, and accessible for the long term?

Describe plans for long-term management of your data after the active phases of your research have concluded including data deposit and sharing. Consider and describe as applicable plans for:

• all versions of data deposited (raw, master, analytic, published)
• all activities (e.g., curation, preservation, ethical compliance, publishing etc.)
• all software and platforms (e.g., data repositories)

• Guidance:
  Describe the steps you will take to ensure your data will remain, as appropriate, discoverable and accessible over time while considering:

• - any obligations from funders, publishers, or your own institution’s policies
  - if any data will be destroyed and, if so, under what conditions.
  - if any data are sensitive and if ethics approval and/or de-identification is required prior to sharing
  - if any software is needed for continued access to the data, as well as if code, scripts, or metadata are required to interpret the data

• Will you deposit any versions of your data into a digital research data repository for open discovery, appropriate access, and potential reuse by others?

• - If yes, provide details including, as best able, where you will deposit your data
  - If no, include an explanation as to why your data will not be deposited.

• When planning to deposit data, consider repositories that assign a persistent identifier as these support both its discovery and citability. Two free-to-use generalist (not discipline specific) data repositories in Canada are:

• UNB Dataverse: institutional research data repository. Contact RDM Services @ UNB Libraries to learn more about it and its key features.

• Federated Research Data Repository (FRDR): Hosted by the Digital Research Alliance of Canada. FRDR, is a national repository service and data discovery platform that can additionally support very large data deposits.

****** END OF TEMPLATE ******

For assistance when considering your project’s long-term management and deposit needs contact RDM Services @ UNB, rdm.services@unb.ca

A Data Availability Statement (DAS) describes where the data supporting the study's findings can be accessed and under what conditions. In simple terms, it answers three critical questions:

1. Where are the data? (repository, supplementary materials, or "upon request")
2. How to access data? (openly, with restrictions, or not at all)?
3. Why the data might not be available? (ethical, legal, or commercial restrictions)

Data Availability Statement is not the same thing as data citation! A DAS describes where to find the data, while citations give proper credit to data creators.

*table is reproduced and adapted from this source 

DAS templates and guidance

Canadian Science Publishing DAS Guide - templates with Canadian context

Taylor & Francis DAS FAQs - publisher-specific examples

Springer Nature DAS Examples - discipline-specific examples 

NISO JATS Tagging Guidelines -  technical standards for tagging