Data Management Plans
Find all the tools you need to create your Data Management Plan
Data Management Plans
A Data Management Plan (DMP) is a formal plan that outlines how a researcher intends to manage research data during and after a research project.
- 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.
DMPs can be developed or expanded for different purposes or modified to accommodate changes throughout the course of a research project.
Data management experts:
- UBC Library Research Data Management Team
- UBC Research Ethics Board
- UBC Support Program to Advance Research Capacity (SPARC)
- UBC University-Industry Liason Office
- FoM Digital Solution Data Management Team
DMP Templates and Tools:
- DMP Assistant
- DMP Examples for UBC FoM
- DMP Exemplars by Portage
- UBC Data Transfer Agreement template
DMP Guides and Training:
|Who Needs a DMP?
|In 2021 the Tri-Agency Research Data Management Policy was released 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.
|Questions to Consider
- Guidance on what to include for data management tools such as REDCap, OpenSpecimen, and Oracle Apex
- Information about UBC resources available to support you in the other areas of DMPs
Intro to DMPs
Click any bar below to learn more about DMPs and related concepts.
What is 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 non-digital content have the potential to become research data.
Research data may be experimental, observational, operational, third-party, public sector, monitoring, processed or repurposed data.
What are the Data Management Principles?
Grant recipients are not required to openly share their project’s research data.
However, 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.
What are the benefits of having a DMP?
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.
More sample DMPs coming soon
Elements to Consider When Creating a DMP
Adapted from the Government of Canada's Guide to Preparing a Data Management Plan
1. Data Collection
- What data will be collected, created, linked to or acquired?
- What file formats?
- What conventions will you use to organize your files?
- Address data collection issues such as data types: think of all quantitative and qualitative data including data from consent forms, clinical data, demographics, self reported questionnaires, in-person assessments, data extracted from samples, data obtained through linkage etc
- Consider file formats: open or proprietary, naming conventions and data organization–factors that will improve the usability of your data and contribute to the success of your project (Ex.REDCap has standardized file naming conventions for exports)
2. Documentation and Metadata
- What documentation is needed to read and interpret data?
- How will you make sure the documentation is consistent?
- Are you using metadata standards or tools to describe data?
- Why include metadata: Because data are rarely self-explanatory, all research data should be accompanied by metadata (information that describes the data according to community best practices). (Ex. REDCap database is documented in data dictionary and codebook)
- 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 and backed up during the research project?
- What are the storage requirements, size and length of time?
- How will the data be accessed?
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.
- Plan how research data (digital and physical) will be stored and backed up throughout and beyond the research project. Data retention time depends on the type of data.
- If your data is being managed through a service, they will have SOPs and policies to refer to, so consult with them
4. Data Preservation
- Where data will be deposited for long-term preservation? (see requirement in section 3.3 of the Tri-Agency Research Data Management Policy)
- How will you ensure data is preservation ready?
- 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 Reusing Data
- What data will be shared and in what form?
- Have you considered end-user license?
- What steps will be taken to help research community know data exists?
- 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.
- 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. This may involve a Data Transfer Agreement.
6. Responsibilities and Resources
- Identify who will be responsible for managing the projects data
- How will responsibilities be managed if personnel overseeing project changes?
- What resources will you require to implement your data management plan?
Think about: 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)
- Establish procedures/policies for ongoing training of project participants on the DMP, including technical elements and repository maintenance.
- 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
- For sensitive data, how will you ensure that it is securely managed?
- What strategies will you undertake to address secondary use?
- How will you manage legal, ethical and intellectual property issues (e.g., how the project will comply with laws and ethical guidelines that apply to the data)
- 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.
- 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
Sample DMP (coming soon)