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Research Data Management

Research Data Management (RDM)

What is RDM?

Research Data Management (RDM) refers to the process applied throughout the lifecycle of a research project to guide the collection, documentation, storage, sharing, and preservation of research data, and allows researchers to find and access data.

What is Research Data?

Research data is information collected or created during research to support and validate findings. This can include observations, survey results, experiments, and interviews in any format, such as text, numbers, audio, or images.

What are the Benefits of Managing Your Research Data?

  • Compliance with funding agency requirements
  • Improve efficiency and organization
  • Make it easier to interpret, share, and reuse research data
  • Increase data security and integrity

Tri-Agency RDM Policy

â–¶ The Tri-Agency Research Data Management Policy (2021)

Developed collectively by three Canadian federal funding agencies - the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Social Sciences and Humanities Research Council of Canada (SSHRC) - the Tri-Agency Research Data Management Policy was introduced to promote sound data management and data stewardship practices among researchers. It applies to grant recipients and to institutions administering Tri-Agency funds. 

The policy outlines three core requirements:

  1. Institutional Strategies: Institutions are required to develop and publish an institutional research data management (RDM) strategy, detailing how they support data stewardship and RDM practices within their organization.
  2. Data Management Plans (requirements for researchers): Researchers applying for funding from any of the three agencies must submit a DMP, outlining how data will be collected, documented, stored, and shared over the course of the research project.
  3. Data Deposit (requirements for researchers): Researchers are expected to deposit their research data in a publicly accessible repository whenever possible, ensuring that data can be accessed, reused, and cited by others, in accordance with ethical, privacy, and legal standards.
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YukonU RDM Strategy

Yukon University’s Research Data Management Strategy articulates our commitment to promoting and supporting researchers in managing data responsibly throughout all stages of the research cycle. This strategy aligns with our institution's vision and values, emphasizing data stewardship, ethical research, and respect for Indigenous data sovereignty.

Open and FAIR data

Government authorities, funding bodies and journals are increasingly encouraging or requiring authors to make data openly accessible without sacrificing the protection of human subjects or other valid subject privacy. Launched in 2016, the FAIR principles provide a set of guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of scientific data. 

Findable
It should be feasible for both humans and computers to find metadata and data.

Accessible
Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorization.
Interoperable
Data should be able to be easily integrated with other datasets, applications, and workflows by both humans and computers.
Reusable
Metadata and data should be described in a way that allows for data replication, so that it can be reused and repurposed by computers.
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Indigenous Data Sovereignty

Open and FAIR data principles, while promoting data sharing, are not universally applicable as they often overlook the rights and interests of Indigenous Peoples. By focusing on accessibility and openness, FAIR principles fail to address power imbalances and historical contexts. The emphasis on greater data sharing alone creates tension for Indigenous Peoples who are also asserting greater control over the application and use of Indigenous data and Indigenous Knowledge for collective benefit, making FAIR an insufficient framework for research involving Indigenous communities. 

Indigenous data sovereignty prioritizes the rights of Indigenous Peoples to govern the collection, ownership, and use of their data. Rooted in self-determination, this approach ensures that data is managed in alignment with Indigenous worldviews, values, and collective benefit. By adopting principles like CARE (Collective benefit, Authority to control, Responsibility, and Ethics) and OCAP (Ownership, Control, Access, Possession), Indigenous data sovereignty provides a framework that respects the cultural and ethical needs of Indigenous communities in research.

 Learn more about Indigenous Data Sovereignty >