Research Data Management (RDM)

Research Data Management (RDM)

• RDM refers to the processes applied throughout the lifecycle of a research project to guide the collection, documentation, storage, sharing, and preservation of research data.
• RDM practices are integral to conducting responsible research and can help researchers save resources by ensuring their data is complete, understandable, and secure.
• RDM practices also follow institutional and funding agency guidelines that protect their investments.
• The broader research community can derive maximum value from research data that can be accessed, shared, reused and repurposed.

(adapted from: Primer: Research Data Management, Portage Training Expert Group, 2019)

What is “research data”?

• Primary sources supporting research, scholarship or artistic endeavours
• Can be used as evidence to validate findings and results
• May take the form of experimental data, observational data, operational data, third party data, public sector data, monitoring data, processed data, or repurposed data
• All other digital and non-digital content have the potential to become research data

(adapted from Primer: Research Data Management, Portage Training Expert Group, 2019)

What does research data management involve?

The following video from the UK Data Service describes typical data management activities that take place at each stage of the research process. (Note: There is no audio in this video.)

Research data management refers to the storage, access and preservation of data produced from a given research project. RDM practices cover the entire data lifecycle: from planning the project through conducting it and disseminating the results; from collecting the data to analysing, cleaning, sharing, and preserving or destroying it after the project has concluded.

Data management activities and topics include:

• accessibility
• Data Management Plans (DMPs)
• documentation and metadata
• file naming and organization
• integrity and authenticity
• licensing and usage rights
• notebook protocols (lab or field)
• preservation
• quality control and quality assurance (QA/QC)
• research team roles and responsibilities
• security and privacy
• sharing and reuse
• storage

(adapted from Research Data Management, CODATA Research Data Management Terminology)

Why is RDM important?

RDM is an integral component of the research process which benefits researchers, the research community, and society at large.  

For researchers, good research data management:

• reduces the risk of data loss
• streamlines research collaboration
• improves research integrity and transparency
• enhances the visibility of research and research data
• increases potential research impact
• facilitates the sharing and reuse of research data

For society, RDM:

• provides more opportunities to build new knowledge upon existing data, maximizing return on research investment dollars
• accelerates the pursuit of innovative solutions to address scientific, economic, and social challenges
• ensures transparency, accountability, and reproducibility of research conducted using public funds

In 2021, the Canadian Tri-Council funding agencies (CIHR, NSERC, SSHRC) introduced the Tri-Agency Research Data Management Policy, which stipulates:

1. Institutions eligible to administer Tri-Agency funding must create an Institutional RDM Strategy, made publicly available on the institution's website.
2. For many funding opportunities, a Data Management Plan (DMP) must be submitted to the agency at the time of application and will be considered in the adjudication process.
3. Future funding opportunities will require grant recipients to deposit into a digital repository all digital research data, metadata and code that directly support the research conclusions in journal publications and pre-prints that arise from agency-supported research

These requirements follow on best practices established in the 2016 Tri-Agency Statement of Principles for Digital Data Management.