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Research Data Management (RDM)

Lists resources to help researchers manage their research data, with an emphasis on Canadian tools.

Research Data Management (RDM)

Resources for managing your research data.

  • 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.

Research data lifecycle: Plan, Create, Process, Analyze, Disseminate, Preserve, Reuse

Source: Primer: Research Data Management by the Portage Training Expert Group, 2019.(CC-BY)


  • 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

Quoted from: Primer: Research Data Management by the Portage Training Expert Group, 2019. (CC-BY)


The following video from the UK Data Archive 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 investigation. RDM practices cover the entire lifecycle of the data, from planning the investigation to conducting it, and from backing up data as it is created and used, to long term preservation of data deliverables after the research investigation has concluded. Specific activities and issues that fall within the category of RDM include:

  • research data management plans
  • file naming (the proper way to name computer files)
  • data quality control and quality assurance
  • data access
  • data documentation (including levels of uncertainty)
  • metadata creation and controlled vocabularies
  • data storage
  • data archiving and preservation
  • data sharing and reuse
  • data integrity
  • data security
  • data privacy
  • data rights
  • notebook protocols (lab or field)

Quoted from: "Research Data Management" in CASRAI Research Data Management Glossary (CC-BY)

RDM is an integral component of the research process which benefits both researchers and society.

For researchers, good research data management:

  • reduces the risk of data loss
  • encourages research collaboration
  • improves research integrity
  • enhances the visibility of research and research data
  • increases the potential impact (e.g. citations) of research work
  • facilitates the sharing and reuse of research data

For society at large, RDM:

  • provides more opportunities to build new knowledge upon existing data
  • assists with helping to find innovative solutions to address scientific, economic, and social challenges

Starting in 2022, the Tri-Agencies will require the submission of research data management plans (DMP's) with many funding applications. See: