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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

Resources for managing your research data.

Research data comes in a variety of formats, shapes, and sizes. Managing research data is a must when it appears that research funders will soon require that research data be stored in an open format for reuse and review. However, a funder mandate is not the only reason researchers should undertake research data management (RDM).

Including plans for research data management at the start of your research project can make your data administration easier throughout the research lifecycle.

Video from UK Data Archive Creative Commons Attribution license (reuse allowed)

What is research data?

Research data is any of the following:

Facts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records. (from the Original RDC Glossary).

What is research data management?

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)

(adapted from The CASRAI Dictionary)

Why is research data management important?

RDM is an integral component of the research process. An increasing number of funding agencies have been advocating that grant recipients make their research data as accessible as possible, including the Tri-Agency with the Tri-Agency Statement of Principles for Digital Data Management. It is anticipated that the Tri-Agencies will soon mandate the sharing of research data as evidenced by the release of the DRAFT Tri-Agency Research Data Management Policy for Consultation.

RDM 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