RDM and DMP: The Basics

The following is a truncated version of the accompanying attachment: Data Management Plans_FAQ and resources_v1

According to the Tri-Agency Research Data Management Policy:

Research data management (RDM) refers to the processes applied through the lifecycle of a research project to guide the collection, documentation, storage, sharing and preservation of research data (Source).

RDM is essential throughout the data lifecycle—from data creation, processing, analysis, preservation, storage and access, to sharing and reuse (where appropriate), at which point the cycle begins again. Data management should be practiced over the entire lifecycle of the data, including planning the investigation, conducting the research, backing up data as it is created and used, disseminating data, and preserving data for the long term after the research investigation has concluded (Source).

The agencies acknowledge the diversity of models of scientific and scholarly inquiry that advance knowledge within and across the disciplines represented by agency mandates. The agencies, therefore, recognize that significant differences exist in standards for RDM—including what counts as relevant research data—among and across the disciplines, areas of research, and modes of inquiry that the agencies support (Source).

According to the Tri-Agency Research Data Management Policy:

RDM enables researchers to organize, store, access, reuse and build upon digital research data. RDM is essential to Canadian researchers’ capacity to securely preserve and use their research data throughout their research projects, reuse their data over the course of their careers and, when appropriate, share their data. Furthermore, as an acknowledged component of research excellence, strong RDM practices support researchers in achieving scientific rigour and enable collaboration in their fields (Source).

Data Management Plans (DMP) are living documents associated with a project, and details the practices, processes, and strategies that the researcher will use to effectively and ethically manage their data before, during, and after. A DMP will also identify potential obstacles in data management and will provide solutions before these arises.

Each funding agency will have their own set list of requirements, but generally, the following components should be expected and included. For further details, please visit Portage Network

  • Data collection: how data will be collected, documented, formatted, protected, and preserved.
  • How existing data will be used
  • How new data will be created
  • Data storage and backup: how and whether data will be shared, and where will the data be deposited
  • Data security: including how is responsible for managing data and succession plans
  • Data preservation
  • Data sharing/reusing (if applicable)
  • Ethical, legal or commercial constraints
  • Methodological considerations

Dawson College is proud to support the Tri-Agency in promoting and ensuring RDM best practices.

An RDM Strategy has been drafted and posted, and we are currently creating a number of tools and a resource hub that will serve the Dawson Research Community in informing and equipping researchers of RDM best practices.

As this is a new area for us all, we will be frequently soliciting input from our researchers and stakeholders, and continuously updating our strategy and offerings to reflect the needs of our researchers, our community, our partners, and the research fields.

Details can be found in the Appendix of the Action Plan. Dawson_Data Management InstitutionalPlan_v1

Ildikó, our Research Coordinator, welcomes all questions, comments, and insight and is available through email. Feel free to drop a line at iglaserhille@dawsoncollege.qc.ca

See also

Research Data Management Strategy Plan

Indigenous Data

Resources and Training Tools

Last Modified: March 21, 2023