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McGill RDM Strategy v3.0


In March 2021, the Tri-Agency[1] launched a Research Data Management (RDM) Policy with the objectives of promoting RDM and data stewardship practices amongst Canadian researchers. The Tri-Agency RDM Policy will be implemented with an incremental approach, in step with continuing development of RDM practices and capacities in Canada and internationally.

Three key deployment phases have been communicated by the Tri-Agency:

  • Institutional strategies: By March 1st, 2023, each post-secondary institution and research hospital eligible to administer Tri-Agency funds is required to create an institutional RDM strategy, publicly post the strategy and notify the agencies when completed.
  • Data management plans: By spring 2022, the agencies will identify an initial set of funding opportunities where researchers will be required to submit a data management plan with their grant proposal.
  • Data deposit: After reviewing the institutional RDM strategies, and in line with the readiness of the Canadian research community, the agencies will phase in the data deposit requirement. In addition to any existing sponsor requirements, grant recipients will be required to archive all digital research data, metadata and code that directly support research conclusions in journal publications and pre-prints into a digital repository, and “provide appropriate access to the data where ethical, cultural, legal and commercial requirements allow[2]”. There is no current timeframe for this requirement.

McGill University is committed to meeting these Tri-Agency RDM requirements and supporting its researchers in adopting these practices. McGill therefore aims to provide researchers with the best possible support in terms of project planning, guidelines, policies, and infrastructure, to foster research excellence across the institution. In 2021, a joint effort from the office of the Vice-Principal - Research and Innovation, the McGill Library and IT Services led to the creation of the McGill Digital Research Services Hub (DRS). The DRS was an important first step in building capacity to coordinate RDM services across institutional units. The DRS currently offers a broad array of support and guidance services.

The present strategy does not mandate RDM requirements nor policies at McGill, but it outlines the high-level approach McGill will take over the next 3 to 5 years to equip its research community with the knowledge, tools, and support to adopt meaningful and robust RDM practices. The RDM Strategy is intended to be a living document that will be reviewed and adapted in pace with the evolving needs and policies in terms of research data management.


What is Research Data Management (RDM)?

RDM is a framework for actively organizing research data through the life cycle of a research project or program (please see Appendix A for a full list of definitions). RDM is both a field within the academic discipline of Information Science and a set of methodological guidelines that involve the planning, organization, description, storage, and sharing of research data in a secure fashion. Good RDM practices are also expected to improve the dissemination and reproducibility of research outcomes.


Why is Research Data Management Important?

Recognizing research data as a major research asset is an important steppingstone in the pursuit of academic excellence. Research activities in many domains create increasingly larger volumes of data that are challenging to manage and analyze effectively. Making research outputs discoverable, reproducible, and reusable, are foundations and principles of modern scholarship. While not all research data are suited to be shared broadly, for ethical, legal, cultural, or commercial reasons, adopting best practices in research data management applicable within and between research units is crucial to maintain and maximize public trust in academic research.

Governments, funders, institutions, and research communities ubiquitously recognize that RDM best practices are essential to raise research standards and increase its potential impact and relevance. Properly managed data have both practical and financial[3] benefits to research, such as reducing research duplication, lowering unnecessary burdens on participants due to repetitive sampling, increasing accountability and transparency, allowing replication of research results, fostering collaborations, and accelerating new discoveries.

RDM is an integral part of research. RDM practices enable compliance with fast evolving ethical, legal, cultural, and commercial requirements and are a key factor in safeguarding research when necessary. Therefore, it is critical to strive to equip researchers, staff, and trainees with sound RDM practices and stewardship to achieve scientific rigor and enable collaboration.


[1] The Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council (NSERC) and the Social Sciences and Humanities Research Council (SSHRC)

[3] European Commission, Directorate-General for Research and Innovation, Cost-benefit analysis for FAIR research data: cost of not having FAIR research data, Publications Office, 2019,

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