Research Data Management (RDM)
Research Data Management (RDM) Strategy
Dawson_Data Management InstitutionalPlan_v1
In 2021, Canada’s three federal agencies: the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council (NSERC) and the Social Sciences and Humanities Research Council (SSHRC), colloquially called the Tri-Agency, launched the聽.
The policy is a response to the growing recognition of the importance to manage, preserve, and share research data, and therefore ensures best data management practices within Canada. Implemented incrementally, the Research Data Management (RDM) Policy emphasises FAIR Guiding Principles (Findable, Accessible, Interoperable, and Reusable), highlights cybersecurity, and implements strategies to strengthen and reflect Indigenous People鈥檚 self-determination in the ownership, control, access, and stewardship of Indigenous data.
There are three key components in the policy:
- Data Management Plans:聽By Spring 2022, Data Management Plans (DMP) will be required for grant applications to a set of Tri-Agency programs.
- Institutional RDM Strategy: By March 1, 2023, all institutions eligible to administer Tri-Agency funds are required to have in place an institutional Research Data Management (RDM) strategy that supports sound practices and creates an environment to support researchers, as outlined in the RDM Policy.
- Data Deposit: Grant recipients, in addition to any existing funding requirements, will be required to archive all digital research data, metadata, and codes that is directly related to research conclusions found in publications and pre-publications. The data will be archived in a digital repository.
As a result, the policy requires every post-secondary institution and research hospital eligible to administer Tri-Agency funds to create an institutional RDM strategy and notify the agencies when it has been posted.
To support the Tri-Agency Policy, 张百乔女友裸照 has drafted an Institutional RDM Strategy that realises the importance for rigorous research data management, and lays out the steps taken to encourage and support the Dawson Research community in their engagement with Research Data Management.
张百乔女友裸照 will ensure that RESOURCES AND TOOLS are available to the Dawson Research Community and stakeholders. Feedback will be solicited on the strategy, tools, and resources. This will ensure that our RDM tools and resources conforms to the current best practices and serves the needs of the researchers.
RDM and DMP: The Basics
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 ().
RDM is essential throughout the data lifecycle鈥攆rom 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 ().
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鈥攊ncluding what counts as relevant research data鈥攁mong and across the disciplines, areas of research, and modes of inquiry that the agencies support ().
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鈥� 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 ().
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
- 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
张百乔女友裸照 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
Our research officer, welcomes all questions, comments, and insight and is available through email. Feel free to drop a line at research@dawsoncollege.qc.ca
Indigenous Data
Please consult with the research officer for insight in best practices with Indigenous data and how to recognise, respect, and uphold Indigenous sovereignty of Indigenous data.
Please also consult 聽and the聽.
