The information provided herein is intended as an overview of research data management (RDM) at Laurentian University, connecting researchers with the services and tools offered by the Department of Library & Archives.
Questions regarding RDM can be directed to: Michael McArthur, Associate Librarian - Health and Data: rdm@laurentian.ca
What is Research Data Management?
Research data management (RDM) encompasses the processes applied throughout the lifecycle of a research project to guide the collection, documentation, storage, sharing, and preservation of data. (Digital Research Alliance, 2024)
Not all data collected during the course of a research project follows this lifecycle exactly. During the planning phase for your project, each of these elements needs to be considered and addressed in your data management plan.
Plan: Design the research and identify the data that needs to be collected.
Create: Find or create the data points needed for the research.
Process: Clean and organize the data.
Analyze: Run models and tests to study the data
Preserve: Back up the data and prepare for long-term preservation.
Share: Publish the research and share data within a subject-appropriate repository.
Reuse: Data is discovered by other researchers, where it can be incorporated into their own research, or the original researcher continues their program based on this data.
Research Lifecycle. (2022, June 9). Network of the National Library of Medicine. https://www.nnlm.gov/guides/data-glossary/research-lifecycle
RDM helps to preserve, protect and proliferate the data behind research discoveries and claims. First and foremost it is about quality and transparency. When research data is managed actively and responsibly, the evidence that underpins research can be made open for anyone to scrutinize and attempt to reproduce findings. This leads to a more robust scholarly record, and helps to discourage and identify academic fraud.
Another primary benefit is protection: the rights and legitimate interests of data subjects and Intellectual Property owners are mindfully protected, and responsible data management reduces the risk of inadvertent data leakage or loss.
Further benefits can be derived from good data management, including:
- Impact: Data linked to publications receive more citations, over longer periods of time;
- Speed: The research process becomes faster, which can be vital in tackling ongoing global challenges;
- Efficiency: Data collection can be funded once, and the data re-used many times for a variety of purposes;
- Accessibility: Interested third parties can (where appropriate) access and build upon publicly-funded research outputs with minimal barriers to access;
- Durability: Simply put, fewer important datasets will be lost or become incomprehensible if they are managed with care.
Laurentian University
Research Data Management Institutional Strategy
The RDM strategy is relevant to all Laurentian University researchers. This includes faculty, postdocs, undergraduate and graduate students, research staff and administrators. It is a living document, that the University will periodically revisit to ensure it continually develops and evolves into a document that encompasses current knowledge and best practices in Research Data Management.
Tri-Agencies (CIHR, NSERC, SSHRC)
Research Data Management Policy
The Tri-Agencies adopted the above policy in 2021 which is comprised of three pillars:
- Institutional RDM strategies must be in place by 2023
- Data management plans (DMPs) will be required for a specific number of funding opportunities starting in 2022
- Data deposit will be phased in after a review of the institutional strategies and an assessment of the readiness of the Canadian research community.
Scholarly Publishers
It is now common for publishers to include stipulations regarding data sharing/availability as a condition for publishing your paper. You should look to the author guidelines for the specific journal you are submitting to. Here are some examples of publisher policies and author guidance.