Data Bites

Your Research Data Management Refresher

News bites about important research data management principles that connects researchers to helpful practical resources. Data Bites is a regular feature in Compass.

Compass Issue 26 – April 3, 2025

Research Data Management and AI Tools

AI tools powered by Large Language Models (LLMs) are revolutionizing the way we handle research data. They can analyze documents, generate code, and create text, images and videos from prompts, making them potentially helpful tools in your research workflow. But before you dive in, let’s talk about two crucial risks you need to be aware of.

Data Protection

Most LLMs operate in the cloud, meaning the company behind them can retain a copy of everything you share with it. This could lead to unintended disclosures of private information. If you are working with non-public data, don’t share it with AI tools without contractual assurance that your data will not be saved or used to train the model. Your research data’s security is paramount!

Data Integrity

LLMs can produce incredibly useful content, but they can also make mistakes that aren’t easy to spot. AI tools can generate false statements, draw inaccurate conclusions, and fabricate non-existent references. These errors undermine research integrity and compromise research replicability. The onus is on researchers to ensure the accuracy of all their research outputs. If you choose to use an AI tool, you must always verify that AI-generated and AI-informed content is accurate and complete.

University Resources for Safe AI Use

University of Toronto researchers have access to the enterprise version of Microsoft Copilot, which is safe to use for up to Level 3 data and ensures your data won’t be retained or used for training. For more details, visit the Microsoft Copilot Tool Guide.

Join us to learn how to use U of T library-licensed AI tools to power your literature exploration at our upcoming webinar on April 9, 2025, and in-person workshops offered on April 30, and May 8, 2025.

Generative AI Considerations in Academic Research

For more information about using GenAI in your research workflow, check out the CRIS guide on Generative AI Considerations in Academic Research.

Compass Issue 25 – February 6, 2025

Data Ownership

Data Ownership – Whose Data Is It, Anyway?

Did you know that Feb 10-14 is International Love Data Week 2025? With the theme of Whose Data Is It, Anyway?, this year’s organizers invite researchers to reflect on where their data came from and who owns the data.

Some key questions to consider about data ownership:

What rights do I have to use and publish the data that my research team has collected?
What legal and ethical obligations do I have regarding the data?
What rights do research participants have to their data?
If I’m conducting research involving Indigenous peoples, how has data ownership been established to respect Indigenous data sovereignty?
How are the contributions of collaborators and students considered?
What are the academic institution’s policies on data ownership?
What happens to the data if I leave the institution or project?
Research data can sometimes be considered intellectual property (IP). How does the institution’s IP policy impact data ownership?
Are there any funder or publisher policies that impact data ownership?
If I’m using existing data, are there any restrictions on data usage due to licensing, data use agreements, or other permission limitations?

These questions don’t have a one-size-fits-all answer. Consult the University of Toronto Institutional Research Data Management Strategy, or connect with the Innovations & Partnerships Office (IPO) or the Centre for Research & Innovation Support (CRIS) for support. Contact the University of Toronto Libraries for more information about data licenses.

Want to Learn More?

Browse the full list of International Love Data Week 2025 events and activities, and view recorded sessions on the ICPSI YouTube channel.

Indigenous Data Sovereignty

Learn about and reflect on Indigenous data sovereignty in CRIS’s Learning Together Discussion Group: The Fundamentals of OCAP® (Ownership, Control, Access and Possession) program.

Compass Issue 24 – December 5, 2024

How to Prevent Data Loss

How Can Data Loss Occur?

While data can be lost through malicious interference or attack, it is most commonly caused by simple human error or a system failure. While the hope is that our data will always be there for us, a good backup strategy will help to minimize such loss and allow you to recover faster to carry on your research.

Strategies for Preventing Data Loss

A simple and effective backup strategy for preventing against data loss is commonly known as the 3-2-1 strategy.

You should have at least
3 copies of your data.

3 Copies

Improve resiliency by storing your backups on at least 2 different storage mediums. For example, 1 backup hosted in the cloud and 1 backup on an external drive.

2 Mediums

Don’t keep all of your backups in close proximity to each other, as a fire, flood or natural disaster could render them all unusable.

1 Off-site

How can I back up my data?

Your divisional or departmental IT staff are a fantastic resource regarding what tools, techniques and services might already be available to you to backup your data. Otherwise, the University’s Information Security Handbook provides practical how-tos on developing a backup strategy and backing up your data.

Securing and Maintaining my Backups

Remember that your backups are a copy of your data and intellectual property, therefore it is essential that just as you encrypt your working data that you encrypt your backups as well.

Having access to a working backup when you need it could be the difference between marginal inconvenience and the irrecoverable loss of years of work. It’s important to periodically test your backups by recovering from them and ensuring that they are usable.

Compass Issue 23 – October 3, 2024

Data Management Plan (DMP)

Creating Your Data Management Plan (DMP)

A Data Management Plan (DMP) is more than just a requirement—it’s a helpful tool for keeping your research organized and on track.

A DMP is a formal document that outlines how you will handle your research data throughout your project, covering everything from storage and security to sharing and preservation. It helps ensure your data is well-organized, easy to find, and secure, reducing the risk of pitfalls like data loss or duplication.

Why Bother with a DMP?

Think of it as your guide to making smart decisions about your data. It helps you plan for challenges, estimate resource needs, and keep your research team aligned. Plus, a solid DMP can make your life easier when meeting funder requirements. In fact, major funding agencies, including the Tri-Agencies, are starting to request a DMP as part of grant applications.

Not Sure Where to Start?

Visit the DRI Portal and The University of Toronto Libraries’ RDM website to take the first step toward a smoother research process and ensure your DMP meets both your project needs and funder expectations. You can also use the DMP Assistant, a free collaborative tool that provides customized templates and guidance. Or check out the McMaster Data Management Plan Database to see a variety of example DMPs.

Sign up to our Compass Newsletter

To stay up to date on important research data management principles, subscribe to the CRIS mailing list to receive our newsletters.

Questions?

Please email cris@utoronto.ca if you have any questions.

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