Health Data Nexus Dataset Grants Applications Close (Deadline: Oct. 10)
Overview
• Value: $50,000 per grant • Number of grants available: Two grants • Duration: One year (can be extended to two years, but no extra funding will be provided) • Application Procedure: Applications must be submitted using this online form:
Application Deadline: October 10, 2025 (Friday @ 5 pm ET)
About Us
T-CAIREM is an interdepartmental Centre that serves as a focal point for collaboration among healthcare providers, trainees, researchers, computer scientists, engineers, and industry. Our goal is to transform health through AI.
To identify relevant problems in health care and medicine that will benefit from AI solutions requiring large datasets
To generate high-value, de-identified datasets for use by a broad range of AI researchers, educators, and trainees in medicine
To enrich the Health Data Nexus and demonstrate its utility for easily and broadly sharing highly impactful health data
To align with T-CAIREM’s mission of accelerating responsible, high-impact AI in medicine and health care.
Eligibility
The Primary Applicant/ Principal Investigator must:
Be a T-CAIREM member,
Hold a University of Toronto faculty appointment, OR hold a faculty appointment at a Canadian university and be a co-Principal Investigator with a University of Toronto faculty appointee, and
Be eligible to hold grant funding.
Priority will be given to interdisciplinary project teams.
Individuals may submit only one application as a Primary Applicant but may participate as co-Applicants or team members on multiple submissions.
Successful proposals will be evaluated according to the following criteria:
Clearly specify the dataset that will be generated, and how it can be used for AI research and education (40%)
-Project Proposal (including the impact of the dataset on AI research and education)
(Max. 800 words)
Datasets that support multiple research questions or educational applications will be prioritized.
Approach to dataset generation (20%) (Please include in detail how the dataset will be generated, curated, checked for validity and reliability, de-identified for use on the Health Data Nexus, and maintained)
(Max. 1200 words)
-Preference is given to projects with a reproducible and sustainable pipeline.
Research team and roles. Preference is given to interdisciplinary project teams (20%)
(Max. 200 words)
Feasibility including project milestones, timelines, and cooperation with institutional REB (15%)
(Max. 600 words)
Budget justification (5%). We welcome projects augmenting current research funds, partnerships, or matching (cash or in-kind) to demonstrate commitment to the initiative. Funds can be used to support student RAs, student stipends, open access fees, etc. Eligible expenses must align with the University of Toronto’s Guide to Financial Management.
(Max. 400 words)
Anticipated zone of access and reasoning (ungraded)
-The Health Data Nexus has three different zones of access for data with increasing controls for data of increasing sensitivity. These are described online.
-Datasets that reside in Zone 1 of the platform, which has the most transparent and equitable access process, will receive priority.
References
(Max. 500 words)
Submission Process
Submissions must include:
A completed application form.
Completed abbreviated CVs of PI and co-PIs (max. 5 pages each) in PDF format. The Canadian Common CV (CCV) is preferred.
The dataset generated from the project must be published on the T-CAIREM Health Data Nexus in Zone 1, 2, or 3 (Zone 1 preferred).
To be published on the Health Data Nexus, the dataset must be de-identified per T-CAIREM guidelines (attached) and must be provided with metadata and documentation to the T-CAIREM Data Steward.
A final summary report must be submitted at the end of the project funding period, which will be published on the T-CAIREM website (within three months).
The dataset may be selected as a research focus for a T-CAIREM Datathon, in which case the PI will be available to discuss its creation and how it might be used by future generations of AI health rese
