Data Access Grants

*** Please note: The deadline for this call has now passed. Thank you to all the applicants and for your interest. The below information is being left up for reference.***

Application Deadline
September 30, 2022
Value and Duration
Up to CDN $10,000, up to 8 months
Application Procedure
Proposals are to be submitted using an online form 


The Data Sciences Institute (DSI) will provide grants of up to $10,000 to cover costs associated with accessing and working with large data sources that are necessary to carry out data-intensive research projects. Projects should bring together researchers from different disciplines to work on projects through a shared trainee or trainees at a Master’s, Doctoral, or Postdoctoral level.

The purpose of these grants is to improve data accessibility for DSI researchers and to foster research by mitigating the high cost of access to data sets. The DSI believes that equitable access to resources is crucial for creating a diverse and inclusive environment, and equity will be considered when reviewing all applications.

Applications are accepted four (4) times a year (September, December, March, May). 


The applicant and co-applicant(s) must have a budgetary appointment* either at the University of Toronto OR an external funding partner institution and:

  • Eligible to hold research funding at the University of Toronto or at a DSI external funding partner.
  • Each proposal must include two faculty members from complementary disciplines. Co-PIs from the same unit can apply as long as they represent different disciplines. The proposal should highlight the complementary academic contributions of the researchers and any trainees.
  • Applicants must be members of the DSI.

*Faculty budgetary appointments for the University of Toronto are continuing, full-time academic appointments with salary commitments from a University of Toronto academic unit.

Allowable Expenses

DSI Data Access Grants are intended to cover the costs of accessing data. Typically, the main cost to be covered is a data access fee, but applications can also include costs such as data transfer. This grant cannot fund project personnel costs. In cases where personnel costs are included within the standard services of a data provider, these costs must be carefully and fully justified.

Submission Process

Applications are made via the online form linked on this page and must include the following:

  • NPI Information
  • Co-PI Information
  • Trainee Information
  • Project Information
    • Title of Research Proposal
    • Research Proposal Description (maximum 4000 characters)
    • Location of data
    • Additional comments (optional)
  • Budget Information
    • Total amount requested
    • Budget outline (maximum 4000 characters)

The Research Proposal Description and Budget Outline should detail requested project costs and what data will be paid for by the DSI Data Access Grant. This should include a description of the costs necessary for data access—that is, the costs reimbursed that will make the difference between being able to gain access to the data for an existing research project or not.

If relevant, please mention how gaining data access for your Project will address equity, diversity, and inclusion in your Research Proposal Description.


Researchers that receive Data Access Grants are required to fulfill the reporting obligations listed below:

  • The Project and related findings must be presented in oral or poster format at a DSI Research Day within 1 to 2 years from when a Notification of Award has been sent.
  • We will contact you one year after funds have been claimed to request that you submit a brief summary report that outlines the use of the data and any resulting abstracts, presentations (poster or oral), and/or publications.
  • We will contact you to act as a Reviewer for future DSI Awards competitions.

Further Information

Please contact

Past Recipients:

Eldan Cohen (Mechanical & Industrial Engineering) & Sheila McIlraith (Computer Science) to access data for Fair and Interpretable Machine Learning in Healthcare Applications.

Seema Mital (The Hospital for Sick Children) & Ryan Yuen (The Hospital for Sick Children) to access data for Case-Control Comparison of Genomic Variants in Congenital Heart Disease.

David Soberman (Rotman School of Management) & Mary L’Abbe (Nutritional Sciences) to access data for Nutrition, Marketing, and Health.

Eyal Cohen (The Hospital for Sick Children) & Sonia Grandi (The Hospital for Sick Children) to access data for Cardiometabolic Health of Mothers with a Sick Child.

Michael Fralick (Lunenfeld-Tanenbaum Research Institute) & Kieran Campbell (Lunenfeld-Tanenbaum Research Institute) to access data for Diabetic Ketoacidosis from New Use of an SGLT2: Can Genomics Accurately Estimate Risk (DaNGER).

Alan Walks (Geography, Geomatics, and Environment, UTM) & David Hulchanski (Factor-Inwentash Faculty of Social Work) to access data for Housing Justice: Link Datasets to Analyze Evictions During the Covid-19 Pandemic.

Christopher Wallis (University Health Network & Surgery, Temerty Faculty of Medicine) & Angela Jerath (University Health Network & Anesthesia, Temerty Faculty of Medicine) to access data for Association Between Patient-Surgeon Sex Discordance and Health System Costs.

Rohan Alexander (Information & Statistical Sciences, Arts & Science) & Monica Alexander (Statistical Sciences and Sociology, Arts & Sciences) to access data for Disparities in climate-induced health outcomes in the Greater Toronto Area 

Benjamin Haibe-Kains (University Health Network) & Trevor Pugh (Medical Biophysics, Temerty Faculty of Medicine) to access data for Validating the Utility of Meta-Analysis for Learning Translatable Predictive Models from in vitro Pharmacogenomics 

Scott MacIvor (Biological Sciences, UTSC) & Marie-Josee Fortin (Ecology & Evolutionary Biology, Arts & Science) to access data for Urban parks for people: anonymized movement data to determine access and equity 

Bruce Perkins (Lunenfeld-Tanenbaum Research Institute) & George Tomlinson (University Health Network) to access data for Working to Mitigate Diabetic Ketoacidosis in Type 1 Diabetes: An Education Tool Combining Novel Trial Data Analysis and Lived Experience