Data Access Grant

*** 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.***

Overview

Value & Duration
Up to CDN $10,000, up to 8 months
Application Deadline
This competition is currently closed to new applications.

Purpose

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.

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. Questions on eligible expenses should be directed to awards.dsi@utoronto.ca. Finally, please note that submitted projects should be prepared to spend the funds within the 8-month timeframe.

Successful applicants will be required to:

  • Present the project and related findings in oral or poster format at a DSI Research Day within 1 to 2 years from when a Notification of Award has been sent.
  • Submit a brief summary report that outlines the use of the data and any resulting abstracts, presentations (poster or oral), and/or publications.

In addition, awardees may be called upon to act as reviewers for future DSI award competitions.

The DSI is strongly committed to diversity within its community and especially welcomes applications from racialized persons / persons of colour, women, Indigenous / Aboriginal People of North America, persons with disabilities, LGBTQ2S+ persons, and others who may contribute to the further diversification of ideas.

Eligibility

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.

Submission Process

How to Apply

The award is open to applicants who meet the following criteria:     

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

Applications are submitted via the DSI Good Grants application portal.

Register an account and select “Start Application” for “Data Access Grant.”

DSI Good Grants Dashboard

The application is divided into tabs; each tab includes a set of instructions and fields to fill out. These instructions are also highlighted below.



Applicants will need to complete the following fields.

Tab 1: Start Here

  • Project Title
Tab 2: Team Information You will need the following information for all PIs and trainees:
  • Role
  • Name
  • Email
  • Institution
  • Division (if applicable)
  • Unit (if applicable)
Tab 3: Proposal

Project Description (maximum 750 words): comment on the following:
  • Project rationale
  • Research objectives
Data: Indicate the location(s) of the data that you will be requesting access to.

Amount: Indicate the total amount requested in Canadian Dollars (CAD).

Budget Justification (maximum 750 words): Please provide a clear breakdown of the costs associated with accessing this data. Typically, the main cost to be covered is a data access fee, but applications can also include costs such as data transfer.

Unit Head Signatures: Please fill out the provided template, convert to .pdf, and upload the unit head signature for the PI.

Tab 4: CVs

Upload pdf CVs for each of the PIs.

Once the applicant has submitted their component of the application on or before July 28, the following will occur:

Demographic Survey

The applicant or the person submitting on their behalf will receive a confirmation email that includes a link to a Demographic Survey. While this survey is required, when filling it out respondents have the option to select “Prefer not to answer” for all questions. This link should be sent to all team PIs, and each should fill it out by the Friday following the deadline.

The DSI will form a Review Committee to lead the review of all eligible proposals received by the submission deadline. Reviewers are asked to consider the project rationale and budget justification and the extent to they align with the Data Access Grants program.

All application materials can be submitted directly onto the form. Certain fields on the form ask for uploads and require the following templates:

Further Information

Please contact awards.dsi@utoronto.ca

Current Recipients:

Rajeev Muni (Unity Health Toronto), Peter J. Kertes (Department of Ophthalmology and Vision Sciences, Temerty Faculty of Medicine, University of Toronto), and Yaping Jin (Department of Ophthalmology and Vision Sciences, Temerty Faculty of Medicine, University of Toronto), to access data for Outcomes of Cataract Surgery in Individuals who Received Intravitreal Anti-Vascular Endothelial Growth Factor Agents.

Frank Wendt (Department of Anthropology, University of Toronto Mississauga, University of Toronto) and Nicole Novroski (Department of Anthropology, University of Toronto Mississauga, University of Toronto), to access data for Tandem repeat variation as a causal factor for hair coloration across ancestries.

Girish Kulkarni (Princess Margaret Cancer Centre, University Health Network) and Alistair Johnson (Child Health Evaluative Sciences Labs, The Hospital for Sick Children), to access data for Development of an early warning system for tumour recurrence and progression in contemporary non-muscle invasive bladder cancer patients using time-series forecasting with artificial intelligence.

Rayjean Hung (Lunenfeld-Tanenbaum Research Institute) and Geoffrey Liu (Princess Margaret Cancer Centre, University Health Network), to access data for Comprehensive investigation of lipidomics and its genetic determinants in cancer risk and prognosis by evidence triangulation.

Deborah Levy (Child Health Evaluative Sciences Labs, The Hospital for Sick Children) and Kuan Liu (Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto), to access data for Childhood-onset Systemic Lupus Erythematosus in Ontario: Determining Long-term Outcomes.

Tara Gomes (Unity Health) and Sara Allin (Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto), to access data for Examining access of publicly funded Flash Glucose Monitoring System’s Among Ontario’s Immigrant Population.

Rohan Alexander (Faculty of Information, University of Toronto) and Christopher Cochrane (Department of Political Science, Faculty of Arts and Science, University of Toronto), to access data for Developing a large-scale lobbying and political donations dashboard to foster access to and interdisciplinary research on democratic accountability and political donations.

Madeline Li (Princess Margaret Cancer Centre, University Health Network) and Michael Brudno (Department of Computer Science, Faculty of Arts and Science, University of Toronto), to access data for Proactive PSO: Enhancing Access to Psychosocial Oncology Care for Marginalized Patients.

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.

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.