Grants of up to $10,000 are available to cover costs associated with accessing and working with large data sources, which are necessary to carry out data-intensive research projects. This program is expected to bring together researchers from different disciplines to work on projects through a shared trainee 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 (November, January, April, July) until five (5) grants have been funded in a given university fiscal year (May-April).
The DSI Data Access Grant is intended to cover the costs of accessing data. Usually, the main cost to be covered is a data access fee, but it could also include costs such as data transfer. This grant cannot fund personnel costs.
Applications must include the following:
The proposal and budget should detail the 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 the data access will address equity, diversity and inclusion.
Researchers that receive Data Access Grants are required to fulfill the reporting obligations listed below:
Please contact firstname.lastname@example.org
January 25, 2022.
We are pleased to announce the recipients of the first round of our Data Access Grants competition:
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