Seed Funding for Methodologists Grant


Application Deadline
This competition is currently closed to new proposals. Please check back in fall 2023.
Value and Duration
CDN $10,000 for up to 8 months


The Data Sciences Institute (DSI) is a central hub and incubator for data science research, training, and partnerships at the University of Toronto. Its goal is to accelerate the impact of data sciences across disciplines to address pressing societal questions and to drive positive social change.   The DSI Seed Funding for Methodologists initiative supports single applicants working in data sciences methodology or theory. In applying for this grant, applicants agree to (a) present their work to an audience of applied researchers and (b) apply for a Catalyst Grant with a new Collaborative Research Team (CRT). Ideal candidates will have a novel methodological or theoretical tool that has potential uses in a variety of applications.  The purpose of this grant is to catalyse new Collaborative Research Teams by encouraging new collaborations of data science methodologists and theorists with applied researchers. By presenting and bringing to the fore innovative methodological and theoretical work, our goal is to spotlight exciting methodological innovations and facilitate new and unexpected connections between data science methodologists and applied researchers to foment cutting edge data science work.  An applicant’s research area should focus on data sciences methodology or theory with the potential to be relevant to applied fields. Applicants should summarize their innovative data sciences work and explain its relevance and potential for engaging applied fields.  If successful, applicants will present their work and funds of up to $10,000 can be used to seed a new Collaborative Research Team with the aim of applying for a DSI Catalyst Grant. Funds can be used for up to eight months to support that team through the application process. The DSI will fund five applicants each year and will hold calls twice yearly until our funding is used.  Successful applicants are required to: 
  1. Present their research and methodology/theory at a seminar, including its potential for applied fields. (The logistics to be supported by the DSI Office.) 
  2. Engage co-PIs to develop a DSI Catalyst Grant application with a new collaborative research team. 
In addition, awardees may be called upon to act as reviewers for future DSI awards 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.

How to Apply

Applicant Eligibility
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. 
Application Process
Applications are submitted via the DSI Good Grants application portal.

Register an account and select “Start Application” for “Seed Funding for Methodologists 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: Applicant Information

You will need the following information:
  • Name
  • Email
  • Institution
  • Division (if applicable)
  • Unit (if applicable)
Tab 3: Proposal

Methodology (maximum 500 words): Please summarize your novel methodological tool or approach.

Methods (maximum 500 words): Please summarize the potential that your tool or approach has for engaging applied fields.

Figures & Supporting Material (maximum 1 page): upload a 1-page .pdf with figures and supporting material.

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

Tab 4: CV Using the provided template, upload the PI’s CV.

Once the applicant has submitted their component of the application on or before April 17, 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. The applicant has until April 24 to fill out the survey.
Evaluation and Selection Process

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 following categories:

  • Project rationale and the extent to which it aligns with the DSI mandate including, if applicable, alignment with the DSI Thematic Programs in Reproducibility or Inequity.
  • The potential impact of the proposal on social science research.
  • The extent to which the proposed project includes the development of novel methodology or the innovative application of existing approaches in the context of the social sciences.
  • The extent to which the project considers how to advance EDI in research and outcomes.
Templates and Forms

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

Past Recipients

Jessica Gronsbell (Department of Statistical Sciences, Faculty of Arts and Science): “Infairness: Algorithmic bias evaluation and mitigation for large unlabeled datasets with broad application”

Joseph Jay Williams (Department of Computer Science, Faculty of Arts and Science): “SMART Systems: Dynamic self-optimizing system based on user input for time-sensitive applications”

Ting Kam Leonard Wong (Department of Computer and Mathematical Sciences, University of Toronto Scarborough): “Macroscopic Models of Equity Markets and Portfolio Selection”

Murat Erdogdu (Department of Computer Science, Faculty of Arts & Science): “Applications of Stein’s Method in ML”

Aya Mitani (Dalla Lana School of Public Health): “Matrix-Variate Regression for Multilevel Data”

Linbo Wang (Department of Computer & Mathematical Sciences, University of Toronto Scarborough): “Causal Inference: From Prediction to Actionable Insights”

Recording of Past Event

Further Information

For more information, please contact