Computational & Quantitative Social Sciences Grant

Computational & Quantitative Social Sciences Grant is sponsored by University of Toronto Data Sciences Institute (DSI). This grant provides seed funding for cutting-edge, high-impact research in computational and/or quantitative social science.

Computational & Quantitative Social Sciences Grant - DSI Computational & Quantitative Social Sciences 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.

*** CDN $10,000 for up to 12 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 promote positive social change.

The DSI at the University of Toronto Scarborough, DSI@UTSC, is a tri-campus initiative to encourage research activity in Computational and Quantitative Social Science (CQSS) that includes grant support for research funding, training, and community-building.

The social sciences are undergoing a data sciences revolution spurred on by new statistical and algorithmic techniques, rapid advances in high-performance computing, and the proliferation of large, complex, and heterogeneous data structures (e.g., spatial, relational, temporal, and textual). These developments present exciting opportunities as well as new challenges for social scientists.

The purpose of the DSI Computational and Quantitative Social Science grant is to capitalize on these changes by providing seed funding to cutting-edge, high-impact research in the area of computational and/or quantitative social science. Single applicants proposing projects in the domain of computational and/or quantitative social science can apply for this grant.

Funds of up to $10,000 can be used over 12 months by successful applicants. The DSI will fund at least five projects each year and will hold rolling calls until our annual funding is used. Ideal candidates are developing or applying statistical, mathematical, or computational methods to uncover new findings relevant to social scientists.

Successful applicants should state clearly the novelty of the method and/or application as well as the expected substantive payoff of their project in a given social science field or subfield. Applicants may be from any field of study but the relevance of their project for social science research must be clearly stated. Applications will be evaluated on the novelty of their proposed method and its application to social science research.

Eligible expenses are to be consistent with the appropriate tri-agency guidelines for NSERC Discovery grants, CIHR Project Grants or SSHRC Insight grants, with the exception of laptops which should only be included in application budgets when required for project-specific purposes (as opposed to general research). Proposals should clearly describe what role all research personnel will play in the research project.

Questions on eligible expenses should be directed to awards. dsi@utoronto. ca .

Successful applicants will be required to: Present their research at a future DSI workshop or seminar (with logistics supported by the DSI@UTSC). Engage with other members of the DSI community to develop a DSI Catalyst Grant application with a new Collaborative Research Team (CRT) .

Generate a public end product, including but not limited to one or more white papers, policy papers, contributions to conference proceedings, open-source software packages, or appropriate journal 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.

The award is open to applicants who meet the following criteria: They have a budgetary appointment at either the University of Toronto* OR an external funding partner institution . They are eligible Principal Investigators, according to the University of Toronto PI eligibility criteria . They are 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.

Tri-Agency and External Grants : The goal of the DSI and other institutional strategic initiatives is to support researchers with initial seed funding, enabling new research projects to progress to a point where researchers can apply for larger, external research funds. PIs must provide evidence that they have either secured or applied for one external funding grant as a lead applicant in the past three years.

Provide the agency and the name of the grant proposal. Your response to this question will undergo an administrative check, in consultation with your faculty or research institute as required, to determine your eligibility to be funded via the DSI. Applications are submitted via the DSI Good Grants application portal .

Register an account and select “Start Application” for “Computational & Qualitative Social Sciences. ” 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 2: Applicant Information You will need the following information: A. Abstract (maximum 150 words) C.

Tri-Agency & External Grants The goal of the DSI and other institutional strategic initiatives is to support researchers with initial seed funding, enabling new research projects to progress to a point where team members can apply for larger, external research funds. Provide evidence that you have either secured or applied for one external funding grant as a lead applicant in the past three years.

Include the agency and the name of the grant proposal. Your response to this question will undergo an administrative check, in consultation with your faculty or research institute as required, to determine your eligibility to be funded via the DSI. D.

Objectives & Impact (maximum 500 words): comment on the following: Project rationale and alignment with the DSI mission Relevance to the DSI Thematic Programs in Reproducibility or Inequity , if applicable Impact on the social sciences E. Methods (maximum 500 words): comment on the following: Explanation of how the method or application is novel for the social sciences F.

EDI Statement (maximum 500 words): summarize the impact of the EDI components of this proposal. This response can focus on your integration of EDI considerations into your research question and methods and/or any EDI-related social outcomes from your research. G.

Figures & Supporting Material (maximum 1 page): optionally upload a 1-page . pdf with figures and supporting material. Upload a CV for the applicant.

No specific format is required, but the file must be a . pdf. All DSI applicants are asked to complete a short Demographic Survey.

If we do not have a response on file, the link to this required component will be included in the applicant’s confirmation email. 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 .

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.

Monica Alexander (Department of Statistical Sciences, Faculty of Arts and Science, University of Toronto): “Deficits of migration in the age of Covid-19: A new approach to studying changing migration patterns. ” Rohan Alexander (Faculty of Information, University of Toronto): “Harmonizing Algorithms and Culture: Reconstructing Spotify’s Danceability Metric to Explore the Algorithmic Rendering of Musical Style and Taste.

” Angelina Grigoryeva (Department of Sociology, University of Toronto Scarborough, University of Toronto): “New Money in the New Economy: The Shift to Stock-Based Compensation and Wealth Inequality. ” Joseph Hermer (Department of Sociology, University of Toronto Scarborough, University of Toronto): “Mapping Anti-Homeless Policing Events Amidst the Extreme Climate of Prince George, BC.

” Eunice Eunhee Jang (Department of Applied Psychology and Human Development, Ontario Institute for Studies in Education, University of Toronto): “Quantifying Oral Proficiency: Leveraging Machine Learning and Prosodic Features for Enhanced Automated Speaking Assessment and Learning. ” Spike W. S.

Lee (Joseph L. Rotman School of Management, University of Toronto): “What Do Fake News and Biased News Look Like? A Text-Analysis and Machine-Learning Approach.

” Monica Ramsey (Department of Anthropology, University of Toronto Mississauga, University of Toronto): “Deep Learning in Archaeology and Paleoethnobotany: Imaging Phytolith Training Data. ” Zahra Shakeri (Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto): “Social and Behavioural Determinants of Health in Prognostic Machine Learning Models for Patient Outcome Prediction.

” Julie Teichroeb (Department of Anthropology, University of Toronto Scarborough, University of Toronto): “Using machine learning to determine how male dispersal shapes a primate multilevel society.

” Jue Wang (Department of Geography, Geomatics, and Environment, University of Toronto Mississauga, University of Toronto): “Leveraging AI and Spatial Big Data to Analyze Perceived Community Environment Disparities for the Underprivileged Population. ” Any questions can be directed to awards. dsi@utoronto.

ca .