The Next Big Question Fund is investing in data-driven research questions that have the potential to transform the big data field.
Whether seeding big data research or supporting knowledge mobilization activities, all researchers are encouraged to submit bold proposals to tell us: What is the next big question in big data? By enabling collaboration, curiosity and intellectual risk-taking, the university is growing the big data research cluster and supporting the goals of SFU’s 2016-2020 Strategic Research Plan.
We empower you to push the boundaries of big data research.
Select projects will be funded up to $25,000. See the Terms of Reference to find out more.
Submit completed Application Forms to firstname.lastname@example.org
Call for Submissions Opens: February 6, 2019
Application Deadline: April 1, 2019
Announcement of Competition Results: Summer 2019
We encourage potential applicants to connect with us at email@example.com to answer any questions they may have.
Previously Funded Projects
Identification of Empirically Grounded Social Science Insights which can Contribute to Solution of Big Data Problems
Big data projects focus on areas such as developing algorithms, computer security and the need for talent, yet fail to address the corresponding social issues. Bridging SFU’s disciplinary gaps improves the value proposition of these projects and its societal benefits. READ MORE
Advancing HIV Cure Research into the ‘Big Data’ Age
Though there is still no cure for HIV, many now believe that it is possible. Realization of this ultimate goal, however, will require advancing HIV research - in particular research on the genetics of HIV latency - into the Big Data age. READ MORE
Physiologically Interpretable Models of Large-Scale Human Performance Data
A major challenge facing sports teams is translating data into models that can predict and optimize training and tactics for elite athletes. Data from GPS devices hold the key to tackling this. READ MORE
Big Data Approaches for Synergy between Artificial Intelligence and Clinical Brain Imaging
How can we better understand neurodevelopment, aging or brain trauma? The answer lies in the vast data collected during diagnosis and treatment. Capitalizing on breakthroughs in computational imaging and machine learning leads to better outcomes for patients. READ MORE
Uncovering Students’ Enrollment Patterns Leading to Dropping Out vs. Success from the SFU Engineering Program
SFU’s Engineering program is highly demanding. Not surprisingly, many students experience academic difficulty. Some students recover and other students drop out. Long-term student success starts with unlocking the university’s own enrollment data. READ MORE
Linked Data for Women’s History
The next big question in big data for digital humanists asks how to connect the high quality but disparate humanities datasets that exist and looks to understand pivotal era’s in women’s history. READ MORE
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