Data Sciences Institute (DSI)

Feb 27 2023

DSI welcomes Women’s College Hospital as a partner

The Data Sciences Institute (DSI) is excited to announce a new partnership with Women’s College Hospital (WCH). For more than 100 years WCH has been developing revolutionary advances in healthcare. Today, WCH is a world leader in health equity and Canada’s leading academic ambulatory hospital focused on delivering innovative solutions that address our most pressing issues related to population health, patient experience and health system costs.

“Women’s College Hospital (WCH) is reimaging and redesigning healthcare to enhance access, address inequities, and innovate more readily. To do that, we are leveraging data insights to identify areas for improvement, test new models of care and ultimately improve care for everyone. As a research leader in the field of data science, this collaboration with DSI will enable our teams to further their work, pursue new opportunities, and expand our partnership network,” said Dr. Rulan Parekh, vice president of Academics at Women’s College Hospital.

DSI collaborates with organizations eager to support world-class researchers, educators, and trainees advancing data sciences. We facilitate inclusive research connections, supporting foundational research in data science, as well as supporting the training of a diverse group of highly qualified personnel for their success in interdisciplinary environments. As one of our external funding partners, WCH researchers can apply for research grants, training and networking opportunities at the DSI.

“We are delighted to announce this partnership with WCH. Our goal is to create a hub to elevate data science research, training, and partnerships. By connecting data science researchers, data and computational platforms, and external partners, the DSI advances research and nurtures the next generation of data- and computationally focused researchers. We are very excited to have WCH researchers join the DSI community.” - Lisa Strug, Director, Data Sciences Institute

Feb 02 2023

DSI launches call for new ideas that push the boundaries of data science

Data science is an ever-evolving field. It continues to change, as new innovations come to light, and data scientists continue to revolutionize the way we use, analyze, collect and store data.

Learn more about how you can apply.

Deadline LOI: March 3, 2023

Deadline full proposal: May 26, 2023

So, what is the next big thing for data science?

The Data Sciences Institute (DSI) is launching a new Emergent Data Sciences Program competition designed to fund researchers and advance cross-disciplinary data science in areas where the University of Toronto is already a leader or has the capacity to become one. The DSI seeks to promote and expand the awareness and role of data science in all research activities across UofT. This call is intended to attract and develop new communities that want to enhance their activities using data science.

Emergent Data Sciences Programs are a DSI core activity that helps fulfil its mission of bringing people together for collaborative generation and application of new ideas that support emergent areas in the data sciences.

“At the DSI, we are proud to be a part of the University of Toronto, one of the world’s leading universities. I have no doubt that we will see many proposals that attempt to push the boundary of what has been done before from across UofT, as well as explore new multi-disciplinary applications of data science. We hope these will establish new fields and techniques that are so often the preludes to future scientific, technological and societal breakthroughs,” says David Lie, DSI Associate Director, Thematic Programming and Data Access.

When asked about what he thinks the next big thing in data science is Dr. Lie says, “The collection, application, and curation of large datasets focused on the public has been largely spearheaded by private entities trying to improve their enterprises and businesses. However, the Covid-19 pandemic demonstrated that there are a host of socially beneficial uses of that data. This is just the tip of the iceberg. I believe the next stage of data science will be to devise new techniques, and governance policies, that will enable data collected by private and public organizations to be shared and applied in other, important socially beneficial uses. To do this, we must overcome significant challenges, such as how we can share large data sets in privacy-preserving ways, and how we can identify and mitigate security risks that might arise. But this is just one example of many.”

Emergent Data Sciences Program proposals should include a broad span of activities that lead to the development of innovative data science methodologies, deep connections with computation and applied disciplines, new training programs, collaboration, knowledge mobilization, and impact. Ideal program proposals should establish or elevate local cross-disciplinary activity that advances the data sciences by pursuing the next big-but-yet-unknown data-driven field or computational or analytic breakthrough. 

Dec 15 2022

Building environmental data sets to illustrate climate change in Northern Canada

DSI Catalyst Grants, supporting collaborative research teams for impact

Arctic regions experience climate change at a significantly faster rate than the rest of the planet. Residents in Northern Canada, and other Arctic regions, have long perceived anomalies in weather patterns, changes in long-standing sea ice patterns, and ecosystem stress. But these changes have been difficult to document, making it challenging to understand how they will ultimately impact human health and food security.

The Data Sciences Institute (DSI) is funding cross-disciplinary research teams focused on using the data sciences to solve complex and pressing problems. Yuhong He (Geography, Geomatics & Environment, UTM) and Kent Moore (Chemical & Physical Science, UTM), one of the multidisciplinary collaborative research teams to receive a DSI Catalyst Grant, are using environmental data to help gain a more complete understanding of the changes happening in Northern Canada.

“Cross-disciplinary data science research has the potential to solve some of the most pressing challenges we face today. Professors He and Moore’s research is just one example of many. We are beginning to see the impact of DSI Grants and the capacity of bringing collaborative research teams together. We are excited to see how Catalyst Grant recipients continue to catalyze the transformative nature of the data sciences,” says Gary Bader, DSI Associate Director, Research and Software.

The power of environmental data science

Professor Moore focuses on the cryosphere. The cryosphere is made up of all the frozen places on our planet like glaciers, continental ice sheets, permafrost, snow and ice. He uses theoretical, computational, and observational techniques to gain insights into the dynamics of the climate system. This helps place observed changes to our climate into a long-term context.

Professor He’s research centers on the biosphere. She integrates multi-source remote sensing big data into ecological research for a better understanding of the drivers and mechanisms shaping these changes in vegetative ecosystems. Her research helps improve conservation efforts.

Together the team uses Earth observation data and machine learning to reveal patterns and trends in land surface changes and their possible impacts on people. These results provide a crucial basis to develop long-term strategies to help cope with the climate crisis and its resulting environmental, societal, and economic impacts.

The funding support from DSI increases the team’s capacity across a range of disciplines and helps them conduct an analysis of the environmental changes impacting northern Canada by developing open-access geospatial datasets. The funding also supports reproducibility and the establishment of an Earth observation data management system for sharing and using these datasets. Reproducibility is a DSI Thematic Program that strives for the development of widely adoptable methodology, processes, and infrastructure to share data and code locally and in privacy-compliant ways.

Helping northern communities access reliable environmental data

“Pressing global issues like climate change require integrated, interdisciplinary approaches to successfully address research questions involving complex environmental systems. Both Professor Moore and I have extensive experience using Earth observation data and machine learning approaches, and our research on the cryosphere and biosphere make us an ideal team to establish a complete Earth observation data management system for northern Canada,” says Professor He.

For many northern communities, access to reliable data that illustrates the impact of climate change on regional ecosystems is difficult to access. An aggregate data set does not exist in a usable or scalable way. Local and regional approaches to environmental and climate action, like those taken by Nunavut’s Qaujigiartiit Health Research Centre, require access to longitudinal data to make informed decisions about the health of residents. The establishment of this Earth observation data management system will enable a network of researchers to upload, share, and download spatial data spanning a nearly 50-year period.

“This research will not only advance and redefine our understanding of climate and ecosystems in this region but also provide potential users with direct knowledge and insights to develop local and regional adaptation strategies,” says Professor He.

Nov 25 2022

Data science to make our society better

How do we get people to understand how data influences their lives?

Data science has infiltrated our everyday lives and, although a powerful tool, with it come cases of bias, injustice, and discrimination. Consider the emerging discourse around the metaverse, within which people only exist as data. These data provide opportunities for research and innovation, but also commodification and surveillance.

So how do we conduct data science responsibly?

That’s exactly what the new DSI@UTM initiative is tackling. The DSI at the University of Toronto Mississauga is leading a tri-campus initiative to encourage research activity in Responsible Data Science that includes community-building, workshops and seed funding for research.

Data science will continue to restructure aspects of our world and it is important to maintain a commitment to questions of power, inequity, responsibility, surveillance, justice, and harm. Especially, to ensure that collecting, manipulating, storing, visualizing, learning from, and extracting useful information from data is done in a reproducible, fair, and ethical way.

Why is UTM the right place for this initiative?

UTM has a cluster of faculty working across questions of responsible data science. One example is the Institute of Communication, Culture, Information and Technology (ICCIT), which looks at technology, media, and society and considers how algorithms affect the world. The campus is also comprised of researchers working on sustainability, management, and geography along with initiatives focused on giving back to the Mississauga community, including working with Indigenous community leaders.

During an interview about this initiative, Associate Director of the DSI@UTM, Professor Bree McEwan, highlighted the revised UTM Strategic Framework. The Framework expresses core priorities and commitments that will strengthen consensus, inspire action, and guide investment. It includes priorities such as embracing place and encouraging collaboration.

“Responsible data science is about how we do data science, not just for the purpose of doing data science, but doing data science in a way that is making our society, our environment, etc. better for everyone. Therefore, the idea of responsible data science fits hand in glove with the other pieces of the Framework at UTM. How do we get lots of people to understand how data influences their lives, the idea of responsible data science? At UTM, we already have some strengths in how what we do here at the University influences the community around us,” says McEwan.

“The University of Toronto Mississauga is brimming with world-class researchers, focused on changing the world. UTM is a great place for this initiative, and we are thrilled to be building this within the DSI, as responsible data science needs to be a key part of both our research at UTM and our daily lives,” says Elspeth Brown, Associate Vice-Principal Research (AVPR) in the Office of the Vice-Principal, Research (OVPR).

Events to look out for

A big focus of this initiative is bringing researchers working with data science at the UTM campus, and beyond, together. On December 7, DSI@UTM will be hosting its first Data Digest, Data & Sustainability. These networking events feature UTM data science researchers and provide attendees with the opportunity to engage in Responsible Data Science. Each month will feature a selection of short interdisciplinary research-based talks on a topic and explore challenges and opportunities related to data science.

In February 2023, DSI@UTM will be hosting a Data in the Metaverse workshop. This event seeks to imagine future possibilities, challenges, and implications of data creation, collection, analysis, and deployment in the metaverse. Current discussions of the metaverse and the increase in VR adoption make this an opportune time to consider how data can, is, and could be employed in virtual reality and immersive environments.

Critical Investigation of Data Science Grant

The DSI@UTM Critical Investigation of Data Science (CIDS) grant is designed to provide seed funding for scholars. Projects can vary in scope from the analysis of specific data science projects and approaches to the articulation of potential harms in data science from a broader perspective.

“It’s about putting our money where our mouth is, in that we should be inviting critique of the data sciences in order to improve the data sciences. These grants will allow people to have some support for exactly those kinds of projects. Building in this critical angle, this self-reflection, into the data sciences is also important to make sure that we are doing data science responsibly,” says McEwan.

Nov 18 2022

New approaches to data unlocking benefits for social scientists

The Data Sciences Institute (DSI) and the University of Toronto Scarborough, DSI@UTSC, are leading a tri-campus initiative to encourage research activity in Computational and Quantitative Social Science (CQSS) that includes community-building, research funding, and training in CQSS.

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

Assistant Professor of Sociology Ethan Fosse is an Associate Director of the DSI. “The Institute came about when a number of scholars across all three campuses recognized that there is an emerging area at the intersection of technical fields such as computer science, theoretical statistics, and mathematics and domain-specific fields such as biology, physics, political science, and sociology,” says Prof. Fosse. “With new data, novel algorithms, and rapid increases in computing power, there is the potential to truly accelerate knowledge production in a wide range of subject areas.”

But what does this mean, practically, for researchers in the social sciences? The answer is that there are new, powerful ways of analyzing complex data structures commonly used by social scientists, such as temporal, textual, spatial, and network-based data. For example, many social scientists are experts at examining unstructured text data, such as open-ended survey responses, in-depth interviews, or ethnographic field notes. However, new computational techniques further allow social scientists to automatically summarize and group the data, significantly reducing the amount of labour, time, and monetary cost required to conduct interpretative analyses.

For Prof. Fosse, who has written on the usefulness of these techniques for social scientists and has used them in his own research, the next step is to increase training on these new methods, in addition to helping social scientists become aware of the benefits such techniques can bring to their research. As well, he is working on developing programs to foster research collaborations between data scientists on the one hand and social scientists on the other. The DSI currently provides a number of training opportunities for social scientists as well as grants for projects using the data sciences in innovative ways.

The Institute aims to build an interdisciplinary network of researchers who can be an advocate for the importance of the data sciences in social science research. DSI membership is free, and members can be faculty, staff, or students of the University of Toronto and/or a member of a University of Toronto affiliate (for example, affiliated hospitals and research institutes).