Big Data

With the advent of the Internet of Things and other technologies, Toronto and other urban areas are looking to be “smarter cities” when tackling critical issues – from climate change to affordable housing.

There's just one problem.

“Smart cities are only smart if they have relevant data,” says Mark Fox, a professor of industrial engineering in the University of Toronto’s Faculty of Applied Science & Engineering and a distinguished professor of urban systems engineering.

Enter the Urban Data Centre at U of T's School of Cities. Created last fall, the centre seeks to enable smarter cities by providing awareness and access to relevant urban data. “We're creating a Canadian catalogue of urban data sets to allow people to discover relevant data, where it's located and any restrictions on use,” says Fox, who holds a cross-appointment to the department of computer science in the Faculty of Arts & Science.

The centre is being supported by a commitment from Tata Consultancy Services (TCS) of $1 million over five years to add core staff and expand operations.

One of the main challenges of achieving awareness is that datasets are spread across repositories, created by municipalities, provincial and federal agencies, NGOs and universities, making it difficult to find what is relevant.

“Getting data is hard work and it requires a lot of discipline,” says Raju Goteti, global vice-president of the TCS Co-Innovation Network.

“It requires innovation from multiple stakeholders and specific elements like the Urban Data Centre. The idea is to create new knowledge in the ecosystem, and we believe the School of Cities is a wonderful partner for this.”

Fox, for his part, says the TCS contribution will advance the centre’s work.

“We already have funding for some work streams within the centre, such as urban ontologies and standards, but this provides us with the funds to focus on core elements of our research program, such as the Urban Data Catalogue and Repository,” he says.

Urban Data Centre Director Mark Fox says relevant urban data enables smarter cities (photo courtesy of Pat Doherty)

The school is building the capacity of urban communities to collect, analyze and visualize data to make cities more efficient and equitable.

“The TCS support will give our Urban Data Centre a jump-start in creating a data ecosystem for universities and cities around Canada,” says Karen Chapple, director of the School of Cities.

For all the buzz about using machine learning to build and operate smarter cities, not many people appreciate how much effort goes into pulling relevant data together, according to Fox.

“About 80 per cent of the time it takes to build a smarter-city application is spent on what's called data wrangling, which is finding the data, cleaning it and integrating it, as opposed to actually building machine learning models.”

Creating the Canadian urban data catalogue is an enormous but crucial first step for the centre’s mission, providing a means for urban researchers, developers and planners to find the data they need, says Fox.

He says the centre is working to create standards for the catalogue system that will provide detailed information on each dataset listed.

“Once we have that standard, we're creating the appropriate software so anybody who has a dataset can go to the web page, fill in the metadata for the set they are submitting, and then it goes into our catalogue.”

The next step will be creating a data repository. Fox says they will focus primarily on data from academic research. One potential treasure trove of smart city data is graduate student research on urban environments, which, at present, is often lost upon graduation. “It either sits on their laptop or gets wiped out,” Fox says. “We’ll make it known to academe that not only can they provide us with the metadata associated with their dataset for the catalogue, they can also download their dataset into our repository.”

The Urban Data Centre is also an integral part of the upcoming School of Cities node in India that is being set up in collaboration with Tata Trusts – a philanthropic organization associated with TCS.

Fox says the centre will work with the School of Cities Alliance in India – a network of Canadian and Indian researchers – to create an Indian catalogue and data repository.

“TCS has been focusing on several smart city initiatives for more than a decade,” says K Ananth Krishnan, executive vice-president and chief technology officer at TCS. “We believe the interplay between smart cities and industries will generate ‘innovation at the intersection,’ a core TCS philosophy. I am excited about the Urban Data Centre at the School of Cities, close to our collaboration hub, TCS Pace Port Toronto. I am sure this will offer deep insights for better citizen and customer experiences.”

“It's all about making good decisions,” adds Fox. “And to make good decisions, you need to be aware of what data exists and where.”

Groundbreakers: U of T’s Data Sciences Institute to help researchers find answers to their biggest questions

Christopher.Sorensen — Thu, 16 Sep 2021 21:38:48 +0000

Groundbreakers: U of T’s Data Sciences Institute to help researchers find answers to their biggest questions

Christopher.Sorensen Thu, 09/16/2021 - 17:38

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Researchers working with the multi-university CHIME radio telescope in B.C. are collaborating with experts at U of T's Data Sciences Institute to solve computational and processing problems (photo courtesy of the Chime Collaboration)

Berton Woodward

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Coronavirus

Data Sciences Institute

Groundbreakers

Institutional Strategic Initiatives

Big Data

Dalla Lana School of Public Health

Dunlap Institute for Astronomy & Astrophysics

Faculty of Arts & Science

Hospital for Sick Children

Research & Innovation

Statistical Sciences

When University of Toronto astronomer Bryan Gaensler looks up at the night sky, he doesn’t just see stars – he sees data. Big data.

Bryan Gaensler
(photo couretsy of the Dunlap Institute)

So big, in fact, that his current research tracking the baffling “fast radio bursts” (FRBs) that bombard Earth from across the universe requires the capture of more data per second than all of Canada’s internet traffic.

“This is probably the most exciting thing in astronomy right now, and it’s a complete mystery,” says Gaensler, director of U of T’s Dunlap Institute for Astronomy & Astrophysics and Canada Research Chair in Radio Astronomy. “Randomly, maybe once a minute, there’s this incredibly bright flash of radio waves – like a one-millisecond burst of static – from random directions all over the sky.

“We now know that they’re from very large distances, up to billions of light-years, so they must be incredibly powerful to be able to be seen this far away.”

Data Sciences Institute

Facilitates research connections across disciplines
Supports data-driven discovery through innovative research methodology development and application
Trains and mentors data scientist practitioners and scholars

Learn more about U of T’s Data Sciences Institute

U of T is a world leader in finding FRBs, using the multi-university CHIME radio telescope in British Columbia’s Okanagan region and a U of T supercomputer. Yet, despite the impressive technology, many daunting challenges remain.

“It’s a massive computational and processing problem that is holding us back,” he says. “We are recording more than the entire internet of Canada, every day, every second. And because there’s no hard drive big enough or fast enough to actually save that data, we end up throwing most of it away. We would obviously like to better handle the data, so that needs better equipment and better algorithms and just better ways of thinking about the data.”

With the creation of U of T’s Data Sciences Institute (DSI), Gaensler and his colleagues now have a new place to turn to for help. The institute, which is holding a launch event tomorrow, is designed to help the university’s wealth of academic experts in a variety of disciplines team up with statisticians, computer scientists, data engineers and other digital experts to create powerful research results that can solve a wide range of problems – from shedding light on interstellar mysteries to finding life-saving genetic therapies.

“The way forward is to bring together new teams of astronomers, computer scientists, artificial intelligence experts and statisticians who can come up with fresh approaches optimized to answer specific scientific questions that we currently don’t know how to address,” Gaensler says.

The Data Sciences Institute is just one of nearly two dozen Institutional Strategic Initiatives (ISI) launched by U of T to address complex, real-world challenges that cut across fields of expertise. Each initiative brings together a flexible, multidisciplinary team of researchers, students and partners from industry, government and the community to take on a “grand challenge.”

“We’re bringing together individuals at the intersection of traditional disciplinary fields and computational and data sciences,” says Lisa Strug, director of the Data Sciences Institute and a professor in the departments of statistical sciences and computer science in the Faculty of Arts & Science, and a senior scientist at the Hospital for Sick Children research institute.

She notes that U of T boasts world-leading experts in fields such as medicine, health, social sciences, astrophysics and the arts, and “some of the top departments in the world in the cognate areas of data science like statistics, mathematics, computer science and engineering.”

Data science techniques can be brought to bear on a near-infinite variety of academic questions – from climate change to transportation, planning to art history. In literature, Strug says, many works from previous centuries are now being digitized, allowing data-based analysis right down to, say, sentence structure.

“New fields of data science are emerging every day,” says Strug, who oversees data-intensive genomics research in complex diseases such as cystic fibrosis that has led to the promise of new drugs to treat the debilitating lung disease. “We have so much computational disciplinary strength we can leverage to define and advance these new fields.

“We want to make sure that faculty have access to the cutting-edge tools and methodology that enable them to push the frontiers of their field forward. They may be answering questions they wouldn’t have been able to ask before, without that data and without those tools.”

A key function of the DSI is the creation and funding of Collaborative Research Teams (CRTs) of professors and students from a variety of disciplines who can work together on important projects with stable support.

Gaensler, who already has statisticians on his team, says he’s looking to the CRTs to greatly expand the scope of his work.

“We have just done the low-hanging fruit,” he says. “There are many deeper problems that we haven’t even started on.”

Laura Rosella (photo by Caitlin Free)

Similarly, Laura Rosella, an associate professor at the Dalla Lana School of Public Health, says the collaborative teams will be a major asset for the university.

“We’re going to dedicate funding to these multi-disciplinary trainees and post-docs so we can start building a critical mass of people that can actually translate between these disciplines,” she says. “To solve problems, you need this connecting expertise.”

Rosella played a key role in how Ontario dealt with COVID-19 in the early part of 2021. By analyzing anonymous cellphone data along with health information, she and her interdisciplinary team were able to see where people were moving and congregating, and then predict in advance likely clusters of the disease that would appear up to two weeks later. Her work helped support the province’s highly successful strategy of targeting so-called “hotspots.”

“We’ve been able to work with diverse data sources in order to generate insights that are used for
high-level pandemic preparedness and planning, in ways that weren’t possible before,” says Rosella, who sits on Ontario’s COVID-19 Modelling Consensus Table. “And we’ve also brought in new angles to the data around the social determinants of health that have shone a light on the policy measures that are needed to truly address disparities in COVID rates.”

Rosella’s population risk tools also include one for diabetes, which health systems can use to estimate the future burden of the disease and guide future planning. This includes inputs about the built environment. For example, if people can walk to a new transit stop, Rosella says, the increased exercise may have an impact on diabetes or other diseases. Potentially, even satellite imaging data could be brought into the prediction mix, she says.

In addition to advancing research in a given field, the Data Sciences Institute is also seeking to advance equity.

That includes tackling societal inequalities uncovered by data research – including how socio-economic factors can determine who is more likely to get COVID-19 – and the way the research itself is being conducted.

For example, Strug says most genomics studies have focused on participants of European origin, even though the genetic risk factors for various diseases can differ between different ethnicities.

“We must make sure we develop and implement the models, tools and research designs – and bring diverse sources of data together – to ensure our understanding of disease risk is applicable to all,” Strug says.

Many algorithms, or the data they use to make predictions, contain unconscious bias that may skew results – which is why Strug says transparency is vital both to support equity and to ensure studies can be reproduced properly.

Gaensler says it’s critical to ensure diversity among researchers, too.

“My department looks very different from the faces that I see on the subway,” he says. “It’s not a random sampling of Canadian society – it’s very male, white and old, and that’s a problem we need to work on.”

Strug hopes the Data Sciences Institute will ultimately become a nucleus for researchers across the university – and beyond.

“There’s never been one entrance to the university to guide people, so it’s so important for us to be that front door,” she says.

“We will make every effort to stay abreast of the different fantastic things that are happening in data sciences and be able to direct people to the right place, as well as provide an inclusive, welcoming and inspiring academic home.”

This article is part of a series about U of T’s Institutional Strategic Initiatives program – which seeks to make life-changing advancements in everything from infectious diseases to social justice – and the research community that’s driving it.

U of T launches Data Sciences Institute to harness global data revolution

Christopher.Sorensen — Wed, 01 Sep 2021 14:10:07 +0000

U of T launches Data Sciences Institute to harness global data revolution

Christopher.Sorensen Wed, 09/01/2021 - 10:10

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(Image by iStock)

Faculty of Arts & Science Staff

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Our Community

Institutional Strategic Initiatives

Big Data

Computer Science

Faculty of Arts & Science

Hospital for Sick Children

Mount Sinai Hospital

Research & Innovation

Statistical Sciences

University Health Network

From health care, economics and astrophysics to climate change, digital humanities and the promise of smart cities, the world is in the midst of a data revolution. Complex, massive and unique data sets are being generated and analyzed across a broad spectrum of disciplines at an unprecedented rate.

To capitalize on its strengths, the University of Toronto has launched the Data Sciences Institute (DSI), a community of talented faculty members, researchers, staff and students spanning fields and faculties pursuing exciting research that addresses a variety of global challenges.

Lisa Strug

“The Data Sciences Institute brings together researchers from various disciplines across the University of Toronto and the University Health Network, the Hospital for Sick Children (SickKids) and the Lunenfeld-Tanenbaum Research Institute, establishing Toronto as a leading centre for research into advancing data science,” said Lisa Strug, academic director of the DSI and a professor in the departments of computer science and statistical sciences in the Faculty of Arts & Science.

“By connecting data science researchers, data and computational platforms, the DSI will both advance research and nurture the next generation of data- and computationally-focused researchers.”

Mobilizing data

Data science – the science of collecting, manipulating, storing, visualizing, learning from and extracting useful information from data – is fast becoming a key tool in the development of solutions designed to address society’s most pressing challenges and opportunities.

Led by the Faculty of Arts & Science, the establishment of the DSI enables U of T to mobilize cross-disciplinary data science research and training and support a data science research network across U of T through which members can learn from each other and foster the development of new methodologies and approaches to data sciences research.

“New technologies in every facet of academic and daily life are producing increasingly large-scale, complex data with the potential to inform some of the most pressing societal questions, in completely new ways,” said Melanie Woodin, dean of the Faculty of Arts & Science. “As an interdisciplinary approach rooted in strong foundational computer, mathematical, statistical and engineering sciences, the Data Sciences Institute will serve as a visible and welcoming entryway for U of T data science activities, and a visible focus to attract world-class faculty and students.”

Driving global change

The DSI, U of T’s latest Institutional Strategic Initiative (ISI), embodies the university’s ability to bring together talented faculty members, students and industry spanning fields and faculties to tackle key world issues. Each ISI is carefully designed to enable strategic, cross-divisional, excellence-driven research.

“Global issues require a global response. As one of the world’s top ten ranked universities producing influential research, U of T is uniquely positioned to become a leader in data sciences,” said Christine Allen, associate vice-president and vice-provost, strategic initiatives at U of T. “The Data Sciences Institute will help cement Toronto and U of T as leading destinations for the 21st century’s most exciting, relevant and promising fields, building an inclusive pipeline for training and research that acts as a global source of talent, innovative ideas and solutions.”

The DSI, officially launching with a Sept. 17 celebration featuring keynote presentations by internationally recognized scholars and data science specialists, demonstrates what is possible when world-class scientists and practitioners converge and work across disciplines. The DSI represents a network of partners from across U of T and partner health-care institutions that include:

Faculty of Arts & Science
Faculty of Applied Science & Engineering
Faculty of Information
Dalla Lana School of Public Health
U of T Scarborough
Temerty Faculty of Medicine
McLaughlin Centre
U of T Mississauga
Rotman School of Management
The Hospital for Sick Children (SickKids)
University Health Network
Lunenfeld-Tanenbaum Research Institute

Call for members and funding opportunities

“We are building a community of – and for – researchers to enable the advancement of data science methodology and application through the provision of funding and networking opportunities among other benefits,” said Strug. “Membership to the DSI is open to faculty members, students, and researchers of any kind from across U of T’s three campuses and partner health-care institutions.”

The DSI will provide more than $10 million in grant funding and research supports to members through various collaborative programs. The first major funding opportunity is a Catalyst Grant competition offering up to $100,000 for collaborative research teams focused on the development of new data science methodology, or the use of existing methodology in innovative ways to address questions of major societal importance. Members will be awarded grants to support investigations of their own design for up to two years. Interested applicants must submit a notice of intent to apply by Oct. 15, 2021. The application deadline is Nov. 1, 2021.

'The future is very bright': U of T researcher on building a data science community in Ontario

Christopher.Sorensen — Fri, 15 Jan 2021 19:37:57 +0000

'The future is very bright': U of T researcher on building a data science community in Ontario

Christopher.Sorensen Fri, 01/15/2021 - 14:37

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Lisa Strug, a professor of statistical sciences and computer science, was recently named the director of CANSSI Ontario, the Ontario Regional Centre of the Canadian Statistical Sciences Institute (photo courtesy of Faculty of Arts & Science)

Topic

Dalla Lana School of Public Health

Faculty of Arts & Science

Statistical Sciences

With 15 years of experience doing data-driven research in the field of genomics, the University of Toronto’s Lisa Strug is perfectly placed to be helping researchers throughout the province build their own data science capabilities.

Strug is the director of CANSSI Ontario, the Ontario Regional Centre of the Canadian Statistical Sciences Institute. With U of T’s Faculty of Arts & Science as its academic host, CANSSI Ontario was established as a multidisciplinary unit in July 2019 with a goal to build statistical and data science capabilities throughout the university and the province.

CANSSI is a national institute launched in 2012 with the mission to support and enhance research and training in data science.

“With the volume of data exploding across all fields, the timing for CANSSI Ontario is fantastic,” says Strug, who is a professor in U of T’s departments of statistical sciences and computer science in the Faculty of Arts & Science and a senior scientist and associate director of the Centre for Applied Genomics at the Hospital for Sick Children. “At CANSSI Ontario, we're developing programs to support multidisciplinary activity that bring researchers together to create new research relationships and solve problems in specific domains.

“Together, we’re learning about the role of data science in public policy, in Indigenous data sovereignty, in biomedical science, in the criminal justice system and more.”

Strug’s research focuses on the development of statistical methods to identify genetic contributors to complex traits in diseases such as cystic fibrosis and epilepsy, and her team is translating genetic discoveries into improved diagnostics and personalized therapies. Earlier this year, she and collaborators began a new research project to identify genetic variation in patients with COVID-19 that might explain why the disease affects people differently.

Chris Sasaki, a writer in the Faculty of Arts & Science, spoke with Strug about CANSSI Ontario and how it can help researchers address some of the world’s biggest global challenges.

Why is CANSSI Ontario’s emphasis on data-driven research so important?

The sheer volume of data – in the humanities, social sciences, sciences – means that new tools need to be developed to handle and optimize the use of this data, or existing tools used in other domains need to be repurposed. We need algorithmic computational techniques and classical statistical techniques. And we need researchers from different disciplines – with their domain-specific knowledge – working together. Tools need to be developed that are tailored to the big global challenges of today and individuals need to be trained in how to use these tools.

How does CANSSI Ontario promote an interdisciplinary and collaborative approach?

One great success story is the Strategic Training for Advanced Genetic Epidemiology program, or STAGE, a successful multidisciplinary training program that has been in existence since 2009. STAGE trains graduate students, post-docs and early-career faculty who want to re-tool and train in the field where genetics, epidemiology and statistics meet. It offers training in the use and development of quantitative tools for genomic data. The program existed before CANSSI, but over the last few years has been unfunded. Now, CANSSI Ontario has adopted it and it’s funded by the Faculty of Arts & Science and is a collaboration with the Dalla Lana School of Public Health.

In fact, STAGE has been used as a model to implement a new multidisciplinary doctoral program that we've implemented in the Faculty of Arts & Science this past year. This is a pilot program with the department of statistical sciences and integrates with any other unit across the university where a doctoral student in statistics is co-trained with a faculty member in another department or field using the STAGE model. And this program goes beyond statistical genetics and genetic epidemiology; our first cohort of students includes co-mentorship between astrophysics and statistics, as well as biomedical science and statistics. It's very exciting to see this new multidisciplinary program evolve, modeled after the successes of STAGE but now open to all domain areas.

What sort of funding support does CANSSI Ontario provide?

One of our mandates is to remove barriers to accessing large, publicly available datasets that could benefit the research of our affiliated faculty and trainees. Several data sources have costs associated with accessing or computing with them, and, in some cases, those costs have become prohibitive. So we started a data-access grant program that removes that barrier by offering to cover those costs.

Funding support also comes in the form of awards. For example, in partnership with the Banting Research Foundation, we have the Banting-CANSSI Ontario Discovery Award in Data Science. It’s a one-year grant of up to $25,000 and it’s designed to encourage new faculty to be engaged in multidisciplinary, data-driven health and biomedical research.

And we’re very proud that in 2020, it was awarded to Dylan Kobsar from McMaster University to develop computational methodologies to analyze human movement, which will in turn have applications in wearable sensors for managing osteoarthritis.

How do you feel about your first year as director of CANSSI Ontario?

I've really enjoyed the role. For the past 15 years, I’ve been focused on data science in genomics, but now I have the opportunity to learn about the data-driven research in other disciplines across the faculty and I’m learning a ton.

I'm enjoying speaking to different communities and understanding what their needs are, so we can then work to develop programs and activities to support them and facilitate the work they are doing to solve some of society’s most challenging problems.

The timing for CANSSI Ontario is right, and the future is very bright. We need this initiative to bring people together to share ideas and to share know-how.

U of T biostatistician uses big data to shed new light on chronic diseases

Christopher.Sorensen — Thu, 27 Sep 2018 18:25:22 +0000

U of T biostatistician uses big data to shed new light on chronic diseases

Christopher.Sorensen Thu, 09/27/2018 - 14:25

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Nathalie Moon recently joined U of T's department of statistical sciences as an assistant professor in the teaching stream (photo by Diana Tyszko)

Dee Keilholz

Topic

Our Community

Big Data

Faculty of Arts & Science

Medical Research

Statistical Sciences

Big data has the potential to create new life-saving knowledge in medicine and health, but only if we know how to make sense of large data sets.

And that’s what biostatistician Nathalie Moon has set out to do.

Moon’s latest research empowers medical professionals to gain critical insights by helping them decide how much data to collect – and which kind. A recent University of Toronto hire with a focus in teaching, she's also making an impact by passing her knowledge along to the next generation of statisticians.

For the last five years, Moon, who joined U of T’s department of statistical sciences as an assistant professor this summer, has been working with researchers at the Centre for Prognosis Studies in the Rheumatic Diseases (CPSRD) at Toronto Western Hospital. The centre closely monitors patients suffering from rheumatic disorders – conditions causing chronic pain of the joints and connective tissue – to gain insights into the progression of disease and to improve treatment. Moon leveraged her statistical skills to design research studies that produce accurate results while making efficient use of resources.

Dee Keilholz of the department of statistical sciences spoke to Moon about her latest research project and her passion for teaching students.

What does a biostatistician do?

A biostatistician develops statistical methods and applies them to address problems in areas such as medicine, biology and health. A big part of my work revolves around designing research studies and, to some degree, analyzing data related to biological processes.

Tell me a bit more about your research with the centre. What was the focus of your research and what problems were you trying to solve?

My research looked at three distinct problems, all under the umbrella of understanding the progression of chronic diseases such as psoriasis and psoriatic arthritis – conditions that cause inflammation of the skin and joints respectively.

One part focused on patients who develop a pretty painful, disabling complication called arthritis mutilans. CPSRD follows patients over time and tracks if they develop this complication. However, some patients stop showing up for follow-up appointments, which leads to loss of information. We just don’t know if those patients developed this complication. So, CPSRD launched so-called “tracing studies”, to track down these “lost” patients to see what happened to them. But what if you have hundreds of patients who dropped out, and you only have resources to track down 50? That’s where my research comes in. I basically looked at the data we had on these individuals before they dropped out to figure out who we should focus our resources on.

Another question was around composition of study cohorts – the group of people who participate in these studies. If you want to track the progression of a disease over a patient’s lifetime, that might mean you have to follow that patient for 20 or 30 years. That’s very expensive, and you have to wait a long time to get results. That’s why I looked into designing studies where you don’t have to track people for decades but still get good estimates by recruiting some patients in the early stages of a disease and others from later stages. I showed that this can lead to large savings without compromising the quality of the estimates.

What motivates you to do this type of research?

I prefer working in an area where I can see the link between what I am doing and its usefulness in real life. I was hooked on biostatistics when I took my first course in survival analysis at Queen’s University, which led me to pursue graduate studies in biostatistics at the University of Waterloo. To give an example, survival analysis looks at questions such as cancer patients and the impact of tumor size on patient survival. I just had that “aha” moment where I realized how relevant these questions are, and I wanted to learn how to answer them.

You’ve just started working at U of T as an assistant professor with a focus in teaching. What interested you in taking on this role?

I’m an assistant professor in the teaching stream, which means I will spend most of my time teaching students. I first taught a course during my PhD and it made me realize how much I enjoy helping students learn and encouraging them to try different things. There’s such a variety of career opportunities in statistics, and I like being part of that journey of figuring out what’s right for them. It’s making a different kind of impact than the research I did during my PhD.

I really look forward to teaching my first- and fourth-year courses this term. I think my students are going to benefit from me being a researcher but also a teacher, because it means that I can bring that research experience to the classroom.

Innovation minister visits StatsCan facility at U of T, emphasizes importance of data-driven research

Christopher.Sorensen — Fri, 28 Jul 2017 21:40:02 +0000

Innovation minister visits StatsCan facility at U of T, emphasizes importance of data-driven research

Christopher.Sorensen Fri, 07/28/2017 - 17:40

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U of T President Meric Gertler and Navdeep Bains, the federal minister of innovation, science and economic development, at Robarts Library on Friday (photo by Geoffrey Vendeville)

Chris Sorensen

Author legacy

Chris Sorensen

Canada wants to go big on Big Data to boost innovation and inform policy-making – and researchers from post-secondary institutions like the University of Toronto will play a key role.

Navdeep Bains, the federal minister of innovation, science and economic development, delivered that message at a roundtable discussion Friday with U of T President Meric Gertler and researchers from U of T and McMaster University.

Bains and Anil Arora, the chief statistician at Statistics Canada, arrived at U of T shortly after Bains delivered a speech at Environics Analytics, a marketing analytics firm, in which he outlined plans to modernize Statistics Canada by finding new ways of collecting and integrating data, as well as sharing it with people and businesses.

The federal government hopes researchers at Canadian universities can use the data to better inform everything from health care to economic policy.

“We see this in the broader context of Big Data and open data,” said Bains. “It's really about how we can solve problems when we're around the cabinet table and we have substantive questions on housing, clean-tech, tourism or Indigenous people, income inequality or health care.”

He added that data-driven decision-making will be critical to transform Canada’s current resource-based economy into a knowledge-based one.

The discussion was held inside U of T’s Research Data Centre, a StatsCan-operated facility on the seventh floor of Robarts Library. The secure facility – users must receive special security clearance, no cellphones or cameras are permitted – makes detailed microdata available to U of T and other researchers on subjects ranging from Canadians’ health to their employment status. It’s part of the Canadian Research Data Centres Network (CRDCN), a network of 16 research data centre clusters located on university campuses across the country.

Also present was Martin Taylor, the executive director of CRDCN.

Among the issues discussed by the roundtable was how to incorporate more data into the CRDCN beyond the typical survey data collected by Statistics Canada, while also being mindful of privacy issues. That includes so-called administrative data from hospitals and licensing bodies, as well as data pertaining to Canadian businesses.

“It’s very exciting to think about how the integration of administrative data, which complements the survey and census data that Statistics Canada produces, will allow us to do amazing things,” said President Gertler.

“Anything we can do to foster that access and lower the barriers to firm-level data is going to be in Canada’s best interest.”

Researchers from U of T and McMaster University meet with CRDCN officials, including (at centre) Executive Director Martin Taylor (photo by Geoffrey Vendeville)

Michael Baker is the academic director of the Toronto Research Data Centre and a professor in U of T’s department of economics. He said other countries are already moving in this direction.

“There’s a recognition Canada needs to step up its game in this area,” said Baker in an interview before the roundtable kicked off. “If the data researchers are using for their research doesn’t exist here, they will go elsewhere.

“They will effectively work on [solving] other countries’ problems.”

Alice Hoe, a candidate for a PhD in sociology at U of T, knows how access to the right data can shed light on important social and economic issues. She estimates she spent more than 1,000 hours in the U of T data centre studying labour and income data. Her research is focused on understanding how new immigrants to Canada fare in the job market – a subject she was drawn to after watching her university-educated parents struggle upon arriving in Canada from Taiwan.

“Basically what I’ve found is immigrants are more likely to be in bad jobs, and once they’re in these bad jobs, they’re more likely to stay in them than Canadians who are born here,” Hoe said in an interview earlier this week. “They are also more likely to fall out of employment.”

Economic underperformance among new Canadians is not a new phenomenon. But the situation appears to be getting worse, according to Hoe. She said the problem is tied to a shift in immigration patterns away from Europe toward Asia, Africa and Latin America, suggesting language issues, racial discrimination and challenges associated with the recognition of foreign education and professional credentials are all playing a role.

However, Hoe said it’s become increasingly difficult to get an accurate picture of how new Canadians are faring in the workplace because the survey she relied upon to do her research – the Survey of Labour and Income Dynamics – was scrapped in 2011 and replaced with one that focuses on income.

“You wouldn’t know whether they have access to health and pension benefits, whether they’re unionized with collective bargaining,” said Hoe.

Alice Hoe is a PhD candidate in sociology at U of T. She's spent over 1,000 hours in U of T's research data centre to determine how new immigrants are faring in the Canadian economy (photo by Chris Sorensen)

Maripier Isabelle is a PhD candidate in U of T’s economics department, a fellow at the Canadian Centre for Health Economics and a junior fellow at Massey College. Her research focuses on the long-term impacts of C-section births on children’s health outcomes, and whether it adds to the costs associated with rising C-section rates in Canada. Her preliminary results suggest there may be a link between C-section births and certain health outcomes, including with children who must regularly take prescription medication for conditions like asthma.

“What I really wanted to accomplish with my research is to inform Canadian public policy,” Isabelle told the roundtable.

Isabelle added that her research would benefit greatly from the addition of administrative health data, which documents a wider variety of health outcomes and doesn’t suffer from the imprecision associated with self-reported survey data.

Hoe and Isabelle were joined by Grant Gibson, a PhD student at McMaster University whose research tries to measure inequality in Canada and whether the methods used to remedy it are working.

The roots of Canada’s network of research data centres can be traced to 1998, when a federal task force recommended university researchers be given access to Statistics Canada’s more detailed microdata files.

The CRDCN receives funding from the Social Sciences and Humanities Research Council, the Canada Foundation for Innovation and the Canadian Institutes of Health Research.

Big data helps autism research: U of T team identifies 18 new genes increasing risk

ullahnor — Thu, 13 Apr 2017 20:00:18 +0000

Big data helps autism research: U of T team identifies 18 new genes increasing risk

ullahnor Thu, 04/13/2017 - 16:00

Cutline

U of T researchers Stephen Scherer (left) and Ryan Huen (right) are part of the MSSNG autism genomics project (photo by Robert Teteruck/SickKids)

Jim Oldfield

Author legacy

Jim Oldfield

Topic

Artificial Intelligence

Scientists in the world’s largest autism genomics project recently identified 18 new genes that increase risk for the condition.

Some of the genes seen in participants also carry risk for heart disease, diabetes and other conditions, opening the potential for more personalized genetic counselling.

The results of the project, named MSSNG, provide more evidence that each person’s autism is unique, meaning researchers will still need a lot more genomic data before they can sort and target the many forms of the condition. However, some families are already benefitting. The MSSNG project includes whole-genome data from more than 7,000 individuals affected by autism, and that data is stored on Google Cloud, which allows access to researchers around the world.

Professor Stephen Scherer, director of both the McLaughlin Centre at the University of Toronto and the Centre for Applied Genomics at the Hospital for Sick Children, is the senior investigator for MSSNG.

He spoke with U of T's Jim Oldfield about how the cloud is enabling a new kind of open science on autism, and what needs to happen next for big data to deliver on its potential to treat the most baffling medical conditions.

How did the MSSNG project come about?

Genome sequencing generates massive amounts of data, and the need to deal with those terabytes of information is what put us into the cloud environment.

The project came together four years ago when we decided to make all that data available. The original vision a few of us had was for truly open science, where you could type a keyword into a database, say if you’re looking for which individuals carry a gene.

We found out along the way that we need more open consent, in part because we’re dealing with clinical research data, even though it’s anonymized. So we now have a system where you apply through a data access committee. You can get anything you want in the cloud, including raw reads from the sequencers and new analytics tools we've developed. Almost 100 researchers at dozens of institutions are using the system, and we expect those numbers to grow. It’s probably one of the most open-science genetics projects right now.

Why is this technology well-suited for autism research?

We need to take this approach because autism is extremely heterogeneous in terms of how it presents clinically and the underlying genetics. There are well over 100 different forms, which is why we sometimes call them the autisms.

To subcategorize these conditions, we need big numbers and whole genomes. We calculated that to get all low hanging fruit – the highly penetrative autisms with the most common genetic variants – we’d need about 10,000 families. To find new impactful variants, including copy number variations or small insertions and deletions, some of which are in the noncoding regions of the genome, we’ll likely need up to 100,000.

Will machine learning help analyze that data?

I hope so. [U of T Professor] Brendan Frey and his group published a paper in Science a couple of years ago using MSSNG data in its early form. They used deep genomics algorithms to analyze hundreds of thousands of variants. We published a follow-up paper using his programs to look for splicing differences in autism subjects versus controls. These are some of the first papers that convincingly show non-genic regions of the genome can be involved in autism. So the short answer is we’re already using machine learning to mine the data we have, and other groups are doing it as well. We do think U of T will have a competitive advantage here.

How is MSSNG benefiting patients now?

We’ve found a total of 63 genes and mutations that increase risk for autism through this project.

That data is communicated back to families that are part of the study, through a genetic counsellor in cases where it’s relevant. Sometimes other conditions are implicated such as epilepsy, anxiety or sleep/mood disorders. In others, a formal diagnosis can help encourage earlier behavioral interventions.

A genetic profile that matches a known subtype of autism can also affect prognosis and assessment of familial recurrence risk. And we’re linking families with one another in cases where they may benefit by talking about what worked and what didn’t. In the future, this data should facilitate clinical trials based on a small number of key neurological pathways affected by the many genetic variants in autism.

What progress might we see in the next five years?

I often say autism is about 10 years behind cancer in terms of how we use genomic data. But, we’re only behind because we started later.

Some people don’t think autism should be an area of research, and some families don’t want interventions. But most want investment and research so the demand for data is very high.

If had my dream – and I think this will happen in Ontario within three years – every child with a diagnosis would have his or her genome sequenced. For about 20 per cent of families, we can now explain why autism comes about in their child. Previous technologies only looked at two per cent of the genome, the genes. Now, most leading-edge labs are studying the other 98 per cent, and whole-genome sequencing provides the fundamental road map for those experiments. We are linking all that high-quality data together and using it to decode evolution. It’s a very exciting time.

Nature Neuroscience published the recent results from MSSNG, which is a collaboration between SickKids, Autism Speaks, Verily (formerly Google Life Sciences) and researchers at the University of Toronto.

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