Grants of up to $10,000 are available to cover costs associated with accessing and working with large data sources. These DSI grants aim to improve data accessibility for data science researchers and foster research by mitigating the high cost of access to data sets. We believe that equitable access to resources is crucial for creating a diverse and inclusive environment.
Deadline for applications: April 29
This Seed Funding is designed to encourage new collaborations between data science methodologists and theorists with applied researchers. Single applicants working in data sciences methodology or theory can apply. An applicant’s research area should focus on data sciences methodology or theory with the potential to be relevant to applied fields.
Applicants will present their research and methodology/theory at a seminar, including its potential for applied fields. Funds of up to $10,000 can be used over 8 months to support successful applicants to seed a new Collaborative Research Team with the aim of applying for a DSI Catalyst Grant.
Deadline for applications: April 14
The growing use of large-scale complex data across disciplines has brought the challenge of reproducibility to the forefront. But how can we foster trust in data-informed research? The Data Sciences Institute (DSI) Reproducibility Thematic Program aims to address such questions by focusing on the development of widely adoptable methodology and processes to share data and code, as well as the development of infrastructure, methods, and models that support reproducible and reliable research. Ambitions for the program include educating the next generation of researchers on the importance of transparency, removing the intimidation factor from reproducibility, and supporting researchers in identifying their path to reproducibility.
“There is a lot of discussion about how the lack of reproducible results may make people question their confidence in science. We saw this play out during the pandemic. Robust and reproducible processes are paramount to maintaining confidence in the research enterprise and ensuring the generation of reliable results upon which science builds. We want to push to make the DSI a centre for reproducible science, and help researchers understand how to adopt best practices in reproducibility,” says Timothy Chan, DSI associate director of research and thematic programming.
Last fall, the DSI held an open call for researchers to co-lead this thematic program. Reproducibility co-leads Professors Rohan Alexander, Benjamin Haibe-Kains and Jason Hattrick-Simpers represent different research fields– from social sciences to life and physical sciences – but they are united in their passion for supporting and advocating for transparency and reproducibility in research. They are in the process of developing programs and community-building activities.
Stay tuned for Reproducibility activities and opportunities.
The Reproducibility Thematic Program kicked off with a multi-day workshop that brought together over 460 academic, industry, and non-profit participants on the critical issue of reproducibility in applied statistics and related areas. The workshop was hosted by the DSI and CANSSI Ontario.
Topics at the workshop ranged from reproducibility in language modelling and machine learning, to biomedical research, to integrating reproducibility in undergraduate social science programs and reproducibility in crowd science.
The workshop featured over forty speakers from the University of Toronto, University Health Networks, Canadian and International research universities and focused on evaluating and teaching reproducibility, as well as reproducibility practices. “We were tremendously pleased with the caliber of the speakers,” says Rohan Alexander, the lead organizer. “The deep engagement speaks to the importance of reproducibility. To create understanding it is important that others can trust results.”
Reproducibility champion and world-renowned computer scientist, Professor Joëlle Pineau, spoke to improving reproducibility in machine learning research. “Reproducibility is a minimum necessary condition for a finding to be believable and informative,” says Pineau, an associate professor at the School of Computer Science at McGill University. She co-directs the Reasoning and Learning Lab at McGill and also leads the Facebook AI Research lab.
Professor Colm-Cille Patrick Caulfield from the University of Cambridge discussed why an honest discussion of uncertainty in models is critical for climate science. “We have such a complex climate system, only by ensuring transparency can we be 100% confident in our predictions,” Caulfield says. The DSI and the C2D3 Cambridge Centre for Data-Driven Disc are planning to host joint workshops around the DSI’s Thematic Programs of Inequity and Reproducibility.
The DSI is excited to announce co-leads for its Thematic Program in Reproducibility. The co-leads are responsible for the thematic program events, activities, and community-building.
Rohan Alexander is an assistant professor at the Faculty of Information and Department of Statistical Sciences at the University of Toronto. He is the assistant director of CANSSI Ontario, a senior fellow at Massey College, and a faculty affiliate at the Schwartz Reisman Institute for Technology and Society. He is interested in using statistics to understand the world.
“I am particularly interested in how we turn something as complicated as society into a dataset that can be analyzed, and what we lose in exchange for the benefits that such statistical modeling brings. I applied to be a Reproducibility co-lead to contribute to the improvements that are happening in the social sciences and to share and learn from other disciplines.”
Benjamin Haibe-Kains is a senior scientist with the University Health Network and an Associate Professor of Medical Biophysics at the Temerty Faculty of Medicine. His research program focuses on developing multimodal models, using radiological images and large-scale genomic data, to predict the survival and therapy response of cancer patients.
“I have always been passionate about research transparency and reproducibility, key components of Open Science. When I saw that the newly created Data Sciences Institute had an open call for leading their Reproducibility Theme, I could not miss this unique opportunity to educate on how to make research more transparent and reproducible.”
Jason Hattrick-Simpers is a Professor at the Department of Materials Science and Engineering, at the University of Toronto and a Research Scientist at CanmetMATERIALS. His research focuses on the creation of tools to enable the discovery of new corrosion-resistant materials or new materials for converting waste heat into usable energy.
“Reproducibility is at the heart of the scientific method and is what allows us to live in a world filled with technological wonders.”
DSI Doctoral Student Fellowships support multi-disciplinary training and collaborative research in data sciences that include faculty from the University of Toronto and external funding partner institutes.
Fellows receive stipend support of $25,000/year for up to 3 years and have access to travel awards to present their work. Fellows will also participate in DSI cohort programming for professional development.
Q&A information session: March 3, 2022, 9-10 am ET
Deadline for applications: April 11, 2022
The Data Sciences Institute (DSI) is excited to announce co-leads for its Thematic Program in Inequity. The availability of new and diverse data in each of these domains has transformed traditional disciplines, encouraging researchers to address pressing questions in innovative, data-driven ways. The opportunity to link these new resources with socioeconomic and demographic data, and to co-develop research projects with under-resourced communities, helps researchers use more data-driven approaches to understand social inequities and empower communities.
“With the Inequity Thematic Program, we hope to encourage the generation of evidence and tools to enhance our understanding of inequity and support equitable social change,” says Timothy Chan, DSI associate director of research and thematic programming. “This is relevant across disciplines, whether it’s the social sciences, the humanities, health sciences, or physical sciences.”
Earlier this year, the DSI had a call for research co-leads for its DSI Inequity Program. Professors Angelina Grigoryeva, Arjumand Siddiqi and Azadeh Yadollahi, the three co-leads, bring a broad disciplinary perspective and will have substantial flexibility in developing the Program and activities. These can include supporting scholarly exchange (workshops, conferences, seminars), organizing new research collaborations, and supporting applications for substantial funding opportunities. Several of the DSI’s recently announced Catalyst Grant awards focus on using the transformative nature of data sciences to address inequities and drive positive social change.
“The DSI encourages innovative data science methodology development and application in these areas by offering support and funding for research efforts and programming. We are very excited to serve as co-leads and work with DSI member researchers to create opportunities for exchange and learning,” says Angelina Grigoryeva, one of the co-leads.
The co-leads plan to bring together DSI member researchers to better understand their interests and needs, conduct a grant writing session, develop a speaker series, and, importantly, facilitate informal presentations and networking with the community.
Angelina Grigoryeva is an assistant professor for the Department of Sociology at the University of Toronto Scarborough. She is also a member of the DSI Research & Academics Committee. She researches social stratification and inequality. More specifically she focuses on patterns of wealth, race and gender inequality and examines household economic lives in the context of large-scale socio-economic transformations, with a focus on both between- and within-household inequalities.
Angelina’s current work focuses on the social implications of the large-scale shift towards finance-based capitalism in North America. More specifically, she examines how families became increasingly involved in financial markets in recent decades, how access to financial markets remains socially stratified, and the consequences for growing wealth inequality.
Angelina dreams of leveling the playing field and addressing growing inequality. “What we do know is that inequality in North America, in the United States and Canada alike, has been on the rise. It has been increasing steadily in the past several decades, and it doesn’t look like this trend will reverse anytime soon based on what we see today.”
Arjumand Siddiqi is the Canada Research Chair in Population Health Equity and a professor and the Division Head of Epidemiology at the Dalla Lana School of Public Health, University of Toronto. She works as a social epidemiologist. This is the study of how social factors contribute to health and disease over a period of time.
“We want to know what’s happening in society and specifically, we’re trying to figure out how that’s influencing health. So, what are the large societal dynamics that are at play?” she says.
Her work looks at what inequality looks like, and who is holding the power. However, she’s not looking at individuals. Instead, she’s looking for groups and that’s why the data becomes important. She’s looking at the population level and trying to figure out what’s happening differently.
“I love that when you look at the data and you try to answer questions, it actually leads to other questions. Sometimes it’s frustrating because you can’t solve everything you want to solve, but in some ways, it’s exciting that you can kind of start to at least get a sense of what we don’t know, which is important.”
Azadeh Yadollahi is a Canada Research Chair, a senior scientist at KITE, Toronto Rehabilitation Institute, at the University Health Network, as well as an associate professor at the Institute of Biomedical Engineering at U of T. Her area of research is health equity and sleep. More specifically she develops technologies, often wearable technologies, to monitor physiological signals, and assess sleep-related breathing problems, such as sleep apnea or snoring.
She works with underserved communities, for example, those experiencing homelessness. She hopes her work will change current policies for diagnosing and treating sleep apnea for these populations because a lot of the policies require individuals to come and sleep in a lab to get treatment, which is not always possible. She also hopes to raise awareness about sleep problems.
“I am very passionate to make a meaningful impact in the lives of individuals who are socially and systematically disadvantaged and do not have equitable access to care.”
