Dr. Prameet Sheth, Dr. Henry Wong, and Dr. Calvin Sjaarda, in collaboration with the CAC, developed the Live Operational Laboratory Analytics (LOLA) platform.

LOLA platform is a sophisticated metrics dashboard that can provide real-time analysis and predictive insights into over 200 lab metrics and patient results.

Researchers are producing unprecedented amounts of data and data intensive research is growing rapidly across disciplines. Research data support services offered at universities are often siloed, which can cause inefficient duplication of services and significant gaps in programming and support. As a result, there is a need to develop a strategic, unified approach to providing data support services to ensure researchers have access to the tools and resources they need.

To address these challenges, we are pleased to announce that the Centre for Advanced Computing at Queen’s University is amongst the institutional partners participating in Ithaka S+R’s initiative, “Building Campus Strategies for Coordinated Data Support,” a cohort-based project with 29 universities across Canada and the US. This two-year initiative will support the Queen’s research community through several deliverables that aim to ensure the future viability of research data support services in the following ways:

• Catalogue and analyze the research data services currently offered locally 
• Investigate the experiences of faculty and students in navigating and accessing existing research data services at Queen’s and identify current barriers and gaps 
• Build tools to assist researchers in navigating existing research data services 
• Develop strategic plans for better coordination of current and future services 

In addition, the project will contribute to developing an international benchmarking dataset on current data support services, which will in turn inform our own strategic planning processes. This project aligns with the University’s Strategic Goals to inspire research excellence and advance social impact and sustainability; enhance research and teaching integration; and strengthen the University’s impact on a global scale.

Watch for project updates on progress and for an invitation to members of the Queen’s community participate in interviews to help the implementation committee better understand researcher experiences with current research data services and identify additional support requirements.

We are delighted to have assembled a project team and steering committee with stakeholders from across the University. We would like to thank the Queen’s University Library for funding this initiative.

For questions about the project, please reach out to rdm.library@queensu.ca.

The NVIDIA Graduate Fellowship Program provides funding in the amount of up to $60,000 per award to PhD students who are researching topics that will lead to major advances in accelerated computing and its applications. Recipients not only receive crucial funding for their research but are able to conduct groundbreaking work with access to NVIDIA products and technology. 

Applications for 2024 are now open.

The Digital Research Alliance of Canada is pleased to announce that Innovation, Science and Economic Development Canada has approved funding of up to $228.3 million over the next two years for Digital Research Infrastructure initiatives related to high performance computing, data management, quantum and more that will directly benefit Canada’s researchers.

The Digital Research Alliance of Canada (the Alliance) has launched a survey to better understand how Canadian researchers use the Alliance community cloud or commercial cloud services and to inform the development of a National Cloud Strategy. Researchers and research staff from all disciplines are encouraged to fill out the survey. Findings will help to identify gaps in cloud service provision and improve the functionality of the Alliance Cloud, to better serve the research community. 

The survey will be open until February 6.

The CAC collaborates with researchers Carolyn DeLoyde and Betsy Donald and local Kingston partners to create new “Kingston Dashboard”, an innovative platform designed to act as a digital connection hub for community information and resources.

Queen’s University is among a group of researchers and computer scientists at Canadian institutes who are working towards identifying a more comprehensive and inclusive methodology to predict Bronchopulmonary Dysplasia (BPD) and death in preterm infants.

Group Details:

Faiza Khurshid, Associate Professor, Department of Pediatrics, School of Medicine, Queen’s University

Helen Coo, Grants & Special Projects Leader, Department of Pediatrics, School of Medicine, Queen’s University

Amal Khalil, Senior Analytics Developer, Centre for Advanced Computing, Queen’s University

Jonathan Messiha, Master of Management in Artificial Intelligence, Smith School of Business, Queen’s University

Joseph Y. Ting, Associate Professor, University of Alberta

Jonathan Wong, Assistant Professor, University of British Columbia

Prakesh Shah, Professor, University of Toronto, Director Canadian Neonatal Network Investigators

Current models for early prediction of BPD and death in preterm infants are limited to traditional logistic regression models. Other types of machine learning (ML) models are presently unexplored. Additionally, current models are not suited for use in Canada. The NICHD’s online risk estimator, the standard web-based tool for BPD estimation, has not been validated for the Canadian population and would require an extensive study for use in Canada. Problematically, existing datasets do not capture all the required variables for an external validation study, thus requiring data collection from a massive cohort of infants. Last, the current online risk estimator requires the input of ethnicity, yet response options are limited to white, black, or Hispanic which results in data that is not inclusive of ethno-diverse populations. The need for a comprehensive and inclusive model designed for use in Canada would aid in the identification of BPD and death in preterm infants. Dr. Faiza Khurshid, Associate Professor, Queen’s University, explains, “Babies born very prematurely are at risk of bronchopulmonary dysplasia (BPD), a lung disease that can have lifelong impacts on respiratory and cardiovascular function. A prediction model would help to target and guide decision-making around preventive treatment and to facilitate more personalized counseling of families.”

The group aims to develop and compare a range of regression-based and machine learning approaches to predict BPD and/or death in preterm infants to more precisely identify risk factors as early as possible. To complete the analysis, Dr. Khurshid, partnered with researchers and computer scientists from Queen’s University, University of Toronto, University of Alberta, and University of British Columbia, and utilized datasets from the Canadian Neonatal Network. The researchers worked collaboratively with ML expert, Dr. Amal Khalil, Centre for Advanced Computing at Queen’s University and Jonathan Messiha, graduate student, Master of Management in Artificial Intelligence, Smith School of Business at Queen’s University, to analyze various logistic regression models against other ML models to identify a more comprehensive and inclusive method of prediction.

The group’s initial analysis, “Comparison of Multivariable Logistic Regression and Machine Learning Models for Predicting Bronchopulmonary Dysplasia or Death in Very Preterm Infants” was published in Frontier’s in Pediatrics. Although the first phase of the study did not yield significant differences between the two model types, it did contribute to narrowing the gaps in clinical identification. This initial step is important as the group moves into the second phase of the project, developing multinomial models to predict BPD severity and death, where it will apply similar logic to further analyze methodologies.

Dr. Khurshid summarizes the impact of the study as she explains, “Our research is using a novel approach in developing clinical prediction models for Canadian preterm infants; a first step towards the ability to accurately predict those at risk.”

More information about the study can be found in the Frontier’s in Pediatrics publication.

Special thanks are extended to the Canadian Neonatal Network as the study would not have been successful without the data and resources provided by their team.

Queen’s University: https://www.queensu.ca/

Centre for Advanced Computing: https://cac.queensu.ca/

Smith School of Business: https://smith.queensu.ca/

Kingston Health Sciences Centre: https://kingstonhsc.ca/

Canadian Neonatal Network: http://www.canadianneonatalnetwork.org/

University of Toronto: https://www.utoronto.ca/

University of Alberta: https://www.ualberta.ca/

University of British Columbia: https://www.ubc.ca/

The CAC is thrilled to announce our partnership as a member of the Queen’s Data Champion’s (qDC) team who are one of eighteen groups or individuals being supported in part by funding from the Digital Research Alliance of Canada.

Nathan Brinklow (Thanyehténhas), a PhD student at Queen’s University, to discusses how advanced research computing resources have supported the revitalization of Indigenous Languages. 

Photo credit: Tsi Tyónnheht Onkwawén:na