QSpace: Queen’s Scholarship & Digital Collections
QSpace is our online repository of digital content produced and collected by the Queen’s community.
QSpace provides access to peer-reviewed articles, conference papers, technical reports, graduate theses and dissertations and other works produced by Queen’s faculty and students.
QSpace is managed by the Library’s Scholarly Publishing service, facilitating seamless access to Queen’s research to the widest possible audience. For more information about QSpace and to contribute your work(s) please contact the Scholarly Publishing Team.
Communities in QSpace
Select a community to browse its collections.
This community includes graduate theses, dissertations and projects produced by students at Queen’s University.
This community includes Queen’s peer-reviewed research publications, including journal articles, book chapters, conference proceedings, and more.
This community includes research data produced by faculty and staff at Queen’s University.
This community includes digital collections produced by members of the Queen’s community, as well as digital special collections made available via W.D. Jordan Rare Books & Special Collections.
This community provides access for staff and students at Queen’s University to degree examination papers and syllabi.
The properties of geomembrane (GMB) sheet material and seams are critical factors controlling the design life of a constructed barrier system. In particular, the resistance to stress cracking and retention of antioxidants ...
"Our Kind of Love": Black and White Interracial Relationships in Nineteenth and Twentieth Century Ontario The subject of Black and White interracial relationships is one that has been neglected in the existing historiography of Black Canadian studies. Historically, scholars have treated these relationships as salacious stories ...
Attentive Cross-Modal Connections for Learning Multimodal Representations from Wearable Signals for Affect Recognition We propose cross-modal attentive connections, a new dynamic and effective technique for multimodal representation learning from wearable data. Our solution can be integrated into any stage of the pipeline, i.e., after any ...
TOWARDS THE DEVELOPMENT OF COST-EFFECTIVE DECENTRALIZED APPLICATIONS: AN INVESTIGATION OF TRANSACTION PROCESSING TIMES ON THE ETHEREUM BLOCKCHAIN PLATFORM Ethereum is one of the most popular blockchain platforms for the development of blockchain-powered applications (i.e. ÐApps). When engineering ÐApps, developers need to translate requests captured in the frontend of their ...
Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning We propose a novel neural pipeline, MSGazeNet, that learns gaze representations by taking advantage of the eye anatomy information through a multistream framework. Our proposed solution comprises two components, first a ...