Knowledge mobilization (KMb) is the movement of research findings between and within academic and non-academic settings. In a recent SSHRC-funded partner development project, Supporting Transparent and Open Research Engagement and Exchange (STOREE), we constructed a KMb plan as part of our funding application. Our work focused on making research more accessible, relevant to, and useful for non-academic audiences, and supporting scholars to change practices around research sharing. Reflecting on the project, team composition, how we worked together, sub-project processes and outcomes, and individual learnings, we gained insights on making alternative outputs and KMb more broadly.

Have you ever struggled to find the right health information, unsure of where to turn or what advice to trust? For many older Chinese adults in Canada, this challenge is even greater. They often face situations such as navigating a complex healthcare system, overcoming language barriers, and balancing traditional health beliefs with Western medical practices. These challenges can impact how they make health decisions and their overall well-being. Through in-depth interviews with 20 older Chinese adults in Canada, our research explores various factors related to how they seek and use health information. What did we uncover? Join us as we delve into their stories and the broader implications for health equity in Canada.

Most search interfaces currently used in public digital libraries have been influenced by design patterns based on web search, even though the complexity of the information seeking process when searching within a public library can be far greater than web search. Web search interfaces work extremely well for lookup search tasks, but they struggle to support complex search, especially those related to exploratory search.

For governments across the world, evaluating the impact of social service programs is a growing challenge – and they are increasingly turning to data and technology to help manage it. This is especially true for programs serving refugees and immigrants to settle in a new country. From tracking who needs settlement support to deciding who gets benefits first, digital systems and artificial intelligence (AI) are becoming key tools in how social services support refugees and immigrants. But what happens when data systems try to capture something as human and complex as “support”?  

In the world we are living in, a digital world, some data slips past our awareness, but very little data ever truly disappears. As we, information scientists, are concerned with reproducibility and responsibility of research, data lifecycle models have been developed to manage the complexity. To foster open, transparent, and collaborative science, data is often archived in a repository at the end of the project according to such data lifecycle models. This is often followed by the last step of the lifecycle models, data reuse. Traditionally, this model is cyclical, with reused data leading to new questions and fueling subsequent rounds of research.

This study explores how artificial intelligence (AI), specifically deep representation learning, can predict the scholarly impact of new scientific papers without relying on citation data. Using the SciBERT model, the research introduces two key indicators—Topicality (τ) and Originality (σ)—to estimate the potential impact of newly published papers. The approach is validated using the COVID-19 Open Research Dataset, demonstrating that papers with high topicality or originality are more likely to gain scholarly attention. The findings suggest that AI can complement traditional citation-based metrics, particularly for early-stage research, offering insights into knowledge creation dynamics and interdisciplinary research potential.