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.

Longitudinal research in information science is rare, but an exception to that rule is a Canadian study that has surveyed academic librarians at multiple points over a 30-year period to understand their instructional goals and practices. This research program highlights the value of longitudinal and geographic comparisons.

Lessons from research on the governance of misinformation in everyday life apply widely to individual and community experiences.

In a landscape saturated with data, the challenge lies in making information meaningful. How data is structured, visualized, and contextualized determines whether it merely informs or truly influences perception and decision-making. However, raw data alone does not communicate meaning. Traditional data visualizations, such as bar charts, scatter plots, and heat maps, often fail to engage audiences emotionally and cognitively. This is where data storytelling emerges as a transformative approach.

Thinking of communities as “information poor” misrepresents the reality of systemic exclusion. Instead, marginalized communities have been intentionally and unintentionally excluded from mainstream information infrastructures. This exclusion is not due to a lack of knowledge on the part of marginalized communities but rather a reflection of structural barriers that limit access to institutionalized information flows. We need to recognize the existence and prevalence of information precarity, and then we need to radically alter how we plan and carry out projects, research, and outreach with—not for—marginalized communities.