This presentation introduces the first comprehensive analysis of how Government of Canada open data is cited in academic literature, addressing a critical challenge digital curators face: how to demonstrate the impact and value of open data collections. Using a fine-tuned BERT language model trained on over 3,000 manually verified citation examples, this study overcame the problem of inconsistent data citation standards. This study leveraged AI to identify 3,953 citing articles with 91% accuracy, significantly outperforming traditional keyword-matching methods at 73% accuracy. The study reveals key usage patterns across disciplines, identifying environmental science, agriculture, and immigration studies as primary users of Canadian government data. The study's findings provide digital curators with evidence-based insights for strategic collection development and resource allocation decisions, while the open-source methodology offers the community immediately deployable tools for impact assessment. In an era of budget cuts where archives must continually justify their value, this study demonstrates how AI can enhance traditional bibliometric approaches to provide more comprehensive and accurate measures of collection impact, directly addressing contemporary challenges in digital curation.
Michael Groenendyk (Wed,) studied this question.
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