This paper's dataset of 10K+ term familiarity annotations has been directly adopted as a benchmark for training and evaluating improved personalized jargon detection models, its finding that jargon familiarity varies by individual background has motivated personalized knowledge support in LLM-based reading tools and audience-aware text simplification systems, its prompt-based methodology for incorporating researcher metadata has been adapted for identifying unfamiliar phrases in document processing pipelines and extended to jargon identification for non-scientist audiences, and it is frequently referenced to justify the importance of addressing jargon as a barrier in interdisciplinary communication and to contextualize efforts in plain language summarization of scientific texts.