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Many academics use yearly publication numbers to quantify academic interestfor their research topic. While such visualisations are ubiquitous in grantapplications, manuscript introductions, and review articles, they fail toaccount for the rapid growth in scientific publications. As a result, anysearch term will likely show an increase in supposed "academic interest". Oneproposed solution is to normalise yearly publication rates by field size, butthis is arduous and difficult. Here, we propose an simpler index thatnormalises keywords of interest by a ubiquitous and innocuous keyword, such as"banana". Alternatively, one could opt for field-specific keywords orhierarchical structures (e.g. PubMed's Medical Subject Headings, MeSH) tocompute "interest market share". Using this approach, we uncovered plausibletrends in academic interest in examples from the medical literature. Inneuroimaging, we found that not the supplementary motor area (as was previouslyclaimed), but the prefrontal cortex is the most interesting part of the brain.In cancer research, we found a contemporary preference for cancers with highprevalence and clinical severity, and notable declines in interest for moretreatable or likely benign neoplasms. Finally, we found that interest inrespiratory viral infections spiked when strains showed potential for pandemicinvolvement, with SARS-CoV-2 and the COVID-19 pandemic being the most extremeexample. In sum, the time is ripe for a quick and easy method to quantifytrends in academic interest for anecdotal purposes. We provide such a method,along with software for researchers looking to implement it in their ownwriting.

Banana for scale: Gauging trends in academic interest by normalising publication rates to common and innocuous keywords