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This script applies topic modeling to analyze literature trends of climate impacts to inland fish based on the papers within the Fish and Climate Change Database (FiCli, DOI: 10.5066/P9973SMC). Sections 1-8 loaded the .bib file with all of the papers in the database and cleaned the text. This included combining the title/abstract/keywords, removing non-informative words, stemming words, removing punctuation, and forming phrases (ie. climate change to climate_change). Sections 9-10 divided the papers into discrete topics by identifying the ideal number of topics and then using Latent Dirichlet Allocation (LDA) modeling and Gibbs sampling to assign topics to each paper. Sections 11-17 analyzed the topic modeling results...