{'fraction_non_alphanumeric': 0.01588502269288956, 'fraction_numerical': 0.0, 'mean_word_length': 5.182242990654205, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Interactive Exploration of the Readability of Science Authors

Here we present a new text analysis tool that consists of a text analysis service and an author search service. These services were created by using or extending many existing Free and Open Source tools, including streamlit, requests, WordCloud, TextStat, and The Natural Language Tool Kit. The tool has the capability to retrieve journal hosting links and journal article content from APIs and journal hosting websites. Together, these services allow the user to review the complexity of a scientist’s published work relative to other online-based text repositories. Rather than providing feedback as to the complexity of a single text as previous tools have done, the tool presented here shows the relative complexity across many texts from the same author, while also comparing the readability of the author’s body of work to a variety of other scientific and lay text types. The goal of this work is to apply a more data-driven approach that provides established academic authors with statistical insights into their body of published peer reviewed work. By monitoring these readability metrics, scientists may be able to cater their writing to reach broader audiences, contributing to an improved global communication and understanding of complex topics.