pyResearchInsights—An open‐source Python package for scientific text analysis - Shetty, Sarthak J.; Ramesh, Vijay (Ecology and evolution, 2021)
With an increasing number of scientific articles published each year, there is a need to synthesize and obtain insights across ever‐growing volumes of literature. Here, we present pyResearchInsights, a novel open‐source automated content analysis package that can be used to analyze scientific s within a natural language processing framework. The package collects s from scientific repositories, identifies topics of research discussed in these s, and presents interactive concept maps to visualize these research topics. To showcase the utilities of this package, we present two examples, specific to the field of ecology and conservation biology. First, we demonstrate the end‐to‐end functionality of the package by presenting topics of research discussed in 1,131 s pertaining to birds of the Tropical Andes. Our results suggest that a large proportion of avian research in this biodiversity hotspot pertains to species distributions, climate change, and plant ecology. Second, we retrieved and analyzed 22,561 s across eight journals in the field of conservation biology to identify twelve global topics of conservation research. Our analysis shows that conservation policy and landscape ecology are focal topics of research. We further examined how these conservation‐associated research topics varied across five biodiversity hotspots. Lastly, we compared the utilities of this package with existing tools that carry out automated content analysis, and we show that our open‐source package has wider functionality and provides end‐to‐end utilities that seldom exist across other tools. With an increasing number of scientific articles being published each year, there is a need to synthesize and obtain insights across large volumes of literature. Hence, we developed pyResearchInsights, a novel open‐source, modular, end‐to‐end automated content analysis package that can be used to analyze scientific s within a natural language processing framework.