Abstract
The importance of academic publications is often evaluated by the number of and impact of its subsequent citing works. These citing works build upon the referenced material, representing both further intellectual insights and additional derived uses. As such, reading peer-reviewed articles which cite one’s work can serve as a way for authors to understand how their research is being adopted and extended by the greater scientific community, further develop the broader impacts of their research, and even find new collaborators. Unfortunately, in today’s rapidly growing and shifting scientific landscape, it is unlikely that a researcher has enough time to read through all articles citing their works, especially in the case of highly-cited broad-impact studies. To address this challenge, we developed the Science Citation Knowledge Extractor (SCKE), a web tool to provide biological and biomedical researchers with an overview of how their work is being utilized by the broader scientific community. SCKE is a web-based tool which utilizes natural language processing and machine learning to retrieve key information from scientific publications citing a given work, analyze the citing material, and and present users with interactive data visualizations which illustrate how their works are contributing to greater scientific pursuits. Results are generally grouped into two categories, aimed at 1) understanding the broad scientific areas which one’s work is impacting and 2) assessing the breadth and impact of one’s work within these areas. As a web application, SCKE is easy to use, with a single input of PubMed ID(s) to analyze. SCKE is available for immediate use by the scientific community as a hosted web application at https://geco.iplantcollaborative.org/scke/. SCKE can also be self-hosted by taking advantage of a fully-integrated VM Image (https://tinyurl.com/y7ggpvaa), Docker container (https://tinyurl.com/y95u9dhw), or open-source code (GPL license) available on GitHub (https://tinyurl.com/yaesue5e).
Highlights
At the core, scientific research is an iterative process where future lines of inquiry are guided by prior findings
We developed the Science Citation Knowledge Extractor (SCKE), a web tool to provide biological and biomedical researchers with an overview of how their work is being utilized by the broader scientific community
The results of natural language processing (NLP) often require additional analyses to visualize salient and emerging themes across multiple sources of publications. It would be largely beneficial for the scientific community as a whole to have an easy-to-use tool for reading and analyzing scientific papers referencing key publications, such a tool is currently lacking. To address this gap in analyzing citing works of biomedical publications, we developed Science Citation Knowledge Extractor (SCKE)
Summary
Scientific research is an iterative process where future lines of inquiry are guided by prior findings. The results of NLP often require additional analyses to visualize salient and emerging themes across multiple sources of publications It would be largely beneficial for the scientific community as a whole to have an easy-to-use tool for reading and analyzing scientific papers referencing key publications, such a tool is currently lacking. SCKE is a web-accessible application that uses natural language processing (NLP) and machine learning (ML) techniques to analyze the content of citing publications and convey important information such as topics and concepts in informative and interactive data visualizations. Insights are derived from the SCKE pipeline, which uses natural language processing (NLP) and machine learning (ML) techniques to analyze the text and metadata of scientific publications which cite an input work(s) (Figure 1).
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