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Science Citation Knowledge Extractor

机译:科学引文知识提取器

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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).
机译:学术出版物的重要性通常通过其后续引用作品的数量和影响来评估。这些引用作品建立在参考资料的基础上,代表了进一步的智力见解和其他衍生用途。因此,阅读引用某人工作的经同行评审的文章可以帮助作者了解自己的研究被更广泛的科学界采用和扩展,进一步发展其研究的广泛影响力,甚至找到新的合作者。不幸的是,在当今快速发展和变化的科学领域中,研究人员不太可能有足够的时间阅读所有引用其著作的文章,尤其是在受到广泛影响的广泛研究的情况下。为了应对这一挑战,我们开发了科学引文知识提取器(SCKE),这是一个网络工具,可为生物和生物医学研究人员提供有关更广泛的科学界如何利用其工作的概述。 SCKE是一个基于Web的工具,利用自然语言处理和机器学习从引用特定作品的科学出版物中检索关键信息,分析引用材料,并为用户提供交互式数据可视化,从而说明他们的工作如何促进科学发展。追求。结果通常分为两类,其目的是:1)了解一个人的工作正在影响的广泛科学领域,以及2)评估一个人在这些领域中工作的广度和影响。作为Web应用程序,SCKE易于使用,只需输入一次PubMed ID即可进行分析。 SCKE可作为托管Web应用程序在https://geco.iplantcollaborative.org/scke/上供科学界立即使用。还可以利用完全集成的VM映像(https://tinyurl.com/y7ggpvaa)、Docker容器(https://tinyurl.com/y95u9dhw)或开源代码(GPL)来自托管SCKE许可证)可在GitHub(https://tinyurl.com/yaesue5e)上获得。

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