Science Citation Knowledge Extractor
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 cit...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-12-01
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| Series: | Frontiers in Research Metrics and Analytics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/frma.2018.00035/full |
| _version_ | 1818907120963158016 |
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| author | Heather Lent Gustave Hahn-Powell Asher Haug-Baltzell Sean Davey Mihai Surdeanu Eric Lyons |
| author_facet | Heather Lent Gustave Hahn-Powell Asher Haug-Baltzell Sean Davey Mihai Surdeanu Eric Lyons |
| author_sort | Heather Lent |
| collection | DOAJ |
| description | 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 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). |
| first_indexed | 2024-12-19T21:50:05Z |
| format | Article |
| id | doaj.art-758f40aaae104b99b61c4f291286b60a |
| institution | Directory Open Access Journal |
| issn | 2504-0537 |
| language | English |
| last_indexed | 2024-12-19T21:50:05Z |
| publishDate | 2018-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Research Metrics and Analytics |
| spelling | doaj.art-758f40aaae104b99b61c4f291286b60a2022-12-21T20:04:26ZengFrontiers Media S.A.Frontiers in Research Metrics and Analytics2504-05372018-12-01310.3389/frma.2018.00035426701Science Citation Knowledge ExtractorHeather Lent0Gustave Hahn-Powell1Asher Haug-Baltzell2Sean Davey3Mihai Surdeanu4Eric Lyons5School of Plant Sciences, BIO5 Institute, University of Arizona, Tucson, AZ, United StatesCLU Lab, Department of Computer Science, University of Arizona, Tucson, AZ, United StatesSchool of Plant Sciences, BIO5 Institute, University of Arizona, Tucson, AZ, United StatesSchool of Plant Sciences, BIO5 Institute, University of Arizona, Tucson, AZ, United StatesCLU Lab, Department of Computer Science, University of Arizona, Tucson, AZ, United StatesSchool of Plant Sciences, BIO5 Institute, University of Arizona, Tucson, AZ, United StatesThe 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 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).https://www.frontiersin.org/article/10.3389/frma.2018.00035/fullcitationsmachine learningnatural language processingscientific publicationsvisualizations |
| spellingShingle | Heather Lent Gustave Hahn-Powell Asher Haug-Baltzell Sean Davey Mihai Surdeanu Eric Lyons Science Citation Knowledge Extractor Frontiers in Research Metrics and Analytics citations machine learning natural language processing scientific publications visualizations |
| title | Science Citation Knowledge Extractor |
| title_full | Science Citation Knowledge Extractor |
| title_fullStr | Science Citation Knowledge Extractor |
| title_full_unstemmed | Science Citation Knowledge Extractor |
| title_short | Science Citation Knowledge Extractor |
| title_sort | science citation knowledge extractor |
| topic | citations machine learning natural language processing scientific publications visualizations |
| url | https://www.frontiersin.org/article/10.3389/frma.2018.00035/full |
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