A keyword-based approach to analyzing scientific research trends: ReRAM present and future
Abstract Research trend analysis is a primary step in defining research structures and predicting research directions from scientific papers. Recently, due to millions of annual scientific publications, researchers demand analytical methods to interpret the research field topologically and temporall...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-93423-5 |
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| author | Hyeon Kim Seong Hun Kim Jaeseon Kim Eun Ho Kim Jun Hyeong Gu Donghwa Lee |
| author_facet | Hyeon Kim Seong Hun Kim Jaeseon Kim Eun Ho Kim Jun Hyeong Gu Donghwa Lee |
| author_sort | Hyeon Kim |
| collection | DOAJ |
| description | Abstract Research trend analysis is a primary step in defining research structures and predicting research directions from scientific papers. Recently, due to millions of annual scientific publications, researchers demand analytical methods to interpret the research field topologically and temporally. In this study, we propose a keyword-based research trend analysis method that automatically and systematically analyzes the research field by extracting keywords and constructing a keyword network. We verified our method on the resistive random-access memory (ReRAM) research field, which is in the limelight as an alternative device for non-volatile memory and artificial synapses. Our method performs three sequential processes: article collection, keyword extraction, and research structuring. We identified three keyword communities of ReRAM based on the processing-structure-property-performance (PSPP) relationship and found an upward trend in Neuromorphic applications. As a result, our method successfully structures the ReRAM research field and is expected to provide detailed insights into various research fields. |
| format | Article |
| id | doaj-art-3b4cd8d176bd4f0e870df4a3ef3a998b |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3b4cd8d176bd4f0e870df4a3ef3a998b2025-08-20T03:10:07ZengNature PortfolioScientific Reports2045-23222025-04-0115111010.1038/s41598-025-93423-5A keyword-based approach to analyzing scientific research trends: ReRAM present and futureHyeon Kim0Seong Hun Kim1Jaeseon Kim2Eun Ho Kim3Jun Hyeong Gu4Donghwa Lee5Department of Materials Science and Engineering (MSE), Pohang University of Science and Technology (POSTECH)Department of Materials Science and Engineering (MSE), Pohang University of Science and Technology (POSTECH)Department of Materials Science and Engineering (MSE), Pohang University of Science and Technology (POSTECH)Department of Materials Science and Engineering (MSE), Pohang University of Science and Technology (POSTECH)Department of Materials Science and Engineering (MSE), Pohang University of Science and Technology (POSTECH)Department of Materials Science and Engineering (MSE), Pohang University of Science and Technology (POSTECH)Abstract Research trend analysis is a primary step in defining research structures and predicting research directions from scientific papers. Recently, due to millions of annual scientific publications, researchers demand analytical methods to interpret the research field topologically and temporally. In this study, we propose a keyword-based research trend analysis method that automatically and systematically analyzes the research field by extracting keywords and constructing a keyword network. We verified our method on the resistive random-access memory (ReRAM) research field, which is in the limelight as an alternative device for non-volatile memory and artificial synapses. Our method performs three sequential processes: article collection, keyword extraction, and research structuring. We identified three keyword communities of ReRAM based on the processing-structure-property-performance (PSPP) relationship and found an upward trend in Neuromorphic applications. As a result, our method successfully structures the ReRAM research field and is expected to provide detailed insights into various research fields.https://doi.org/10.1038/s41598-025-93423-5Research trend analysisBibliometricsNetwork theoryNatural Language processingReRAMMaterials science |
| spellingShingle | Hyeon Kim Seong Hun Kim Jaeseon Kim Eun Ho Kim Jun Hyeong Gu Donghwa Lee A keyword-based approach to analyzing scientific research trends: ReRAM present and future Scientific Reports Research trend analysis Bibliometrics Network theory Natural Language processing ReRAM Materials science |
| title | A keyword-based approach to analyzing scientific research trends: ReRAM present and future |
| title_full | A keyword-based approach to analyzing scientific research trends: ReRAM present and future |
| title_fullStr | A keyword-based approach to analyzing scientific research trends: ReRAM present and future |
| title_full_unstemmed | A keyword-based approach to analyzing scientific research trends: ReRAM present and future |
| title_short | A keyword-based approach to analyzing scientific research trends: ReRAM present and future |
| title_sort | keyword based approach to analyzing scientific research trends reram present and future |
| topic | Research trend analysis Bibliometrics Network theory Natural Language processing ReRAM Materials science |
| url | https://doi.org/10.1038/s41598-025-93423-5 |
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