Analysis of Domestic Research Trends on Technoparks(1997~2022) (테크노파크 국내 학술 연구동향 분석(1997~2022))
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- Journal of Korean Society of Industrial and Systems Engineering
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- v.47 no.3
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- pp.104-113
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- 2024
This study aims to examine domestic research trends on technoparks and to explore future research directions in this field. For this purpose, 493 articles were collected from academic journal sites, covering the period from 1997, when the pilot technoparks were designated, to 2022. To avoid duplication of identical titles and content, theses and conference papers were excluded. Only articles registered or candidate-registered in the Korea Citation Index (KCI) were selected. After reviewing the research topics and content, a total of 74 papers were used for the final analysis. The data analysis involved descriptive analyses of the research period, research areas, research methods, research subjects, and research topics. Furthermore, a word cloud text analysis was conducted using 305 keywords related to technoparks. This study is significant as the first comprehensive analysis of research trends on technoparks and aims to provide meaningful foundational data to explore future directions for research and innovation policy related to technoparks.
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (