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Analyzing Iranian Research on Air Pollution with Scientometric Approach
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Ali Akbar Khasseh    , Faramarz Soheili , Zahra Mehri * , Mostafa Rostami , Najmeh Navarbafzadeh |
| MA. in Scientometrics, Payame Noor University, Tehran, Iran , mehri.zahra08@gmil.com |
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Abstract: (5069 Views) |
Background and aim: Today, in scientometrics, the keywords of scientific products are used to evaluate the scientific outputs of different scientific fields. In this research, the keywords of scientific products of Iranian experts in the field of air pollution indexed in international databases were used for content analysis.
Materials and methods: This applied study was done using scientometric techniques and software. The data of this research were extracted from the Web of Science from 1983 to 2018. The research population consisted of 1054 documents using Latin keywords related to infection and its roots and in accordance with the database search strategy. The Bib Excel, SPSS, UCINET and Excel were used to analyze the data.
Findings: The findings of this study showed that in terms of frequency, the keywords "Tehran" and "suspended particles" had the highest frequency. Findings also indicated that the scientific products of Iranian researchers in the field of air pollution consisted of 11 clusters so that clusters 1 and 11 were the most frequent clusters with 14 and 13 words, respectively. According to the findings, American researchers had the most cooperation with Iranian researchers by producing 91 documents. Moreover, in the period under study, research and executive institutions in the field of air pollution had the lowest scientific products.
Conclusion: The scientific productions of Iranian researchers have been growing in the specified years. Furthermore, the word density maps in the field of air pollution illustrate the word density of "air quality" and "suspended particles" in the maps. |
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| Keywords: Air pollution, Scientometrics, Iranian researchers, Content analysis |
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Full-Text [PDF 900 kb]
(2113 Downloads)
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Type of Study: Orginal |
Subject:
Scientometrics
Received: 2020/04/3 | Revised: 2021/04/19 | Accepted: 2020/09/9 | ePublished: 2020/08/31
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