Analysis of research trends in Korean dentistry journals by assigning MeSH to author keywords
Researchers seek to identify optimal journals for submission based on their studies but tend to rely on journal impact factors or scientific journal rankings. We investigated research trends by selecting high-frequency words from author keywords (AKs), analyzing subject areas, and performing quantit...
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Published in | Medicine (Baltimore) Vol. 99; no. 38; p. e22190 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
United States
Lippincott Williams & Wilkins
18.09.2020
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Subjects | |
Online Access | Get full text |
ISSN | 0025-7974 1536-5964 1536-5964 |
DOI | 10.1097/MD.0000000000022190 |
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Abstract | Researchers seek to identify optimal journals for submission based on their studies but tend to rely on journal impact factors or scientific journal rankings. We investigated research trends by selecting high-frequency words from author keywords (AKs), analyzing subject areas, and performing quantitative data analysis of Korean dental journals. Consequently, we suggest a method for choosing journals that fit a specific subject area.We used a corpus of 9 Korean dentistry journals regarded in Korea as quality internationally approved journals. AKs occurring more than 10 times were assigned to Medical Subject Headings (MeSH) terms and subcategories, which were then categorized using the MeSH tree structure. KnowledgeMatrix Plus and VOSviewer were used to analyze network relationships, density, and clustering.The AKs were of 7527 types, 15,960 terms, and formed 54 clusters. The AKs with 10+ occurrence were 199 types, 4289 terms, and formed 9 clusters. Assigning the AKs with 10+ occurrence to MeSH terms led to expanding 732 types of AK terms into 249 types with 9 clusters and 4268 links. Core study areas over the past 10 years were facial asymmetry, a topic under oral surgery and medicine, and orthognathic surgery focused on mandibular fractures, followed by shear bond strength of zirconia. Analyzing 16 MeSH subject categories, we found that the "analytical, diagnostic and therapeutic techniques and equipment" category had the largest distribution of AKs (40.7%). This was followed by "diseases" (21.2%) and "anatomy" (14.90%). The orthognathic surgery cluster was the largest, followed by the shear bond strength cluster. Dental implants is a core area with strong links to high-occurrence words, such as cone-beam computed tomography and mandible, which were distributed in the order of The Journal of Advanced Prosthodontics (37.8%) and Journal of Periodontal & Implant Science (30.6%). Five clusters were closely packed in the center, 2 clusters were formed above the center, 1 cluster was formed below the center, and a cluster on the right was widespread.Cluster analysis using AKs and MeSH may be a good analytic method for researchers to determine expanding research areas and select optimal journals for paper submission. |
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AbstractList | Supplemental Digital Content is available in the text
Researchers seek to identify optimal journals for submission based on their studies but tend to rely on journal impact factors or scientific journal rankings. We investigated research trends by selecting high-frequency words from author keywords (AKs), analyzing subject areas, and performing quantitative data analysis of Korean dental journals. Consequently, we suggest a method for choosing journals that fit a specific subject area.
We used a corpus of 9 Korean dentistry journals regarded in Korea as quality internationally approved journals. AKs occurring more than 10 times were assigned to Medical Subject Headings (MeSH) terms and subcategories, which were then categorized using the MeSH tree structure. KnowledgeMatrix Plus and VOSviewer were used to analyze network relationships, density, and clustering.
The AKs were of 7527 types, 15,960 terms, and formed 54 clusters. The AKs with 10+ occurrence were 199 types, 4289 terms, and formed 9 clusters. Assigning the AKs with 10+ occurrence to MeSH terms led to expanding 732 types of AK terms into 249 types with 9 clusters and 4268 links. Core study areas over the past 10 years were facial asymmetry, a topic under oral surgery and medicine, and orthognathic surgery focused on mandibular fractures, followed by shear bond strength of zirconia. Analyzing 16 MeSH subject categories, we found that the “analytical, diagnostic and therapeutic techniques and equipment” category had the largest distribution of AKs (40.7%). This was followed by “diseases” (21.2%) and “anatomy” (14.90%). The orthognathic surgery cluster was the largest, followed by the shear bond strength cluster. Dental implants is a core area with strong links to high-occurrence words, such as cone-beam computed tomography and mandible, which were distributed in the order of The Journal of Advanced Prosthodontics (37.8%) and Journal of Periodontal & Implant Science (30.6%). Five clusters were closely packed in the center, 2 clusters were formed above the center, 1 cluster was formed below the center, and a cluster on the right was widespread.
Cluster analysis using AKs and MeSH may be a good analytic method for researchers to determine expanding research areas and select optimal journals for paper submission. Researchers seek to identify optimal journals for submission based on their studies but tend to rely on journal impact factors or scientific journal rankings. We investigated research trends by selecting high-frequency words from author keywords (AKs), analyzing subject areas, and performing quantitative data analysis of Korean dental journals. Consequently, we suggest a method for choosing journals that fit a specific subject area.We used a corpus of 9 Korean dentistry journals regarded in Korea as quality internationally approved journals. AKs occurring more than 10 times were assigned to Medical Subject Headings (MeSH) terms and subcategories, which were then categorized using the MeSH tree structure. KnowledgeMatrix Plus and VOSviewer were used to analyze network relationships, density, and clustering.The AKs were of 7527 types, 15,960 terms, and formed 54 clusters. The AKs with 10+ occurrence were 199 types, 4289 terms, and formed 9 clusters. Assigning the AKs with 10+ occurrence to MeSH terms led to expanding 732 types of AK terms into 249 types with 9 clusters and 4268 links. Core study areas over the past 10 years were facial asymmetry, a topic under oral surgery and medicine, and orthognathic surgery focused on mandibular fractures, followed by shear bond strength of zirconia. Analyzing 16 MeSH subject categories, we found that the "analytical, diagnostic and therapeutic techniques and equipment" category had the largest distribution of AKs (40.7%). This was followed by "diseases" (21.2%) and "anatomy" (14.90%). The orthognathic surgery cluster was the largest, followed by the shear bond strength cluster. Dental implants is a core area with strong links to high-occurrence words, such as cone-beam computed tomography and mandible, which were distributed in the order of The Journal of Advanced Prosthodontics (37.8%) and Journal of Periodontal & Implant Science (30.6%). Five clusters were closely packed in the center, 2 clusters were formed above the center, 1 cluster was formed below the center, and a cluster on the right was widespread.Cluster analysis using AKs and MeSH may be a good analytic method for researchers to determine expanding research areas and select optimal journals for paper submission.Researchers seek to identify optimal journals for submission based on their studies but tend to rely on journal impact factors or scientific journal rankings. We investigated research trends by selecting high-frequency words from author keywords (AKs), analyzing subject areas, and performing quantitative data analysis of Korean dental journals. Consequently, we suggest a method for choosing journals that fit a specific subject area.We used a corpus of 9 Korean dentistry journals regarded in Korea as quality internationally approved journals. AKs occurring more than 10 times were assigned to Medical Subject Headings (MeSH) terms and subcategories, which were then categorized using the MeSH tree structure. KnowledgeMatrix Plus and VOSviewer were used to analyze network relationships, density, and clustering.The AKs were of 7527 types, 15,960 terms, and formed 54 clusters. The AKs with 10+ occurrence were 199 types, 4289 terms, and formed 9 clusters. Assigning the AKs with 10+ occurrence to MeSH terms led to expanding 732 types of AK terms into 249 types with 9 clusters and 4268 links. Core study areas over the past 10 years were facial asymmetry, a topic under oral surgery and medicine, and orthognathic surgery focused on mandibular fractures, followed by shear bond strength of zirconia. Analyzing 16 MeSH subject categories, we found that the "analytical, diagnostic and therapeutic techniques and equipment" category had the largest distribution of AKs (40.7%). This was followed by "diseases" (21.2%) and "anatomy" (14.90%). The orthognathic surgery cluster was the largest, followed by the shear bond strength cluster. Dental implants is a core area with strong links to high-occurrence words, such as cone-beam computed tomography and mandible, which were distributed in the order of The Journal of Advanced Prosthodontics (37.8%) and Journal of Periodontal & Implant Science (30.6%). Five clusters were closely packed in the center, 2 clusters were formed above the center, 1 cluster was formed below the center, and a cluster on the right was widespread.Cluster analysis using AKs and MeSH may be a good analytic method for researchers to determine expanding research areas and select optimal journals for paper submission. Researchers seek to identify optimal journals for submission based on their studies but tend to rely on journal impact factors or scientific journal rankings. We investigated research trends by selecting high-frequency words from author keywords (AKs), analyzing subject areas, and performing quantitative data analysis of Korean dental journals. Consequently, we suggest a method for choosing journals that fit a specific subject area.We used a corpus of 9 Korean dentistry journals regarded in Korea as quality internationally approved journals. AKs occurring more than 10 times were assigned to Medical Subject Headings (MeSH) terms and subcategories, which were then categorized using the MeSH tree structure. KnowledgeMatrix Plus and VOSviewer were used to analyze network relationships, density, and clustering.The AKs were of 7527 types, 15,960 terms, and formed 54 clusters. The AKs with 10+ occurrence were 199 types, 4289 terms, and formed 9 clusters. Assigning the AKs with 10+ occurrence to MeSH terms led to expanding 732 types of AK terms into 249 types with 9 clusters and 4268 links. Core study areas over the past 10 years were facial asymmetry, a topic under oral surgery and medicine, and orthognathic surgery focused on mandibular fractures, followed by shear bond strength of zirconia. Analyzing 16 MeSH subject categories, we found that the "analytical, diagnostic and therapeutic techniques and equipment" category had the largest distribution of AKs (40.7%). This was followed by "diseases" (21.2%) and "anatomy" (14.90%). The orthognathic surgery cluster was the largest, followed by the shear bond strength cluster. Dental implants is a core area with strong links to high-occurrence words, such as cone-beam computed tomography and mandible, which were distributed in the order of The Journal of Advanced Prosthodontics (37.8%) and Journal of Periodontal & Implant Science (30.6%). Five clusters were closely packed in the center, 2 clusters were formed above the center, 1 cluster was formed below the center, and a cluster on the right was widespread.Cluster analysis using AKs and MeSH may be a good analytic method for researchers to determine expanding research areas and select optimal journals for paper submission. |
Author | Jeong, Sona Jeong, Ji Na |
AuthorAffiliation | Department of Health Management, Jeonju University, Jeonju, Korea |
AuthorAffiliation_xml | – name: Department of Health Management, Jeonju University, Jeonju, Korea – name: a Department of Library & Information Science, Sookmyung Women's University – name: c Department of Health Management, Jeonju University, Jeonju, Korea – name: b Medical Library, the Catholic University of Korea, Seoul |
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BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32957346$$D View this record in MEDLINE/PubMed |
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References | Borgatti (R10-20230103) 2005; 27 van Eck Nees (R9-20230103) 2010; 84 Gan (R13-20230103) 2019; 98 Yeung (R22-20230103) 2018; 33 Dehdarirad (R12-20230103) 2019; 366 Kim (R25-20230103) 2019; 45 Gu (R20-20230103) 2020; 22 Liu (R11-20230103) 2020; 00 Gasparyan (R1-20230103) 2017; 32 Yang (R15-20230103) 2020; 192 Hernandez-Vasquez (R14-20230103) 2018; 11 Tijssen (R2-20230103) 1994; 18 Gonzalez (R19-20230103) 2018; 13 Wang (R18-20230103) 2018; 15 Romero (R21-20230103) 2019; 10 Jayaratne (R17-20230103) 2015; 10 Fardi (R23-20230103) 2017; 32 Mazaheri (R16-20230103) 2019; 10 |
References_xml | – volume: 33 start-page: 1240 year: 2018 ident: R22-20230103 article-title: Citation network analysis of dental implant literature from 2007 to 2016 publication-title: Int J Oral Maxillofac Implants doi: 10.11607/jomi.6727 – volume: 98 start-page: e16782 year: 2019 ident: R13-20230103 article-title: Mapping the knowledge structure and trends of epilepsy genetics over the past decade: A co-word analysis based on medical subject headings terms publication-title: Medicine (Baltimore) doi: 10.1097/MD.0000000000016782 – volume: 10 start-page: 564 year: 2019 ident: R21-20230103 article-title: Trends in sigma-1 receptor research: a 25-year bibliometric analysis publication-title: Front Pharmacol doi: 10.3389/fphar.2019.00564 – volume: 84 start-page: 523 year: 2010 ident: R9-20230103 article-title: Software survey: VOSviewer, a computer program for bibliometric mapping publication-title: Scientometrics doi: 10.1007/s11192-009-0146-3 – volume: 10 start-page: 201 year: 2019 ident: R16-20230103 article-title: Comparison of Intellectual Structure of Knowledge in International Journal of Preventive Medicine with MeSH: A Co-Word Analysis publication-title: Int J Prev Med doi: 10.4103/ijpvm.IJPVM_346_18 – volume: 13 start-page: e0201435 year: 2018 ident: R19-20230103 article-title: An author keyword analysis for mapping sport sciences publication-title: PLoS One doi: 10.1371/journal.pone.0201435 – volume: 27 start-page: 55 year: 2005 ident: R10-20230103 article-title: Centrality and network flow publication-title: Soc Netw doi: 10.1016/j.socnet.2004.11.008 – volume: 192 start-page: 105740 year: 2020 ident: R15-20230103 article-title: Research trends of stem cells in ischemic stroke from 1999 to 2018: A bibliometric analysis publication-title: Clin Neurol Neurosurg doi: 10.1016/j.clineuro.2020.105740 – volume: 15 start-page: 2181 year: 2018 ident: R18-20230103 article-title: Exploring the emerging evolution trends of urban resilience research by scientometric analysis publication-title: Int J Environ Res Public Health doi: 10.3390/ijerph15102181 – volume: 00 start-page: 1 year: 2020 ident: R11-20230103 article-title: Worldwide tendency and perspectives in traumatic dental injuries: A bibliometric analysis over two decades (1999-2018) publication-title: Dent Traumatol – volume: 45 start-page: 186 year: 2019 ident: R25-20230103 article-title: Classification of the journal category “oral surgery” in the Scopus and the science citation index expanded: flaws and suggestions publication-title: J Korean Assoc Oral Maxillofac Surg doi: 10.5125/jkaoms.2019.45.4.186 – volume: 32 start-page: 173 year: 2017 ident: R1-20230103 article-title: The journal impact factor: moving toward an alternative and combined scientometric approach publication-title: J Korean Med Sci doi: 10.3346/jkms.2017.32.2.173 – volume: 11 year: 2018 ident: R14-20230103 article-title: A bibliometric analysis of the global research on biosimilars publication-title: J Pharm Policy Pract doi: 10.1186/s40545-018-0133-2 – volume: 366 year: 2019 ident: R12-20230103 article-title: Bibliometric mapping of microbiology research topics (2012-16): a comparison by socioeconomic development and infectious disease vulnerability values publication-title: FEMS Microbiol Lett doi: 10.1093/femsle/fnz004 – volume: 10 start-page: e0119503 year: 2015 ident: R17-20230103 article-title: The evolution of dental journals from 2003 to 2012: a bibliometric analysis publication-title: PLoS One doi: 10.1371/journal.pone.0119503 – volume: 22 start-page: e15142 year: 2020 ident: R20-20230103 article-title: Tracking knowledge evolution in cloud health care research: knowledge map and common word analysis publication-title: J Med Internet Res doi: 10.2196/15142 – volume: 18 start-page: 98 year: 1994 ident: R2-20230103 article-title: Mapping changes in science and technology: bibliometric co-occurrence analysis of the R&D literature publication-title: Eval Rev doi: 10.1177/0193841X9401800110 – volume: 32 start-page: 555 year: 2017 ident: R23-20230103 article-title: Top-cited articles in implant dentistry publication-title: Int J Oral Maxillofac Implants doi: 10.11607/jomi.5331 |
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Snippet | Researchers seek to identify optimal journals for submission based on their studies but tend to rely on journal impact factors or scientific journal rankings.... Supplemental Digital Content is available in the text Researchers seek to identify optimal journals for submission based on their studies but tend to rely on... |
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Title | Analysis of research trends in Korean dentistry journals by assigning MeSH to author keywords |
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