A Bibliometric Study of Traditional Medicinal Plant Database Research, 2001 – 2021

The main objective of this study is to conduct a bibliometric study of Traditional Medicinal Plant Database research analysis of twenty years (2001 – 2021) of trends in Traditional Medicinal Plant Database research topics. The literature was extracted and analyzed using the Web of Science database. VOSViewer software was used to identify and visualize key trends, influential authors, and journals. The 654 filtered documents were selected based on three main criteria which are (i) Topics on Traditional Medicinal Plant Database, (ii) Type of documents on ‘Article’, and (iii) Year Published within 2001 to 2021. We conducted several types of analyses on the body of research using VOSViewer which are (i) Co-authorship analysis, (ii) Co-occurrence analysis, (iii) Citation analysis, and (iv) Co-citation analysis. The main contribution and motivation for this study are in the form of a conceptual framework of Traditional Medicinal Plant Database research topics in guiding future research in supporting the UN Sustaina ble Development Goals agenda on ‘Quality Education’ and ‘Good Health and Well- being’. There are five major keyword theme clusters concerning the Traditional Medicinal Plant Database that we had determined based on the clusters which are (i) Plant Identification, (ii) Bioactive Activities, (iii) Medicinal Properties, (iv) Plant Classification, and (v) Plant Species themes. ‘molecular docking’, ‘natural - products’, ‘network pharmacology’, ‘nf -kappa- b’, ‘oxidative stress’, ‘prediction’, ‘protein’, ‘proteomics’ and ‘traditional chinese medicine’. Third, the theme we classified as ‘Medicinal Properties’ oriented -


Introduction
There are ongoing trends Traditional Medicinal Plant Database research topics (Ningthoujam et al., 2012) (Kumar et al., 2018)   . The following discusses the results and discussion for (i) 'Coauthorship analysis', (ii) 'Co-occurrence analysis', (iii) 'Citation analysis', and (iv) 'Co-citation analysis'. A conceptual framework was also being developed.

Co-authorship Analysis
In general, 'co-authorship analysis can be described as the greater the number of coauthored papers, the higher the relatedness of authors, institutions, and countries' (Van Eck and Waltman, 2010) (Park et al., 2020). In total, 3938 authors were involved in writing the 654 articles that comprised the Web of Science results related to the Traditional Medicinal Plant Database from the year 2001 to 2021. By using VOSviewer, the minimum number of documents published by an author was set to one and the minimum number of citations of an author to 150. 92 authors who met this threshold. Subsequently, the result of coauthorship analysis is shown in Fig. 2 which includes one prominent cluster (16 authors). The cluster (red node) comprise of 16 authors (The names of the authors are Barikmo, Ingrid; Berhe, Nega; Blomhoff, Rune; Bohn, Siv K.; Carlsen, Monica H., Dragland, Steinar; Halvorsen, Bente I., Holte, Kari; Jacobs, Daviord r., jr.; Phillips, Katherine M.; Sampson, Laura; Sanada, Chiko; Senoo, Haruki; Umezono, Yuko; Willett, Walter C., Wiley, Carol).

Fig. 2. Co-authorship diagram (Generated by VOSviewer)
The top six countries in terms of the number of papers published are listed in Table 1. Scholars from the China and USA have the most papers and have the most citations (by country).

Co-occurrence Analysis
In general, 'the bigger the number of papers in which two keywords appear together, the higher the relatedness of these keywords, according to co-occurrence analysis' (Van Eck and Waltman, 2010;Park et al., 2020). VOSViewer collects 'co-occurrences of both author keywords and all other keywords, demonstrating their frequency and relatedness' (Van Eck and Waltman, 2010;Park et al., 2020). Co-occurrence analysis includes 'measuring the number of documents in which two terms or words are found together' (Van Eck and Waltman, 2010;Park et al., 2020). VOSViewer was set for a threshold of ten documents in which a keyword had to appear for it to be included. Out of 4061 keywords, the data subsequently resulted in 88 keywords with accord to the aforementioned threshold. Table 2 lists the ten most commonly occurring keywords that appeared in our sample of 654 papers.  Fig. 4 shows the mapping of the keyword co-occurrences and also depicts the dominant links between keywords and clusters. First, the shown in red that we classify as 'Plant Identification' oriented-keywords comprises 'accumulation', 'annotation', 'arabidopsis', 'biosynthesis', 'classification', 'database', 'evolution', 'expression', 'family', 'flavonoid biosynthesis', 'gene', 'generation', 'genes', 'genome', 'identification', 'metabolism', 'metabolomics', 'molecular-cloning', 'pathway', 'rna-seq', 'sequence', 'stress', 'tool' and 'transcriptome'.

Citation Analysis
In general, 'the more the number of times authors, journals, and publications cite each other, the more connected these items are, according to citation analysis' (Van Eck and Waltman, 2010;Park et al., 2020). Citation analysis is 'based on the relatedness of entities like authors and journals, which is determined by how many times they cite each other' (Van Eck and Waltman, 2010;Park et al., 2020). Which documents in the field of Traditional Medicinal Plant Database research cite each other? We use VOSviewer and set the threshold that a paper is cited at least thirty times. Out of 654 documents, only 91 documents met this threshold which created nine clusters as shown in Fig. 5.  Fig. 5. Citations by paper are shown on the mapping (Created by VOSviewer) The threshold was set in VOSviewer that a journal had to be cited at least five times to be included in the map and the minimum number of a document of a source is three. 18 journals out of 307 sources met this criterion and of these and created four main clusters as shown in Fig. 6

Co-citation Analysis
In general, 'the greater the number of times authors, journals, and publications are referenced together, the stronger the relatedness of these items, according to the co-citation analysis' (Van Eck and Waltman, 2010) (Park et al., 2020). Co-citation analysis looks at 'how closely elements like authors, journals, and publications are mentioned together and how it has shaped academic discussions in the subject' (Van Eck and Waltman, 2010) (Park et al., 2020).

Conclusion
The theme and sub-themes as shown in Fig. 7 are important to be referred to for future possible research in Traditional Medicinal Plant Database research topics. The main contribution and motivation for this study are in the form of the conceptual framework of Traditional Medicinal Plant Database research topics in supporting the UN Sustainable Development Goals agenda on 'Quality Education' and 'Good Health and Well-being'.