Eld [23,24]. In our study, a keyword co-occurrence network was built to Trolox Protocol represent

Eld [23,24]. In our study, a keyword co-occurrence network was built to Trolox Protocol represent subjects, recognize the relationships in between these topics, and define clusters of closely related topics within a topic region (Step 3 in Table 1). This type of analysis demonstrates the interaction within and in between clusters primarily based on keyword phrases in each topic location. A cluster represents a collection of closely connected elements (topics) which are homogeneous [25]. In this study, each and every constructed network represented a topic location (defined by Scopus classification). Every network had several clusters to represent closely related subjects. In order to build such networks, we employed the VOSViewer package. To carry out this technical task, we downloaded the articles from Scopus for every topic location separately and constructed the networks with clusters utilizing the co-word analysis function of VOSViewer. This function is performed working with key phrases extracted in the Scopus database and applies a counting technique inside the VOSViewer. The counting method is “full counting” exactly where each keyword has exactly the same weight, without any influence around the JR-AB2-011 Formula number of search phrases for every write-up. Offered that some topic regions had a scarcity of articles (with only a few key phrases) affiliated with Kazakhstan, we kept the minimum quantity of co-occurrences to get a keyword as 1. two.2.3. Author-Based Analysis Productivity Evaluation An analysis of a country’s study productivity is as important as an assessment of publication and topical trends for any provided study field. It’s reflected by the number of publications scholars contribute to an overall understanding base within a particular time frame [26]. Numerous approaches are accessible to evaluate author-based research productivity, such as Lotka’s law [27,28]. In Step 4 (Table 1), we utilized this law to assess the scholarly productivity from the researchers from Kazakhstan and to evaluate the relative productivity (improvement) of 25 subject regions. Lotka’s law makes use of the number of articles and the quantity of authors inside a given subject location and presents the frequency of publication by authors for this region [29]. It really is defined as per Equation (1). f(x) = k/xn , (1) where f(x) calculates the number of authors contributing x articles each and every, x could be the number of articles by an author, k can be a provided continuous which represents the number of authors who published only 1 write-up, and n may be the parameter which represents the distribution from the research productivity (articles) by all authors.Publications 2021, 9,five ofIn this equation, theoretically, the n-parameter is equal to about 2. In that case, in line with this law, about 60 of all authors within a provided topic area make a single contribution (represented by the k-constant as 0.60), about 25 (1/2^2), 2 contributions, about 11 (1/3^2), 3 contributions, and so forth. [30,31]. The connection between the n-parameter and k-constant implies that the amount of scholars publishing a given variety of articles is fixed to the quantity of scholars publishing only one particular article. In the literature, Voos [32] applied Lotka’s law in the facts science literature and identified that the n-parameter was three.five. Pao [33] empirically tested this law around the quantity of study fields and determined that the parameter value ranged from 1.eight to 3.8. For that reason, the case with all the n-parameter equal to about two is deemed a generalization [30,34]. It is actually regarded that these subject regions with larger n-parameter values are much less created (significantly less maturely represented by fewer researche.