Eld [23,24]. In our study, a keyword co-occurrence network was constructed to represent topics, recognize the relationships between these topics, and define clusters of closely related topics inside a topic area (Step 3 in Table 1). This kind of evaluation demonstrates the interaction inside and between clusters based on keywords in each topic region. A cluster represents a collection of closely associated elements (subjects) that happen to be homogeneous [25]. Within this study, each constructed network represented a topic region (defined by Scopus classification). Every single network had numerous clusters to represent closely connected subjects. So that you can make such networks, we used the VOSViewer package. To execute this technical job, we downloaded the articles from Scopus for every single subject area separately and constructed the networks with clusters using the co-word evaluation function of VOSViewer. This function is performed utilizing keywords extracted in the Scopus database and applies a counting technique within the VOSViewer. The counting method is “full counting” where every single keyword has the identical weight, Telenzepine medchemexpress without any influence on the variety of keyword phrases for each and every short article. Provided that some topic locations had a scarcity of articles (with only a few keywords) affiliated with Kazakhstan, we kept the minimum variety of co-occurrences for a keyword as 1. 2.two.3. Author-Based Analysis Productivity Evaluation An evaluation of a country’s investigation productivity is as important as an assessment of publication and topical trends for a offered analysis field. It can be reflected by the amount of publications scholars contribute to an all round information base inside a specific time frame [26]. Numerous procedures are readily available to evaluate author-based analysis productivity, including 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 (development) of 25 subject regions. Lotka’s law uses the amount of articles plus the variety of authors in a given subject area and presents the frequency of publication by authors for this region [29]. It is actually defined as per Equation (1). f(x) = k/xn , (1) exactly where f(x) calculates the number of authors contributing x articles each, x will be the quantity of articles by an author, k is a given continuous which represents the amount of authors who published only one post, and n is the parameter which represents the distribution of your study productivity (articles) by all authors.Publications 2021, 9,five ofIn this equation, theoretically, the n-parameter is equal to about two. If so, as outlined by this law, about 60 of all authors in a given subject region 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, etc. [30,31]. The partnership among the n-parameter and k-constant implies that the amount of scholars publishing a offered variety of articles is fixed for the quantity of scholars publishing only one particular article. Within the literature, Voos [32] applied Lotka’s law inside the data science literature and located that the n-parameter was 3.5. Pao [33] empirically tested this law around the variety of study fields and determined that the parameter worth ranged from 1.eight to 3.eight. As a result, the case with all the n-parameter equal to about two is regarded a generalization [30,34]. It can be regarded that those subject locations with larger n-parameter values are less developed (much less maturely represented by fewer researche.
