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Objective. This study aimed to identify the primary research areas, countries, and organizational involvement in publications on neurological disorders through an analysis of human-assigned keywords. These results were then compared with unsupervised and machine-algorithm-based extracted terms from the title and abstract of the publications to gain knowledge about deficiencies of both techniques. This has enabled us to understand how far machine-derived terms through titles and abstracts can be a substitute for human-assigned keywords of scientific research articles. Design/Methodology/Approach. While significant research areas on neurological disorders were identified from the author-provided keywords of downloaded publications of Web of Science and PubMed, these results were compared by the terms extracted from titles and abstracts through unsupervised based models like VOSviewer and machine-algorithm-based techniques like YAKE and CounterVectorizer. Results/Discussion. We observed that the post-covid-19 era witnessed more research on various neurological disorders, but authors still chose more generic terms in the keyword list than specific ones. The unsupervised extraction tool, like VOSviewer, identified many other extraneous and insignificant terms along with significant ones. However, our self-developed machine learning algorithm using CountVectorizer and YAKE provided precise results subject to adding more stop-words in the dictionary of the stop-word list of the NLTK tool kit. Conclusion. We observed that although author provided keywords play a vital role as they are assigned in a broader sense by the author to increase readability, these concept terms lacked specificity for in-depth analysis. We suggested that the ML algorithm being more compatible with unstructured data was a valid alternative to the author-generated keywords for more accurate results. Originality/Value. To our knowledge, this is the first-ever study that compared the results of author-p

The study helped with the subject mapping of the resources on 22 languages included in the Indian constitution that were indexed in the National Digital Library of India from 2018 and covering the different fields of arts and humanities, social sciences, science, engineering, technology, and history. The subject mapping is based on the Dewey Decimal Classification (DDC) 22nd edition. The relevant/required data was collected from the official website of the National Digital Library of India (https://ndl.iitkgp.ac.in/) on 4th April 2021, Sunday by browsing subject-wise. (https://ndl.iitkgp.ac.in/result?q={"t":"subject","b":{"browse":"subject","filters":[]}}) and filtering one by one of 22 officially recognized Indian languages in the Indian constitution. A total number of 398793 documents on different subjects indexed in NDLI till 4th April 2021, The maximum number of documents indexed in Computer science, Information & general works (000) subject [88.98%] and the larger number of documents indexed in the Bengali language [40.71%].

In recent decay, the scientometric study is one of the major research areas in scholarly communication. Researchers have conducted their research in the scientometric field from different core subject areas. Using bibliographic records on a scientometric field from the SCOPUS database, this paper tries to give a complete view of the evaluation of Indian research in the domain of scientometric. From 2010-2019 researchers have published 41462 publications out of the 334 number publications belongs to the scientometric domain of Indian research. Researchers have critically analyzed the collected data on various aspects like year-wise publication, author collaboration, authorship pattern, degree of collaboration, collaborative coefficient (CC), leading authors, productive journal, state-wise production in India, and mostly used keyword. The finding of the study disclosed that the maximum number of articles (97) published in the year 2019 with 222 citations. In the year 2015 got the highest number of citations (355) from only 31 publications. The highest number of articles are two-authored (140) followed by three-authored (89) and single-authored (54) respectively, and the average number of authors per article is 2.13. In respect of state-wise production, New Delhi has stood the first position with 191 publications. The word "scientometric” is the most used keyword and the top productive journal is Library Philosophy and Practice (114).