Background: Scientometrics studies are one of the most efficient methods of quantitative evaluation of the scientific outputs of valuable information and citation databases for understanding and observing the status of scientific publications in different subject areas. The main aim of this article was to study the 50 years of Coronavirus scientific publications in the world. Materials & Methods: This applied research was carried out using scientometrics methods and an analytical approach. The statistical population of this article includes 5128 Coronavirus subject area documents indexed on the WoS from 1970 to 2019. The keywords were extracted from MeSH and analyzed using Excel 2016. Results: Data analysis showed that the highest science production was in 2005, and the highest citation number was in 2019. "Enjuanes L." is the most proliferated author, the United States, the most productive country, and the University of Hong Kong, the top organization in Coronavirus in the last half-century. Conclusion: The results showed, there is a direct relationship between the Coronavirus outbreaks and the amount of Scientific Publications in this area in the World. The quality of the researchers' productions in this area can be deliberated by scientific methods and researchers' self-citation has affected their h-index. For health care researchers, policymakers, and planners, it is necessary to be aware of the results of scientific studies of strategic and vital research areas, such as Coronavirus, to identify more appropriate therapeutic goals, make better decisions, and provide more effective solutions in the shortest time.

PurposeThe present article's primary purpose is the topic modeling of the global coronavirus publications in the last 50 years.Design/methodology/approachThe present study is applied research that has been conducted using text mining. The statistical population is the coronavirus publications that have been collected from the Web of Science Core Collection (1970–2020). The main keywords were extracted from the Medical Subject Heading browser to design the search strategy. Latent Dirichlet allocation and Python programming language were applied to analyze the data and implement the text mining algorithms of topic modeling.FindingsThe findings indicated that the SARS, science, protein, MERS, veterinary, cell, human, RNA, medicine and virology are the most important keywords in the global coronavirus publications. Also, eight important topics were identified in the global coronavirus publications by implementing the topic modeling algorithm. The highest number of publications were respectively on the following topics: “structure and proteomics,” “Cell signaling and immune response,” “clinical presentation and detection,” “Gene sequence and genomics,” “Diagnosis tests,” “vaccine and immune response and outbreak,” “Epidemiology and Transmission” and “gastrointestinal tissue.”Originality/valueThe originality of this article can be considered in three ways. First, text mining and Latent Dirichlet allocation were applied to analyzing coronavirus literature for the first time. Second, coronavirus is mentioned as a hot topic of research. Finally, in addition to the retrospective approaches to 50 years of data collection and analysis, the results can be exploited with prospective approaches to strategic planning and macro-policymaking.

COVID-19 is a threat to the lives of people all over the world. As a result of the new and unknown nature of COVID-19, much research has been conducted recently. In order to increase and enhance the growth rate of Iranian publications on COVID-19, this article aims to analyze these publications in LitCovid to identify the topical and content structure and topic modeling of scientific publications in the mentioned subject area. The present article is applied research performed by using an analytical approach as well as text mining techniques. The statistical population is all the publications of Iranian researchers in LitCovid. Latent Dirichlet Allocation (LDA) and Python were used to analyze the data and implement text mining and topic modeling algorithms. Data analysis shows that the percentage of Iranian publications in the eight topical groups in LitCovid is as follows: prevention (39.57%), treatment (18.99%), diagnosis (18.99%), forecasting (7.83%), case report (6.52%), mechanism (3.91%), transmission (3.62%), and general (0.58%). The results indicate that patient, pandemic, outbreak, case, Iranian, model, care, health, coronavirus, and disease are the most important words in the publications of Iranian researchers in LitCovid. Six topics for prevention; four topics for treatment and case report and forecasting; three topics for diagnosis, mechanism, and transmission in general have been obtained by implementing the topic modeling algorithm. Most of the Iranian publications in LitCovid are related to the topic “pandemic status,” with 22.47% in the prevention category, and the lowest number of publications is related to the topic “environment,” with 11.11% in the transmission category. The present study indicates a better understanding of essential and strategic issues of Iranian publications in LitCovid. The results reveal that many Iranian studies on COVID-19 were primarily on the issues related to prevention, management, and control. These findings provided a structured and research-based viewpoint of COVID-19 in Iran to guide researchers and policymakers.

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The aim of this paper is to prepare and propose a job description for and identify the organizational position of systems librarians in university libraries in Iran. Design/methodology/approach -The required data were collected in two ways: literature review and survey of opinions. The respondents completed a questionnaire designed by the researchers in order to specify the skills, duties, characteristics and other qualifications of the librarians in Iranian university libraries. The material covered by the questionnaire was extracted from the existing cultural and organizational structure systems librarianship texts and their recruitment advertisements. Differences between Iranian university libraries and those of developed nations are noted. The research population included all administrators and supervisors of information sections in university libraries of Iran. Findings -It was found that in Iran information sciences librarians require assistance from computer experts, since the IT training programme on offer does not meet the required standard. This has resulted in a reduced ability to perform their duties adequately, prompting the proposed survey for the purpose of producing a revised job description. Practical implications -Based on the findings of this research, a job description is formulated for systems librarians in Iranian university libraries. The job description thus presented includes: responsibilities, skills, job features, factors, tools of work, condition of work environment, intellectual capabilities required, body posture, and required technologies. Originality/value -The survey is unique in that the focus is on an Iranian systems librarian's job description based on requirements resulting from the opinions expressed by the managers and the librarians of the Iranian university libraries. Its structure therefore corresponds to the occupations widespread in Iran.

This paper aims to identify the stratification of Iranian Library and Information Science academics in terms of visibility, effectiveness and scientific and professional performance. The present study is applied and is implemented through survey and webometrics methods and with a descriptive approach. The research population includes all Iranian academics working in Library and Information Science departments with a PhD with the titles Assistant Professor, Associate Professor and Full Professor. Google Scholar is used to gather web data. A researcher-constructed questionnaire is also used to gather data from the research population in order to stratify them in terms of professional and scientific performance. J Mehrad, MH Dayani and R Fattahi achieved the first to the third ranks respectively in terms of professional and scientific performance. There is also a direct and significant relationship between stratification of the visibility and effectiveness and professional and scientific performance. Graduation from foreign universities, proficiency in English language, writing team papers, scientific collaboration with international scientists, membership of valid national and international research groups, employment in university departments with high experience and also employment in university departments which offer postgraduate qualifications are considered the main factors behind some members' visibility in the research community.