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“COVID” which stands for corona virus disease, has become the world’s most infamous acronym. Previous analysis of acronyms in health and medical journals found a growing use of acronyms over time in titles and abstracts, with “DNA” as the most common. Here we examine acronyms in the pandemic year of 2020 to show the dramatic rise of COVID-related research. “COVID” was over five times more frequently used than “DNA” in 2020, and in just one year it has become the sixth most popular acronym of all time, surpassing “AIDS”, “PCR” and “MRI”. Refereed/Peer-reviewed

The Yule-Simpson paradox refers to the fact that outcomes of comparisons between groups are reversed when groups are combined. Using Essential Sciences Indicators, a part of InCites (Clarivate), data for countries, it is shown that although the Yule-Simpson phenomenon in citation analysis and research evaluation is not common, it isn't extremely rare either. The Yule-Simpson paradox is a phenomenon one should be aware of, otherwise one may encounter unforeseen surprises in scientometric studies. ispartof: SCIENTOMETRICS vol:126 issue:4 pages:3501-3511 ispartof: location:Switzerland status: published

Identifying and monitoring business and technological trends are crucial for innovation and competitiveness of businesses. Exponential growth of data across the world is invaluable for identifying emerging and evolving trends. On the other hand, the vast amount of data leads to information overload and can no longer be adequately processed without the use of automated methods of extraction, processing, and generation of knowledge. There is a growing need for information systems that would monitor and analyse data from heterogeneous and unstructured sources in order to enable timely and evidence-based decision-making. Recent advancements in computing and big data provide enormous opportunities for gathering evidence on future developments and emerging opportunities. The present study demonstrates the use of text-mining and semantic analysis of large amount of documents for investigating in business trends in mobile commerce (m-commerce). Particularly with the on-going COVID-19 pandemic and resultant social isolation, m-commerce has become a large technology and business domain with ever growing market potentials. Thus, our study begins with a review of global challenges, opportunities and trends in the development of m-commerce in the world. Next, the study identifies critical technologies and instruments for the full utilization of the potentials in the sector by using the intelligent big data analytics system based on in-depth natural language processing utilizing text-mining, machine learning, science bibliometry and technology analysis. The results generated by the system can be used to produce a comprehensive and objective web of interconnected technologies, trends, drivers and barriers to give an overview of the whole landscape of m-commerce in one business intelligence (BI) data mart diagram.

This commentary identifies and characterizes correction and erratum in COVID-19 publications with a scientometric approach by considering their rate of growth, reasons for correction, the time-span between publishing the original and corrected versions, as well as their citation status in four questions. It also suggestions to solve the current issues regarding indexing, retrieving, publishing, and research evaluation.

The COVID-19 pandemic has vast global consequences. Yet, effective mitigation strategies and economic and medical outfall differ extensively across the globe. It is currently unclear how well researchers from all continents are represented in the unsolicited and solicited publications. A literature review was performed in SCOPUS on COVID-19 oriented publications in the four most impactful medical journals. These included the British Medical Journal, Journal of the American Medical Association, the New England Journal of Medicine and The Lancet. We identified 809 eligible publications out of identified 924 records. The vast majority of publications on COVID-19, in the four can be considered European (47.7%) or North-American (37.3%) research. Chinese reports were relatively common (8.8%); however, reports from other Asian countries (3.2%) were minimal. Research from the African (1.0%) and South-American continents (0.6%) was rarely published in these journals. These observations are not surprising, as they reflect global academic publishing. However, involving all continents into COVID-19 research is important as COVID-19 management strategies and societal and economic consequences differ extensively across the globe. We see an important role for medical journals in encouraging global voices through solicited articles, to ensure a weighted research and humanitarian response. Electronic supplementary material The online version of this article (10.1007/s11192-020-03730-z) contains supplementary material, which is available to authorized users.

In recent months the COVID-19 (also known as SARS-CoV-2 and Coronavirus) pandemic has spread throughout the world. In parallel, extensive scholarly research regarding various aspects of the pandemic has been published. In this work, we analyse the changes in biomedical publishing patterns due to the pandemic. We study the changes in the volume of publications in both peer reviewed journals and preprint servers, average time to acceptance of papers submitted to biomedical journals, international (co-)authorship of these papers (expressed by diversity and volume), and the possible association between journal metrics and said changes. We study these possible changes using two approaches: a short-term analysis through which changes during the first six months of the outbreak are examined for both COVID-19 related papers and non-COVID-19 related papers; and a longitudinal approach through which changes are examined in comparison to the previous four years. Our results show that the pandemic has so far had a tremendous effect on all examined accounts of scholarly publications: A sharp increase in publication volume has been witnessed and it can be almost entirely attributed to the pandemic; a significantly faster mean time to acceptance for COVID-19 papers is apparent, and it has (partially) come at the expense of non-COVID-19 papers; and a significant reduction in international collaboration for COVID-19 papers has also been identified. As the pandemic continues to spread, these changes may cause a slow down in research in non-COVID-19 biomedical fields and bring about a lower rate of international collaboration. Comment: 26 pages, 9 figures, 11 tables

The SARS-CoV-2 virus, which causes Covid-19, induced a global pandemic for which an effective cure, either in the form of a drug or vaccine, has yet to be discovered. In the few brief months that the world has known Covid-19, there has been an unprecedented volume of papers published related to this disease, either in a bid to find solutions, or to discuss applied or related aspects. Data from Clarivate Analytics’ Web of Science, and Elsevier’s Scopus, which do not index preprints, were assessed. Our estimates indicate that 23,634 unique documents, 9960 of which were in common to both databases, were published between January 1 and June 30, 2020. Publications include research articles, letters, editorials, notes and reviews. As one example, amongst the 21,542 documents in Scopus, 47.6% were research articles, 22.4% were letters, and the rest were reviews, editorials, notes and other. Based on both databases, the top three countries, ranked by volume of published papers, are the USA, China, and Italy while BMJ, Journal of Medical Virology and The Lancet published the largest number of Covid-19-related papers. This paper provides one snapshot of how the publishing landscape has evolved in the first six months of 2020 in response to this pandemic and discusses the risks associated with the speed of publications.

Intriguing unforced regularities in human behaviors have been reported in varied research domains, including scientometrics. In this study we examine the manuscript submission behavior of researchers, with a focus on its monthly pattern. With a large and reliable dataset which records the submission history of articles published on 10 multidisciplinary journals and 10 management journals over a five-year period (2013-2017), we observe a prominent turn-of-the-month submission effect for accepted papers in management journals but not multidisciplinary journals. This effect gets more pronounced in submissions to top-tier journals and when the first day of a month happens to be a Saturday or Sunday. Sense of ceremony is proposed as a likely explanation of this effect, since the first day of a month is a fundamental temporal landmark which has a 'fresh start effect' on researchers. To conclude, an original and interesting day-of-the-month effect in the academia is reported in this study, which calls for more research attention.

We investigate the accuracy of how author names are reported in bibliographic records excerpted from four prominent sources: WoS, Scopus, PubMed, and CrossRef. We take as a case study 44,549 publications stored in the internal database of Sapienza University of Rome, one of the largest universities in Europe. While our results indicate generally good accuracy for all bibliographic data sources considered, we highlight a number of issues that undermine the accuracy for certain classes of author names, including compound names and names with diacritics, which are common features to Italian and other Western languages.

Automatically generated papers have been used to manipulate bibliography indexes on numerous occasions. This paper is interested in different means to generate texts such as recurrent neural network, Markov model, or probabilistic context free grammar, and if it is possible to detect them using a current approach. Then, probabilistic context free grammar (PCFG) is focused on as the one most used. However, even though there have been multiple approaches to detect such types of paper, they are all working at the document level and are unable to detect a small amount of generated text inside a larger body of genuinely written text. Thus, we present the grammatical structure similarity measurement to detect sentences or short fragments of automatically generated text from known PCFG generators. The proposed approach is tested against a pattern checker and various common machine learning methods. Additionally, the ability to detect a modified PCFG generator is also tested.