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  • 05 social sciences
  • 050105 experimental psychology
  • Scientometrics

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  • Open Access English
    Authors: 
    Adrian G. Barnett; Zoë A. Doubleday;
    Publisher: Netherlands : Springer
    Country: Australia
    Project: ARC | ARC Future Fellowships - ... (FT190100244)

    “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

  • Open Access
    Authors: 
    Robin Haunschild; Lutz Bornmann;
    Publisher: Springer Science and Business Media LLC

    AbstractMethodological mistakes, data errors, and scientific misconduct are considered prevalent problems in science that are often difficult to detect. In this study, we explore the potential of using data from Twitter for discovering problems with publications. In this case study, we analyzed tweet texts of three retracted publications about COVID-19 (Coronavirus disease 2019)/SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) and their retraction notices. We did not find early warning signs in tweet texts regarding one publication, but we did find tweets that casted doubt on the validity of the two other publications shortly after their publication date. An extension of our current work might lead to an early warning system that makes the scientific community aware of problems with certain publications. Other sources, such as blogs or post-publication peer-review sites, could be included in such an early warning system. The methodology proposed in this case study should be validated using larger publication sets that also include a control group, i.e., publications that were not retracted.

  • Open Access
    Authors: 
    Jorge A. V. Tohalino; Laura V. C. Quispe; Diego R. Amancio;
    Publisher: Springer Science and Business Media LLC

    Predicting the output of research grants is of considerable relevance to research funding bodies, scientific entities and government agencies. In this study, we investigate whether text features extracted from projects title and abstracts are able to identify productive grants. Our analysis was conducted in three distinct areas, namely Medicine, Dentistry and Veterinary Medicine. Topical and complexity text features were used to identify predictors of productivity. The results indicate that there is a statistically significant relationship between text features and grants productivity, however such a dependence is weak. A feature relevance analysis revealed that the abstract text length and metrics derived from lexical diversity are among the most discriminative features. We also found that the prediction accuracy has a dependence on the considered project language and that topical features are more discriminative than text complexity measurements. Our findings suggest that text features should be used in combination with other features to assist the identification of relevant research ideas.

  • Open Access English
    Authors: 
    Bakthavachalam Elango;
    Publisher: Springer International Publishing

    The aim of the present study is to identify retracted articles in the biomedical literature (co) authored by Indian authors and to examine the features of retracted articles. The PubMed database was searched to find the retracted articles in order to reach the goal. The search yielded 508 records and retrieved for the detailed analysis of: authorships and collaboration type, funding information, who retracts? journals and impact factors, and reasons for retraction. The results show that most of the biomedical articles retracted were published after 2010 and common reasons are plagiarism and fake data for retraction. More than half of the retracted articles were co-authored within the institutions and there is no repeat offender. 25% of retracted articles were published in the top 15 journals and 33% were published in the non-impact factor journals. Average time from publication to retraction is calculated to 2.86 years and retractions due to fake data takes longest period among the reasons. Majority of the funded research was retracted due to fake data whereas it is plagiarism for non-funded.

  • Open Access English
    Authors: 
    Zhiqi Wang; Ronald Rousseau;
    Publisher: Springer International Publishing
    Country: Belgium

    Abstract: 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.

  • Open Access
    Authors: 
    Mei Hsiu-Ching Ho; John S. Liu;
    Publisher: Springer Science and Business Media LLC

    Scholars all over the world have produced a large body of COVID-19 literature in an exceptionally short period after the outbreak of this rapidly-spreading virus. An analysis of the literature accumulated in the first 150 days hints that the rapid knowledge accumulation in its early-stage development was expedited through a wide variety of journal platforms, a sense and pressure of national urgency, and inspiration from journal editorials.

  • Open Access English
    Authors: 
    Ozcan Saritas; Pavel Bakhtin; Ilya Kuzminov; Elena Khabirova;
    Publisher: Springer International Publishing

    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.

  • Open Access
    Authors: 
    Kun Sun; Haitao Liu; Wenxin Xiong;
    Publisher: Springer Science and Business Media LLC
    Project: EC | WIDE (742545), EC | WIDE (742545)

    AbstractScientific writings, as one essential part of human culture, have evolved over centuries into their current form. Knowing how scientific writings evolved is particularly helpful in understanding how trends in scientific culture developed. It also allows us to better understand how scientific culture was interwoven with human culture generally. The availability of massive digitized texts and the progress in computational technologies today provide us with a convenient and credible way to discern the evolutionary patterns in scientific writings by examining the diachronic linguistic changes. The linguistic changes in scientific writings reflect the genre shifts that took place with historical changes in science and scientific writings. This study investigates a general evolutionary linguistic pattern in scientific writings. It does so by merging two credible computational methods: relative entropy; word-embedding concreteness and imageability. It thus creates a novel quantitative methodology and applies this to the examination of diachronic changes in the Philosophical Transactions of Royal Society (PTRS, 1665–1869). The data from two computational approaches can be well mapped to support the argument that this journal followed the evolutionary trend of increasing professionalization and specialization. But it also shows that language use in this journal was greatly influenced by historical events and other socio-cultural factors. This study, as a “culturomic” approach, demonstrates that the linguistic evolutionary patterns in scientific discourse have been interrupted by external factors even though this scientific discourse would likely have cumulatively developed into a professional and specialized genre. The approaches proposed by this study can make a great contribution to full-text analysis in scientometrics.

  • Open Access English
    Authors: 
    Shima Moradi; Sajedeh Abdi;
    Publisher: Springer International Publishing

    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.

  • Open Access English
    Authors: 
    Stefano Mammola; Diego Fontaneto; Alejandro Martínez; Filipe Chichorro;
    Countries: Finland, Italy

    AbstractMany believe that the quality of a scientific publication is as good as the science it cites. However, quantifications of how features of reference lists affect citations remain sparse. We examined seven numerical characteristics of reference lists of 50,878 research articles published in 17 ecological journals between 1997 and 2017. Over this period, significant changes occurred in reference lists’ features. On average, more recent papers have longer reference lists and cite more high Impact Factor papers and fewer non-journal publications. We also show that highly cited articles across the ecological literature have longer reference lists, cite more recent and impactful references, and include more self-citations. Conversely, the proportion of ‘classic’ papers and non-journal publications cited, as well as the temporal span of the reference list, have no significant influence on articles’ citations. From this analysis, we distill a recipe for crafting impactful reference lists, at least in ecology.