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The currently prevailing international ranking systems for institutions are limited in their assessment as they only provide assessments either at an overall level or at very broad subject levels such as Science, Engineering, Medicine, etc. While these rankings have their own usage, they cannot be used to identify best institutions in a specific subject (say Computer Science) by taking into account their performance in different thematic areas of research of the given subject (say Artificial Intelligence or Machine Learning or Computer Vision etc. for the subject Computer Science). This paper tries to bridge this gap by proposing a framework that uses the NLP and Network approach for identifying the core competency of institutions and their thematic research strengths. The core competency can be viewed as a measure of breadth of research capability of an institution in a given subject, whereas thematic research strength can be viewed as depth of research of the institution in a specific theme of a subject. The working of the framework is demonstrated in the area of Computer Science for 195 Indian institutions. The framework can be useful for institutions and the scientometrics research community as a system providing a detailed assessment of the core competency and the research strengths of institutions in different thematic areas. The framework and outcomes can also be useful for funding agencies in devising programs for ‘performance-based funding’ in ‘thrust areas’ or ‘national priority areas’.

The growth of the retraction databases reveals the disturbing trend in science and also the rising trend of citations of retracted papers is a serious concern. The objective of the study is to investigate the patterns of retractions through the team size and retracted citations. The publication records of 12,231 retracted papers indexed by Web of Science (WoS) are analyzed to investigate (i) the patterns of retraction associated with collaboration and team size; and (ii) the impact of retracted papers on the papers that are citing the retracted papers (retracted citations). The study demonstrates the collaboration patterns of retracted publications where 61.5% of authors have only one and 24.6% have two retracted papers; however, 2% of authors have more than retracted papers. Also, the temporal evolution of the team size reveals that teams smaller in size have more retractions. The impact of citing retracted papers reveals that 55.2% of retracted papers have been cited at least once. 1/4th of the citations to the retracted papers are self-citations which themselves are retractions. On average 71.4% citations are the non-retracted citations and 28.6% citations are retracted citations which are mostly the self-citations. Last, the variation in average team size and average retracted citations in various research areas (having high retraction) is presented. Retracted publications in high-impact journals are highly cited.

AbstractThe motivation for our research is the view, widespread among Polish scientists, that under the Communist Party’s rule it was always necessary to refer to Marx, Engels, Lenin or Stalin (we call them ‘classics’), especially in the highly-politicised fields like humanities and social sciences, in order for the work to pass the censorship procedures and be published. Therefore, in this paper, we aim to determine whether the 'classics' were commonly cited in a formally socialist country under the rule of the Communist Party (Polish Workers' Party/Polish United Workers’ Party). To address the main research question, we use the Citation Index of the History of Polish Media that covers all publications, whether scholarly articles or book publications, on the history of Polish media; in total, 6880 publications and 59,827 citations from the 1945‒2009 period. We found that citations of the works of the ‘classics’ (N = 296) constitute 0.49% of all citations in the database used and that the practice of citing the 'classics' was extremely rare (just 64 occurrences in the analysed sample). Our research also contributes to the development of reflection in historical bibliometrics and argues that bibliographical databases need to cover various types of publications, especially scholarly book publications, written in different languages (not only in English).

This study aims to analyze 343 retraction notices indexed in the Scopus database, published in 2001-2019, related to scientific articles (co-)written by at least one author affiliated with an Iranian institution. In order to determine reasons for retractions, we merged this database with the database from Retraction Watch. The data were analyzed using Excel 2016 and IBM-SPSS version 24.0, and visualized using VOSviewer software. Most of the retractions were due to fake peer review (95 retractions) and plagiarism (90). The average time between a publication and its retraction was 591 days. The maximum time-lag (about 3,000 days) occurred for papers retracted due to duplicate publications; the minimum time-lag (fewer than 100 days) was for papers retracted due to ''unspecified cause'' (most of these were conference papers). As many as 48 (14%) of the retracted papers were published in two medical journals: Tumor Biology (25 papers) and Diagnostic Pathology (23 papers). From the institutional point of view, Islamic Azad University was the inglorious leader, contributing to over one-half (53.1%) of retracted papers. Among the 343 retraction notices, 64 papers pertained to international collaborations with researchers from mainly Asian and European countries; Malaysia having the most retractions (22 papers). Since most retractions were due to fake peer review and plagiarism, the peer review system appears to be a weak point of the submission/publication process; if improved, the number of retractions would likely drop because of increased editorial control. 29 pages, 7 figures, 5 tables, 41 references

In bibliometric analysis, ambiguity in author names may lead to erroneous aggregation of records. The author name disambiguation techniques attempt to address this issue by attributing records to the corresponding author. The name disambiguation has been widely studied as a clustering task. However, maintaining consistent accuracy levels over datasets is still a major challenge. Recent efforts have witnessed the use of representation learning based techniques to map the records to an embedding space that can be used to determine the clusters. However, some of these models that use supervised global embedding fail to generalize across different datasets, while others lag in the accuracy. In this paper, we propose a method that uses two independent relations among the documents-co-authorship and meta-content of document, to generate a latent representation of documents that is capable of generalizing over various datasets (consisting different sets of features). Through rigorous validation, we discover that the proposed approach outperforms several state-of-the-art methods by a significant margin in terms of standard measures like pairwise F1, K metric, and BF1 scores. Moreover, we have also validated the performance of our method with the statistical test.

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