本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

国际刊物《Scientometrics》文献计量研究

This article aims to provide a systematic and comprehensive comparison of the coverage of the three major bibliometric databases: Google Scholar, Scopus and the Web of Science. Based on a sample of 146 senior academics in five broad disciplinary areas, we therefore provide both a longitudinal and a cross-disciplinary comparison of the three databases. Our longitudinal comparison of eight data points between 2013 and 2015 shows a consistent and reasonably stable quarterly growth for both publications and citations across the three databases. This suggests that all three databases provide sufficient stability of coverage to be used for more detailed cross-disciplinary comparisons. Our cross-disciplinary comparison of the three databases includes four key research metrics (publications, citations, h-index, and hI, annual, an annualised individual h-index) and five major disciplines (Humanities, Social Sciences, Engineering, Sciences and Life Sciences). We show that both the data source and the specific metrics used change the conclusions that can be drawn from cross-disciplinary comparisons.

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Google Scholar, Scopus and the Web of Science: a longitudinal and cross-disciplinary comparison

Google Scholar (GS), a commonly used web-based academic search engine, catalogues between 2 and 100 million records of both academic and grey literature (articles not formally published by commercial academic publishers). Google Scholar collates results from across the internet and is free to use. As a result it has received considerable attention as a method for searching for literature, particularly in searches for grey literature, as required by systematic reviews. The reliance on GS as a standalone resource has been greatly debated, however, and its efficacy in grey literature searching has not yet been investigated. Using systematic review case studies from environmental science, we investigated the utility of GS in systematic reviews and in searches for grey literature. Our findings show that GS results contain moderate amounts of grey literature, with the majority found on average at page 80. We also found that, when searched for specifically, the majority of literature identified using Web of Science was also found using GS. However, our findings showed moderate/poor overlap in results when similar search strings were used in Web of Science and GS (10–67%), and that GS missed some important literature in five of six case studies. Furthermore, a general GS search failed to find any grey literature from a case study that involved manual searching of organisations’ websites. If used in systematic reviews for grey literature, we recommend that searches of article titles focus on the first 200 to 300 results. We conclude that whilst Google Scholar can find much grey literature and specific, known studies, it should not be used alone for systematic review searches. Rather, it forms a powerful addition to other traditional search methods. In addition, we advocate the use of tools to transparently document and catalogue GS search results to maintain high levels of transparency and the ability to be updated, critical to systematic reviews.

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The Role of Google Scholar in Evidence Reviews and Its Applicability to Grey Literature Searching.

A review of the literature on citation impact indicators

Google Scholar, Web of Science, and Scopus: A systematic comparison of citations in 252 subject categories

In this letter we propose the development of guidelines by the main editors associations as well as protocols within online journal management systems for keeping track of rejected manuscripts that are resubmitted as well as for the interchange of referees reports between journals.

Letter to the editor: Against the Resilience of Rejected Manuscripts.

COVID-19 has greatly impacted science. It has become a global research front that constitutes a unique phenomenon of interest for the scientometric community. Accordingly, there has been a proliferation of descriptive studies on COVID-19 papers using altmetrics. Social media metrics serve to elucidate how research is shared and discussed, and one of the key points is to determine which factors are well-conditioned altmetric values. The main objective of this study is to analyze whether the altmetric mentions of COVID-19 medical studies are associated with the type of study and its level of evidence. Data were collected from the PubMed and Altmetric.com databases. A total of 16,672 publications by study types (e.g., case reports, clinical trials, or meta-analyses) that were published in the year 2021 and that had at least one altmetric mention were retrieved. The altmetric indicators considered were Altmetric Attention Score (AAS), news mentions, Twitter mentions, and Mendeley readers. Once the dataset of COVID-19 had been created, the first step was to carry out a descriptive study. Then, a normality hypothesis was evaluated by means of the Kolmogorov–Smirnov test, and since this was significant in all cases, the overall comparison of groups was performed using the nonparametric Kruskal–Wallis test. When this test rejected the null hypothesis, pairwise comparisons were performed with the Mann–Whitney U test, and the intensity of the possible association was measured using Cramer’s V coefficient. The results suggest that the data do not fit a normal distribution. The Mann–Whitney U test revealed coincidences in five groups of study types: The altmetric indicator with most coincidences was news mentions, and the study types with the most coincidences were the systematic reviews together with the meta-analyses, which coincided with four altmetric indicators. Likewise, between the study types and the altmetric indicators, a weak but significant association was observed through the chi-square and Cramer’s V. It can thus be concluded that the positive association between altmetrics and study types in medicine could reflect the level of the “pyramid” of scientific evidence.

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Altmetrics can capture research evidence: an analysis across types of studies in COVID-19 literature

Letter accepted for publication in Journal of the American Society for Information Science and Technology. http://www.asis.org/jasist.html

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Against the resilience of rejected manuscripts

Abstract The growing importance of public innovation has been manifested through the creation of policy labs: spaces for policy experimentation and innovation that work for or within a government entity. The rise of this phenomenon in Europe was evidenced by the creation of a policy lab by the European Commission (EC) in 2016 and the publication by the EC of a report identifying policy labs and their influencers in Europe. Public innovation is increasingly based on national and international networks, giving rise to complex ecosystems involving participation by multiple actors from countries with different administrative approaches. Our study uses social network analysis of these labs’ Twitter profile data to map the European Union’s (EU) public innovation ecosystem and identify the major influencers. Policy labs and their influencers are analyzed by administration style by using a large geographical database. The results reveal a complex global network of influencers and a strong predominance of the Anglo-Saxon administration style. From an EU perspective, our systematic analysis of influence is particularly important in the post-Brexit context, helping to foster a genuine public innovation ecosystem that is both autonomous and interconnected with the aim of facing challenges such as the Sustainable Development Agenda and COVID-19 crisis recovery.

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Who influences policy labs in the European Union? A social network approach

In countries like Denmark and Spain classified journal lists are now being produced and used in the calculation of nationwide performance indicators. As a result, Danish and Spanish scholars are advised to contribute to journals of high 'authority' (as in the former) or those within a high class (as in the latter). This can create a few problems. The aim of this paper is to analyse the potential use of network centrality measures to identify possible mismatches of journal categories. It analysis the Danish National Authority List and the Spanish CIRC Classification. Based on a sample of Library and Information Science publications, it analyses centrality measures that can assess on the importance of journals to given fields, correcting mismatches in these classifications. We conclude by emphasising the use of these measures to better calibrate journal classifications as we observe a general bias in these lists towards older journals. Centrality measures can allow to identify periphery-to-core journals' transitions.

Daniel Torres-Salinas

Papers

Letter to the editor: Against the Resilience of Rejected Manuscripts.

Altmetrics can capture research evidence: an analysis across types of studies in COVID-19 literature

Against the resilience of rejected manuscripts

Who influences policy labs in the European Union? A social network approach

Using network centrality measures to improve national journal classification lists