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Several bibliometric indicators that are extensively used to estimate research performance have not been validated against an external criterion of excellence. This paper aims to investigate whether this validation is possible using the number of Nobel Prize awards.

This study uses several analytical treatments of the data to investigate: whether Nobel Prize awards are sporadic events or they depend on the scientific activity of countries or institutions and can be used in research evaluation; and the association between the number of Nobel Prize achievements and conventional bibliometric indicators across countries and institutions.

This study finds that conventional bibliometric indicators, numbers of publications, citations, and top 1 per cent most cited publications, correlate with the number of Nobel Prize achievements in several advanced countries with similar research abilities. Contrarily, in countries and institutions with more variable research characteristics, there is no association between conventional bibliometric indicators and the number of Nobel Prize achievements, and their use as indicators of research excellence is not valid. In contrast, the number of national articles in Nature and Science correlates with the number of Nobel Prize achievements across countries and institutions.

Science administrators implementing research evaluations and research incentives based on conventional bibliometric indicators should consider that increasing the scores of these indicators does not imply an improvement in research excellence.

The study demonstrates that Nobel Prize achievements are not singular events that occur by chance. Therefore, the number of Nobel Prize achievements can be used to validate bibliometric indicators.

The purpose of this paper is to analyse the metrics provided by Publons about the scoring of publications and their relationship with impact measurements (bibliometric and altmetric indicators).

In January 2018, 45,819 research articles were extracted from Publons, including all their metrics (scores, number of pre and post reviews, reviewers, etc.). Using the DOI identifier, other metrics from altmetric providers were gathered to compare the scores of those publications in Publons with their bibliometric and altmetric impact in PlumX, Altmetric.com and Crossref Event Data.

The results show that: there are important biases in the coverage of Publons according to disciplines and publishers; metrics from Publons present several problems as research evaluation indicators; and correlations between bibliometric and altmetric counts and the Publons metrics are very weak (r<0.2) and not significant.

This is the first study about the Publons metrics at article level and their relationship with other quantitative measures such as bibliometric and altmetric indicators.

The purpose of this paper is to provide to scientific researchers and academic students some subsidies related to information reliability that may be used during information search and retrieval for academic research based on the author’s experience as a university librarian and interdisciplinary researcher.

The paper is composed of a brief conceptualization of the term information reliability, followed by a practical guide containing three processes for obtaining reliable information: knowledge of bibliometric indicators, evaluation of sources of information and analysis of the content of recovered publications.

Currently, there are few publications about information reliability that target the practical and effective learning of academic researchers and students, based on the experience of librarians who work directly with bibliographic search and training based on scientific literature.

The great demand of questions submitted by academic users on how to obtain reliable information shows the need for a paper like this to clarify them.

This paper shares best practices for search and discovery of information reliability, which are based on experiences that are not commonly addressed in the literature, and presents processes covering the bibliometric indicators, information sources and publication contents.

Lean healthcare (LHC) applies lean philosophy in the healthcare sector to promote a culture of continuous improvement through the elimination of non-value-added activities. Studies on the subject can be classified as conceptual (theoretical) or analytical (applied). Therefore, this research compares bibliometric indicators between conceptual and analytical articles on LHC.

For data collection, the PRISMA Protocol was employed, and 488 articles published from 2009 to 2021, indexed in the Scopus and WoS databases, were retrieved.

This study reveals how conceptual and analytical LHC studies are organized in terms of the most relevant journals, articles, institutions, countries, the total number of citations, collaboration networks (co-authorship, international collaboration network and institutional collaboration network) and main co-words.

Only four papers conducting bibliometric analysis on LHC studies were identified in the Scopus and Web of Science databases. In addition, none of these papers compared conceptual and analytical bibliometric indicators to reveal the evolution, organization and trends of each category. Therefore, this work is not only the first to make this comparison but also the first to analyze the collaboration between authors, institutions and countries in relation to studies on LHC. The analyses performed in this work allow one new possible understanding, by researchers and health professionals, of the literature behavior in this field of study.

The purpose of this paper is twofold. First, the study aims to investigate the relationship between the altmetric indicators from ResearchGate (RG) and the bibliometric indicators from the Scopus database. Second, the study seeks to examine the relationship amongst the RG altmetric indicators themselves. RG is a rich source of altmetric indicators such as Citations, RGScore, Impact Points, Profile Views, Publication Views, etc.

For establishing whether RG metrics showed the same results as the established sources of metrics, Pearson’s correlation coefficients were calculated between the metrics provided by RG and the metrics obtained from Scopus. Pearson’s correlation coefficients were also calculated for the metrics provided by RG. The data were collected by visiting the profile pages of all the members who had an account in RG under the Department of Physics, Panjab University, Chandigarh (India).

The study showed that most of the RG metrics showed strong positive correlation with the Scopus metrics, except for RGScore (RG) and Citations (Scopus), which showed moderate positive correlation. It was also found that the RG metrics showed moderate to strong positive correlation amongst each other.

The limitation of this study is that more and more scientists and researchers may join RG in the future, therefore the data may change. The study focuses on the members who had an account in RG under the Department of Physics, Panjab University, Chandigarh (India). Perhaps further studies can be conducted by increasing the sample size and by taking a different sample size having different characteristics.

Being an emerging field, not much has been conducted in the area of altmetrics. Very few studies have been conducted on the reach of academic social networks like RG and their validity as sources of altmetric indicators like RGScore, Impact Points, etc. The findings offer insights to the question whether RG can be used as an alternative to traditional sources of bibliometric indicators, especially with reference to a rapidly developing country such as India.

The purpose of this paper is to study the presence of highly cited papers of Nature in social media websites and tools. It also tries to examine the correlation between altmetric and bibliometric indicators.

This descriptive study was carried out using altmetric indicators. The research sample consisted of 1,000 most-cited articles in Nature. In February 2019, the bibliographic information of these articles was extracted from the Scopus database. Then, the titles of all articles were manually searched on Google, and by referring to the article in the journal website and altmetric institution, the data related to social media presence and altmetric score of articles were collected. The data were analyzed using Microsoft Excel and SPSS.

According to the results of the study, from 1,000 articles, 989 of them (98.9 per cent) were mentioned at least once in different social media websites and tools. The most used altmetric source in highly cited articles was Mendeley (98.9 per cent), followed by Citeulike (79.8 per cent) and Wikipedia (69.4 per cent). Most Tweets, blog posts, Facebook posts, news stories, readers in Mendeley, Citeulike and Connotea and Wikipedia citations belonged to the article titled “Mastering the game of Go with deep neural networks and tree search”. The highest altmetric score was 3,135 which belonged to this paper. Most tweeters and articles’ readers were from the USA. The membership type of the tweeters was public membership. In terms of fields of study, most readers were PhD students in Agricultural and Biological Sciences. Finally, the results of Spearman’s Correlation revealed positive significant statistical correlation between all altmetric indicators and received citations of highly cited articles (p-value = 0.0001).

The results of this study can help researchers, editors and editorial boards of journals better understand the importance and benefits of using social media and tools to publish articles.

Altmetrics is a relatively new field, and in particular, there are not many studies related to the presence of articles in various social media until now. Accordingly, in this study, a comprehensive altmetric analysis was carried out on 1000 most-cited articles of one of the world's most reliable journals.

Although the role of enterprise in R&D is broadly acknowledged, few attempts have been made to gather data for analyzing the nature and scope of private sector collaboration. This study aims to deliver empirical results based on quantitative data to gain insight into the role of private enterprise as an indispensable actor in scientific development and innovation.

The study aimed to deliver empirical results based on quantitative data to gain insight into the role of private enterprise as an indispensable actor in scientific development and innovation. To this end, an analysis was conducted of the contribution made by Spanish business, focusing on the 50 most active companies in terms of internationally visible scientific output, from three perspectives.

The findings provide insight into business involvement in the R&D system based on: research papers published; national, international and sectoral collaboration patterns; structural patterns; and the identification of the most prominent companies from a systematic comparison of their research results and their position in the resulting collaboration network.

Bibliometric analyses do not measure all types of publications. Indicators are usually based on data in the Thomson Reuters databases, which are regarded as being representative of peer‐reviewed, publicly accessible papers with high international visibility and impact. The Thomson Reuters databases feature a series of advantages that make them indispensable for studies on scientific collaboration.

One of the core ideas of this study is the emphasis on the essential role of collaboration in improving scientific results, as borne out by the correlation between the clustering coefficient and the hybrid indicators. The findings also provide proof of the success of strategies for institutional collaboration. The foregoing shows that the application of hybrid indicators to institutional aggregates yields novel results not explored in preceding studies.

The purpose of this study is to map the intellectual structure of the research concerning conflict and conflict management in virtual teams (VT), to contribute to the further integration of knowledge among different streams of research and to develop an interpretative framework to stimulate future research.

A data set of 107 relevant papers on the topic was retrieved using the Web of Science Core Collection database covering a period ranging from 2001 to 2019. A comparative bibliometric analysis consisting of the integration of results from the citation, co-citation and bibliographic coupling was performed to identify the most influential papers. The systematic literature review complemented the bibliometric results by clustering the most influential papers.

The results revealed different intellectual structures across several types of analyses. Despite such differences, 41 papers resulted as the most impactful and provided evidence of the emergence of five thematic clusters: trust, performance, cultural diversity, knowledge management and team management.

Based on the bibliometric analyses an interpretative research agenda has been developed that unveils the main future research avenues. The paper also offers important theoretical contributions by systematizing knowledge on conflict in identifying VTs. Managerial contributions in the form of the identification of best practices are also developed to guide conflict management in VTs.

The uniqueness of this paper is related to its effort in studying, mapping and systematizing the knowledge concerning the topic of handling conflicts in VTs. Considering the current contingencies, this research is particularly timely.

Technology is sometimes used to support assessments of academic research in the form of automatically generated bibliometrics for reviewers to consult during their evaluations or by replacing some or all human judgements. With artificial intelligence (AI), there is increasing scope to use technology to assist research assessment processes in new ways. Since transparency and fairness are widely considered important for research assessment and AI introduces new issues, this review investigates their implications.

This article reviews and briefly summarises transparency and fairness concerns in general terms and through the issues that they raise for various types of Technology Assisted Research Assessment (TARA).

Whilst TARA can have varying levels of problems with both transparency and bias, in most contexts it is unclear whether it worsens the transparency and bias problems that are inherent in peer review.

This is the first analysis that focuses on algorithmic bias and transparency issues for technology assisted research assessment.

The purpose of this study is to investigate whether news media as a form of social communication regarding pressing, important, and contemporary issues could be used as a leading indicator of technology adoption. For technology foresight, monitoring and analyzing energy technologies is in the spotlight due to their strategic importance to the agenda of competitive and sustainable economic growth.

The trends in renewable energy production and news media are determined and compared to draw inferences in foresight concerning the use of renewable energy technologies in energy production. Consequently, the data concern wind and solar energy production and their representation in news media in Germany and the UK. The analysis proceeds as a quasi-experiment treating each yearly observation of energy production as a function of news media data with lagged variables.

The study reveals consistent dynamics in the lag in the primary production of energy in related technologies compared to the media visibility of the respective technologies.

This study explores the prospects of using news media data in foresight analysis concerning renewable energy production and provides many fruitful avenues for foresight research in investigating relationships between technology adoption and media exposure.