On the shoulders of students? The contribution of PhD students to the advancement of knowledge

Using the participation in peer reviewed publications of all doctoral students in Quebec over the 2000-2007 period this paper provides the first large scale analysis of their research effort. It shows that PhD students contribute to about a third of the publication output of the province, with doctoral students in the natural and medical sciences being present in a higher proportion of papers published than their colleagues of the social sciences and humanities. Collaboration is an important component of this socialization: disciplines in which student collaboration is higher are also those in which doctoral students are the most involved in peer-reviewed publications. In terms of scientific impact, papers co-signed by doctorate students obtain significantly lower citation rates than other Quebec papers, except in natural sciences and engineering. Finally, this paper shows that involving doctoral students in publications is positively linked with degree completion and ulterior career in research.Comment: 41 pages, 7 figures, forthcoming in Scientometric

A Bibliometric Analysis and Visualization of the Scientific Publications of Universities: A Study of Hamadan University of Medical Sciences during 1992-2018

The evaluation of universities from different perspectives is important for their scientific development. Analyzing the scientific papers of a university under the bibliometric approach is one main evaluative approach. The aim of this study was to conduct a bibliometric analysis and visualization of papers published by Hamadan University of Medical Science (HUMS), Iran, during 1992-2018. This study used bibliometric and visualization techniques. Scopus database was used for data collection. 3753 papers were retrieved by applying Affiliation Search in Scopus advanced search section. Excel and VOSviewer software packages were used for data analysis and bibliometric indicator extraction. An increasing trend was seen in the numbers of HUMS's published papers and received citations. The highest rate of collaboration in national level was with Tehran University of Medical Sciences. Internationally, HUMS's researchers had the highest collaboration with the authors from the United States, the United Kingdom and Switzerland, respectively. All highly-cited papers were published in high level Q1 journals. Term clustering demonstrated four main clusters: epidemiological studies, laboratory studies, pharmacological studies, and microbiological studies. The results of this study can be beneficial to the policy-makers of this university. In addition, researchers and bibliometricians can use this study as a pattern for studying and visualizing the bibliometric indicators of other universities and research institutions

Application of machine learning in dementia diagnosis: a systematic literature review

According to the World Health Organization forecast, over 55 million people worldwide have dementia, and about 10 million new cases are detected yearly. Early diagnosis is essential for patients to plan for the future and deal with the disease. Machine Learning algorithms allow us to solve the problems associated with early disease detection. This work attempts to identify the current relevance of the application of machine learning in dementia prediction in the scientific world and suggests open fields for future research. The literature review was conducted by combining bibliometric and content analysis of articles originating in a period of 20 years in the Scopus database. Twenty-seven thousand five hundred twenty papers were identified firstly, of which a limited number focused on machine learning in dementia diagnosis. After the exclusion process, 202 were selected, and 25 were chosen for analysis. The recent increasing interest in the past five years in the theme of machine learning in dementia shows that it is a relevant field for research with still open questions. The methods used to identify dementia or what features are used to identify or predict this disease are explored in this study. The literature review revealed that most studies used magnetic resonance imaging (MRI) and its types as the main feature, accompanied by demographic data such as age, gender, and the mini-mental state examination score (MMSE). Data are usually acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Classification of Alzheimer’s disease is more prevalent than prediction of Mild Cognitive Impairment (MCI) or their combination. The authors preferred machine learning algorithms such as SVM, Ensemble methods, and CNN because of their excellent performance and results in previous studies. However, most use not one machine-learning technique but a combination of techniques. Despite achieving good results in the studies considered, there are new concepts for future investigation declared by the authors and suggestions for improvements by employing promising methods with potentially significant results.info:eu-repo/semantics/publishedVersio

Impact Factor: outdated artefact or stepping-stone to journal certification?

A review of Garfield's journal impact factor and its specific implementation as the Thomson Reuters Impact Factor reveals several weaknesses in this commonly-used indicator of journal standing. Key limitations include the mismatch between citing and cited documents, the deceptive display of three decimals that belies the real precision, and the absence of confidence intervals. These are minor issues that are easily amended and should be corrected, but more substantive improvements are needed. There are indications that the scientific community seeks and needs better certification of journal procedures to improve the quality of published science. Comprehensive certification of editorial and review procedures could help ensure adequate procedures to detect duplicate and fraudulent submissions.Comment: 25 pages, 12 figures, 6 table

Top 50 most-cited articles in medicine and science in football

To conduct a comprehensive mapping analysis to the scientific literature published in football aiming to identify the areas of bigger interest and potential for further exploration.info:eu-repo/semantics/publishedVersio

COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts

© 2020 The Authors. Published by MIT Press. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1162/qss_a_00066The COVID-19 pandemic requires a fast response from researchers to help address biological, medical and public health issues to minimize its impact. In this rapidly evolving context, scholars, professionals and the public may need to quickly identify important new studies. In response, this paper assesses the coverage of scholarly databases and impact indicators during 21 March to 18 April 2020. The rapidly increasing volume of research, is particularly accessible through Dimensions, and less through Scopus, the Web of Science, and PubMed. Google Scholar’s results included many false matches. A few COVID-19 papers from the 21,395 in Dimensions were already highly cited, with substantial news and social media attention. For this topic, in contrast to previous studies, there seems to be a high degree of convergence between articles shared in the social web and citation counts, at least in the short term. In particular, articles that are extensively tweeted on the day first indexed are likely to be highly read and relatively highly cited three weeks later. Researchers needing wide scope literature searches (rather than health focused PubMed or medRxiv searches) should start with Dimensions (or Google Scholar) and can use tweet and Mendeley reader counts as indicators of likely importance

The nexus between digital skills/competences and work: A bibliometric study

The widespread use of computers and other new information and communication technologies (ICT) in every realm of society has increased the demand for specific skills and competences for people at any age and stage of life to use and work with ICT effectively. Summarised under the terms "digital skills" and "digital competences" by the European Commission in 2018, these concepts still lack clarity and are characterised by some ambiguity though much research has been devoted to them. Given that these two concepts are of high topicality with regard to current labour market developments, like skills mismatch, the digital divide or the design and implementation of occupational retraining programmes, the main purpose of this paper is to contribute to a more clear-cut understanding of the nexus between digital skills/competences and work. To accomplish this goal, we carry out a bibliometric study consisting of both quantitative and qualitative analysis. Our main findings are that research on the nexus between digital skills/competences and work is evolving and this research field is anchored in many different scientific disciplines and shares thematic overlaps with various other areas such as higher education research. The qualitative part of our analysis reveals that this research field is defined by six building blocks with one motor theme on "digital literacy". Furthermore, employment or employability as well as the effects of changing technologies at the workplace are the most crucial topics addressed in this research field, reflecting the high value attributed to digital skills/competences in determining the employability of the current and future workforce

Bibliometrics and Social Network Analysis of Doctoral Research: Research Trends In Distance Learning

The study investigated research topics of doctoral dissertations that examined issues in distance learning from 2000-2014. Twelve reviews of research on distance learning, spanning from 1997-2015, were identified. It was found that only one of these reviews of research (Davies, Howell, & Petri, 2010) looked at doctoral dissertations. The authors noted that investigating dissertations was complicated and daunting because 1) only a fraction made full text available and 2) there were a large number of dissertations in the area. To counter for these complications the current study utilized bibliometric and social network analysis to investigate dissertation database listings, including abstracts, keywords, classifications, and other bibliographic data. Bibliographic data for dissertation listings (n=3,954) was exported from the ProQuest Dissertations & Theses A&I (PQDT) database. Software developed for the study formatted the data and imported it into a series of databases. Natural language processing techniques were utilized to pull emergent keywords from dissertation abstracts. Department and University types were analyzed. Dissertation reference sections were investigated utilizing co-citation analysis. Author generated keywords and emergent keywords from abstracts were investigated utilizing keyword co-occurrence network analysis. Findings indicated that dissertations came from 17 department types including education-oriented department types, such as Educational Leadership, Educational Technology, and Educational Psychology, as well as non-education-oriented departments, such as Business, Psychology, and Nursing. Seven research topics were found to be pervasive in dissertations from 2000-2014: Student, Instructor, Interaction, Administration and Management, Design, Educational Context, and Technological Medium. No change was found over time; rather these seven topics remained the most central nodes in each of the keyword co-occurrence networks. Finally this method of investigation relied heavily on algorithms developed for the study to aid in data formatting and analysis. The merits of this highly automated SNA approach were discussed. Use of abstracts and natural language processing enabled a much higher n size (n=3954) to be investigated than in comparison with the only other study to analyze distance education dissertations Davies et al. (2010) where n=100. This method enabled the heavy lifting to be dedicated to the interpretation of the results, rather than data preparation