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Mike Thelwall

Publications and source records attributed to Mike Thelwall.

At least 91 records · Page 5Linked to original sources

The rhetorical structure of science? A multidisciplinary analysis of article headings

An effective structure helps an article to convey its core message. The optimal structure depends on the information to be conveyed and the expectations of the audience. In the current increasingly interdisciplinary era, structural norms can be confusing to the authors, reviewers and audiences of scientific articles. Despite this, no prior study has attempted to assess variations in the structure of academic papers across all disciplines. This article reports on the headings commonly used by over 1 million research articles from the PubMed Central Open Access collection, spanning 22 broad categories covering all academia and 172 out of 176 narrow categories. The results suggest that no headings are close to ubiquitous in any broad field and that there are substantial differences in the extent to which most headings are used. In the humanities, headings may be avoided altogether. Researchers should therefore be aware of unfamiliar structures that are nevertheless legitimate when reading, writing and reviewing articles.

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Can Google Scholar and Mendeley help to assess the scholarly impacts of dissertations?

Dissertations can be the single most important scholarly outputs of junior researchers. Whilst sets of journal articles are often evaluated with the help of citation counts from the Web of Science or Scopus, these do not index dissertations and so their impact is hard to assess. In response, this article introduces a new multistage method to extract Google Scholar citation counts for large collections of dissertations from repositories indexed by Google. The method was used to extract Google Scholar citation counts for 77,884 American doctoral dissertations from 2013-2017 via ProQuest, with a precision of over 95%. Some ProQuest dissertations that were dual indexed with other repositories could not be retrieved with ProQuest-specific searches but could be found with Google Scholar searches of the other repositories. The Google Scholar citation counts were then compared with Mendeley reader counts, a known source of scholarly-like impact data. A fifth of the dissertations had at least one citation recorded in Google Scholar and slightly fewer had at least one Mendeley reader. Based on numerical comparisons, the Mendeley reader counts seem to be more useful for impact assessment purposes for dissertations that are less than two years old, whilst Google Scholar citations are more useful for older dissertations, especially in social sciences, arts and humanities. Google Scholar citation counts may reflect a more scholarly type of impact than that of Mendeley reader counts because dissertations attract a substantial minority of their citations from other dissertations. In summary, the new method now makes it possible for research funders, institutions and others to systematically evaluate the impact of dissertations, although additional Google Scholar queries for other online repositories are needed to ensure comprehensive coverage.

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Readership Data and Research Impact

Reading academic publications is a key scholarly activity. Scholars accessing and recording academic publications online are producing new types of readership data. These include publisher, repository, and academic social network download statistics as well as online reference manager records. This chapter discusses the use of download and reference manager data for research evaluation and library collection development. The focus is on the validity and application of readership data as an impact indicator for academic publications across different disciplines. Mendeley is particularly promising in this regard, although all data sources are not subjected to rigorous quality control and can be manipulated.

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Gender and Research Publishing in India: Uniformly high inequality?

Women's access to academic careers has been historically limited by discrimination and cultural constraints. Comprehensive information about gender inequality within disciplines is needed to understand the problem and target remedial action. India is the fifth largest research producer but has a low international index of gender inequality and so is an important case. This study assesses gender inequalities in Indian journal article publishing in 2017 for 186 research fields. It also seeks overall gender differences in interests across academia by comparing the terms used in 27,710 articles with an Indian male or female first author. The data show that there are at least 1.5 male first authors per female first author in each of 26 broad fields and 2.8 male first authors per female first author overall. Compared to the USA, India has a much lower share of female first authors but smaller variations in gender differences between broad fields. Dentistry, Economics and Maths are all more female in India, but Veterinary is much less female than in the USA. There is a tendency for males to research thing-oriented topics and for females to research helping people and some life science topics. More initiatives to promote gender equality in science are needed to address the overall imbalance, but care should be taken to avoid creating the larger between-field gender differences found in the USA.

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Gender differences in research areas and topics: An analysis of publications in 285 fields

Although the gender gap in academia has narrowed, females are underrepresented within some fields in the USA. Prior research suggests that the imbalances between science, technology, engineering and mathematics fields may be partly due to greater male interest in things and greater female interest in people, or to off-putting masculine cultures in some disciplines. To seek more detailed insights across all subjects, this article compares practising US male and female researchers between and within 285 narrow Scopus fields inside 26 broad fields from their first-authored articles published in 2017. The comparison is based on publishing fields and the words used in article titles, abstracts, and keywords. The results cannot be fully explained by the people/thing dimensions. Exceptions include greater female interest in veterinary science and cell biology and greater male interest in abstraction, patients, and power/control fields, such as politics and law. These may be due to other factors, such as the ability of a career to provide status or social impact or the availability of alternative careers. As a possible side effect of the partial people/thing relationship, females are more likely to use exploratory and qualitative methods and males are more likely to use quantitative methods. The results suggest that the necessary steps of eliminating explicit and implicit gender bias in academia are insufficient and might be complemented by measures to make fields more attractive to minority genders.

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She's Reddit: A source of statistically significant gendered interest information?

Information about gender differences in interests is necessary to disentangle the effects of discrimination and choice when gender inequalities occur, such as in employment. This article assesses gender differences in interests within the popular social news and entertainment site Reddit. A method to detect terms that are statistically significantly used more by males or females in 181 million comments in 100 subreddits shows that gender affects both the selection of subreddits and activities within most of them. The method avoids the hidden gender biases of topic modelling for this task. Although the method reveals statistically significant gender differences in interests for topics that are extensively discussed on Reddit, it cannot give definitive causes, and imitation and sharing within the site mean that additional checking is needed to verify the results. Nevertheless, with care, Reddit can serve as a useful source of insights into gender differences in interests.

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Do females create higher impact research? Scopus citations and Mendeley readers for articles from five countries

There are known gender imbalances in participation in scientific fields, from female dominance of nursing to male dominance of mathematics. It is not clear whether there is also a citation imbalance, with some claiming that male-authored research tends to be more cited. No previous study has assessed gender differences in the readers of academic research on a large scale, however. In response, this article assesses whether there are gender differences in the average citations and/or Mendeley readers of academic publications. Field normalised logged Scopus citations and Mendeley readers from mid-2018 for articles published in 2014 were investigated for articles with first authors from India, Spain, Turkey, the UK and the USA in up to 251 fields with at least 50 male and female authors. Although female-authored research is less cited in Turkey (-4.0%) and India (-3.6%), it is marginally more cited in Spain (0.4%), the UK (0.4%), and the USA (0.2%). Female-authored research has fewer Mendeley readers in India (-1.1%) but more in Spain (1.4%), Turkey (1.1%), the UK (2.7%) and the USA (3.0%). Thus, whilst there may be little practical gender difference in citation impact in countries with mature science systems, the higher female readership impact suggests a wider audience for female-authored research. The results show that the conclusions from a gender analysis depend on the field normalisation method. A theoretically informed decision must therefore be made about which normalisation to use. The results also suggest that arithmetic mean-based field normalisation is favourable to males.

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Can Microsoft Academic help to assess the citation impact of academic books?

Despite recent evidence that Microsoft Academic is an extensive source of citation counts for journal articles, it is not known if the same is true for academic books. This paper fills this gap by comparing citations to 16,463 books from 2013-2016 in the Book Citation Index (BKCI) against automatically extracted citations from Microsoft Academic and Google Books in 17 fields. About 60% of the BKCI books had records in Microsoft Academic, varying by year and field. Citation counts from Microsoft Academic were 1.5 to 3.6 times higher than from BKCI in nine subject areas across all years for books indexed by both. Microsoft Academic found more citations than BKCI because it indexes more scholarly publications and combines citations to different editions and chapters. In contrast, BKCI only found more citations than Microsoft Academic for books in three fields from 2013-2014. Microsoft Academic also found more citations than Google Books in six fields for all years. Thus, Microsoft Academic may be a useful source for the impact assessment of books when comprehensive coverage is not essential.

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Which US and European Higher Education Institutions are visible in ResearchGate and what affects their RG Score?

While ResearchGate has become the most popular academic social networking site in terms of regular users, not all institutions have joined and the scores it assigns to academics and institutions are controversial. This paper assesses the presence in ResearchGate of higher education institutions in Europe and the US in 2017, and the extent to which institutional ResearchGate Scores reflect institutional academic impact. Most of the 2258 European and 4355 US higher educational institutions included in the sample had an institutional ResearchGate profile, with near universal coverage for PhD-awarding institutions found in the Web of Science (WoS). For non-PhD awarding institutions that did not publish, size (number of staff members) was most associated with presence in ResearchGate. For PhD-awarding institutions in WoS, presence in RG was strongly related to the number of WoS publications. In conclusion, a) institutional RG scores reflect research volume more than visibility and b) this indicator is highly correlated to the number of WoS publications. Hence, the value of RG Scores for institutional comparisons is limited.

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Dimensions: A Competitor to Scopus and the Web of Science?

Dimensions is a partly free scholarly database launched by Digital Science in January 2018. Dimensions includes journal articles and citation counts, making it a potential new source of impact data. This article explores the value of Dimensions from an impact assessment perspective with an examination of Food Science research 2008-2018 and a random sample of 10,000 Scopus articles from 2012. The results include high correlations between citation counts from Scopus and Dimensions (0.96 by narrow field in 2012) as well as similar average counts. Almost all Scopus articles with DOIs were found in Dimensions (97% in 2012). Thus, the scholarly database component of Dimensions seems to be a plausible alternative to Scopus and the Web of Science for general citation analyses and for citation data in support of some types of research evaluations.

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Can Microsoft Academic assess the early citation impact of in-press articles? A multi-discipline exploratory analysis

Many journals post accepted articles online before they are formally published in an issue. Early citation impact evidence for these articles could be helpful for timely research evaluation and to identify potentially important articles that quickly attract many citations. This article investigates whether Microsoft Academic can help with this task. For over 65,000 Scopus in-press articles from 2016 and 2017 across 26 fields, Microsoft Academic found 2-5 times as many citations as Scopus, depending on year and field. From manual checks of 1,122 Microsoft Academic citations not found in Scopus, Microsoft Academic's citation indexing was faster but not much wider than Scopus for journals. It achieved this by associating citations to preprints with their subsequent in-press versions and by extracting citations from in-press articles. In some fields its coverage of scholarly digital libraries, such as arXiv.org, was also an advantage. Thus, Microsoft Academic seems to be a more comprehensive automatic source of citation counts for in-press articles than Scopus.

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Could scientists use Altmetric.com scores to predict longer term citation counts?

Altmetrics from Altmetric.com are widely used by publishers and researchers to give earlier evidence of attention than citation counts. This article assesses whether Altmetric.com scores are reliable early indicators of likely future impact and whether they may also reflect non-scholarly impacts. A preliminary factor analysis suggests that the main altmetric indicator of scholarly impact is Mendeley reader counts, with weaker news, informational and social network discussion/promotion dimensions in some fields. Based on a regression analysis of Altmetric.com data from November 2015 and Scopus citation counts from October 2017 for articles in 30 narrow fields, only Mendeley reader counts are consistent predictors of future citation impact. Most other Altmetric.com scores can help predict future impact in some fields. Overall, the results confirm that early Altmetric.com scores can predict later citation counts, although less well than journal impact factors, and the optimal strategy is to consider both Altmetric.com scores and journal impact factors. Altmetric.com scores can also reflect dimensions of non-scholarly impact in some fields.

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The Research Production of Nations and Departments: A Statistical Model for the Share of Publications

Policy makers and managers sometimes assess the share of research produced by a group (country, department, institution). This takes the form of the percentage of publications in a journal, field or broad area that has been published by the group. This quantity is affected by essentially random influences that obscure underlying changes over time and differences between groups. A model of research production is needed to help identify whether differences between two shares indicate underlying differences. This article introduces a simple production model for indicators that report the share of the world's output in a journal or subject category, assuming that every new article has the same probability to be authored by a given group. With this assumption, confidence limits can be calculated for the underlying production capability (i.e., probability to publish). The results of a time series analysis of national contributions to 36 large monodisciplinary journals 1996-2016 are broadly consistent with this hypothesis. Follow up tests of countries and institutions in 26 Scopus subject categories support the conclusions but highlight the importance of ensuring consistent subject category coverage.

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Microsoft Academic: A multidisciplinary comparison of citation counts with Scopus and Mendeley for 29 journals

Microsoft Academic is a free citation index that allows large scale data collection. This combination makes it useful for scientometric research. Previous studies have found that its citation counts tend to be slightly larger than those of Scopus but smaller than Google Scholar, with disciplinary variations. This study reports the largest and most systematic analysis so far, of 172,752 articles in 29 large journals chosen from different specialisms. From Scopus citation counts, Microsoft Academic citation counts and Mendeley reader counts for articles published 2007-2017, Microsoft Academic found a slightly more (6%) citations than Scopus overall and especially for the current year (51%). It found fewer citations than Mendeley readers overall (59%), and only 7% as many for the current year. Differences between journals were probably due to field preprint sharing cultures or journal policies rather than broad disciplinary differences.

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Microsoft Academic Automatic Document Searches: Accuracy for Journal Articles and Suitability for Citation Analysis

Microsoft Academic is a free academic search engine and citation index that is similar to Google Scholar but can be automatically queried. Its data is potentially useful for bibliometric analysis if it is possible to search effectively for individual journal articles. This article compares different methods to find journal articles in its index by searching for a combination of title, authors, publication year and journal name and uses the results for the widest published correlation analysis of Microsoft Academic citation counts for journal articles so far. Based on 126,312 articles from 323 Scopus subfields in 2012, the optimal strategy to find articles with DOIs is to search for them by title and filter out those with incorrect DOIs. This finds 90% of journal articles. For articles without DOIs, the optimal strategy is to search for them by title and then filter out matches with dissimilar metadata. This finds 89% of journal articles, with an additional 1% incorrect matches. The remaining articles seem to be mainly not indexed by Microsoft Academic or indexed with a different language version of their title. From the matches, Scopus citation counts and Microsoft Academic counts have an average Spearman correlation of 0.95, with the lowest for any single field being 0.63. Thus, Microsoft Academic citation counts are almost universally equivalent to Scopus citation counts for articles that are not recent but there are national biases in the results.

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Confidence intervals for normalised citation counts: Can they delimit underlying research capability?

Normalised citation counts are routinely used to assess the average impact of research groups or nations. There is controversy over whether confidence intervals for them are theoretically valid or practically useful. In response, this article introduces the concept of a group's underlying research capability to produce impactful research. It then investigates whether confidence intervals could delimit the underlying capability of a group in practice. From 123120 confidence interval comparisons for the average citation impact of the national outputs of ten countries within 36 individual large monodisciplinary journals, moderately fewer than 95% of subsequent indicator values fall within 95% confidence intervals from prior years, with the percentage declining over time. This is consistent with confidence intervals effectively delimiting the research capability of a group, although it does not prove that this is the cause of the results. The results are unaffected by whether internationally collaborative articles are included.

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Do ResearchGate Scores create ghost academic reputations?

The academic social network site ResearchGate (RG) has its own indicator, RG Score, for its members. The high profile nature of the site means that the RG score may be used for recruitment, promotion and other tasks for which researchers are evaluated. In response, this study investigates whether it is reasonable to employ the RG Score as evidence of scholarly reputation. For this, three different author samples were investigated. An outlier sample includes 104 authors with high values. A Nobel sample comprises 73 Nobel winners from Medicine & Physiology, Chemistry, Physics and Economics (from 1975 to 2015). A longitudinal sample includes weekly data on 4 authors with different RG Scores. The results suggest that high RG Scores are built primarily from activity related to asking and answering questions in the site. In particular, it seems impossible to get a high RG Score solely through publications. Within RG it is possible to distinguish between (passive) academics that interact little in the site and active platform users, who can get high RG Scores through engaging with others inside the site (questions, answers, social networks with influential researchers). Thus, RG Scores should not be mistaken for academic reputation indicators.

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The Accuracy of Confidence Intervals for Field Normalised Indicators

When comparing the average citation impact of research groups, universities and countries, field normalisation reduces the influence of discipline and time. Confidence intervals for these indicators can help with attempts to infer whether differences between sets of publications are due to chance factors. Although both bootstrapping and formulae have been proposed for these, their accuracy is unknown. In response, this article uses simulated data to systematically compare the accuracy of confidence limits in the simplest possible case, a single field and year. The results suggest that the MNLCS (Mean Normalised Log-transformed Citation Score) confidence interval formula is conservative for large groups but almost always safe, whereas bootstrap MNLCS confidence intervals tend to be accurate but can be unsafe for smaller world or group sample sizes. In contrast, bootstrap MNCS (Mean Normalised Citation Score) confidence intervals can be very unsafe, although their accuracy increases with sample sizes.

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