The effect of the rapid growth of covid-19 publications on citation indicators

A concern has been raised that "covidization" of research would cause an overemphasizes on covid-19 and pandemics at the expense of other research. The rapid growth of publications related to the Covid-19 pandemic renders a vast amount of citations from this literature. This growth may affect bibliometric indicators. In this paper I explored how the growth of covid-19 publications influences bibliometric indicators commonly used in university rankings, research evaluation and research allocation, namely the field normalized citation score and the journal impact factor. I found that the burst of publications in the early stage of the covid-19 pandemic affects field-normalized citation scores and will affect the journal impact factor. Publications unrelated to covid-19 are also heavily affected. I conclude that there is a considerable risk to draw misleading conclusions from citation indicators spanning over the beginning of the covid-19 pandemic, in particular when time series are used and when the biomedical literature is assessed

Mapping the structure of science through clustering in citation networks : granularity, labeling and visualization

The science system is large, and millions of research publications are published each year. Within the field of scientometrics, the features and characteristics of this system are studied using quantitative methods. Research publications constitute a rich source of information about the science system and a means to model and study science on a large scale. The classification of research publications into fields is essential to answer many questions about the features and characteristics of the science system. Comprehensive, hierarchical, and detailed classifications of large sets of research publications are not easy to obtain. A solution for this problem is to use network-based approaches to cluster research publications based on their citation relations. Clustering approaches have been applied to large sets of publications at the level of individual articles (in contrast to the journal level) for about a decade. Such approaches are addressed in this thesis. I call the resulting classifications “algorithmically constructed, publications-level classifications of research publications” (ACPLCs). The aim of the thesis is to improve interpretability and utility of ACPLCs. I focus on some issues that hitherto have not received much attention in the previous literature: (1) Conceptual framework. Such a framework is elaborated throughout the thesis. Using the social science citation theory, I argue that citations contextualize and position publications in the science system. Citations may therefore be used to identify research fields, defined as focus areas of research at various granularity levels. (2) Granularity levels corresponding to conceptual framework. In Articles I and II, a method is proposed on how to adjust the granularity of ACPLCs in order to obtain clusters corresponding to research fields at two granularity levels: topics and specialties. (3) Cluster labeling. Article III addresses labeling of clusters at different semantic levels, from broad and large to narrow and small, and compares the use of data from various bibliographic fields and different term weighting approaches. (4) Visualization. The methods resulting from Articles I-III are applied in Article IV to obtain a classification of about 19 million biomedical articles. I propose a visualization methodology that provides overview of the classification, using clusters at coarse levels, as well as the possibility to zoom into details, using clusters at a granular level. In conclusion, I have improved interpretability and utility of ACPLCs by providing a conceptual framework, adjusting granularity of clusters, labeling clusters and, finally, by visualizing an ACPLC in a way that provides both overview and detail. I have demonstrated how these methods can be applied to obtain ACPLCs that are useful to, for example, identify and explore focus areas of research

The Share of Open Access in Sweden 2011

Under 2013 genomfördes en studie över andelen open access (OA) i Sverige. Projektet finansierades av Kungliga bibliotekets program Openaccess.se. I undersökningen, som använde SwePub som index, studerades hur stor del av svenska forskningsartiklar med publiceringsår 2011 som är OA. Analysen gäller dels andelen guld och grön OA, men också hur stor del av de publicerade artiklarna som skulle kunna parallellpubliceras enligt förlagens villkor. Analysen visar att andelen OA-artiklar som publicerades under år 2011 uppgick till cirka 17 procent, varav guld OA 10 procent och grön OA 10 procent. Detta indikerar ett överlapp av artiklar som är både guld och grön OA på cirka 3 procent. Räknar man även in så kallat fördröjd OA så uppgår den totala mängden OA-artiklar år 2011 till uppemot 25 procent. Resultatet visar vidare att ytterligare en stor mängd artiklar skulle kunna parallellpubliceras enligt informationen i Sherpa/RoMEO

Bibliometriska indikatorer för KTH 2007-2011

QC 20200131</p

Jämförelse mellan Google Scholar, Scopus och Web of Science – En fallstudie av en Unit of Assessment i RAE2012

QC 20200131</p

Jämförelse mellan Scopus och Web of Science för utvärdering av KTH:s publicering

QC 20200131</p

Jämförelse mellan Scopus och Web of Science för utvärdering av KTH:s publicering

QC 20200131</p

Bibliometriska indikatorer för KTH 2007-2011

QC 20200131</p