PurposeThe introduction of “altmetrics” as new tools to analyze scientific impact within the reward system of science has challenged the hegemony of citations as the predominant source for... Show morePurposeThe introduction of “altmetrics” as new tools to analyze scientific impact within the reward system of science has challenged the hegemony of citations as the predominant source for measuring scientific impact. Mendeley readership has been identified as one of the most important altmetric sources, with several features that are similar to citations. The purpose of this paper is to perform an in-depth analysis of the differences and similarities between the distributions of Mendeley readership and citations across fields.Design/methodology/approachThe authors analyze two issues by using in each case a common analytical framework for both metrics: the shape of the distributions of readership and citations, and the field normalization problem generated by differences in citation and readership practices across fields. In the first issue the authors use the characteristic scores and scales method, and in the second the measurement framework introduced in Crespo et al. (2013).FindingsThere are three main results. First, the citations and Mendeley readership distributions exhibit a strikingly similar degree of skewness in all fields. Second, the results on “exchange rates (ERs)” for Mendeley readership empirically supports the possibility of comparing readership counts across fields, as well as the field normalization of readership distributions using ERs as normalization factors. Third, field normalization using field mean readerships as normalization factors leads to comparably good results.Originality/valueThese findings open up challenging new questions, particularly regarding the possibility of obtaining conflicting results from field normalized citation and Mendeley readership indicators; this suggests the need for better determining the role of the two metrics in capturing scientific recognition. Show less
The research presented in this PhD thesis describes ways of identifying at an early-stage, 2-3 years after their publication, discoveries in science that are expected to have a major impact on... Show moreThe research presented in this PhD thesis describes ways of identifying at an early-stage, 2-3 years after their publication, discoveries in science that are expected to have a major impact on science. Bibliographic information extracted from those scientific publications is analysed to select patterns that may identify such `scientific breakthroughs’. A distinction is made between different types of breakthroughs. A major methodological issue is the differentiation between discoveries that actually achieve a long-term major impact on science and those where the impact has not occurred within the first three years. These impacts are measured in terms of `citations’ from subsequent research publications.This thesis introduces the conceptual framework that is used to analyses four case studies, each study focussing on a specific well-documented breakthrough discovery. The citation impact patterns are analysed in a search for bibliographic markers that are characteristic for a breakthrough discovery. The significance of the various markers, one set of markers for each type of breakthrough, were tested in a large-scale validation study.This study resulted in several early-stage identification algorithms. Five of these algorithms were implemented as automated computerized search methods. The algorithms have proved able, retrospectively, to identify early-stage publications that present (potential) breakthrough discoveries. Show less
The research described in this thesis aims to establish the use of detailed collaboration and citation analysis combined with other forms of bibliometric analysis as a tool enabling a better... Show moreThe research described in this thesis aims to establish the use of detailed collaboration and citation analysis combined with other forms of bibliometric analysis as a tool enabling a better understanding of the organization of scientific communities and the way knowledge is spread inside scientific communities. In the study of collaboration networks the main goal is to identify existing research groups, potential research groups, and patterns of collaboration. The analysis of citations networks through specific measures and metrics, on the other hand, makes it possible to identify main lines of research through the years. Thus, such analyses improve our understanding of the growth and decline of fields, including phenomena such as paradigm shifts and emerging research themes. Network measures and metrics also allow for the identification of important nodes (e.g., journals, articles) embedded in the citation network. Show less
