Showing papers in "Journal of Informetrics in 2021"

TL;DR: In this paper, the authors used various classification and regression models and evaluated their performance, finding neural networks and ensemble models to perform best for these tasks, finding that Mendeley readership is the most important factor in predicting the early citations, followed by other factors such as the academic status of the readers (e.g., student, postdoc, professor), followers on Twitter, online post length, author count, and the number of mentions on Wikipedia, and across different countries.

TL;DR: In this paper, the authors examined the male-female collaboration practices of all internationally visible Polish university professors (N = 25,463) based on their Scopus-indexed publications from 2009-2018 (158,743 journal articles).

TL;DR: It is demonstrated that there is a significant positive effect of Project 5−100 on quantitative university research performance, and participating universities demonstrate a substantial, steady increase in publications measured in total numbers and per capita.

TL;DR: This study analyzed the technological changes in a technology domain by eliminating the effects on evolution path caused by patent families and discussed the implications of the technological path obtained from different parameters.

TL;DR: A Supervised Rebel Identification (SRI) framework to identify rebels on Twitter consists of a novel mechanism to structure the users’ tweets into a directed user graph that links predicates (verbs) with the subject and object words to understand semantics of the underlying data.

TL;DR: The triangular citation proposed in this paper provides a new research perspective for bibliometrics, and it can be used to explore hidden literature relationships, citation mechanisms and citation motivations.

TL;DR: This paper contributes to improve the understanding of the effects of funding on research performance through the analysis of the scientific publications of Spain-based researchers in seven disciplines during a five-year period.

TL;DR: A novel approach to structure scientific fields is proposed, which uses semantic analysis from natural language texts to construct knowledge graphs and automatically extracts information such as the most relevant concepts in topics and overlapping concepts between topics.

TL;DR: This study proposes an improved methodology on the basis of entities and semantic relations (functional and non-functional relations), which takes semantic direction of each sequence structure and the word order information of each component into consideration.

TL;DR: The research result shows that the proposed main path analysis method can resolve the problem of discipline bias and time bias in interdisciplinary research and provides a more differentiated ranking for citation linkages in the network.

TL;DR: The authors explored the relationship between interdisciplinarity and the citation impact of highly cited papers by comparing the dimensions of disciplinary diversity (variety, balance, and disparity) and the typical integration indicators (i.e., the Rao-Stirling index (RS and the Leinster-Cobbold Diversity Index (LCDiv)) of all papers published in 2000 and indexed in Clarivate Analytics’ Web of Science.

TL;DR: The notion that researchers seek collaborators to augment their scientific and technical human capital is supported, with an inverted-U relationship in which the probability of collaboration initially increases with topic similarity, then rapidly declines after peaking at a similarity "sweet spot".

TL;DR: Investigation of the changes in the self-citation behavior of Italian professors following the introduction of a citation-based incentive scheme, for national accreditation to academic appointments shows that there is much heterogeneity in the individual patterns of self- citing behavior, albeit with very few outliers.

TL;DR: In this article, Wu et al. used a list of milestone papers, selected and published by editors of Physical Review Letters, and investigated whether this human (experts)-based list is related to values of the several disruption indicators variants and which variants show the highest correlation with expert judgements.

TL;DR: A methodology based on bi-layer network analytics that characterizes emerging general-purpose technologies and incorporates three novel indicators that quantify a technology's technical potential and social impacts, not just in one specific technological area but in a wide range of domains is proposed.

TL;DR: Analyzing bibliometric data from six disciplines reveals four categories of significant drivers for the collaboration closeness between countries, namely science, economy, geopolitics, and culture respectively, and countries with large and equivalent scientific sizes are more likely to collaborate closely with each other.

TL;DR: Experiments on various real-world multiplex networks prove the capability of the proposed Community-guided Link Prediction based on External Similarity (CLPES) method for producing improved results and its superiority against other link prediction algorithms.

TL;DR: The axiomatic characterisation of WTC shows why it is especially suitable for scientometric purposes and the size of the stable sets with respect to exclusiveness can be plotted to obtain an overview of the competitiveness of the field.

TL;DR: This work takes articles in PLoS as the data and characterizes the references from different disciplines based on Citation Content Analysis (CCA), concluding that the distributions of references fromDifferent disciplines are significantly different.

TL;DR: An entropy-based indicator is proposed, as well as a quantitative method based on the S-curve of entropy is established to identify the stages of TLC, which reveals that 3D printing is still in its growth stage.

TL;DR: It was found that the influence of multiple factors in the novelty category on citation counts is higher than the bibliometric and academic-network categories, while the individual factor is not always the most influential factor.

TL;DR: Findings support earlier studies indicating that journals from post-socialist countries are used mainly for local promotions and formal fulfilment of policy rules and there is no correlation between the ratio of national vs international publications to GDP.

TL;DR: The results show that the proposed model can not only uncover the evolutionary process appropriately, but also can effectively distinguish the citations in the network by taking the influence difference into account.

TL;DR: A publication delay adjusted impact factor (PDAIF) which takes publication delay into consideration to reduce the negative effect on the quality of the impact factor determination is proposed.

TL;DR: Using the lognormal cumulative distribution function, the equation that defines the h index can be formulated and this equation shows that h has a complex dependence on the number of papers (N), which means that in real citation distributions there are other difficulties.

TL;DR: A novel knowledge network coupling approach to gauge network linkage between S&T by integrating knowledge linkages and structural linkages is proposed and provides references for policymakers to conduct policy adjustments, by identifying the lead-lag relationship between S &T.

TL;DR: This study shows that the frequently used metrics to compare the impact of papers and to select a sample of "most impactful papers" published in each year and each field may privilege specific periods while neglecting others.

TL;DR: The integer DEA model is feasible and could be used by universities to form suitable journal ranking list and it is proposed that CRS is more suitable in academic journal evaluation than VRS because there is no effect of increasing or decreasing returns to scale in the process of journal publication and citation.

TL;DR: This work tries to fill the gap examining the citation-based impact of Italian 2010–2017 publications distinguishing authorship by the private sector from the public sector, and investigates the relation between different forms of collaboration and impact.

TL;DR: A novel dataset at European level which includes data on academic staff and students of universities from official National Statistical Authorities, bibliometric indicators on publications, and socio-economic indicators at regional level finds strong support for peer effects at institutional level and for the positive effect of international collaborations and attraction of foreign PhD students.