Scientometrics

Citation and referencing data from recent biotechnology patents and bioscience papers is used to show that the bibliometric properties in these two realms are quite similar. Specifically, it is shown that the time distribution of references from both patents and papers are similar, with peak citing at two to four years prior to publication or issue. This is shown to hold for patents citing patents, for papers citing papers, and for patents citing papers. Furthermore, it is shown that there is a very skewed distribution of cited material in both patents and papers, with a relatively small number of highly cited patents and papers, and a relatively large number of documents which are cited only once or twice, or not at all. Finally, it is shown that there is a substantial amount of citation from biotechnology patents to the central scientific literature. We conclude from this that science and technology are far more closely linked today than is normally perceived, and that, in fact, the division between leading edge biotechnology and modern bioscience has alsmot completely disappeared.

Author bibliographic coupling analysis (ABCA) is an extension of bibliographic coupling theory at the author level and is widely used in mapping intellectual structures and scholarly communities. However, the assumption of equal citations and the complete dependence on explicit counts may affect its effectiveness in today’s complex context of discipline development. This research proposes a new approach that uses multiple full-text data to improve ABCA called enhanced author bibliographic coupling analysis. By mining the semantic and syntactic information of citations, the new approach considers more diverse dimensions as the basis of author bibliographic coupling strength. Comparative empirical research was then conducted in the field of oncology. The results show that the new approach can more accurately reveal the relevant relations between authors and map a more detailed domain intellectual structure.

Clarivate Analytics’s Web of Science (WoS) is the world’s leading scientific citation search and analytical information platform. It is used as both a research tool supporting a broad array of scientific tasks across diverse knowledge domains as well as a dataset for large-scale data-intensive studies. WoS has been used in thousands of published academic studies over the past 20 years. It is also the most enduring commercial legacy of Eugene Garfield. Despite the central position WoS holds in contemporary research, the quantitative impact of WoS has not been previously examined by rigorous scientific studies. To better understand how this key piece of Eugene Garfield’s heritage has contributed to science, we investigated the ways in which WoS (and associated products and features) is mentioned in a sample of 19,478 English-language research and review papers published between 1997 and 2017, as indexed in WoS databases. We offered descriptive analyses of the distribution of the papers across countries, institutions and knowledge domains. We also used natural language processingtechniques to identify the verbs and nouns in the abstracts of these papers that are grammatically connected to WoS-related phrases. This is the first study to empirically investigate the documentation of the use of the WoS platform in published academic papers in both scientometric and linguistic terms.

This paper explores scale, scope and trade-off effects in scientific research and education. External conditions may dramatically affect the measurement of performance. We apply theDaraio&Simar's (2005) nonparametric methodology to robustlytake into account these factors and decompose the indicators of productivity accordingly. From a preliminary investigation on the Italian system of universities, we find that economies of scale and scope are not significant factors in explaining research and education productivity. We do not find any evidence of the trade-off research vs teaching. About the trade-off academic publications vs industry oriented research, it seems that, initially, collaboration with industry may improve productivity, but beyond a certain level the compliance with industry expectations may be too demanding and deteriorate the publication profile. Robust nonparametric methods in efficiency analysis are shown as useful tools for measuring and explaining the performance of a public research system of universities.

The understanding of how science works can contribute to making scientific development more effective. In this paper, we report an analysis of the organization and the interconnection between unbalanced areas of study. More specifically, we considered two important subareas in analytical chemistry, namely capillary electrophoresis (CE) and mass spectrometry (MS). These areas are particularly interesting because MS is employed in a bigger range of applications than CE. Consequently, these different portions of papers can interfere in the quality of the searches for papers devoted to CE–MS. Here, we considered a citation network in which the nodes and connections represent papers and citations, respectively. The network clusters were detected by employing the Infomap algorithm. By considering the clusters and the respective abstracts, the subjects were identified. Interesting results were found, including a marked separation between some clusters of articles devoted to instrumentation techniques and applications, which was quantified from the abstract contents. For instance, the most well-defined community was assigned as CE (Instru.), with 73.8% of the papers having abstracts that include the word “electrophoresis” and not the word “mass”. However, the papers that describe CE–MS did not lead to a well-defined cluster. In order to better understand the organization of the citation network, we considered a multi-scale analysis, in which we used the information regarding sub-clusters. Firstly, we analyzed the sub-cluster that contains the first article devoted to the coupling between CE and MS, whose subject was found to be a good representation of its sub-cluster. The second analysis was about the sub-cluster of a seminal paper known to be the first that dealt with protein analysis by using CE–MS and a similar result was obtained. By considering the proposed methodologies, our paper can contribute to researchers working with similar scenarios, since it shows that a given subject can be spread on many clusters of the network, therefore, lead to better literature reviews.

The citation analysis of the research output of the German economic research institutes presented here is based on publications in peer-reviewed journals listed in the Social Science Citation Index for the 2000–2009 period. The novel feature of the paper is that a count data model quantifies the determinants of citation success and simulates their citation potential. Among the determinants of the number of cites the quality of the publication outlet exhibits a strong positive effect. The same effect has the number of the published pages, but journals with size limits also yield more cites. Field journals get less citations in comparison to general journals. Controlling for journal quality, the number of co-authors of a paper has no effect, but it is positive when co-authors are located outside the own institution. We find that the potential citations predicted by our best model lead to different rankings across the institutes than current citations indicating structural change.

Three characteristics of scientific research (subject matter, researchers' institutional sectors, and funding sources) were compared using bibliographic and survey data from a study of restorative dental materials research. Both types of data yielded similar findings on the distribution of research across subject areas and the distribution of researchers in government, university and industry sectors. Findings on the sources of research funding, however, were dissimilar and university research support appeared underreported in the bibliographic data. In general, data on publications (from bibliographic files or surveys) yielded lower estimates of industrial participation in research than data pertaining to projects.

Online bibliographic databases are powerful resources for research in data mining and social network analysis especially co-author networks. Predicting future rising stars is to find brilliant scholars/researchers in co-author networks. In this paper, we propose a solution for rising star prediction by applying machine learning techniques. For classification task, discriminative and generative modeling techniques are considered and two algorithms are chosen for each category. The author, co-authorship and venue based information are incorporated, resulting in eleven features with their mathematical formulations. Extensive experiments are performed to analyze the impact of individual feature, category wise and their combination w.r.t classification accuracy. Then, two ranking lists for top 30 scholars are presented from predicted rising stars. In addition, this concept is demonstrated for prediction of rising stars in database domain. Data from DBLP and Arnetminer databases (1996–2000 for wide disciplines) are used for algorithms’ experimental analysis.