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Register a new userScientific mobility indicators in practice: International mobility profiles at the country level
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Nicolas Robinson-Garcia
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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This paper presents and describes the methodological opportunities offered by bibliometric data to produce indicators of scientific mobility. Large bibliographic datasets of disambiguated authors and their affiliations allow for the possibility of tracking the affiliation changes of scientists. Using the Web of Science as data source, we analyze the distribution of types of mobile scientists for a selection of countries. We explore the possibility of creating profiles of international mobility at the country level, and discuss potential interpretations and caveats. Five countries (Canada, The Netherlands, South Africa, Spain, and the United States) are used as examples. These profiles enable us to characterize these countries in terms of their strongest links with other countries. This type of analysis reveals circulation among and between countries with strong policy implications.
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This paper analyses the potential use of bibliometric data for mapping and applying network analysis to mobility flows. We show case mobility networks at three different levels of aggregation: at the country level, at the city level and at the institutional level. We reflect on the potential uses of bibliometric data to inform research policies with regard to scientific mobility.
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Tatiana Savina
, Ivan Sterligov (National Research University Highern School of Economics
, Moscow
2020
We present the results of a large-scale study of potentially predatory journals (PPJ) represented in the Scopus database, which is widely used for research evaluation. Both journal metrics and country, disciplinary data have been evaluated for different groups of PPJ: those listed by Jeffrey Beall and those delisted by Scopus because of publication concerns. Our results show that even after years of delisting, PPJ are still highly visible in the Scopus database with hundreds of active potentially predatory journals. PPJ papers are continuously produced by all major countries, but with different shares. All major subject areas are affected. The largest number of PPJ papers are in engineering and medicine. On average, PPJ have much lower citation metrics than other Scopus-indexed journals. We conclude with a brief survey of the case of Kazakhstan where the share of PPJ papers at one time amounted to almost a half of all Kazakhstan papers in Scopus, and propose a link between PPJ share and national research evaluation policies (in particular, rules of awarding academic degrees). The progress of potentially predatory journal research will be increasingly important because such evaluation methods are becoming more widespread in times of the Metric Tide.
The purpose of this study is to explore the relationship between the first affiliation and the corresponding affiliation at the different levels via the scientometric analysis We select over 18 million papers in the core collection database of Web of Science (WoS) published from 2000 to 2015, and measure the percentage of match between the first and the corresponding affiliation at the country and institution level. We find that a papers the first affiliation and the corresponding affiliation are highly consistent at the country level, with over 98% of the match on average. However, the match at the institution level is much lower, which varies significantly with time and country. Hence, for studies at the country level, using the first and corresponding affiliations are almost the same. But we may need to take more cautions to select affiliation when the institution is the focus of the investigation. In the meanwhile, we find some evidence that the recorded corresponding information in the WoS database has undergone some changes since 2013, which sheds light on future studies on the comparison of different databases or the affiliation accuracy of WoS. Our finding relies on the records of WoS, which may not be entirely accurate. Given the scale of the analysis, our findings can serve as a useful reference for further studies when country allocation or institute allocation is needed. Existing studies on comparisons of straight counting methods usually cover a limited number of papers, a particular research field or a limited range of time. More importantly, using the number counted can not sufficiently tell if the corresponding and first affiliation are similar. This paper uses a metric similar to Jaccard similarity to measure the percentage of the match and performs a comprehensive analysis based on a large-scale bibliometric database.
In this article, we conduct data mining to discover the countries, universities and companies, produced or collaborated the most research on Covid-19 since the pandemic started. We present some interesting findings, but despite analysing all available records on COVID-19 from the Web of Science Core Collection, we failed to reach any significant conclusions on how the world responded to the COVID-19 pandemic. Therefore, we increased our analysis to include all available data records on pandemics and epidemics from 1900 to 2020. We discover some interesting results on countries, universities and companies, that produced collaborated most the most in research on pandemic and epidemics. Then we compared the results with the analysing on COVID-19 data records. This has created some interesting findings that are explained and graphically visualised in the article.
Bornmann, Stefaner, de Moya Anegon, and Mutz (in press) have introduced a web application (www.excellencemapping.net) which is linked to both academic ranking lists published hitherto (e.g. the Academic Ranking of World Universities) as well as spatial visualization approaches. The web application visualizes institutional performance within specific subject areas as ranking lists and on custom tile-based maps. The new, substantially enhanced version of the web application and the multilevel logistic regression on which it is based are described in this paper. Scopus data were used which have been collected for the SCImago Institutions Ranking. Only those universities and research-focused institutions are considered that have published at least 500 articles, reviews and conference papers in the period 2006 to 2010 in a certain Scopus subject area. In the enhanced version, the effect of single covariates (such as the per capita GDP of a country in which an institution is located) on two performance metrics (best paper rate and best journal rate) is examined and visualized. A covariate-adjusted ranking and mapping of the institutions is produced in which the single covariates are held constant. The results on the performance of institutions can then be interpreted as if the institutions all had the same value (reference point) for the covariate in question. For example, those institutions can be identified worldwide showing a very good performance despite a bad financial situation in the corresponding country.
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