The mobility network of scientists: analyzing temporal correlations in scientific careers

Vaccario, Giacomo; Verginer, Luca; Schweitzer, Frank

doi:10.1007/s41109-020-00279-x

Cited by 20 publications

(14 citation statements)

References 31 publications

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“…Previous research on the mobility of scientists has used, among other approaches, large-scale surveys ( Franzoni, Scellato, Stephan, 2012 , Franzoni, Scellato, Stephan, 2014 , Franzoni, Scellato, Stephan, 2018 ) and, more recently, large bibliographic datasets ( Bohannon, Doran, 2017 , Deville, Wang, Sinatra, Song, Blondel, Barabási, 2014 , Graf, Kalthaus, 2018 , Vaccario, Verginer, Schweitzer, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Talent goes to global cities: The world network of scientists’ mobility

Verginer

Riccaboni

2021

Research Policy

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Section: Methodsmentioning

confidence: 99%

Talent goes to global cities: The world network of scientists’ mobility

Verginer

Riccaboni

2021

Research Policy

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“…Researcher mobility is considered in Deville et al (2014), James et al (2018 and Vaccario et al (2019), but from a different perspective. James et al (2018) tackle the problem of predicting the next career move of a researcher.…”

Section: Related Workmentioning

confidence: 99%

“…James et al (2018) tackle the problem of predicting the next career move of a researcher. Vaccario et al (2019) reconstruct the temporal graph of worldwide movements of scientists and study its properties. Deville et al (2014) measure the impact of a movement to a higher-or a lower-ranked institution.…”

Section: Related Workmentioning

confidence: 99%

“…APS has been often used as reference repository in the related literature (Deville et al 2014;Petersen 2018;James et al 2018;Wang et al 2013;Qi et al 2017). MedLine was used in Vaccario et al (2019). However, while the affiliation per se is useful in studying academic mobility [i.e., movements from one institution to another one, as in Deville et al ( 2014)], it needs to be further processed if the goal is to study geographic mobility as well.…”

Section: The Academic Data Landscapementioning

confidence: 99%

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The academic wanderer: structure of collaboration network and relation with research performance

et al. 2021

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Thanks to the widespread availability of large-scale datasets on scholarly outputs, science itself has come under the microscope with the aim of capturing a quantitative understanding of its workings. In this study, we leverage well-established cognitive models coming from anthropology in order to characterise the personal network of collaborations between scientists, i.e., the network considered from the standpoint of each individual researcher (referred to as ego network), in terms of the cognitive investment they devote to the different collaborations. Building upon these models, we study the interplay between the structure of academic collaborations, academic performance, and academic mobility at different career stages. We take into account both purely academic mobility (i.e., the number of affiliation changes) and geographical mobility (i.e., physical relocations to different countries). For our investigation, we rely on a dataset comprising the geo-referenced publications of a group of 81,500 authors extracted from Scopus, one of the biggest repositories of academic knowledge. Our main finding is that there is a clear correlation between the structure of co-authorship ego networks and academic performance indices: the more one publishes and the higher their impact, the larger their collaboration network. However, we observe a capacity bound effect, whereby, beyond a certain point, higher performances become increasingly less correlated with large collaboration networks. We also find that international academic migrants are better at growing their networks than researchers that only migrate within the same country, but the latter seem to be better in exploiting their collaboration to achieve higher impact. High academic mobility does not appear to translate into better academic performance or larger collaboration networks. This shows a different finding with respect to related literature, where scientific productivity is seen as directly linked to mobility. Our results show that, when looking at impact of research, this is not necessarily the case.

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“…Параллельно с базами Web of Science и Scopus развивается новая наукометрическая база данных Microsoft Academic, покрывающая посвященные компьютерным наукам журналы и конференции лучше, чем первые две. Примером использования базы данных Microsoft Academic для отслеживания академической мобильности исследователей может служить работа [58]. Авторы данной работы отмечают особенность агрегаторов Microsoft Academic и Scopus: обе базы содержат идентификаторы аффилиаций не только отдельно каждого вуза, но и идентификаторы различных его подразделений.…”

Section: библиографическая и реферативная база данных Scopus неоднокрunclassified

Academic Mobility Assessment: the Strengths and Limitations of Different Approaches

Kryachko¹

2020

UMPA

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The article discusses methodological approaches to measuring academic mobility when a researcher changes his place of work within the academic environment. Researchers’ academic mobility is a factor of their productivity, it provides knowledge transfer, renews scientific ideas, and in general contributes to the active development of higher education and science. However, in Russia the phenomenon of academic mobility is not studied sufficiently and demands an estimation of both mobility as a whole, and its factors, and its consequences. There are not many data sources on mobility, and getting corresponding information is often problematic – these factors make it difficult to empirically measure researchers’ academic mobility. This review might be the first step towards filling the gap in empirical research on academic mobility in Russia. The author suggests a detailed analysis of academic mobility assessment methodologies, including surveys, CVs, bibliometric and combined approaches. The article summarizes the experience of practical approaches application, identifies advantages and disadvantages of each of them, and provides their comparative analysis. The author comes to the conclusion that in the absence of a unique approach to measuring the academic mobility, the bibliometric approach, which provides large representative samplings, research results reproducibility, and greater possibilities of mobility factors assessment, best meets the goals of modern research on causality of academic mobility. This paper should help those who study science and higher education in selecting the most suitable approach to academic mobility measurement and in considering a number of nuances of each approach as practically applied in the research planning process.

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The mobility network of scientists: analyzing temporal correlations in scientific careers

Cited by 20 publications

References 31 publications

Talent goes to global cities: The world network of scientists’ mobility

Talent goes to global cities: The world network of scientists’ mobility

The academic wanderer: structure of collaboration network and relation with research performance

Academic Mobility Assessment: the Strengths and Limitations of Different Approaches

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