Where do you go to, my lovely [scientist]?
The mobility of scientific human capital is a key channel for exchanging ideas and disseminating scientific knowledge. In our recent blog post, Martin Srholec (Deputy Director for Research at CERGE-EI, Prague/CZ), PhD student Vít Macháček (CERGE-EI), PostDoc Nicolás Robinson-García (TU Delft/NL), Senior Researcher Rodrigo Costas (Leiden Univ/NL) and I demonstrate how scientometrics can help trace mobility patterns at the institutional level, using the Dimensions database.
So far it has been hard to trace the mobility of researchers between institutions. Thanks to the implementation of advanced approaches for affiliation harmonization, the situation has changed.
In our blog post, we illustrate how author-affiliation information can be used to develop mobility indicators at the institutional level and explore the many possibilities this offers for the study of scientific mobility.
In order to track scholars’ movements at the institutional level, we combine the affiliation information given in bibliometric data (available in the Leiden Ranking) with the unique institutional affiliation names available in the Global Research Identifier Database (GRID).
Of course many other/additional perspectives and alternative indicators can be formulated to fully understand global mobility patterns, such as the economic, social, reputational, linguistic, geographical, generational, systemic, or political aspects of scientific mobility (e.g., youth and size of the different scientific systems, travel bans, war conflicts, or the effects of crises like the current COVID-19 pandemic, etc.).
But our preliminary analysis shows what possibilities biblometrics offer to monitoring and studying of mobility patterns at the institutional level.
N. Cao, M. Meyer, L. Thiele, O. Saukh
Pollen video library for benchmarking detection, classification, tracking and novelty detection tasks: dataset
in: DATA '20: Proceedings of the Third Workshop on Data: Acquisition To Analysis. Association for Computing Machinery, NY (2020) 23–25
Privacy-preserving machine learning for time series data
in: SenSys '20: The 18th ACM Conf on Embedded Networked Sensor Systems (2020) 813–814
F. Papst, N. Stricker, O. Saukh
Localization from activity sensor data: poster abstract
in: SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems (2020) 703–704