<b>NWB2023_A preliminary exploration on the dynamics of international scientific mobility</b>

International scientific mobility is the most important feature of the globalization of modern science. In the last few decades, the changes in technological conditions and contemporary culture have profoundly reshaped disciplinary development in various countries as well as the pattern of international mobility. With the help of large-scale datasets to capture longitudinal, country-level patterns of migrations in science, this paper outlines the evolution of scientific mobility over the past 100 years. There has been a dramatic growth in scientific mobility both at the global and regional levels. As one of the manifestations of multi-polarization in science, the disparity in the attractiveness of countries for scientists is decreasing, and this is partly attributable to stronger relations among higher- and lower-income countries in mobility networks. Another interesting finding was that scientists from high-income countries now have a lower probability of returning to their original countries than those from lower-income countries, unlike the trend that was observed in the 1990s. Additionally, we examined the factors that might affect the number of scientific immigrants, and highlighted the importance of social capital connections between countries and “pull” factors like economic and scientific strength of countries. This paper reviewed the dynamics of scientific mobility from different aspects and shed light on the studies of evolution of science.</p

Inclusion and exclusion criteria.

Inclusion and exclusion criteria.</p

Details of the search strategy for Embase.

Details of the search strategy for Embase.</p

Search terms for study screening.

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PICOS of the study.

PICOS of the study.</p

The worldwide estimated number of cancer in 2020.

(A) Estimated number of prevalent cases (5-year) in 2020, worldwide, both sexes, all ages. (B) Estimated number of new cases in 2020, worldwide, both sexes, all ages. (C) Estimated number of deaths in 2020, worldwide, both sexes, all ages. (Datasource: Globocan2020 Graph production: Global Cancer Observatory).</p

Additional file 1 of An interpretable DIC risk prediction model based on convolutional neural networks with time series data

Additional file 1. Related acronyms and abbreviations

The Gantt chart of the status and timeline of the study.

The Gantt chart of the status and timeline of the study.</p

PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) 2015 checklist: Recommended items to address in a systematic review protocol*.

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PRISMA-P 2015 checklist.

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