scholarly journals Brain drain and brain gain in Russia: Analyzing international migration of researchers by discipline using Scopus bibliometric data 1996–2020

2021 ◽  
Author(s):  
Alexander Subbotin ◽  
Samin Aref

AbstractWe study international mobility in academia, with a focus on the migration of published researchers to and from Russia. Using an exhaustive set of over 2.4 million Scopus publications, we analyze all researchers who have published with a Russian affiliation address in Scopus-indexed sources in 1996–2020. The migration of researchers is observed through the changes in their affiliation addresses, which altered their mode countries of affiliation across different years. While only 5.2% of these researchers were internationally mobile, they accounted for a substantial proportion of citations. Our estimates of net migration rates indicate that while Russia was a donor country in the late 1990s and early 2000s, it has experienced a relatively balanced circulation of researchers in more recent years. These findings suggest that the current trends in scholarly migration in Russia could be better framed as brain circulation, rather than as brain drain. Overall, researchers emigrating from Russia outnumbered and outperformed researchers immigrating to Russia. Our analysis on the subject categories of publication venues shows that in the past 25 years, Russia has, overall, suffered a net loss in most disciplines, and most notably in the five disciplines of neuroscience, decision sciences, mathematics, biochemistry, and pharmacology. We demonstrate the robustness of our main findings under random exclusion of data and changes in numeric parameters. Our substantive results shed light on new aspects of international mobility in academia, and on the impact of this mobility on a national science system, which have direct implications for policy development. Methodologically, our novel approach to handling big data can be adopted as a framework of analysis for studying scholarly migration in other countries.

2020 ◽  
Vol 5 (3) ◽  
pp. 33-56 ◽  
Author(s):  
Mudassar Arsalan ◽  
Omar Mubin ◽  
Abdullah Al Mahmud

AbstractPurposeThis study aims to classify research impact indicators based on their characteristics and scope. A concept of evidence-based nomenclature of research impact (RI) indicator has been introduced for generalization and transformation of scope.Design/methodology/approchLiterature was collected related to the research impact assessment. It was categorized in conceptual and applied case studies. One hundred and nineteen indicators were selected to prepare classification and nomenclature. The nomenclature was developed based on the principle—“every indicator is a contextual-function to explain the impact”. Every indicator was disintegrated into three parts, i.e. Function, Domain, and Target Areas.FindingsThe main functions of research impact indicators express improvement (63%), recognition (23%), and creation/development (14%). The focus of research impact indicators in literature is more towards the academic domain (59%) whereas the environment/sustainability domain is least considered (4%). As a result, research impact related to the research aspects is felt the most (29%). Other target areas include system and services, methods and procedures, networking, planning, policy development, economic aspects and commercialisation, etc.Research limitationsThis research applied to 119 research impact indicators. However, the inclusion of additional indicators may change the result.Practical implicationsThe plausible effect of nomenclature is a better organization of indicators with appropriate tags of functions, domains, and target areas. This approach also provides a framework of indicator generalization and transformation. Therefore, similar indicators can be applied in other fields and target areas with modifications.Originality/valueThe development of nomenclature for research impact indicators is a novel approach in scientometrics. It is developed on the same line as presented in other scientific disciplines, where fundamental objects need to classify on common standards such as biology and chemistry.


Author(s):  
Shuyu Xue ◽  
Bohui Zhang ◽  
Xiaofeng Zhao

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e043010
Author(s):  
Jane Lyons ◽  
Ashley Akbari ◽  
Fatemeh Torabi ◽  
Gareth I Davies ◽  
Laura North ◽  
...  

IntroductionThe emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions.Methods and analysisTwo privacy-protecting population-level cohorts have been created and derived from multisourced demographic and healthcare data. The C20 cohort consists of 3.2 million people in Wales on the 1 January 2020 with follow-up until 31 May 2020. The complete cohort dataset will be updated monthly with some individual datasets available daily. The C16 cohort consists of 3 million people in Wales on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed as a counterfactual cohort to provide contextual comparative population data on disease, health service utilisation and mortality. Study outcomes will: (a) characterise the epidemiology of COVID-19, (b) assess socioeconomic and demographic influences on infection and outcomes, (c) measure the impact of COVID-19 on short -term and longer-term population outcomes and (d) undertake studies on the transmission and spatial spread of infection.Ethics and disseminationThe Secure Anonymised Information Linkage-independent Information Governance Review Panel has approved this study. The study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.


2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.


Coatings ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 758
Author(s):  
Cibi Pranav ◽  
Minh-Tan Do ◽  
Yi-Chang Tsai

High Friction Surfaces (HFS) are applied to increase friction capacity on critical roadway sections, such as horizontal curves. HFS friction deterioration on these sections is a safety concern. This study deals with characterization of the aggregate loss, one of the main failure mechanisms of HFS, using texture parameters to study its relationship with friction. Tests are conducted on selected HFS spots with different aggregate loss severity levels at the National Center for Asphalt Technology (NCAT) Test Track. Friction tests are performed using a Dynamic Friction Tester (DFT). The surface texture is measured by means of a high-resolution 3D pavement scanning system (0.025 mm vertical resolution). Texture data are processed and analyzed by means of the MountainsMap software. The correlations between the DFT friction coefficient and the texture parameters confirm the impact of change in aggregates’ characteristics (including height, shape, and material volume) on friction. A novel approach to detect the HFS friction coefficient transition based on aggregate loss, inspired by previous works on the tribology of coatings, is proposed. Using the proposed approach, preliminary outcomes show it is possible to observe the rapid friction coefficient transition, similar to observations at NCAT. Perspectives for future research are presented and discussed.


2021 ◽  
Vol 13 (5) ◽  
pp. 874
Author(s):  
Yu Chen ◽  
Mohamed Ahmed ◽  
Natthachet Tangdamrongsub ◽  
Dorina Murgulet

The Nile River stretches from south to north throughout the Nile River Basin (NRB) in Northeast Africa. Ethiopia, where the Blue Nile originates, has begun the construction of the Grand Ethiopian Renaissance Dam (GERD), which will be used to generate electricity. However, the impact of the GERD on land deformation caused by significant water relocation has not been rigorously considered in the scientific research. In this study, we develop a novel approach for predicting large-scale land deformation induced by the construction of the GERD reservoir. We also investigate the limitations of using the Gravity Recovery and Climate Experiment Follow On (GRACE-FO) mission to detect GERD-induced land deformation. We simulated three land deformation scenarios related to filling the expected reservoir volume, 70 km3, using 5-, 10-, and 15-year filling scenarios. The results indicated: (i) trends in downward vertical displacement estimated at −17.79 ± 0.02, −8.90 ± 0.09, and −5.94 ± 0.05 mm/year, for the 5-, 10-, and 15-year filling scenarios, respectively; (ii) the western (eastern) parts of the GERD reservoir are estimated to move toward the reservoir’s center by +0.98 ± 0.01 (−0.98 ± 0.01), +0.48 ± 0.00 (−0.48 ± 0.00), and +0.33 ± 0.00 (−0.33 ± 0.00) mm/year, under the 5-, 10- and 15-year filling strategies, respectively; (iii) the northern part of the GERD reservoir is moving southward by +1.28 ± 0.02, +0.64 ± 0.01, and +0.43 ± 0.00 mm/year, while the southern part is moving northward by −3.75 ± 0.04, −1.87 ± 0.02, and −1.25 ± 0.01 mm/year, during the three examined scenarios, respectively; and (iv) the GRACE-FO mission can only detect 15% of the large-scale land deformation produced by the GERD reservoir. Methods and results demonstrated in this study provide insights into possible impacts of reservoir impoundment on land surface deformation, which can be adopted into the GERD project or similar future dam construction plans.


2013 ◽  
Vol 13 (15) ◽  
pp. 7875-7894 ◽  
Author(s):  
I. El Haddad ◽  
B. D'Anna ◽  
B. Temime-Roussel ◽  
M. Nicolas ◽  
A. Boreave ◽  
...  

Abstract. As part of the FORMES summer 2008 experiment, an Aerodyne compact time-of-flight aerosol mass spectrometer (cToF-AMS) was deployed at an urban background site in Marseille to investigate the sources and aging of organic aerosols (OA). France's second largest city and the largest port in the Mediterranean, Marseille, provides a locale that is influenced by significant urban industrialized emissions and an active photochemistry with very high ozone concentrations. Particle mass spectra were analyzed by positive matrix factorization (PMF2) and the results were in very good agreement with previous apportionments obtained using a chemical mass balance (CMB) approach coupled to organic markers and metals (El Haddad et al., 2011a). AMS/PMF2 was able to identify for the first time, to the best of our knowledge, the organic aerosol emitted by industrial processes. Even with significant industries in the region, industrial OA was estimated to contribute only ~ 5% of the total OA mass. Both source apportionment techniques suggest that oxygenated OA (OOA) constitutes the major fraction, contributing ~ 80% of OA mass. A novel approach combining AMS/PMF2 data with 14C measurements was applied to identify and quantify the fossil and non-fossil precursors of this fraction and to explicitly assess the related uncertainties. Results show with high statistical confidence that, despite extensive urban and industrial emissions, OOA is overwhelmingly non-fossil, formed via the oxidation of biogenic precursors, including monoterpenes. AMS/PMF2 results strongly suggest that the variability observed in the OOA chemical composition is mainly driven in our case by the aerosol photochemical age. This paper presents the impact of photochemistry on the increase of OOA oxygenation levels, formation of humic-like substances (HULIS) and the evolution of α-pinene SOA (secondary OA) components.


2021 ◽  
pp. 215336872110075
Author(s):  
TaLisa J. Carter ◽  
Lallen T. Johnson

This study demonstrates that racially disparate fare evasion citation outcomes are the product of racialized social systems that allow transit police officers to determine the belongingness of Black riders in systems of mass transit. Using citation data from the Washington Metropolitan Area Transit Authority, we test the impact of race and place attributes on transit officer decisions to allocate punishment for subway fare evasion using mixed effects logistic regression controlling for individual and contextual predictors. Although rider racial identity alone proves statistically irrelevant, Black riders suspected of fare evasion possess an elevated risk for being fined as opposed to merely being warned at stations located within predominately white neighborhoods and as stations increase in ridership. These findings demonstrate how transit police officer discretion challenges Black belongingness on systems of public transportation. Broader implications of this work include the importance of scholarship linking statistical disparities to organizational intent and integrating diverse voices in policing policy development.


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