大数据视角下的学术流动

IF 3.4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Data Science and Analytics Pub Date : 2023-10-06 DOI:10.1007/s41060-023-00432-6

Laura Pollacci, Letizia Milli, Tuba Bircan, Giulio Rossetti

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引用次数: 0

摘要

在当今的全球知识经济中，了解高技能人才的职业和流动发挥着越来越重要的作用。研究人员和学者是政府和机构创新和发展的源泉。我们的研究使用与科学相关的数据来跟踪职业发展和研究人员的运动。为此，我们定义了年度合作程度指数(annual Degree of collaboration Index)，该指数衡量研究人员在国内合作的年度趋势，以及两个分数来衡量国家内外的流动性及其平衡。

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Academic mobility from a big data perspective

Abstract Understanding the careers and movements of highly skilled people plays an ever-increasing role in today’s global knowledge-based economy. Researchers and academics are sources of innovation and development for governments and institutions. Our study uses scientific-related data to track careers evolution and Researchers’ movements over time. To this end, we define the Yearly Degree of Collaborations Index, which measures the annual tendency of researchers to collaborate intra-nationally, and two scores to measure the mobility in and out of countries, as well as their balance.

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来源期刊

International Journal of Data Science and Analytics Multiple-

CiteScore

6.40

自引率

8.30%

发文量

期刊介绍： Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations. The journal is composed of three streams: Regular, to communicate original and reproducible theoretical and experimental findings on data science and analytics; Applications, to report the significant data science applications to real-life situations; and Trends, to report expert opinion and comprehensive surveys and reviews of relevant areas and topics in data science and analytics.Topics of relevance include all aspects of the trends, scientific foundations, techniques, and applications of data science and analytics, with a primary focus on:statistical and mathematical foundations for data science and analytics;understanding and analytics of complex data, human, domain, network, organizational, social, behavior, and system characteristics, complexities and intelligences;creation and extraction, processing, representation and modelling, learning and discovery, fusion and integration, presentation and visualization of complex data, behavior, knowledge and intelligence;data analytics, pattern recognition, knowledge discovery, machine learning, deep analytics and deep learning, and intelligent processing of various data (including transaction, text, image, video, graph and network), behaviors and systems;active, real-time, personalized, actionable and automated analytics, learning, computation, optimization, presentation and recommendation; big data architecture, infrastructure, computing, matching, indexing, query processing, mapping, search, retrieval, interoperability, exchange, and recommendation;in-memory, distributed, parallel, scalable and high-performance computing, analytics and optimization for big data;review, surveys, trends, prospects and opportunities of data science research, innovation and applications;data science applications, intelligent devices and services in scientific, business, governmental, cultural, behavioral, social and economic, health and medical, human, natural and artificial (including online/Web, cloud, IoT, mobile and social media) domains; andethics, quality, privacy, safety and security, trust, and risk of data science and analytics

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