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EPJ Data Science
基本信息
期刊名称 EPJ Data Science
EPJ DATA SCI
期刊ISSN 2193-1127
期刊官方网站 https://link.springer.com/journal/13688
是否OA Yes
出版商 Springer Science + Business Media
出版周期
文章处理费 登录后查看
始发年份 2012
年文章数 59
最新影响因子 3.0(2023)  scijournal影响因子  greensci影响因子
中科院SCI期刊分区
大类学科 小类学科 Top 综述
工程技术2区 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 数学跨学科应用2区
CiteScore
CiteScore排名 CiteScore SJR SNIP
学科 排名 百分位 6.1 0.829 1.355
Mathematics
Computational Mathematics
26/189 86%
Mathematics
Modeling and Simulation
52/324 84%
Computer Science
Computer Science Applications
244/817 70%
补充信息
自引率 6.7%
H-index 21
SCI收录状况 Science Citation Index Expanded
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网友分享审稿时间 数据统计中,敬请期待。
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PubMed Central (PMC) http://www.ncbi.nlm.nih.gov/nlmcatalog?term=2193-1127%5BISSN%5D
投稿指南
期刊投稿网址 https://www.editorialmanager.com/EPDS
收稿范围
The 21st century is witnessing the establishment of data-driven science as a complementary approach to the traditional hypothesis-driven method. This (r)evolution accompanying the paradigm shift from reductionism to complex systems sciences has already transformed the natural sciences and is about to bring the same changes to the techno-socio-economic sciences, viewed broadly.

EPJ Data Science offers a publication platform to showcase the latest contributions to the study of techno-socio-economic systems, wherein “digital traces” of human activity are used as first-order objects for the investigation. Specifically, the focus of the journal is on analyzing and synthesizing massive data sets to achieve new insights into societal phenomena and behavior. Application domains include, but are not limited to, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice. Methodologically, EPJ Data Science welcomes approaches from a broad range of disciplines, spanning statistically rigorous analysis of data, social network analysis, complex systems, applied machine learning, and more.

Papers submitted to this journal should not only strive to improve on existing data science methodologies but must provide new insight into human or social behavior or systems, in the areas outlined above. Submissions that focus on purely descriptive statistics or apply standard techniques to mainstream datasets are unlikely to be considered for publication.

Thus, EPJ Data Science offers a publication platform to bring together diverse academic disciplines concerned with challenges around:

How to extract signals about techno-socio-economic systems from large, complex data
How to interpret these signals in the theoretical context of the relevant disciplines
How to find new empirical laws, or fundamental theories, concerning how societies work
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投稿指南 https://epjdatascience.springeropen.com/submission-guidelines
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