EPJ Data Science is a peer-reviewed open access journal published under the SpringerOpen brand.Data-driven science is rapidly emerging as a complementary approach to the traditional hypothesis-driven method. This revolution accompanying the paradigm shift from reductionism to complex systems sciences has already largely transformed the natural sciences and is about to bring the same changes to the techno-socio-economic sciences, viewed broadly.The journal EPJ Data Science addresses the challenges of the data revolution across academic disciplines:· how to extract meaningful data from systems with ever-increasing complexity· how to analyze data in ways that inspire new insights· how to generate data that is needed but not yet available· how to develop new empirical laws, or more fundamental theories, concerning the function of complex natural or artificial systemsEPJ Data Science spans a broad range of research areas and applications with a focus on social systems, where it comprises those research lines that regard digital traces of human behavior as first-order objects for scientific investigation. This includes human and animal social behavior and interaction, 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.
EPJ数据科学是一个同行评审的开放获取期刊，以SpringerOpen品牌出版。数据驱动科学正迅速成为传统假设驱动方法的补充方法。伴随范式从还原论向复杂系统科学转变而来的这场革命，已经在很大程度上改变了自然科学，而且从广泛的角度来看，即将给技术社会经济科学带来同样的变化。EPJ数据科学杂志探讨了跨学科的数据革命的挑战:·如何从日益复杂的系统中提取有意义的数据·如何分析数据，激发新的见解·如何生成需要但尚未可用的数据·关于复杂的自然或人工系统的功能，如何发展新的经验法则，或更基本的理论EPJ数据科学涵盖了广泛的研究领域和应用，以社会系统为重点，包括那些把人类行为的数字痕迹作为科学研究的一级对象的研究路线。这包括人类和动物的社会行为和相互作用、经济和金融系统、管理和商业网络、社会技术基础设施、卫生和环境系统、科学以及一般风险和危机情景预测。
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