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數據科學是一個同行評審的開放獲取期刊，以SpringerOpen品牌出版。數據驅動科學正迅速成為傳統假設驅動方法的補充方法。伴隨范式從還原論向復雜系統科學轉變而來的這場革命，已經在很大程度上改變了自然科學，而且從廣泛的角度來看，即將給技術社會經濟科學帶來同樣的變化。EPJ數據科學雜志探討了跨學科的數據革命的挑戰:·如何從日益復雜的系統中提取有意義的數據·如何分析數據，激發新的見解·如何生成需要但尚未可用的數據·關于復雜的自然或人工系統的功能，如何發展新的經驗法則，或更基本的理論EPJ數據科學涵蓋了廣泛的研究領域和應用，以社會系統為重點，包括那些把人類行為的數字痕跡作為科學研究的一級對象的研究路線。這包括人類和動物的社會行為和相互作用、經濟和金融系統、管理和商業網絡、社會技術基礎設施、衛生和環境系統、科學以及一般風險和危機情景預測。