EPJ Data Science
Scope & Guideline
Fostering Collaboration in Computational Mathematics
Introduction
Aims and Scopes
- Interdisciplinary Data Science Research:
Highlights the integration of data science methodologies across various fields such as social sciences, economics, urban studies, and public health. - Quantitative Analysis and Empirical Methods:
Emphasizes rigorous quantitative methods, including statistical modeling, machine learning, and network analysis to derive insights from data. - Focus on Societal Challenges:
Addresses pressing societal issues through data analysis, including public health, social media dynamics, and economic behavior, contributing to informed policy-making. - Advancements in Computational Techniques:
Showcases innovative computational techniques and tools, such as large language models and network embeddings, enhancing data analysis capabilities. - Exploration of Human Behavior:
Investigates human mobility, social interactions, and behavioral patterns using data-driven insights to understand complex social phenomena.
Trending and Emerging
- Social Media Analysis and Misinformation:
A growing emphasis on analyzing social media dynamics, misinformation campaigns, and their societal impacts, particularly in the context of recent geopolitical events. - Human Mobility and Urban Dynamics:
Increased focus on understanding human mobility patterns and their implications for urban planning and public health, especially in light of the COVID-19 pandemic. - Artificial Intelligence and Ethics:
Emerging discussions around AI ethics, transparency, and accountability, as researchers explore the implications of AI technologies in various applications. - Network Science:
A trend towards utilizing network science to understand complex interactions across social, economic, and biological systems, highlighting the interconnectedness of data. - Sustainability and Environmental Impact:
Growing interest in assessing sustainability through data science, including the analysis of environmental factors and their influence on social behaviors.
Declining or Waning
- Traditional Statistical Methods:
There is a noticeable reduction in the application of traditional statistical methods in favor of more advanced computational techniques and machine learning approaches. - Isolated Case Studies:
The journal has shifted towards broader analyses and comprehensive data-driven investigations rather than isolated case studies, which are becoming less frequent. - Descriptive Analytics:
A decline in purely descriptive analytics is observed as the journal increasingly favors predictive modeling and causal inference methodologies. - Single-Domain Studies:
Research that focuses exclusively on single domains without interdisciplinary connections appears to be waning, with a stronger emphasis on interdisciplinary approaches.
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