EPJ Data Science

Scope & Guideline

Fostering Collaboration in Computational Mathematics

Introduction

Explore the comprehensive scope of EPJ Data Science through our detailed guidelines, including its aims and scope. Stay updated with trending and emerging topics, and delve into declining areas to understand shifts in academic interest. Our guidelines also showcase highly cited topics, featuring influential research making a significant impact. Additionally, discover the latest published papers and those with high citation counts, offering a snapshot of current scholarly conversations. Use these guidelines to explore EPJ Data Science in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN-
PublisherSPRINGER
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationEPJ DATA SCI / EPJ Data Sci.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressONE NEW YORK PLAZA, SUITE 4600 , NEW YORK, NY 10004, UNITED STATES

Aims and Scopes

The journal EPJ Data Science focuses on the intersection of data science with various domains, emphasizing empirical research methodologies and quantitative analysis. It seeks to advance the understanding of complex systems through data-driven approaches, fostering interdisciplinary collaboration and innovation.
  1. Interdisciplinary Data Science Research:
    Highlights the integration of data science methodologies across various fields such as social sciences, economics, urban studies, and public health.
  2. Quantitative Analysis and Empirical Methods:
    Emphasizes rigorous quantitative methods, including statistical modeling, machine learning, and network analysis to derive insights from data.
  3. 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.
  4. Advancements in Computational Techniques:
    Showcases innovative computational techniques and tools, such as large language models and network embeddings, enhancing data analysis capabilities.
  5. Exploration of Human Behavior:
    Investigates human mobility, social interactions, and behavioral patterns using data-driven insights to understand complex social phenomena.
The journal EPJ Data Science is currently witnessing a dynamic evolution of themes, reflecting emerging trends in data science and its applications across various sectors. The following themes have gained traction in recent publications, indicating areas of heightened interest and research activity.
  1. 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.
  2. 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.
  3. Artificial Intelligence and Ethics:
    Emerging discussions around AI ethics, transparency, and accountability, as researchers explore the implications of AI technologies in various applications.
  4. Network Science:
    A trend towards utilizing network science to understand complex interactions across social, economic, and biological systems, highlighting the interconnectedness of data.
  5. 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

While EPJ Data Science continues to explore a wide array of topics, certain areas of focus appear to be diminishing in prominence over recent years. This decline may reflect shifts in research priorities or the maturation of methodologies.
  1. 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.
  2. 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.
  3. Descriptive Analytics:
    A decline in purely descriptive analytics is observed as the journal increasingly favors predictive modeling and causal inference methodologies.
  4. 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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