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Computing and Software for Big Science - Springer Nature | 2024 Cite Score: 6.4 | Q1

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Computing and Software for Big Science - Springer Nature | 2024 Cite Score: 6.4 | Q1

  • About this Journal:
  • This single-blind peer-reviewed journal is dedicated to the publication of high-quality material originating from all current and emerging scientific communities in which experimental research is increasingly based on large-scale research infrastructures. With particle, nuclear and astrophysics as prime examples, the journal welcomes contributions from all areas of fundamental and applied physics where collective international efforts take center stage. Computing and Software for Big Science is an international open-access journal dedicated to software engineering, computing infrastructures, and data management for large-scale scientific research.
  • Research in these fields is characterized by huge amounts of data generated at the very frontiers of energy, intensity and detector technology and call for substantial advances to address the highly specialized and ever more demanding requirements in the areas of computing, software, data science and numerical simulations. Future experimental and theoretical challenges will require extensive collaborative efforts and may yield novel approaches and solutions to a range of different scientific communities.The journal focuses on the computational challenges and technological innovations that enable big science experiments in fields such as particle physics, astronomy, astrophysics, and bioinformatics.
  • It serves as a multidisciplinary forum for scientists, engineers, and developers working on large-scale data processing, simulation, and distributed computing systems.
  • The publication emphasizes open science, reproducibility, and collaboration across large research infrastructures and international scientific collaborations.

  • Editor-in-Chief:  Markus Elsing PhD

  • Scope of the Journal:
  • High-performance computing (HPC) and distributed computing for large-scale experiments.
  • Software frameworks, middleware, and workflow management systems for scientific applications.
  • Big data management, storage, and processing architectures.
  • Machine learning, AI, and data analytics for scientific discovery.
  • Cloud and grid computing infrastructures for collaborative research.
  • Software sustainability, validation, and reproducibility in scientific computing.
  • Case studies from major research collaborations (e.g., CERN, astrophysics observatories, genomics projects).
  • Latest Research Topics for PhD in Computer Science
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  • Latest Research Topics for PhD in Machine Learning
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  • Print ISSN:  25102044

    Electronic ISSN:  

  • Abstracting and Indexing:  Scopus

  • Imapct Factor :  

  • Subject Area and Category:  Computer Science (miscellaneous), Software, Nuclear and High Energy Physics

  • Publication Frequency:  Continuous (Article-based publishing)

  • H Index:  24

  • Best Quartile:

    Q1:  Computer Science (miscellaneous)

    Q2:  

    Q3:  

    Q4:  

  • Cite Score:  6.4

  • SNIP:  1.440

  • Journal Rank(SJR):  1.449