Presents new concepts for large-scale, collaborative computing and software development for particle, astroparticle, and nuclear physics domains, as well as observational astronomy and cosmology, or high-brilliance light sources.

Addressing challenges ranging from data reduction via data sharing, to the need for data-driven modeling, this journal explores concepts for large-scale, collaborative computing and software development as well as new algorithms and techniques for data processing.

Papers solicited include primarily research articles presenting new and original results, review papers (including white papers), advanced, self-contained tutorials, as well as documentation papers with the explicit aim to collect and combine knowledge spread over many internal documents to foster proper technology transfer.

  • Explores emerging issues in big-science development
  • Includes coverage of distributed data analysis and deep learning algorithms
  • Presents articles on software benchmarking, performance assessment and on data-quality monitoring on or off-line
  • Discusses aspects of evolving computing infrastructures
  • Investigates physics event generation and detector simulation
  • Volker Beckmann,
  • Markus Elsing,
  • Günter Quast
Publishing model
Hybrid. Open Choice – What is this?
Submission to first decision: 56 days
Acceptance to publication: 27 days
Downloads: 4,897 (2018)


  1. Content type: Original Article

    Authors: Martin Barisits, Thomas Beermann, Frank Berghaus, Brian Bockelman, Joaquin Bogado, David Cameron, Dimitrios Christidis, Diego Ciangottini, Gancho Dimitrov, Markus Elsing, Vincent Garonne, Alessandro di Girolamo, Luc Goossens, Wen Guan, Jaroslav Guenther, Tomas Javurek…


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  1. EBSCO Discovery Service
  2. Google Scholar
  5. Institute of Scientific and Technical Information of China
  6. Japanese Science and Technology Agency (JST)
  7. Naver
  8. OCLC WorldCat Discovery Service
  9. ProQuest-ExLibris Primo
  10. ProQuest-ExLibris Summon
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