COMPUTING IN SCIENCE & ENGINEERING

Scope & Guideline

Advancing Knowledge in Science and Engineering Computing.

Introduction

Welcome to the COMPUTING IN SCIENCE & ENGINEERING information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of COMPUTING IN SCIENCE & ENGINEERING, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN1521-9615
PublisherIEEE COMPUTER SOC
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 1999 to 2024
AbbreviationCOMPUT SCI ENG / Comput. Sci. Eng.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314

Aims and Scopes

The journal 'Computing in Science & Engineering' primarily focuses on the intersection of computational methodologies and their applications across various scientific and engineering domains. The core aims and scopes can be summarized as follows:
  1. High-Performance Computing (HPC):
    The journal emphasizes advancements in high-performance computing, including discussions on exascale systems and their applications in scientific research.
  2. Software Development and Engineering Practices:
    It explores best practices in software development tailored for scientific computing, including continuous integration, reproducibility, and sustainable software practices.
  3. Data Management and Visualization:
    The journal covers innovative techniques in data management, visualization, and analysis, particularly in complex scientific datasets.
  4. Interdisciplinary Collaboration:
    Promoting collaboration across disciplines is a key focus, encouraging the integration of computing methodologies in diverse fields such as physics, climate science, and biomedical research.
  5. Emerging Technologies:
    The journal addresses the impact of emerging technologies, such as quantum computing and AI, on scientific research and engineering applications.
  6. Educational Approaches in Computational Science:
    It highlights educational methodologies aimed at improving the skills of future scientists and engineers in computational techniques.
The journal has been evolving to address the dynamic landscape of computational science and engineering. Recent publications indicate several emerging themes that are gaining traction:
  1. Artificial Intelligence and Machine Learning:
    There is a significant increase in research focusing on AI and machine learning applications in scientific discovery, automation, and data analysis, reflecting the growing importance of these technologies in computational science.
  2. Software Sustainability and Community Building:
    A trend towards sustainability in software development practices and the establishment of communities supporting collaborative software engineering efforts is evident.
  3. Cloud Computing and Data Accessibility:
    Emerging themes include the utilization of cloud-native approaches for scientific computing, emphasizing data accessibility and collaborative research environments.
  4. Reproducibility and Transparency:
    A heightened focus on reproducibility in scientific research practices indicates a commitment to transparency and validation of computational results.
  5. Quantum Computing Integration:
    The exploration of quantum computing paradigms and their integration into high-performance computing frameworks is becoming a prominent theme.
  6. Diversity, Equity, and Inclusion in STEM:
    There is an emerging emphasis on addressing diversity and inclusivity within computing and engineering fields, fostering a more equitable research environment.

Declining or Waning

While 'Computing in Science & Engineering' continues to thrive in several core areas, certain themes have shown a decline in prominence over recent years. The following scopes are witnessing reduced focus:
  1. Traditional Programming Languages:
    There is a noticeable decrease in publications focused on traditional programming languages like Fortran, as the community shifts towards more modern languages such as Python and Julia.
  2. General Computational Models Without Specific Applications:
    Research that presents generic computational models without a clear application context is becoming less frequent, as the journal favors case studies with practical implications.
  3. Standalone Research Software:
    The focus is moving away from isolated software projects towards integrated systems that encompass broader software ecosystems and collaborative tools.
  4. Basic Algorithmic Studies:
    There is a waning interest in purely theoretical algorithmic studies that do not translate into practical applications, as the journal leans towards applied research.
  5. Single-Disciplinary Research:
    Research confined to a single discipline is less prominent, with a growing emphasis on interdisciplinary approaches that leverage computing across various scientific fields.

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