COMPUTING IN SCIENCE & ENGINEERING
Scope & Guideline
Driving Progress in Computing for Scientific Discovery.
Introduction
Aims and Scopes
- High-Performance Computing (HPC):
The journal emphasizes advancements in high-performance computing, including discussions on exascale systems and their applications in scientific research. - Software Development and Engineering Practices:
It explores best practices in software development tailored for scientific computing, including continuous integration, reproducibility, and sustainable software practices. - Data Management and Visualization:
The journal covers innovative techniques in data management, visualization, and analysis, particularly in complex scientific datasets. - 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. - Emerging Technologies:
The journal addresses the impact of emerging technologies, such as quantum computing and AI, on scientific research and engineering applications. - Educational Approaches in Computational Science:
It highlights educational methodologies aimed at improving the skills of future scientists and engineers in computational techniques.
Trending and Emerging
- 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. - 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. - Cloud Computing and Data Accessibility:
Emerging themes include the utilization of cloud-native approaches for scientific computing, emphasizing data accessibility and collaborative research environments. - Reproducibility and Transparency:
A heightened focus on reproducibility in scientific research practices indicates a commitment to transparency and validation of computational results. - Quantum Computing Integration:
The exploration of quantum computing paradigms and their integration into high-performance computing frameworks is becoming a prominent theme. - 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
- 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. - 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. - Standalone Research Software:
The focus is moving away from isolated software projects towards integrated systems that encompass broader software ecosystems and collaborative tools. - 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. - 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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