MSSEOct 1, 2012

Best Practices for Scientific Computing

arXiv:1210.0530v4682 citations
Originality Synthesis-oriented
AI Analysis

This addresses the issue of low software efficiency for scientists, but it is incremental as it compiles existing practices rather than introducing new methods.

The paper tackles the problem of inefficient scientific software development by scientists lacking formal training, and proposes a set of best practices to improve productivity and software reliability.

Scientists spend an increasing amount of time building and using software. However, most scientists are never taught how to do this efficiently. As a result, many are unaware of tools and practices that would allow them to write more reliable and maintainable code with less effort. We describe a set of best practices for scientific software development that have solid foundations in research and experience, and that improve scientists' productivity and the reliability of their software.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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