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Software engineering for scientific big data analysis.

Björn A Grüning1,2, Samuel Lampa3,4, Marc Vaudel5,6

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Summary
This summary is machine-generated.

Scientists often develop software without formal training. This study offers 10 guidelines for creating robust, command-line data analysis tools that integrate with modern computational workflows.

Keywords:
big datacodingcomputational toolsdata analysisintegration systemsscientific softwaresoftware developmentsoftware engineeringstandardsworkflow

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Scientific Computing

Background:

  • Increasing data complexity necessitates advanced analysis methods.
  • Scientists increasingly perform software engineering without formal training.
  • Existing resources inadequately address the development of robust, large-scale analysis tools.

Purpose of the Study:

  • To provide guidelines for developing advanced computational tools.
  • To facilitate the integration of scientific software into workflow systems.
  • To improve the usability, reliability, and extensibility of research software.

Main Methods:

  • Development of 10 practical guidelines for command-line tool creation.
  • Focus on modern coding practices and workflow integration standards.
  • Emphasis on robustness, data handling, logging, and flow control.

Main Results:

  • A comprehensive set of 10 guidelines for scientific software development.
  • Guidelines address requirements for integration into workflow management systems.
  • Focus on creating usable, reliable, and extensible command-line tools.

Conclusions:

  • Adherence to these guidelines enhances the quality and interoperability of scientific software.
  • Empowers scientists to develop sophisticated, workflow-ready computational tools.
  • Promotes modern coding standards in scientific software engineering.