Scikick: A sidekick for workflow clarity and reproducibility during extensive data analysis

Area of Science:

• Computational science
• Data science
• Scientific reproducibility

Background:

• Reproducibility is vital for scientific progress but implementing clear data analysis workflows is difficult.
• A lack of detailed computational step records hinders the validation of research findings.

Purpose of the Study:

• To introduce Scikick, a software tool designed to simplify the configuration, execution, and presentation of scientific computational analyses.
• To address the challenges in maintaining transparent and reproducible data analysis workflows.

Main Methods:

• Scikick utilizes notebooks as the primary units for workflow execution.
• It establishes a standardized project structure and automatically tracks dependencies between analysis steps.
• The tool includes methods for compiling diverse research results into a unified final report.

Main Results:

• Scikick facilitates the creation of transparent data analysis workflows.
• It transforms the complex management of computational analyses into a standardized and manageable practice.
• The software provides utilities to ensure the traceability of computational steps.

Conclusions:

• Scikick offers a practical solution for enhancing the reproducibility of scientific research.
• The tool promotes transparency and standardization in computational data analysis.
• Scikick is available as open-source software with comprehensive documentation.