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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Structuring data analysis projects in the Open Science era with Kerblam!

Luca Visentin1, Luca Munaron1, Federico Alessandro Ruffinatti1

  • 1Department of Life Sciences and Systems Biology, University of Turin, Turin, 10136, Italy.

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Summary
This summary is machine-generated.

Applying Open Science principles to data analysis project structures enhances transparency and understanding. This study proposes design principles and introduces Kerblam!, a tool to streamline data handling and sharing for reproducible research.

Keywords:
Project Managementdata managementopen sciencereproducibilityworkflows

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Area of Science:

  • Data Science
  • Computational Biology
  • Research Reproducibility

Background:

  • Data analysis project structuring often relies on personal preferences, hindering accessibility and transparency.
  • Open Science principles advocate for more open, understandable, and reproducible research practices.
  • Applying Open Science to project structure can improve research accessibility and collaboration.

Purpose of the Study:

  • To investigate common data analysis project structures and identify best practices.
  • To develop fundamental design principles for structuring data analysis projects.
  • To introduce Kerblam!, a tool to support these principles and improve data management.

Main Methods:

  • Analysis of data analysis project templates from GitHub repositories.
  • Visualization of consensus structures to identify common patterns and characteristics.
  • Development of design principles based on Open Science philosophy.

Main Results:

  • Limited overlap exists among project templates, but distinct practices were identified.
  • Fundamental design principles for structuring data analysis projects were formulated.
  • Kerblam! was developed as a project management tool to facilitate data handling and workflow execution.

Conclusions:

  • Adherence to proposed design principles and use of Kerblam! can increase transparency and understandability of data analysis projects.
  • The proposed framework aims to make data analysis projects more useful to the wider research community.
  • Promoting standardized project structures fosters reproducibility and facilitates collaboration in data science.