Digital open science-Teaching digital tools for reproducible and transparent research

Area of Science:

• Life Sciences
• Scientific Computing
• Research Methodology

Background:

• Reproducibility is a cornerstone of scientific integrity.
• Traditional life science curricula often lack training in modern computational and data analysis tools.
• Increasing mandates from funders and journals necessitate robust reproducibility.

Purpose of the Study:

• To design and outline an introductory course focused on reproducible science workflows.
• To address the skills gap in computational reproducibility for life science students.
• To provide a framework for integrating reproducible science training into existing academic programs.

Main Methods:

• Development of a structured curriculum for an introductory course on reproducible science.
• Identification of key internet-based tools and technologies for scientific transparency.
• Exploration of challenges and solutions for implementing such a course.

Main Results:

• The course content covers essential skills for reproducible research.
• The outlined curriculum provides a foundation for students to adopt reproducible workflows.
• Initial challenges and potential extensions for the course have been identified.

Conclusions:

• Implementing dedicated courses is crucial for equipping life scientists with reproducible research skills.
• Such training is vital for meeting the evolving demands of scientific funding and publication.
• Integrating reproducible science into curricula enhances the transparency and reliability of future research.