"Ethics When You Least Expect It": A Modular Approach to Short Course Data Ethics Instruction.

Area of Science:

• Data Science
• Research Ethics
• Computational Science

Background:

• Data science skills are essential in modern research.
• Existing research data science curricula often lack explicit ethics components.
• Developing effective data ethics training can be challenging.

Purpose of the Study:

• To describe a novel approach for teaching data ethics in short research data science courses.
• To center ethics instruction on "responsible and open (data) science citizenship."
• To integrate ethics into daily research practices.

Main Methods:

• Developed ethics content for CODATA-RDA Schools for Research Data Science.
• Utilized a virtue ethics framework to promote ethics of practice.
• Distributed ethics content across technical modules using lectures, discussions, and exercises.

Main Results:

• Students developed an understanding of responsible data science citizenship.
• Students learned to implement ethical responsibilities in their daily research.
• The approach successfully integrated ethics into practical data science activities.

Conclusions:

• Ethics instruction can be effectively embedded within technical data science modules.
• This approach helps researchers recognize how small actions contribute to larger ethical considerations.
• Data ethics is a vital concern for all data scientists, not just data generators or end-users.