Developing and communicating responsible data management policies to trainees and colleagues

Area of Science:

• Scientific Research
• Data Science
• Information Management

Background:

• Data management encompasses critical stages: ownership, collection, analysis, storage, and sharing.
• Best practices ensure experimental reproducibility, data security, and regulatory compliance.
• Effective communication of data management principles is vital for trainees and collaborators.

Purpose of the Study:

• To outline fundamental components of scientific data management.
• To highlight essential principles for best practices in data handling.
• To propose effective strategies for teaching data management to researchers.

Main Methods:

• Review of core data management components and underlying principles.
• Incorporation of practical experience from scientists and educators.
• Development of didactic and experiential teaching approaches.

Main Results:

• Identified key data management elements: ownership, collection, recording, analysis, storage, retention, destruction, and sharing.
• Emphasized principles like experimental reproducibility, data security, and pre-collaboration sharing agreements.
• Proposed discipline-focused didactic instruction and "teachable moments" for effective training.

Conclusions:

• Data management requires a comprehensive understanding of its lifecycle and best practices.
• Teaching data management principles, rather than rigid rules, is crucial for adaptability.
• Educational strategies should integrate discipline-specific training with practical application to address evolving data handling needs.