Best practices for data management and sharing in experimental biomedical research.

Area of Science:

• Biomedical Research
• Data Science
• Scientific Integrity

Background:

• Scientific progress relies on data integrity and reproducibility.
• Effective data management involves organization, documentation, storage, sharing, and preservation.
• Robust data management builds credibility and benefits researchers' careers.

Purpose of the Study:

• To provide critical insights into data management practices for experimental biomedical researchers.
• To systematically present resources and optimal practices for data handling.
• To address the specific challenges of managing low-throughput experimental data.

Main Methods:

• Review of raw and processed data management.
• Analysis of metadata and dataset types (high-throughput vs. low-throughput).
• Examination of documentation standards, reporting guidelines, and data sharing practices.

Main Results:

• Highlights the importance of a common language for documentation.
• Discusses efficient data management systems and relevant repositories.
• Emphasizes transparency and accountability in research practices.

Conclusions:

• Comprehensive data management is vital in experimental biomedicine for validation and cumulative knowledge.
• Adherence to standards, documentation, and data sharing are increasingly required by funding agencies.
• Implementing optimal data management practices ensures long-term accessibility and reproducibility of scientific findings.