Common-sense approaches to sharing tabular data alongside publication.

Area of Science:

• Data Science
• Computational Science
• Research Reproducibility

Background:

• Open science offers societal and individual benefits, including increased trust and transparency.
• Sharing research data can improve reproducibility and foster new collaborations.
• However, research data are often inaccessible or lack sufficient metadata for reuse.

Purpose of the Study:

• To identify barriers to widespread research data sharing and reuse.
• To provide practical guidance for making tabular data reusable for publication.
• To bridge the incentive gap for data sharing in academic fields.

Main Methods:

• Comparison of data sharing practices with code sharing.
• Examination of computational environments for research reproducibility.
• Development of common-sense strategies for sharing tabular data.

Main Results:

• Lack of incentives and infrastructure are significant barriers to data sharing.
• Useful metadata and documentation are crucial for data reuse.
• Practical guidance can improve the reusability of shared tabular data.

Conclusions:

• Creating a culture of widespread data sharing requires addressing incentive and infrastructure issues.
• Focusing on practical, common-sense approaches can facilitate data sharing for academics.
• Improved data sharing practices enhance research transparency, reproducibility, and collaboration.