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Common-sense approaches to sharing tabular data alongside publication.

Nicholas J Tierney1,2,3, Karthik Ram4

  • 1Monash University, Department of Econometrics and Business Statistics, Melbourne, Australia.

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Summary

Open science sharing, including research data, enhances transparency and collaboration. Addressing the lack of incentives and infrastructure is key to widespread data sharing and reuse.

Keywords:
DSML 4: Production: Data science output is validated, understood, and regularly used for multiple domains/platforms

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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.