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Microbiome research generates large, complex data. Improving data sharing requires addressing challenges and incentivizing best practices for better resource accessibility and reproducibility.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Data Science

Background:

  • Microbiome research generates vast and complex data, including data, metadata, and analytical workflows.
  • Existing infrastructure supports data sharing, but the interdisciplinary and multi-omic nature of microbiome research complicates resource identification and utilization.
  • Adhering to data deposition best practices demands significant effort with limited immediate rewards, and gaps persist in microbiome-specific data sharing and reproducibility resources.

Purpose of the Study:

  • To outline current best practices for microbiome data sharing.
  • To identify challenges hindering the adoption of these best practices.
  • To highlight opportunities for advancing data sharing and reproducibility in microbiome research.

Main Methods:

  • Review and synthesis of existing literature on microbiome data sharing practices.
  • Analysis of challenges associated with data deposition and resource utilization.
  • Identification of potential solutions and incentives for promoting data sharing.

Main Results:

  • Several best practices for microbiome data sharing exist, but their adoption is hampered by complexity and lack of clear incentives.
  • Gaps remain in dedicated microbiome resources for data sharing and ensuring research reproducibility.
  • Enforcement and incentivization of data sharing, including recognition for curation efforts, are crucial.

Conclusions:

  • Advancing microbiome data sharing necessitates addressing current challenges and implementing supportive policies.
  • Recognizing and incentivizing data curation and sharing activities by researchers, institutions, journals, and funders is essential.
  • Future microbiome-specific databases should integrate advanced methods for data analysis, integration, and reuse to maximize research impact.