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A roadmap for equitable reuse of public microbiome data

  • Laura A. Hug
  • , Roland Hatzenpichler
  • , Cristina Moraru
  • , André R. Soares
  • , Folker Meyer
  • , Anke Heyder
  • , The Data Reuse Consortium
  • , Tom Williams
  • , Alexander J. Probst
  • University of Waterloo
  • Montana State University
  • Universitat Duisburg-Essen
  • Ruhr-Universität Bochum

Research output: Contribution to journal › Article › peer-review

19   Link opens in a new tab Citations (SciVal)

Abstract

Science benefits from rapid open data sharing, but current guidelines for data reuse were established two decades ago, when databases were several million times smaller than they are today. These guidelines are largely unfamiliar to the scientific community, and, owing to the rapid increase in biological data generated in the past decade, they are also outdated. As a result, there is a lack of community standards suited to the current landscape and inconsistent implementation of data sharing policies across institutions. Here we discuss current sequence data sharing policies and their benefits and drawbacks, and present a roadmap to establish guidelines for equitable sequence data reuse, developed in consultation with a data consortium of 167 microbiome scientists. We propose the use of a Data Reuse Information (DRI) tag for public sequence data, which will be associated with at least one Open Researcher and Contributor ID (ORCID) account. The machine-readable DRI tag indicates that the data creators prefer to be contacted before data reuse, and simultaneously provides data consumers with a mechanism to get in touch with the data creators. The DRI aims to facilitate and foster collaborations, and serve as a guideline that can be expanded to other data types.
Original languageEnglish
JournalNature Microbiology
Early online date26 Sept 2025
DOIs
Publication statusE-pub ahead of print - 26 Sept 2025

Acknowledgements

The Data Reuse Core Team thanks the numerous participants of our survey who chose not to be listed as authors on this manuscript for their valuable perspectives and the many people who discussed this endeavour with us on multiple occasions. We thank L. Rothe for consultations on data visualizations.

Funding

A.J.P. acknowledges funding by the German Research Foundation (DFG), CRC 1439/1 and CRC 1439/2, project number 426547801 (project INF). L.A.H. acknowledges support from the Canada Research Chairs. R.H. acknowledges support from the US National Science Foundation (OCE-2049445). C. Moraru acknowledges funding by the German Research Foundation (DFG), Priority Program SPP 2330, project number MO 3498/2-1. F.M. acknowledges support from the German Federal Ministry of Education and Research (BMBF project number 01ZZ2013).

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