Microbiome metadata standards: Report of the national microbiome data collaborative's workshop and follow-on activities

Pajau Vangay, Josephine Burgin, Anjanette Johnston, Kristen L. Beck, Daniel C. Berrios, Kai Blumberg, Shane Canon, Patrick Chain, John Marc Chandonia, Danielle Christianson, Sylvain V. Costes, Joan Damerow, William D. Duncan, Jose Pablo Dundore-Arias, Kjiersten Fagnan, Jonathan M. Galazka, Sean M. Gibbons, David Hays, Judson Hervey, Bin HuBonnie L. Hurwitz, Pankaj Jaiswal, Marcin P. Joachimiak, Linda Kinkel, Joshua Ladau, Stanton L. Martin, Lee Ann McCue, Kayd Miller, Nigel Mouncey, Chris Mungall, Evangelos Pafilis, T. B.K. Reddy, Lorna Richardson, Simon Roux, Justin P. Shaffer, Jagadish Chandrabose Sundaramurthi, Luke R. Thompson, Ruth E. Timme, Jie Zheng, Elisha M. Wood-Charlson, Emiley A. Eloe-Fadrosh

Research output: Contribution to journalReview articlepeer-review

Abstract

Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical. Important contributions have been made in the development of community-driven metadata standards; however, these standards have not been uniformly embraced by the microbiome research community. To understand how these standards are being adopted, or the barriers to adoption, across research domains, institutions, and funding agencies, the National Microbiome Data Collaborative (NMDC) hosted a workshop in October 2019. This report provides a summary of discussions that took place throughout the workshop, as well as outcomes of the working groups initiated at the workshop.

Original languageEnglish (US)
Article numbere01194-20
JournalmSystems
Volume6
Issue number1
DOIs
StatePublished - 2021

Keywords

  • Data standards
  • Metadata
  • Microbiome
  • Ontology

ASJC Scopus subject areas

  • Microbiology
  • Ecology, Evolution, Behavior and Systematics
  • Biochemistry
  • Physiology
  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computer Science Applications

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