Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing

Kevin M. Mendez, Leighton Pritchard, Stacey N. Reinke, David I. Broadhurst

Research output: Contribution to journalArticle

3 Citations (Scopus)
27 Downloads (Pure)

Abstract

BACKGROUND: A lack of transparency and reporting standards in the scientific community has led to increasing and widespread concerns relating to reproduction and integrity of results. As an omics science, which generates vast amounts of data and relies heavily on data science for deriving biological meaning, metabolomics is highly vulnerable to irreproducibility. The metabolomics community has made substantial efforts to align with FAIR data standards by promoting open data formats, data repositories, online spectral libraries, and metabolite databases. Open data analysis platforms also exist; however, they tend to be inflexible and rely on the user to adequately report their methods and results. To enable FAIR data science in metabolomics, methods and results need to be transparently disseminated in a manner that is rapid, reusable, and fully integrated with the published work. To ensure broad use within the community such a framework also needs to be inclusive and intuitive for both computational novices and experts alike. AIM OF REVIEW: To encourage metabolomics researchers from all backgrounds to take control of their own data science, mould it to their personal requirements, and enthusiastically share resources through open science. KEY SCIENTIFIC CONCEPTS OF REVIEW: This tutorial introduces the concept of interactive web-based computational laboratory notebooks. The reader is guided through a set of experiential tutorials specifically targeted at metabolomics researchers, based around the Jupyter Notebook web application, GitHub data repository, and Binder cloud computing platform.

Original languageEnglish
Article number125
Number of pages16
JournalMetabolomics
Volume15
Early online date14 Sep 2019
DOIs
Publication statusPublished - 31 Oct 2019

Keywords

  • open access
  • reproducibility
  • data science
  • statistics
  • cloud computing
  • Jupyter
  • Python

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  • Prizes

    2020 Metabolomics Publication Award

    Kevin Mendez (Recipient), Pritchard, Leighton (Recipient), Stacey Reinke (Recipient) & David Broadhurst (Recipient), 11 Mar 2020

    Prize: Prize (including medals and awards)

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