Evidence map›Paper›PMID 31522294›Full record

ReviewMetabolomics : Official journal of the Metabolomic Society2019

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

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

Open access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Metabolomics : Official journal of the Metabolomic Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
3.9field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 73 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Biotechnology Data Analysis Training with Jupyter Notebooks.Journal of microbiology & biology education · 2023
    Article
  10. Providing metabolomics education and training: pedagogy and considerations.Metabolomics : Official journal of the Metabolomic Society · 2022
    Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Kevin M MendezCentre for Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, 6027, Australia.ORCID 0000-0002-8832-2607
Leighton PritchardStrathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Cathedral Street, Glasgow, G1 1XQ, Scotland, UK.ORCID 0000-0002-8392-2822
Stacey N ReinkeCentre for Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, 6027, Australia. stacey.n.reinke@ecu.edu.au.ORCID 0000-0002-0758-0330
David I BroadhurstCentre for Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, 6027, Australia. d.broadhurst@ecu.edu.au.ORCID 0000-0003-0775-9581
Edith Cowan University · AUUniversity of Strathclyde · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA 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.

Indexed as

Cloud ComputingData ScienceMetabolomicsSoftwareAnimalsHumansCloud computingData scienceJupyterOpen accessReproducibilityStatistics

Identifiers

PMID31522294
PMCPMC6745024
OpenAlexW2972529175

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.