Evidence map›Paper›PMID 26612985›Full record

ReviewMetabolomics : Official journal of the Metabolomic Society2016

Data standards can boost metabolomics research, and if there is a will, there is a way.

Philippe Rocca-Serra, Reza M Salek, Masanori Arita, Elon Correa, Saravanan Dayalan, Alejandra Gonzalez-Beltran, Tim Ebbels, Royston Goodacre, Janna Hastings, Kenneth Haug and 9 more

Open access · hybridAbstract readReview
In one paragraph

Review in Metabolomics : Official journal of the Metabolomic Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 3 pooled it
11.6field-weighted citation impact, top 1% 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

63 citing papers in PubMed, 3 syntheses or guidelines pooled it, 153 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
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  7. Review
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  9. Data Treatment for LC-MS Untargeted Analysis.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  10. Article
  11. Plant Metabolomics: The Future of Anticancer Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2024
    Review
  12. Review
  13. Review
  14. Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research.Toxicological sciences : an official journal of the Society of Toxicology · 2022
    Review
  15. Review
  16. Review
  17. Review
  18. Review
  19. Article
  20. Article

3 more citing papers are in PubMed but not listed here.

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

19 authors at 12 institutions in 6 countries.

Philippe Rocca-Serra *Oxford e-Research Centre, University of Oxford, 7 Keble Road, Oxford, OX1 3QG UK.
Reza M Salek *European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK.
Masanori AritaNational Institute of Genetics, Mishima, Shizuoka 411-8540 Japan.
Elon CorreaUniversity of Manchester, Centre for Endocrinology and Diabetes, Old St Mary's Building, Hathersage Road, Manchester, M13 9WL UK.
Saravanan DayalanMetabolomics Australia, The University of Melbourne, Parkville, VIC 3010 Australia.
Alejandra Gonzalez-BeltranOxford e-Research Centre, University of Oxford, 7 Keble Road, Oxford, OX1 3QG UK.
Tim EbbelsComputational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington, London, SW7 2AZ UK.
Royston GoodacreSchool of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN UK.
Janna HastingsEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK.
Kenneth HaugEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK.
Albert KoulmanMRC Human Nutrition Research, Elsie Widdowson Laboratory, 120 Fulbourn Road, Cambridge, CB1 9NL UK.
Macha NikolskiBordeaux Bioinformatics Center, Université de Bordeaux, Bordeaux, France.
Matej OresicSteno Diabetes Center, 2820 Gentofte, Denmark.
Susanna-Assunta SansoneOxford e-Research Centre, University of Oxford, 7 Keble Road, Oxford, OX1 3QG UK.
Daniel SchoberDepartment of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany.
James SmithMRC Human Nutrition Research, Elsie Widdowson Laboratory, 120 Fulbourn Road, Cambridge, CB1 9NL UK.
Christoph SteinbeckEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK.
Mark R ViantSchool of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK.
Steffen NeumannDepartment of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany.
University of Oxford · GBWellcome Trust · GBLeibniz Institute of Plant Biochemistry · DEMRC Human Nutrition Research · GBUniversity of Manchester · GBEuropean Bioinformatics Institute · GBImperial College London · GBLaboratoire Bordelais de Recherche en Informatique · FRRIKEN Center for Sustainable Resource Science · JPSteno Diabetes Center · DKUniversity of Birmingham · GBUniversity of Melbourne · AU

Funding

Biotechnology and Biological Sciences Research Council BB/E025080/1Biotechnology and Biological Sciences Research Council BB/H024921/1Biotechnology and Biological Sciences Research Council BB/I000771/1Biotechnology and Biological Sciences Research Council BB/I000933/1Medical Research Council MC_EX_G0800783Medical Research Council MR/L01632X/1
6 · The paper itself

Abstract

Thousands of articles using metabolomics approaches are published every year. With the increasing amounts of data being produced, mere description of investigations as text in manuscripts is not sufficient to enable re-use anymore: the underlying data needs to be published together with the findings in the literature to maximise the benefit from public and private expenditure and to take advantage of an enormous opportunity to improve scientific reproducibility in metabolomics and cognate disciplines. Reporting recommendations in metabolomics started to emerge about a decade ago and were mostly concerned with inventories of the information that had to be reported in the literature for consistency. In recent years, metabolomics data standards have developed extensively, to include the primary research data, derived results and the experimental description and importantly the metadata in a machine-readable way. This includes vendor independent data standards such as mzML for mass spectrometry and nmrML for NMR raw data that have both enabled the development of advanced data processing algorithms by the scientific community. Standards such as ISA-Tab cover essential metadata, including the experimental design, the applied protocols, association between samples, data files and the experimental factors for further statistical analysis. Altogether, they pave the way for both reproducible research and data reuse, including meta-analyses. Further incentives to prepare standards compliant data sets include new opportunities to publish data sets, but also require a little "arm twisting" in the author guidelines of scientific journals to submit the data sets to public repositories such as the NIH Metabolomics Workbench or MetaboLights at EMBL-EBI. In the present article, we look at standards for data sharing, investigate their impact in metabolomics and give suggestions to improve their adoption.

Indexed as

Data sharingData standardsExperimental metadataMass spectrometryMetabolomicsNMR

Identifiers

PMID26612985
PMCPMC4648992
OpenAlexW2173728492

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.