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Metabolomics. 2016;12:14. doi: 10.1007/s11306-015-0879-3. Epub 2015 Nov 17.

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

Author information

1
Oxford e-Research Centre, University of Oxford, 7 Keble Road, Oxford, OX1 3QG UK.
2
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK.
3
National Institute of Genetics, Mishima, Shizuoka 411-8540 Japan.
4
RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa 230-0045 Japan.
5
University of Manchester, Centre for Endocrinology and Diabetes, Old St Mary's Building, Hathersage Road, Manchester, M13 9WL UK.
6
School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN UK.
7
Metabolomics Australia, The University of Melbourne, Parkville, VIC 3010 Australia.
8
Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington, London, SW7 2AZ UK.
9
MRC Human Nutrition Research, Elsie Widdowson Laboratory, 120 Fulbourn Road, Cambridge, CB1 9NL UK.
10
Bordeaux Bioinformatics Center, Université de Bordeaux, Bordeaux, France.
11
CNRS/LaBRI, Université de Bordeaux, Talence, France.
12
Steno Diabetes Center, 2820 Gentofte, Denmark.
13
Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany.
14
Department of Applied Mathematics and Theoretical Physics, Cambridge Computational Biology Institute, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA UK.
15
School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK.
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Contributed equally

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.

KEYWORDS:

Data sharing; Data standards; Experimental metadata; Mass spectrometry; Metabolomics; NMR

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