Imperial College London


Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Reader in Computational Bioinformatics



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BibTex format

author = {Rocca-Serra, P and Salek, RM and Arita, M and Correa, E and Dayalan, S and Gonzalez-Beltran, A and Ebbels, T and Goodacre, R and Hastings, J and Haug, K and Koulman, A and Nikolski, M and Oresic, M and Sansone, S-A and Schober, D and Smith, J and Steinbeck, C and Viant, MR and Neumann, S},
doi = {10.1007/s11306-015-0879-3},
journal = {Metabolomics},
title = {Data standards can boost metabolomics research, and if there is a will, there is a way},
url = {},
volume = {12},
year = {2015}

RIS format (EndNote, RefMan)

AB - 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.
AU - Rocca-Serra,P
AU - Salek,RM
AU - Arita,M
AU - Correa,E
AU - Dayalan,S
AU - Gonzalez-Beltran,A
AU - Ebbels,T
AU - Goodacre,R
AU - Hastings,J
AU - Haug,K
AU - Koulman,A
AU - Nikolski,M
AU - Oresic,M
AU - Sansone,S-A
AU - Schober,D
AU - Smith,J
AU - Steinbeck,C
AU - Viant,MR
AU - Neumann,S
DO - 10.1007/s11306-015-0879-3
PY - 2015///
SN - 1573-3890
TI - Data standards can boost metabolomics research, and if there is a will, there is a way
T2 - Metabolomics
UR -
UR -
VL - 12
ER -