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Cited by 507

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

The role of reporting standards for metabolite annotation and identification in metabolomic studies

Open access
Salek, Reza M, Steinbeck, Christoph, Viant, Mark R, Goodacre, Royston, Dunn, Warwick B·2013·GigaScience

Abstract

The application of reporting standards in metabolomics allow data from different laboratories to be shared, integrated and interpreted. Although minimum reporting standards related to metabolite identification were published in 2007, it is clear that significant efforts are required to ensure their continuous update and appropriate use by the metabolomics community. These include their use in metabolomics data submission (e.g., MetaboLights) and as a requirement for publication in peer-reviewed journals (e.g., Metabolomics). The Data Standards and Metabolite Identification Task Groups of the international Metabolomics Society are actively working to develop and promote these standards and educate the community on their use.

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Crossref · 10.1186/2047-217x-2-13

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Metadata

Authors
Salek, R. M., Steinbeck, C., Viant, M. R., Goodacre, R., & Dunn, W. B.
Venue
GigaScience
Published
2013-10-16
Volume
2
Issue
1
Pages
13-13
ISSN
2047-217X (ISSN-L)
OpenAlex
W2132400619
Cited by
507
References
8
FWCI
5.24
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: The role of reporting standards for metabolite annotation and identification in metabolomic studies. https://app.cochat.ai/papers/10.1186/2047-217x-2-13
The paper
Salek, R. M., Steinbeck, C., Viant, M. R., Goodacre, R., & Dunn, W. B. (2013). The role of reporting standards for metabolite annotation and identification in metabolomic studies. GigaScience. https://doi.org/10.1186/2047-217x-2-13

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.