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

Overview of attention for article published in Metabolomics, November 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#37 of 937)
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

31 tweeters


68 Dimensions

Readers on

183 Mendeley
5 CiteULike
Data standards can boost metabolomics research, and if there is a will, there is a way
Published in
Metabolomics, November 2015
DOI 10.1007/s11306-015-0879-3
Pubmed ID

Philippe Rocca-Serra, Reza M. Salek, Masanori Arita, Elon Correa, Saravanan Dayalan, Alejandra Gonzalez-Beltran, Tim Ebbels, Royston Goodacre, Janna Hastings, Kenneth Haug, Albert Koulman, Macha Nikolski, Matej Oresic, Susanna-Assunta Sansone, Daniel Schober, James Smith, Christoph Steinbeck, Mark R. Viant, Steffen Neumann


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.

Twitter Demographics

The data shown below were collected from the profiles of 31 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 183 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 5 3%
United States 4 2%
Netherlands 2 1%
Brazil 2 1%
Spain 1 <1%
Italy 1 <1%
Denmark 1 <1%
South Africa 1 <1%
Belgium 1 <1%
Other 3 2%
Unknown 162 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 54 30%
Student > Ph. D. Student 42 23%
Student > Master 23 13%
Professor > Associate Professor 14 8%
Student > Bachelor 11 6%
Other 39 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 50 27%
Chemistry 32 17%
Biochemistry, Genetics and Molecular Biology 27 15%
Computer Science 19 10%
Medicine and Dentistry 18 10%
Other 37 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 20. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 August 2017.
All research outputs
of 13,755,511 outputs
Outputs from Metabolomics
of 937 outputs
Outputs of similar age
of 358,475 outputs
Outputs of similar age from Metabolomics
of 39 outputs
Altmetric has tracked 13,755,511 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 937 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 358,475 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.