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First steps towards semantic descriptions of electronic laboratory notebook records

Overview of attention for article published in Journal of Cheminformatics, December 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

twitter
14 tweeters
video
1 video uploader

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
54 Mendeley
citeulike
2 CiteULike
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Title
First steps towards semantic descriptions of electronic laboratory notebook records
Published in
Journal of Cheminformatics, December 2013
DOI 10.1186/1758-2946-5-52
Pubmed ID
Authors

Simon J Coles, Jeremy G Frey, Colin L Bird, Richard J Whitby, Aileen E Day

Abstract

In order to exploit the vast body of currently inaccessible chemical information held in Electronic Laboratory Notebooks (ELNs) it is necessary not only to make it available but also to develop protocols for discovery, access and ultimately automatic processing. An aim of the Dial-a-Molecule Grand Challenge Network is to be able to draw on the body of accumulated chemical knowledge in order to predict or optimize the outcome of reactions. Accordingly the Network drew up a working group comprising informaticians, software developers and stakeholders from industry and academia to develop protocols and mechanisms to access and process ELN records. The work presented here constitutes the first stage of this process by proposing a tiered metadata system of knowledge, information and processing where each in turn addresses a) discovery, indexing and citation b) context and access to additional information and c) content access and manipulation. A compact set of metadata terms, called the elnItemManifest, has been derived and caters for the knowledge layer of this model. The elnItemManifest has been encoded as an XML schema and some use cases are presented to demonstrate the potential of this approach.

Twitter Demographics

The data shown below were collected from the profiles of 14 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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 5 9%
Germany 2 4%
United States 2 4%
Czechia 1 2%
Netherlands 1 2%
Unknown 43 80%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 20%
Student > Ph. D. Student 8 15%
Student > Master 7 13%
Librarian 6 11%
Professor 3 6%
Other 13 24%
Unknown 6 11%
Readers by discipline Count As %
Computer Science 17 31%
Chemistry 14 26%
Agricultural and Biological Sciences 5 9%
Medicine and Dentistry 3 6%
Biochemistry, Genetics and Molecular Biology 2 4%
Other 6 11%
Unknown 7 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 08 September 2015.
All research outputs
#3,005,659
of 21,358,488 outputs
Outputs from Journal of Cheminformatics
#313
of 779 outputs
Outputs of similar age
#40,752
of 303,935 outputs
Outputs of similar age from Journal of Cheminformatics
#20
of 45 outputs
Altmetric has tracked 21,358,488 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 779 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.1. This one has gotten more attention than average, scoring higher than 59% 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 303,935 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.