The ChEMBL database as linked open data

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

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#5 of 300)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

blogs
3 blogs
twitter
30 tweeters
peer_reviews
1 peer review site
googleplus
3 Google+ users

Readers on

mendeley
75 Mendeley
citeulike
5 CiteULike
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Title
The ChEMBL database as linked open data
Published in
Journal of Cheminformatics, May 2013
DOI 10.1186/1758-2946-5-23
Pubmed ID
Authors

Egon L Willighagen, Andra Waagmeester, Ola Spjuth, Peter Ansell, Antony J Williams, Valery Tkachenko, Janna Hastings, Bin Chen, David J Wild, Willighagen EL, Waagmeester A, Spjuth O, Ansell P, Williams AJ, Tkachenko V, Hastings J, Chen B, Wild DJ

Abstract

Making data available as Linked Data using Resource Description Framework (RDF) promotes integration with other web resources. RDF documents can natively link to related data, and others can link back using Uniform Resource Identifiers (URIs). RDF makes the data machine-readable and uses extensible vocabularies for additional information, making it easier to scale up inference and data analysis.

Twitter Demographics

The data shown below were collected from the profiles of 30 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 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 4 5%
United States 4 5%
Germany 3 4%
United Kingdom 2 3%
Netherlands 2 3%
Japan 1 1%
China 1 1%
Austria 1 1%
Turkey 1 1%
Other 2 3%
Unknown 54 72%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 24%
Student > Ph. D. Student 15 20%
Student > Master 15 20%
Professor > Associate Professor 8 11%
Student > Postgraduate 6 8%
Other 13 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 35%
Computer Science 22 29%
Chemistry 18 24%
Medicine and Dentistry 3 4%
Biochemistry, Genetics and Molecular Biology 2 3%
Other 4 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 47. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score was calculated when the research output was last mentioned on 18 June 2016.
All research outputs
#114,985
of 5,376,909 outputs
Outputs from Journal of Cheminformatics
#5
of 300 outputs
Outputs of similar age
#2,640
of 95,167 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 12 outputs
Altmetric has tracked 5,376,909 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 300 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has done particularly well, scoring higher than 98% 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 95,167 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 97% of its contemporaries.
We're also able to compare this research output to 12 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 91% of its contemporaries.