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Computational toxicology using the OpenTox application programming interface and Bioclipse

Overview of attention for article published in BMC Research Notes, November 2011
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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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

blogs
2 blogs
twitter
3 tweeters
googleplus
2 Google+ users

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
63 Mendeley
citeulike
3 CiteULike
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Title
Computational toxicology using the OpenTox application programming interface and Bioclipse
Published in
BMC Research Notes, November 2011
DOI 10.1186/1756-0500-4-487
Pubmed ID
Authors

Egon L Willighagen, Nina Jeliazkova, Barry Hardy, Roland C Grafström, Ola Spjuth

Abstract

Toxicity is a complex phenomenon involving the potential adverse effect on a range of biological functions. Predicting toxicity involves using a combination of experimental data (endpoints) and computational methods to generate a set of predictive models. Such models rely strongly on being able to integrate information from many sources. The required integration of biological and chemical information sources requires, however, a common language to express our knowledge ontologically, and interoperating services to build reliable predictive toxicology applications.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 3 5%
Netherlands 2 3%
Bulgaria 2 3%
United Kingdom 2 3%
Brazil 1 2%
Germany 1 2%
Denmark 1 2%
United States 1 2%
Unknown 50 79%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 27%
Researcher 16 25%
Other 6 10%
Student > Master 6 10%
Professor > Associate Professor 5 8%
Other 8 13%
Unknown 5 8%
Readers by discipline Count As %
Computer Science 15 24%
Chemistry 11 17%
Agricultural and Biological Sciences 9 14%
Pharmacology, Toxicology and Pharmaceutical Science 6 10%
Biochemistry, Genetics and Molecular Biology 5 8%
Other 13 21%
Unknown 4 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 18 February 2017.
All research outputs
#765,327
of 12,440,173 outputs
Outputs from BMC Research Notes
#108
of 2,782 outputs
Outputs of similar age
#11,875
of 220,513 outputs
Outputs of similar age from BMC Research Notes
#3
of 40 outputs
Altmetric has tracked 12,440,173 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,782 research outputs from this source. They receive a mean Attention Score of 4.4. 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 220,513 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 94% of its contemporaries.
We're also able to compare this research output to 40 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 95% of its contemporaries.