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Automated extraction of chemical structure information from digital raster images

Overview of attention for article published in Chemistry Central Journal, February 2009
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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 (#17 of 583)
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

blogs
3 blogs
twitter
3 tweeters
patent
2 patents

Citations

dimensions_citation
42 Dimensions

Readers on

mendeley
54 Mendeley
citeulike
5 CiteULike
connotea
2 Connotea
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Title
Automated extraction of chemical structure information from digital raster images
Published in
Chemistry Central Journal, February 2009
DOI 10.1186/1752-153x-3-4
Pubmed ID
Authors

Jungkap Park, Gus R Rosania, Kerby A Shedden, Mandee Nguyen, Naesung Lyu, Kazuhiro Saitou

Abstract

To search for chemical structures in research articles, diagrams or text representing molecules need to be translated to a standard chemical file format compatible with cheminformatic search engines. Nevertheless, chemical information contained in research articles is often referenced as analog diagrams of chemical structures embedded in digital raster images. To automate analog-to-digital conversion of chemical structure diagrams in scientific research articles, several software systems have been developed. But their algorithmic performance and utility in cheminformatic research have not been investigated.

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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 4%
Austria 1 2%
Italy 1 2%
Netherlands 1 2%
Kenya 1 2%
United Kingdom 1 2%
United States 1 2%
Unknown 46 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 31%
Student > Ph. D. Student 14 26%
Student > Master 7 13%
Student > Bachelor 4 7%
Other 2 4%
Other 5 9%
Unknown 5 9%
Readers by discipline Count As %
Chemistry 14 26%
Computer Science 12 22%
Agricultural and Biological Sciences 5 9%
Biochemistry, Genetics and Molecular Biology 3 6%
Engineering 2 4%
Other 9 17%
Unknown 9 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 24. 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 28 October 2020.
All research outputs
#997,100
of 17,525,923 outputs
Outputs from Chemistry Central Journal
#17
of 583 outputs
Outputs of similar age
#12,434
of 263,795 outputs
Outputs of similar age from Chemistry Central Journal
#3
of 165 outputs
Altmetric has tracked 17,525,923 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 583 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 97% 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 263,795 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 95% of its contemporaries.
We're also able to compare this research output to 165 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 98% of its contemporaries.