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OSCAR4: a flexible architecture for chemical text-mining

Overview of attention for article published in Journal of Cheminformatics, October 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 (95th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

blogs
3 blogs
twitter
2 X users
googleplus
1 Google+ user

Citations

dimensions_citation
151 Dimensions

Readers on

mendeley
155 Mendeley
citeulike
7 CiteULike
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Title
OSCAR4: a flexible architecture for chemical text-mining
Published in
Journal of Cheminformatics, October 2011
DOI 10.1186/1758-2946-3-41
Pubmed ID
Authors

David M Jessop, Sam E Adams, Egon L Willighagen, Lezan Hawizy, Peter Murray-Rust

Abstract

The Open-Source Chemistry Analysis Routines (OSCAR) software, a toolkit for the recognition of named entities and data in chemistry publications, has been developed since 2002. Recent work has resulted in the separation of the core OSCAR functionality and its release as the OSCAR4 library. This library features a modular API (based on reduction of surface coupling) that permits client programmers to easily incorporate it into external applications. OSCAR4 offers a domain-independent architecture upon which chemistry specific text-mining tools can be built, and its development and usage are discussed.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 155 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 3%
Portugal 3 2%
Germany 2 1%
United Kingdom 2 1%
Canada 1 <1%
Netherlands 1 <1%
Spain 1 <1%
Iran, Islamic Republic of 1 <1%
Unknown 139 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 25%
Researcher 35 23%
Student > Master 20 13%
Student > Bachelor 9 6%
Other 8 5%
Other 22 14%
Unknown 22 14%
Readers by discipline Count As %
Computer Science 47 30%
Chemistry 21 14%
Agricultural and Biological Sciences 21 14%
Engineering 13 8%
Materials Science 7 5%
Other 21 14%
Unknown 25 16%
Attention Score in Context

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 August 2012.
All research outputs
#1,553,731
of 25,079,131 outputs
Outputs from Journal of Cheminformatics
#104
of 943 outputs
Outputs of similar age
#6,945
of 141,523 outputs
Outputs of similar age from Journal of Cheminformatics
#5
of 21 outputs
Altmetric has tracked 25,079,131 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 943 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has done well, scoring higher than 89% 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 141,523 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 21 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.