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LibKiSAO: a Java library for Querying KiSAO

Overview of attention for article published in BMC Research Notes, January 2012
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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 (80th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

twitter
8 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
15 Mendeley
citeulike
3 CiteULike
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Title
LibKiSAO: a Java library for Querying KiSAO
Published in
BMC Research Notes, January 2012
DOI 10.1186/1756-0500-5-520
Pubmed ID
Authors

Anna Zhukova, Richard Adams, Camille Laibe, Nicolas Le Novère

Abstract

The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of Systems Biology models, their characteristics, parameters and inter-relationships. KiSAO enables the unambiguous identification of algorithms from simulation descriptions. Information about analogous methods having similar characteristics and about algorithm parameters incorporated into KiSAO is desirable for simulation tools. To retrieve this information programmatically an application programming interface (API) for KiSAO is needed.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 7%
Unknown 14 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 47%
Student > Bachelor 3 20%
Professor > Associate Professor 2 13%
Student > Ph. D. Student 1 7%
Student > Master 1 7%
Other 1 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 47%
Computer Science 4 27%
Engineering 2 13%
Social Sciences 1 7%
Unknown 1 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 04 October 2012.
All research outputs
#2,761,631
of 12,519,627 outputs
Outputs from BMC Research Notes
#444
of 2,804 outputs
Outputs of similar age
#25,270
of 127,517 outputs
Outputs of similar age from BMC Research Notes
#3
of 17 outputs
Altmetric has tracked 12,519,627 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,804 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done well, scoring higher than 84% 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 127,517 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 80% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.