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Erratum to: Weakly supervised learning of biomedical information extraction from curated data

Overview of attention for article published in BMC Bioinformatics, February 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
3 Mendeley
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Title
Erratum to: Weakly supervised learning of biomedical information extraction from curated data
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0925-9
Pubmed ID
Authors

Suvir Jain, Kashyap R. Tumkur, Tsung-Ting Kuo, Shitij Bhargava, Gordon Lin, Chun-Nan Hsu

Twitter Demographics

The data shown below were collected from the profiles of 2 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 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 33%
Other 1 33%
Unknown 1 33%
Readers by discipline Count As %
Computer Science 1 33%
Social Sciences 1 33%
Unknown 1 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 13 February 2016.
All research outputs
#3,357,779
of 7,187,728 outputs
Outputs from BMC Bioinformatics
#2,132
of 3,306 outputs
Outputs of similar age
#151,674
of 320,712 outputs
Outputs of similar age from BMC Bioinformatics
#110
of 144 outputs
Altmetric has tracked 7,187,728 research outputs across all sources so far. This one has received more attention than most of these and is in the 52nd percentile.
So far Altmetric has tracked 3,306 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
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 320,712 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 144 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.