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A hybrid blob-slice model for accurate and efficient detection of fluorescence labeled nuclei in 3D

Overview of attention for article published in BMC Bioinformatics, November 2010
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About this Attention Score

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

patent
1 patent

Citations

dimensions_citation
105 Dimensions

Readers on

mendeley
92 Mendeley
citeulike
1 CiteULike
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Title
A hybrid blob-slice model for accurate and efficient detection of fluorescence labeled nuclei in 3D
Published in
BMC Bioinformatics, November 2010
DOI 10.1186/1471-2105-11-580
Pubmed ID
Authors

Anthony Santella, Zhuo Du, Sonja Nowotschin, Anna-Katerina Hadjantonakis, Zhirong Bao

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 3%
Brazil 2 2%
Japan 2 2%
Switzerland 1 1%
Portugal 1 1%
United States 1 1%
Unknown 82 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 26%
Researcher 24 26%
Student > Master 9 10%
Professor > Associate Professor 8 9%
Professor 4 4%
Other 15 16%
Unknown 8 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 38%
Biochemistry, Genetics and Molecular Biology 14 15%
Computer Science 10 11%
Engineering 6 7%
Neuroscience 4 4%
Other 13 14%
Unknown 10 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 January 2016.
All research outputs
#7,550,598
of 23,035,022 outputs
Outputs from BMC Bioinformatics
#3,042
of 7,318 outputs
Outputs of similar age
#54,516
of 181,429 outputs
Outputs of similar age from BMC Bioinformatics
#22
of 61 outputs
Altmetric has tracked 23,035,022 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,318 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 181,429 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.