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Similarity maps and hierarchical clustering for annotating FT-IR spectral images

Overview of attention for article published in BMC Bioinformatics, January 2013
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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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

news
1 news outlet
twitter
1 tweeter

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
53 Mendeley
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Title
Similarity maps and hierarchical clustering for annotating FT-IR spectral images
Published in
BMC Bioinformatics, January 2013
DOI 10.1186/1471-2105-14-333
Pubmed ID
Authors

Qiaoyong Zhong, Chen Yang, Frederik Großerüschkamp, Angela Kallenbach-Thieltges, Peter Serocka, Klaus Gerwert, Axel Mosig

Abstract

Unsupervised segmentation of multi-spectral images plays an important role in annotating infrared microscopic images and is an essential step in label-free spectral histopathology. In this context, diverse clustering approaches have been utilized and evaluated in order to achieve segmentations of Fourier Transform Infrared (FT-IR) microscopic images that agree with histopathological characterization.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Unknown 52 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 30%
Researcher 10 19%
Student > Master 8 15%
Student > Bachelor 4 8%
Other 3 6%
Other 7 13%
Unknown 5 9%
Readers by discipline Count As %
Engineering 9 17%
Computer Science 8 15%
Agricultural and Biological Sciences 7 13%
Medicine and Dentistry 5 9%
Biochemistry, Genetics and Molecular Biology 4 8%
Other 15 28%
Unknown 5 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 07 January 2014.
All research outputs
#1,815,288
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#753
of 5,420 outputs
Outputs of similar age
#31,884
of 254,431 outputs
Outputs of similar age from BMC Bioinformatics
#63
of 424 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 86% 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 254,431 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 87% of its contemporaries.
We're also able to compare this research output to 424 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.