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Attention Score in Context
Title |
Similarity maps and hierarchical clustering for annotating FT-IR spectral images
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Published in |
BMC Bioinformatics, November 2013
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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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Norway | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 1 | 2% |
Unknown | 53 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 16 | 30% |
Researcher | 9 | 17% |
Student > Master | 6 | 11% |
Student > Bachelor | 5 | 9% |
Professor | 3 | 6% |
Other | 8 | 15% |
Unknown | 7 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 9 | 17% |
Engineering | 8 | 15% |
Agricultural and Biological Sciences | 7 | 13% |
Medicine and Dentistry | 6 | 11% |
Physics and Astronomy | 4 | 7% |
Other | 13 | 24% |
Unknown | 7 | 13% |
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
#3,184,745
of 22,731,677 outputs
Outputs from BMC Bioinformatics
#1,176
of 7,266 outputs
Outputs of similar age
#38,438
of 302,010 outputs
Outputs of similar age from BMC Bioinformatics
#18
of 104 outputs
Altmetric has tracked 22,731,677 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,266 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 done well, scoring higher than 83% 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 302,010 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 104 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.