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X Demographics
Mendeley readers
Attention Score in Context
Title |
Distinguishing highly similar gene isoforms with a clustering-based bioinformatics analysis of PacBio single-molecule long reads
|
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Published in |
BioData Mining, April 2016
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DOI | 10.1186/s13040-016-0090-8 |
Pubmed ID | |
Authors |
Ma Liang, Castle Raley, Xin Zheng, Geetha Kutty, Emile Gogineni, Brad T. Sherman, Qiang Sun, Xiongfong Chen, Thomas Skelly, Kristine Jones, Robert Stephens, Bin Zhou, William Lau, Calvin Johnson, Tomozumi Imamichi, Minkang Jiang, Robin Dewar, Richard A. Lempicki, Bao Tran, Joseph A. Kovacs, Da Wei Huang |
X Demographics
The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 22% |
Norway | 1 | 11% |
Germany | 1 | 11% |
Unknown | 5 | 56% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 6 | 67% |
Members of the public | 3 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 4% |
United States | 2 | 4% |
Japan | 1 | 2% |
Unknown | 52 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 12 | 21% |
Student > Ph. D. Student | 8 | 14% |
Other | 7 | 12% |
Student > Bachelor | 6 | 11% |
Student > Master | 4 | 7% |
Other | 7 | 12% |
Unknown | 13 | 23% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 16 | 28% |
Agricultural and Biological Sciences | 16 | 28% |
Immunology and Microbiology | 5 | 9% |
Medicine and Dentistry | 3 | 5% |
Veterinary Science and Veterinary Medicine | 2 | 4% |
Other | 2 | 4% |
Unknown | 13 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. 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 01 December 2019.
All research outputs
#2,422,784
of 25,837,817 outputs
Outputs from BioData Mining
#38
of 324 outputs
Outputs of similar age
#38,004
of 318,129 outputs
Outputs of similar age from BioData Mining
#3
of 11 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 324 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.5. This one has done well, scoring higher than 88% 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 318,129 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 11 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.