You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
A normalization strategy for comparing tag count data
|
|---|---|
| Published in |
Algorithms for Molecular Biology, April 2012
|
| DOI | 10.1186/1748-7188-7-5 |
| Pubmed ID | |
| Authors |
Koji Kadota, Tomoaki Nishiyama, Kentaro Shimizu |
| Abstract |
High-throughput sequencing, such as ribonucleic acid sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) analyses, enables various features of organisms to be compared through tag counts. Recent studies have demonstrated that the normalization step for RNA-seq data is critical for a more accurate subsequent analysis of differential gene expression. Development of a more robust normalization method is desirable for identifying the true difference in tag count data. |
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 % |
|---|---|---|
| Unknown | 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 242 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 11 | 5% |
| Japan | 4 | 2% |
| Brazil | 4 | 2% |
| Germany | 2 | <1% |
| Mexico | 2 | <1% |
| France | 1 | <1% |
| Indonesia | 1 | <1% |
| Italy | 1 | <1% |
| Norway | 1 | <1% |
| Other | 9 | 4% |
| Unknown | 206 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 71 | 29% |
| Student > Ph. D. Student | 54 | 22% |
| Student > Master | 26 | 11% |
| Professor > Associate Professor | 17 | 7% |
| Professor | 13 | 5% |
| Other | 44 | 18% |
| Unknown | 17 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 137 | 57% |
| Biochemistry, Genetics and Molecular Biology | 44 | 18% |
| Computer Science | 11 | 5% |
| Mathematics | 4 | 2% |
| Neuroscience | 4 | 2% |
| Other | 19 | 8% |
| Unknown | 23 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 18 June 2013.
All research outputs
#3,963,354
of 22,710,079 outputs
Outputs from Algorithms for Molecular Biology
#31
of 264 outputs
Outputs of similar age
#26,602
of 161,328 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 6 outputs
Altmetric has tracked 22,710,079 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one has done well, scoring higher than 87% 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 161,328 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 83% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them