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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Matataki: an ultrafast mRNA quantification method for large-scale reanalysis of RNA-Seq data
|
|---|---|
| Published in |
BMC Bioinformatics, July 2018
|
| DOI | 10.1186/s12859-018-2279-y |
| Pubmed ID | |
| Authors |
Yasunobu Okamura, Kengo Kinoshita |
X Demographics
The data shown below were collected from the profiles of 35 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 | 5 | 14% |
| Germany | 5 | 14% |
| Canada | 2 | 6% |
| Sweden | 1 | 3% |
| Austria | 1 | 3% |
| Australia | 1 | 3% |
| China | 1 | 3% |
| Slovenia | 1 | 3% |
| Luxembourg | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 15 | 43% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 23 | 66% |
| Members of the public | 12 | 34% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 28 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 29% |
| Researcher | 7 | 25% |
| Student > Bachelor | 3 | 11% |
| Student > Master | 3 | 11% |
| Professor | 1 | 4% |
| Other | 2 | 7% |
| Unknown | 4 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 29% |
| Biochemistry, Genetics and Molecular Biology | 7 | 25% |
| Computer Science | 4 | 14% |
| Medicine and Dentistry | 3 | 11% |
| Immunology and Microbiology | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 4 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 18. 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 08 September 2018.
All research outputs
#1,953,325
of 24,272,486 outputs
Outputs from BMC Bioinformatics
#448
of 7,510 outputs
Outputs of similar age
#40,569
of 330,538 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 100 outputs
Altmetric has tracked 24,272,486 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,510 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 94% 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 330,538 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 100 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.