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X Demographics
Mendeley readers
Attention Score in Context
| Title |
SWAP-Assembler: scalable and efficient genome assembly towards thousands of cores
|
|---|---|
| Published in |
BMC Bioinformatics, September 2014
|
| DOI | 10.1186/1471-2105-15-s9-s2 |
| Pubmed ID | |
| Authors |
Jintao Meng, Bingqiang Wang, Yanjie Wei, Shengzhong Feng, Pavan Balaji |
| Abstract |
There is a widening gap between the throughput of massive parallel sequencing machines and the ability to analyze these sequencing data. Traditional assembly methods requiring long execution time and large amount of memory on a single workstation limit their use on these massive 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 8% |
| Taiwan | 1 | 4% |
| Unknown | 22 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 20% |
| Researcher | 4 | 16% |
| Student > Bachelor | 3 | 12% |
| Other | 3 | 12% |
| Student > Master | 2 | 8% |
| Other | 4 | 16% |
| Unknown | 4 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 32% |
| Computer Science | 6 | 24% |
| Biochemistry, Genetics and Molecular Biology | 5 | 20% |
| Sports and Recreations | 1 | 4% |
| Medicine and Dentistry | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 3 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 16 December 2014.
All research outputs
#20,237,640
of 22,764,165 outputs
Outputs from BMC Bioinformatics
#6,845
of 7,273 outputs
Outputs of similar age
#200,248
of 238,990 outputs
Outputs of similar age from BMC Bioinformatics
#108
of 117 outputs
Altmetric has tracked 22,764,165 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 238,990 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 117 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.