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X Demographics
Mendeley readers
Attention Score in Context
| Title |
FASTQSim: platform-independent data characterization and in silico read generation for NGS datasets
|
|---|---|
| Published in |
BMC Research Notes, August 2014
|
| DOI | 10.1186/1756-0500-7-533 |
| Pubmed ID | |
| Authors |
Anna Shcherbina |
| Abstract |
High-throughput next generation sequencing technologies have enabled rapid characterization of clinical and environmental samples. Consequently, the largest bottleneck to actionable data has become sample processing and bioinformatics analysis, creating a need for accurate and rapid algorithms to process genetic data. Perfectly characterized in silico datasets are a useful tool for evaluating the performance of such algorithms.Background contaminating organisms are observed in sequenced mixtures of organisms. In silico samples provide exact truth. To create the best value for evaluating algorithms, in silico data should mimic actual sequencer data as closely as possible. |
X Demographics
The data shown below were collected from the profiles of 2 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 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 4% |
| France | 1 | 1% |
| Germany | 1 | 1% |
| Spain | 1 | 1% |
| Brazil | 1 | 1% |
| Unknown | 66 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 22 | 30% |
| Student > Ph. D. Student | 16 | 22% |
| Student > Master | 15 | 21% |
| Student > Bachelor | 8 | 11% |
| Other | 3 | 4% |
| Other | 6 | 8% |
| Unknown | 3 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 26 | 36% |
| Biochemistry, Genetics and Molecular Biology | 20 | 27% |
| Computer Science | 9 | 12% |
| Immunology and Microbiology | 5 | 7% |
| Mathematics | 1 | 1% |
| Other | 5 | 7% |
| Unknown | 7 | 10% |
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 19 August 2014.
All research outputs
#17,286,379
of 25,374,647 outputs
Outputs from BMC Research Notes
#2,501
of 4,513 outputs
Outputs of similar age
#145,468
of 243,218 outputs
Outputs of similar age from BMC Research Notes
#79
of 133 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,513 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 32nd percentile – i.e., 32% 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 243,218 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 133 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.