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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Impact of variance components on reliability of absolute quantification using digital PCR
|
|---|---|
| Published in |
BMC Bioinformatics, August 2014
|
| DOI | 10.1186/1471-2105-15-283 |
| Pubmed ID | |
| Authors |
Bart KM Jacobs, Els Goetghebeur, Lieven Clement |
| Abstract |
Digital polymerase chain reaction (dPCR) is an increasingly popular technology for detecting and quantifying target nucleic acids. Its advertised strength is high precision absolute quantification without needing reference curves. The standard data analytic approach follows a seemingly straightforward theoretical framework but ignores sources of variation in the data generating process. These stem from both technical and biological factors, where we distinguish features that are 1) hard-wired in the equipment, 2) user-dependent and 3) provided by manufacturers but may be adapted by the user. The impact of the corresponding variance components on the accuracy and precision of target concentration estimators presented in the literature is studied through simulation. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Denmark | 2 | 2% |
| Sweden | 1 | 1% |
| Germany | 1 | 1% |
| Unknown | 87 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 24 | 26% |
| Student > Ph. D. Student | 13 | 14% |
| Student > Master | 8 | 9% |
| Other | 5 | 5% |
| Professor > Associate Professor | 4 | 4% |
| Other | 11 | 12% |
| Unknown | 26 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 19 | 21% |
| Biochemistry, Genetics and Molecular Biology | 16 | 18% |
| Engineering | 5 | 5% |
| Computer Science | 3 | 3% |
| Immunology and Microbiology | 3 | 3% |
| Other | 17 | 19% |
| Unknown | 28 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 05 January 2023.
All research outputs
#4,332,782
of 23,485,296 outputs
Outputs from BMC Bioinformatics
#1,645
of 7,394 outputs
Outputs of similar age
#43,035
of 237,130 outputs
Outputs of similar age from BMC Bioinformatics
#26
of 116 outputs
Altmetric has tracked 23,485,296 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,394 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 77% 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 237,130 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 80% of its contemporaries.
We're also able to compare this research output to 116 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.