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X Demographics
Mendeley readers
Attention Score in Context
| Title |
MetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approach
|
|---|---|
| Published in |
BMC Bioinformatics, November 2013
|
| DOI | 10.1186/1471-2105-14-338 |
| Pubmed ID | |
| Authors |
Gift Nyamundanda, Isobel Claire Gormley, Yue Fan, William M Gallagher, Lorraine Brennan |
| Abstract |
Determining sample sizes for metabolomic experiments is important but due to the complexity of these experiments, there are currently no standard methods for sample size estimation in metabolomics. Since pilot studies are rarely done in metabolomics, currently existing sample size estimation approaches which rely on pilot data can not be applied. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 20% |
| United Kingdom | 1 | 20% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
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 Kingdom | 2 | <1% |
| Spain | 2 | <1% |
| Austria | 1 | <1% |
| Brazil | 1 | <1% |
| South Africa | 1 | <1% |
| Colombia | 1 | <1% |
| Denmark | 1 | <1% |
| Italy | 1 | <1% |
| Japan | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 230 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 58 | 24% |
| Student > Ph. D. Student | 36 | 15% |
| Student > Master | 29 | 12% |
| Student > Doctoral Student | 18 | 7% |
| Professor > Associate Professor | 15 | 6% |
| Other | 44 | 18% |
| Unknown | 42 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 61 | 25% |
| Biochemistry, Genetics and Molecular Biology | 36 | 15% |
| Medicine and Dentistry | 25 | 10% |
| Chemistry | 20 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 9 | 4% |
| Other | 39 | 16% |
| Unknown | 52 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 29 June 2016.
All research outputs
#13,321,125
of 22,977,819 outputs
Outputs from BMC Bioinformatics
#4,021
of 7,308 outputs
Outputs of similar age
#161,956
of 303,307 outputs
Outputs of similar age from BMC Bioinformatics
#46
of 104 outputs
Altmetric has tracked 22,977,819 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,308 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 42nd percentile – i.e., 42% 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 303,307 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 104 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.