| Title |
Chemometrics models for overcoming high between subject variability: applications in clinical metabolic profiling studies
|
|---|---|
| Published in |
Metabolomics, December 2013
|
| DOI | 10.1007/s11306-013-0616-8 |
| Authors |
Yun Xu, Stephen J. Fowler, Ardeshir Bayat, Royston Goodacre |
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 % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Austria | 1 | 2% |
| South Africa | 1 | 2% |
| United Kingdom | 1 | 2% |
| Denmark | 1 | 2% |
| Japan | 1 | 2% |
| Unknown | 50 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 33% |
| Student > Ph. D. Student | 15 | 27% |
| Student > Doctoral Student | 4 | 7% |
| Student > Bachelor | 3 | 5% |
| Student > Master | 3 | 5% |
| Other | 6 | 11% |
| Unknown | 6 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 24 | 44% |
| Agricultural and Biological Sciences | 15 | 27% |
| Biochemistry, Genetics and Molecular Biology | 4 | 7% |
| Physics and Astronomy | 2 | 4% |
| Medicine and Dentistry | 2 | 4% |
| Other | 1 | 2% |
| Unknown | 7 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 14 May 2014.
All research outputs
#14,653,295
of 22,755,127 outputs
Outputs from Metabolomics
#821
of 1,292 outputs
Outputs of similar age
#183,268
of 307,086 outputs
Outputs of similar age from Metabolomics
#7
of 12 outputs
Altmetric has tracked 22,755,127 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,292 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 35th percentile – i.e., 35% 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 307,086 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.