| Title |
NS-kNN: a modified k-nearest neighbors approach for imputing metabolomics data
|
|---|---|
| Published in |
Metabolomics, November 2018
|
| DOI | 10.1007/s11306-018-1451-8 |
| Pubmed ID | |
| Authors |
Justin Y. Lee, Mark P. Styczynski |
X Demographics
The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 2 | 15% |
| United Kingdom | 1 | 8% |
| Korea, Republic of | 1 | 8% |
| France | 1 | 8% |
| Canada | 1 | 8% |
| United States | 1 | 8% |
| Comoros | 1 | 8% |
| Unknown | 5 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 54% |
| Scientists | 4 | 31% |
| Science communicators (journalists, bloggers, editors) | 2 | 15% |
Mendeley readers
The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 43 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 14% |
| Student > Master | 6 | 14% |
| Researcher | 5 | 12% |
| Lecturer > Senior Lecturer | 2 | 5% |
| Student > Postgraduate | 2 | 5% |
| Other | 7 | 16% |
| Unknown | 15 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 6 | 14% |
| Agricultural and Biological Sciences | 5 | 12% |
| Mathematics | 4 | 9% |
| Biochemistry, Genetics and Molecular Biology | 4 | 9% |
| Engineering | 2 | 5% |
| Other | 6 | 14% |
| Unknown | 16 | 37% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 15 July 2022.
All research outputs
#4,508,153
of 25,081,285 outputs
Outputs from Metabolomics
#226
of 1,370 outputs
Outputs of similar age
#91,706
of 449,608 outputs
Outputs of similar age from Metabolomics
#11
of 31 outputs
Altmetric has tracked 25,081,285 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,370 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done well, scoring higher than 83% 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 449,608 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 79% of its contemporaries.
We're also able to compare this research output to 31 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 67% of its contemporaries.