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X Demographics
Mendeley readers
Attention Score in Context
| Title |
KAML: improving genomic prediction accuracy of complex traits using machine learning determined parameters
|
|---|---|
| Published in |
Genome Biology, June 2020
|
| DOI | 10.1186/s13059-020-02052-w |
| Pubmed ID | |
| Authors |
Lilin Yin, Haohao Zhang, Xiang Zhou, Xiaohui Yuan, Shuhong Zhao, Xinyun Li, Xiaolei Liu |
X Demographics
The data shown below were collected from the profiles of 30 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 | 5 | 17% |
| United Kingdom | 2 | 7% |
| Canada | 2 | 7% |
| Italy | 1 | 3% |
| Iraq | 1 | 3% |
| San Marino | 1 | 3% |
| Brazil | 1 | 3% |
| Germany | 1 | 3% |
| Sweden | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 14 | 47% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 18 | 60% |
| Scientists | 10 | 33% |
| Science communicators (journalists, bloggers, editors) | 2 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 87 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 18 | 21% |
| Researcher | 18 | 21% |
| Student > Master | 7 | 8% |
| Other | 4 | 5% |
| Student > Doctoral Student | 4 | 5% |
| Other | 13 | 15% |
| Unknown | 23 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 29 | 33% |
| Biochemistry, Genetics and Molecular Biology | 16 | 18% |
| Computer Science | 6 | 7% |
| Engineering | 3 | 3% |
| Unspecified | 2 | 2% |
| Other | 5 | 6% |
| Unknown | 26 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 21. 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 17 August 2020.
All research outputs
#1,753,028
of 25,387,668 outputs
Outputs from Genome Biology
#1,450
of 4,470 outputs
Outputs of similar age
#49,830
of 433,999 outputs
Outputs of similar age from Genome Biology
#42
of 86 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,470 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 67% 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 433,999 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 88% of its contemporaries.
We're also able to compare this research output to 86 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 51% of its contemporaries.