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X Demographics
Mendeley readers
Attention Score in Context
| Title |
RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants
|
|---|---|
| Published in |
Genome Biology, November 2019
|
| DOI | 10.1186/s13059-019-1847-4 |
| Pubmed ID | |
| Authors |
Hai Lin, Katherine A. Hargreaves, Rudong Li, Jill L. Reiter, Yue Wang, Matthew Mort, David N. Cooper, Yaoqi Zhou, Chi Zhang, Michael T. Eadon, M. Eileen Dolan, Joseph Ipe, Todd C. Skaar, Yunlong Liu |
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 % |
|---|---|---|
| United States | 5 | 38% |
| United Kingdom | 2 | 15% |
| Azerbaijan | 1 | 8% |
| Netherlands | 1 | 8% |
| Thailand | 1 | 8% |
| Unknown | 3 | 23% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 62% |
| Scientists | 4 | 31% |
| Science communicators (journalists, bloggers, editors) | 1 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 93 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 93 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 15% |
| Student > Bachelor | 12 | 13% |
| Researcher | 9 | 10% |
| Student > Master | 9 | 10% |
| Student > Postgraduate | 6 | 6% |
| Other | 15 | 16% |
| Unknown | 28 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 31 | 33% |
| Agricultural and Biological Sciences | 13 | 14% |
| Immunology and Microbiology | 4 | 4% |
| Medicine and Dentistry | 3 | 3% |
| Unspecified | 2 | 2% |
| Other | 10 | 11% |
| Unknown | 30 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 12 December 2019.
All research outputs
#6,377,981
of 25,387,668 outputs
Outputs from Genome Biology
#3,050
of 4,470 outputs
Outputs of similar age
#127,673
of 473,746 outputs
Outputs of similar age from Genome Biology
#86
of 98 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
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 is in the 31st percentile – i.e., 31% 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 473,746 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 98 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.