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X Demographics
Mendeley readers
Attention Score in Context
| Title |
LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis
|
|---|---|
| Published in |
Bioinformatics, September 2016
|
| DOI | 10.1093/bioinformatics/btw613 |
| Pubmed ID | |
| Authors |
Jie Zheng, A Mesut Erzurumluoglu, Benjamin L Elsworth, John P Kemp, Laurence Howe, Philip C Haycock, Gibran Hemani, Katherine Tansey, Charles Laurin, Beate St Pourcain, Nicole M Warrington, Hilary K Finucane, Alkes L Price, Brendan K Bulik-Sullivan, Verneri Anttila, Lavinia Paternoster, Tom R Gaunt, David M Evans, Benjamin M Neale |
X Demographics
The data shown below were collected from the profiles of 21 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 Kingdom | 4 | 19% |
| United States | 4 | 19% |
| Australia | 1 | 5% |
| Netherlands | 1 | 5% |
| Spain | 1 | 5% |
| Argentina | 1 | 5% |
| Italy | 1 | 5% |
| Norway | 1 | 5% |
| Chile | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 6 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 12 | 57% |
| Scientists | 8 | 38% |
| Practitioners (doctors, other healthcare professionals) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 613 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | <1% |
| United States | 2 | <1% |
| Finland | 1 | <1% |
| Canada | 1 | <1% |
| United Kingdom | 1 | <1% |
| Unknown | 606 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 140 | 23% |
| Student > Ph. D. Student | 134 | 22% |
| Student > Master | 51 | 8% |
| Student > Bachelor | 48 | 8% |
| Other | 34 | 6% |
| Other | 87 | 14% |
| Unknown | 119 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 137 | 22% |
| Agricultural and Biological Sciences | 93 | 15% |
| Medicine and Dentistry | 89 | 15% |
| Neuroscience | 33 | 5% |
| Computer Science | 29 | 5% |
| Other | 72 | 12% |
| Unknown | 160 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 28 December 2020.
All research outputs
#2,672,363
of 25,837,817 outputs
Outputs from Bioinformatics
#2,038
of 12,966 outputs
Outputs of similar age
#43,581
of 331,213 outputs
Outputs of similar age from Bioinformatics
#21
of 185 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,966 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. 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 331,213 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 86% of its contemporaries.
We're also able to compare this research output to 185 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.