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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Open-source QSAR models for pKa prediction using multiple machine learning approaches
|
|---|---|
| Published in |
Journal of Cheminformatics, September 2019
|
| DOI | 10.1186/s13321-019-0384-1 |
| Pubmed ID | |
| Authors |
Kamel Mansouri, Neal F. Cariello, Alexandru Korotcov, Valery Tkachenko, Chris M. Grulke, Catherine S. Sprankle, David Allen, Warren M. Casey, Nicole C. Kleinstreuer, Antony J. Williams |
X Demographics
The data shown below were collected from the profiles of 19 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 | 3 | 16% |
| Germany | 2 | 11% |
| United Kingdom | 1 | 5% |
| Chile | 1 | 5% |
| Thailand | 1 | 5% |
| Spain | 1 | 5% |
| Japan | 1 | 5% |
| Unknown | 9 | 47% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 10 | 53% |
| Members of the public | 8 | 42% |
| Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 200 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 200 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 41 | 21% |
| Student > Ph. D. Student | 30 | 15% |
| Student > Master | 21 | 11% |
| Student > Bachelor | 15 | 8% |
| Other | 10 | 5% |
| Other | 26 | 13% |
| Unknown | 57 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 57 | 28% |
| Chemical Engineering | 9 | 5% |
| Biochemistry, Genetics and Molecular Biology | 8 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 8 | 4% |
| Computer Science | 8 | 4% |
| Other | 33 | 17% |
| Unknown | 77 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 19. 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 29 July 2022.
All research outputs
#1,798,558
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#148
of 891 outputs
Outputs of similar age
#38,475
of 346,474 outputs
Outputs of similar age from Journal of Cheminformatics
#3
of 13 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 891 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. 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 346,474 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 13 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.