| Title |
Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies
|
|---|---|
| Published in |
Journal of Cheminformatics, September 2019
|
| DOI | 10.1186/s13321-019-0381-4 |
| Pubmed ID | |
| Authors |
Christoph A. Bauer, Gisbert Schneider, Andreas H. Göller |
X Demographics
The data shown below were collected from the profiles of 14 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 | 2 | 14% |
| Germany | 1 | 7% |
| Canada | 1 | 7% |
| Japan | 1 | 7% |
| India | 1 | 7% |
| Sweden | 1 | 7% |
| Unknown | 7 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 50% |
| Scientists | 6 | 43% |
| Science communicators (journalists, bloggers, editors) | 1 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 67 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 18% |
| Student > Ph. D. Student | 11 | 16% |
| Student > Bachelor | 9 | 13% |
| Student > Master | 9 | 13% |
| Lecturer | 3 | 4% |
| Other | 4 | 6% |
| Unknown | 19 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 18 | 27% |
| Pharmacology, Toxicology and Pharmaceutical Science | 5 | 7% |
| Biochemistry, Genetics and Molecular Biology | 4 | 6% |
| Computer Science | 3 | 4% |
| Engineering | 3 | 4% |
| Other | 14 | 21% |
| Unknown | 20 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 23. 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 March 2021.
All research outputs
#1,540,327
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#115
of 891 outputs
Outputs of similar age
#33,195
of 344,659 outputs
Outputs of similar age from Journal of Cheminformatics
#2
of 14 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 93rd 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 87% 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 344,659 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.