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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A hybrid framework for improving uncertainty quantification in deep learning-based QSAR regression modeling
|
|---|---|
| Published in |
Journal of Cheminformatics, September 2021
|
| DOI | 10.1186/s13321-021-00551-x |
| Pubmed ID | |
| Authors |
Dingyan Wang, Jie Yu, Lifan Chen, Xutong Li, Hualiang Jiang, Kaixian Chen, Mingyue Zheng, Xiaomin Luo |
X Demographics
The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 2 | 22% |
| United States | 1 | 11% |
| Sweden | 1 | 11% |
| Israel | 1 | 11% |
| Unknown | 4 | 44% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 44% |
| Members of the public | 4 | 44% |
| Science communicators (journalists, bloggers, editors) | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 39 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 23% |
| Researcher | 7 | 18% |
| Student > Master | 4 | 10% |
| Professor | 3 | 8% |
| Other | 1 | 3% |
| Other | 2 | 5% |
| Unknown | 13 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 5 | 13% |
| Social Sciences | 3 | 8% |
| Engineering | 3 | 8% |
| Materials Science | 3 | 8% |
| Computer Science | 2 | 5% |
| Other | 8 | 21% |
| Unknown | 15 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 07 October 2021.
All research outputs
#6,982,354
of 23,344,526 outputs
Outputs from Journal of Cheminformatics
#573
of 862 outputs
Outputs of similar age
#138,215
of 433,089 outputs
Outputs of similar age from Journal of Cheminformatics
#19
of 28 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 862 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 32nd percentile – i.e., 32% 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 433,089 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 67% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.