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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Explainable uncertainty quantifications for deep learning-based molecular property prediction
|
|---|---|
| Published in |
Journal of Cheminformatics, February 2023
|
| DOI | 10.1186/s13321-023-00682-3 |
| Pubmed ID | |
| Authors |
Chu-I Yang, Yi-Pei Li |
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 % |
|---|---|---|
| Japan | 2 | 14% |
| Ireland | 1 | 7% |
| Germany | 1 | 7% |
| United States | 1 | 7% |
| Unknown | 9 | 64% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 57% |
| Scientists | 5 | 36% |
| Science communicators (journalists, bloggers, editors) | 1 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 53 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 17% |
| Student > Master | 4 | 8% |
| Other | 2 | 4% |
| Researcher | 2 | 4% |
| Student > Bachelor | 2 | 4% |
| Other | 2 | 4% |
| Unknown | 32 | 60% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 4 | 8% |
| Environmental Science | 2 | 4% |
| Chemistry | 2 | 4% |
| Chemical Engineering | 2 | 4% |
| Computer Science | 1 | 2% |
| Other | 9 | 17% |
| Unknown | 33 | 62% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 27 March 2023.
All research outputs
#4,695,169
of 25,576,801 outputs
Outputs from Journal of Cheminformatics
#423
of 974 outputs
Outputs of similar age
#94,267
of 475,699 outputs
Outputs of similar age from Journal of Cheminformatics
#15
of 38 outputs
Altmetric has tracked 25,576,801 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 974 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 10.0. This one has gotten more attention than average, scoring higher than 56% 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 475,699 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 80% of its contemporaries.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.