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X Demographics
Mendeley readers
Attention Score in Context
| Title |
kGCN: a graph-based deep learning framework for chemical structures
|
|---|---|
| Published in |
Journal of Cheminformatics, May 2020
|
| DOI | 10.1186/s13321-020-00435-6 |
| Pubmed ID | |
| Authors |
Ryosuke Kojima, Shoichi Ishida, Masateru Ohta, Hiroaki Iwata, Teruki Honma, Yasushi Okuno |
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 | 3 | 21% |
| India | 2 | 14% |
| United Kingdom | 1 | 7% |
| France | 1 | 7% |
| Singapore | 1 | 7% |
| Israel | 1 | 7% |
| Mexico | 1 | 7% |
| Unknown | 4 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 64% |
| Scientists | 4 | 29% |
| Science communicators (journalists, bloggers, editors) | 1 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 141 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 141 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 25 | 18% |
| Student > Ph. D. Student | 16 | 11% |
| Student > Master | 10 | 7% |
| Student > Bachelor | 10 | 7% |
| Professor | 8 | 6% |
| Other | 19 | 13% |
| Unknown | 53 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 24 | 17% |
| Computer Science | 15 | 11% |
| Biochemistry, Genetics and Molecular Biology | 9 | 6% |
| Agricultural and Biological Sciences | 7 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 6 | 4% |
| Other | 21 | 15% |
| Unknown | 59 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 14 October 2020.
All research outputs
#5,229,531
of 24,903,209 outputs
Outputs from Journal of Cheminformatics
#452
of 934 outputs
Outputs of similar age
#112,946
of 390,660 outputs
Outputs of similar age from Journal of Cheminformatics
#15
of 24 outputs
Altmetric has tracked 24,903,209 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 934 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has gotten more attention than average, scoring higher than 51% 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 390,660 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 70% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.