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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules
|
|---|---|
| Published in |
Journal of Cheminformatics, May 2018
|
| DOI | 10.1186/s13321-018-0280-0 |
| Pubmed ID | |
| Authors |
Ilia Korvigo, Maxim Holmatov, Anatolii Zaikovskii, Mikhail Skoblov |
X Demographics
The data shown below were collected from the profiles of 6 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 | 1 | 17% |
| United States | 1 | 17% |
| Spain | 1 | 17% |
| Germany | 1 | 17% |
| Israel | 1 | 17% |
| Unknown | 1 | 17% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 67% |
| Science communicators (journalists, bloggers, editors) | 1 | 17% |
| Scientists | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 74 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 23% |
| Researcher | 12 | 16% |
| Student > Master | 9 | 12% |
| Lecturer | 4 | 5% |
| Other | 4 | 5% |
| Other | 10 | 14% |
| Unknown | 18 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 19 | 26% |
| Chemistry | 10 | 14% |
| Agricultural and Biological Sciences | 6 | 8% |
| Engineering | 5 | 7% |
| Biochemistry, Genetics and Molecular Biology | 3 | 4% |
| Other | 8 | 11% |
| Unknown | 23 | 31% |
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 05 July 2019.
All research outputs
#8,057,120
of 24,903,209 outputs
Outputs from Journal of Cheminformatics
#618
of 934 outputs
Outputs of similar age
#129,847
of 336,245 outputs
Outputs of similar age from Journal of Cheminformatics
#9
of 11 outputs
Altmetric has tracked 24,903,209 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
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 is in the 33rd percentile – i.e., 33% 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 336,245 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 60% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.