You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
The kernel-weighted local polynomial regression (KwLPR) approach: an efficient, novel tool for development of QSAR/QSAAR toxicity extrapolation models
|
|---|---|
| Published in |
Journal of Cheminformatics, February 2021
|
| DOI | 10.1186/s13321-021-00484-5 |
| Pubmed ID | |
| Authors |
Agnieszka Gajewicz-Skretna, Supratik Kar, Magdalena Piotrowska, Jerzy Leszczynski |
X Demographics
The data shown below were collected from the profiles of 10 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 | 20% |
| Sweden | 2 | 20% |
| United Kingdom | 1 | 10% |
| United States | 1 | 10% |
| Unknown | 4 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 5 | 50% |
| Members of the public | 4 | 40% |
| Science communicators (journalists, bloggers, editors) | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 37 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 11% |
| Student > Doctoral Student | 3 | 8% |
| Other | 2 | 5% |
| Student > Ph. D. Student | 2 | 5% |
| Student > Master | 2 | 5% |
| Other | 5 | 14% |
| Unknown | 19 | 51% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 5 | 14% |
| Mathematics | 2 | 5% |
| Engineering | 2 | 5% |
| Social Sciences | 2 | 5% |
| Computer Science | 1 | 3% |
| Other | 6 | 16% |
| Unknown | 19 | 51% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 28 February 2021.
All research outputs
#6,343,339
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#514
of 891 outputs
Outputs of similar age
#155,639
of 521,827 outputs
Outputs of similar age from Journal of Cheminformatics
#18
of 28 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
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 is in the 41st percentile – i.e., 41% 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 521,827 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 69% 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 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.