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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A strategy to apply machine learning to small datasets in materials science
|
|---|---|
| Published in |
npj Computational Materials, May 2018
|
| DOI | 10.1038/s41524-018-0081-z |
| Authors |
Ying Zhang, Chen Ling |
X Demographics
The data shown below were collected from the profiles of 13 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 States | 2 | 15% |
| France | 1 | 8% |
| India | 1 | 8% |
| United Kingdom | 1 | 8% |
| Unknown | 8 | 62% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 11 | 85% |
| Science communicators (journalists, bloggers, editors) | 1 | 8% |
| Scientists | 1 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 866 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 866 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 184 | 21% |
| Researcher | 100 | 12% |
| Student > Master | 94 | 11% |
| Student > Bachelor | 63 | 7% |
| Student > Doctoral Student | 43 | 5% |
| Other | 118 | 14% |
| Unknown | 264 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Materials Science | 138 | 16% |
| Engineering | 121 | 14% |
| Computer Science | 64 | 7% |
| Chemistry | 50 | 6% |
| Physics and Astronomy | 49 | 6% |
| Other | 131 | 15% |
| Unknown | 313 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 19. 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 10 March 2023.
All research outputs
#1,859,806
of 25,130,202 outputs
Outputs from npj Computational Materials
#110
of 1,023 outputs
Outputs of similar age
#38,746
of 333,466 outputs
Outputs of similar age from npj Computational Materials
#3
of 13 outputs
Altmetric has tracked 25,130,202 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,023 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.5. This one has done well, scoring higher than 89% 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 333,466 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 88% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.