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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Early Prediction of Cognitive Deficit in Very Preterm Infants Using Brain Structural Connectome With Transfer Learning Enhanced Deep Convolutional Neural Networks
|
|---|---|
| Published in |
Frontiers in Neuroscience, September 2020
|
| DOI | 10.3389/fnins.2020.00858 |
| Pubmed ID | |
| Authors |
Ming Chen, Hailong Li, Jinghua Wang, Weihong Yuan, Mekbib Altaye, Nehal A. Parikh, Lili He |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 22% |
| Unknown | 7 | 78% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 56% |
| Scientists | 2 | 22% |
| Science communicators (journalists, bloggers, editors) | 1 | 11% |
| Practitioners (doctors, other healthcare professionals) | 1 | 11% |
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 % |
|---|---|---|
| Student > Ph. D. Student | 7 | 19% |
| Student > Master | 6 | 16% |
| Researcher | 3 | 8% |
| Professor | 2 | 5% |
| Lecturer | 1 | 3% |
| Other | 3 | 8% |
| Unknown | 15 | 41% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 14% |
| Medicine and Dentistry | 3 | 8% |
| Psychology | 3 | 8% |
| Agricultural and Biological Sciences | 2 | 5% |
| Engineering | 2 | 5% |
| Other | 5 | 14% |
| Unknown | 17 | 46% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. 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 15 October 2020.
All research outputs
#2,425,962
of 25,619,480 outputs
Outputs from Frontiers in Neuroscience
#1,445
of 11,639 outputs
Outputs of similar age
#63,140
of 430,921 outputs
Outputs of similar age from Frontiers in Neuroscience
#102
of 346 outputs
Altmetric has tracked 25,619,480 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,639 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has done well, scoring higher than 87% 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 430,921 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 85% of its contemporaries.
We're also able to compare this research output to 346 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 70% of its contemporaries.