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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Using Deep Convolutional Neural Networks for Neonatal Brain Image Segmentation
|
|---|---|
| Published in |
Frontiers in Neuroscience, March 2020
|
| DOI | 10.3389/fnins.2020.00207 |
| Pubmed ID | |
| Authors |
Yang Ding, Rolando Acosta, Vicente Enguix, Sabrina Suffren, Janosch Ortmann, David Luck, Jose Dolz, Gregory A. Lodygensky |
X Demographics
The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 3 | 25% |
| United States | 2 | 17% |
| Switzerland | 1 | 8% |
| Unknown | 6 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 75% |
| Scientists | 3 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 55 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 15% |
| Student > Master | 7 | 13% |
| Researcher | 7 | 13% |
| Lecturer | 4 | 7% |
| Other | 4 | 7% |
| Other | 5 | 9% |
| Unknown | 20 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 8 | 15% |
| Neuroscience | 6 | 11% |
| Computer Science | 6 | 11% |
| Medicine and Dentistry | 2 | 4% |
| Nursing and Health Professions | 1 | 2% |
| Other | 7 | 13% |
| Unknown | 25 | 45% |
Attention Score in Context
This research output has an Altmetric Attention Score of 44. 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 12 December 2020.
All research outputs
#947,838
of 25,622,179 outputs
Outputs from Frontiers in Neuroscience
#407
of 11,639 outputs
Outputs of similar age
#25,451
of 393,987 outputs
Outputs of similar age from Frontiers in Neuroscience
#13
of 339 outputs
Altmetric has tracked 25,622,179 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% 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 particularly well, scoring higher than 96% 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 393,987 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 339 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.