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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Measuring Efficiency of Semi-automated Brain Tumor Segmentation by Simulating User Interaction
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, April 2020
|
| DOI | 10.3389/fncom.2020.00032 |
| Pubmed ID | |
| Authors |
David Gering, Aikaterini Kotrotsou, Brett Young-Moxon, Neal Miller, Aaron Avery, Lisa Kohli, Haley Knapp, Jeffrey Hoffman, Roger Chylla, Linda Peitzman, Thomas R. Mackie |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 25% |
| United States | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 15 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 3 | 20% |
| Researcher | 2 | 13% |
| Unspecified | 1 | 7% |
| Student > Doctoral Student | 1 | 7% |
| Lecturer > Senior Lecturer | 1 | 7% |
| Other | 2 | 13% |
| Unknown | 5 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 2 | 13% |
| Medicine and Dentistry | 2 | 13% |
| Computer Science | 2 | 13% |
| Unspecified | 1 | 7% |
| Biochemistry, Genetics and Molecular Biology | 1 | 7% |
| Other | 2 | 13% |
| Unknown | 5 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 08 May 2020.
All research outputs
#15,292,727
of 25,563,770 outputs
Outputs from Frontiers in Computational Neuroscience
#616
of 1,469 outputs
Outputs of similar age
#205,744
of 387,880 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#17
of 27 outputs
Altmetric has tracked 25,563,770 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,469 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has gotten more attention than average, scoring higher than 55% 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 387,880 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.