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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation With Clustered Initial Points
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, April 2019
|
| DOI | 10.3389/fncom.2019.00014 |
| Pubmed ID | |
| Authors |
Mohammad Reza Mohebian, Hamid Reza Marateb, Saeed Karimimehr, Miquel Angel Mañanas, Jernej Kranjec, Ales Holobar |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 33% |
| Switzerland | 1 | 33% |
| United States | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 48 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 17% |
| Researcher | 7 | 15% |
| Student > Ph. D. Student | 7 | 15% |
| Student > Bachelor | 4 | 8% |
| Student > Doctoral Student | 2 | 4% |
| Other | 6 | 13% |
| Unknown | 14 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 15 | 31% |
| Sports and Recreations | 3 | 6% |
| Immunology and Microbiology | 2 | 4% |
| Nursing and Health Professions | 2 | 4% |
| Environmental Science | 2 | 4% |
| Other | 8 | 17% |
| Unknown | 16 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 April 2019.
All research outputs
#15,567,535
of 23,138,859 outputs
Outputs from Frontiers in Computational Neuroscience
#875
of 1,360 outputs
Outputs of similar age
#219,120
of 351,414 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#17
of 24 outputs
Altmetric has tracked 23,138,859 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,360 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 29th percentile – i.e., 29% 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 351,414 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.