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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, September 2018
|
| DOI | 10.3389/fncom.2018.00076 |
| Pubmed ID | |
| Authors |
Nitin J. Williams, Ian Daly, Slawomir J. Nasuto |
X Demographics
The data shown below were collected from the profiles of 9 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 | 22% |
| United Kingdom | 1 | 11% |
| Unknown | 6 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 67% |
| Scientists | 3 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 96 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 31 | 32% |
| Researcher | 19 | 20% |
| Student > Bachelor | 9 | 9% |
| Student > Master | 5 | 5% |
| Professor | 4 | 4% |
| Other | 13 | 14% |
| Unknown | 15 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 30 | 31% |
| Engineering | 13 | 14% |
| Computer Science | 9 | 9% |
| Psychology | 6 | 6% |
| Medicine and Dentistry | 5 | 5% |
| Other | 11 | 11% |
| Unknown | 22 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 06 October 2018.
All research outputs
#7,238,286
of 26,017,215 outputs
Outputs from Frontiers in Computational Neuroscience
#327
of 1,476 outputs
Outputs of similar age
#117,463
of 355,313 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#3
of 25 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1,476 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 done well, scoring higher than 77% 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 355,313 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.