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Attention Score in Context
| Title |
Parallel workflow tools to facilitate human brain MRI post-processing
|
|---|---|
| Published in |
Frontiers in Neuroscience, May 2015
|
| DOI | 10.3389/fnins.2015.00171 |
| Pubmed ID | |
| Authors |
Zaixu Cui, Chenxi Zhao, Gaolang Gong |
| Abstract |
Multi-modal magnetic resonance imaging (MRI) techniques are widely applied in human brain studies. To obtain specific brain measures of interest from MRI datasets, a number of complex image post-processing steps are typically required. Parallel workflow tools have recently been developed, concatenating individual processing steps and enabling fully automated processing of raw MRI data to obtain the final results. These workflow tools are also designed to make optimal use of available computational resources and to support the parallel processing of different subjects or of independent processing steps for a single subject. Automated, parallel MRI post-processing tools can greatly facilitate relevant brain investigations and are being increasingly applied. In this review, we briefly summarize these parallel workflow tools and discuss relevant issues. |
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 % |
|---|---|---|
| United Kingdom | 1 | 33% |
| Switzerland | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | 3% |
| France | 1 | 3% |
| Unknown | 32 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 26% |
| Researcher | 6 | 18% |
| Student > Master | 5 | 15% |
| Student > Postgraduate | 4 | 12% |
| Professor > Associate Professor | 3 | 9% |
| Other | 3 | 9% |
| Unknown | 4 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 24% |
| Medicine and Dentistry | 5 | 15% |
| Psychology | 5 | 15% |
| Neuroscience | 4 | 12% |
| Physics and Astronomy | 1 | 3% |
| Other | 6 | 18% |
| Unknown | 5 | 15% |
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 03 June 2015.
All research outputs
#15,983,535
of 25,374,647 outputs
Outputs from Frontiers in Neuroscience
#6,982
of 11,542 outputs
Outputs of similar age
#148,694
of 279,148 outputs
Outputs of similar age from Frontiers in Neuroscience
#82
of 124 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one is in the 38th percentile – i.e., 38% 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 279,148 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 124 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.