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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, November 2016
|
| DOI | 10.3389/fninf.2016.00049 |
| Pubmed ID | |
| Authors |
Natalia Y. Bilenko, Jack L. Gallant |
| Abstract |
In this article we introduce Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables. Pyrcca supports CCA with or without regularization, and with or without linear, polynomial, or Gaussian kernelization. We first use an abstract example to describe Pyrcca functionality. We then demonstrate how Pyrcca can be used to analyze neuroimaging data. Specifically, we use Pyrcca to implement cross-subject comparison in a natural movie functional magnetic resonance imaging (fMRI) experiment by finding a data-driven set of functional response patterns that are similar across individuals. We validate this cross-subject comparison method in Pyrcca by predicting responses to novel natural movies across subjects. Finally, we show how Pyrcca can reveal retinotopic organization in brain responses to natural movies without the need for an explicit model. |
X Demographics
The data shown below were collected from the profiles of 23 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 | 12 | 52% |
| Netherlands | 3 | 13% |
| United Kingdom | 2 | 9% |
| Japan | 1 | 4% |
| Canada | 1 | 4% |
| Switzerland | 1 | 4% |
| Germany | 1 | 4% |
| Unknown | 2 | 9% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 13 | 57% |
| Scientists | 10 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 214 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 1% |
| France | 1 | <1% |
| Germany | 1 | <1% |
| Israel | 1 | <1% |
| Italy | 1 | <1% |
| Unknown | 207 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 55 | 26% |
| Researcher | 40 | 19% |
| Student > Master | 32 | 15% |
| Student > Bachelor | 15 | 7% |
| Student > Doctoral Student | 12 | 6% |
| Other | 30 | 14% |
| Unknown | 30 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 35 | 16% |
| Neuroscience | 29 | 14% |
| Engineering | 24 | 11% |
| Psychology | 23 | 11% |
| Agricultural and Biological Sciences | 14 | 7% |
| Other | 40 | 19% |
| Unknown | 49 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 July 2021.
All research outputs
#2,398,389
of 23,685,936 outputs
Outputs from Frontiers in Neuroinformatics
#95
of 774 outputs
Outputs of similar age
#48,600
of 419,112 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#3
of 11 outputs
Altmetric has tracked 23,685,936 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 774 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.1. This one has done well, scoring higher than 87% 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 419,112 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.