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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Multidimensional Recurrence Quantification Analysis (MdRQA) for the Analysis of Multidimensional Time-Series: A Software Implementation in MATLAB and Its Application to Group-Level Data in Joint Action
|
|---|---|
| Published in |
Frontiers in Psychology, November 2016
|
| DOI | 10.3389/fpsyg.2016.01835 |
| Pubmed ID | |
| Authors |
Sebastian Wallot, Andreas Roepstorff, Dan Mønster |
| Abstract |
We introduce Multidimensional Recurrence Quantification Analysis (MdRQA) as a tool to analyze multidimensional time-series data. We show how MdRQA can be used to capture the dynamics of high-dimensional signals, and how MdRQA can be used to assess coupling between two or more variables. In particular, we describe applications of the method in research on joint and collective action, as it provides a coherent analysis framework to systematically investigate dynamics at different group levels-from individual dynamics, to dyadic dynamics, up to global group-level of arbitrary size. The Appendix in Supplementary Material contains a software implementation in MATLAB to calculate MdRQA measures. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Denmark | 2 | 17% |
| United States | 1 | 8% |
| Unknown | 9 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 58% |
| Scientists | 5 | 42% |
Mendeley readers
The data shown below were compiled from readership statistics for 154 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Austria | 2 | 1% |
| Germany | 1 | <1% |
| Turkey | 1 | <1% |
| Unknown | 150 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 36 | 23% |
| Researcher | 24 | 16% |
| Student > Master | 15 | 10% |
| Professor > Associate Professor | 12 | 8% |
| Student > Doctoral Student | 11 | 7% |
| Other | 29 | 19% |
| Unknown | 27 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 40 | 26% |
| Computer Science | 14 | 9% |
| Engineering | 14 | 9% |
| Neuroscience | 11 | 7% |
| Social Sciences | 10 | 6% |
| Other | 28 | 18% |
| Unknown | 37 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 27 August 2020.
All research outputs
#5,526,469
of 26,154,612 outputs
Outputs from Frontiers in Psychology
#8,877
of 35,022 outputs
Outputs of similar age
#96,111
of 419,770 outputs
Outputs of similar age from Frontiers in Psychology
#150
of 416 outputs
Altmetric has tracked 26,154,612 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 35,022 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.7. This one has gotten more attention than average, scoring higher than 74% 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 419,770 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 77% of its contemporaries.
We're also able to compare this research output to 416 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.