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The modelling cycle for collective animal behaviour

Overview of attention for article published in Interface Focus, August 2012
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4 X users

Citations

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72 Dimensions

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189 Mendeley
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Title
The modelling cycle for collective animal behaviour
Published in
Interface Focus, August 2012
DOI 10.1098/rsfs.2012.0031
Pubmed ID
Authors

David J. T. Sumpter, Richard P. Mann, Andrea Perna

Abstract

Collective animal behaviour is the study of how interactions between individuals produce group level patterns, and why these interactions have evolved. This study has proved itself uniquely interdisciplinary, involving physicists, mathematicians, engineers as well as biologists. Almost all experimental work in this area is related directly or indirectly to mathematical models, with regular movement back and forth between models, experimental data and statistical fitting. In this paper, we describe how the modelling cycle works in the study of collective animal behaviour. We classify studies as addressing questions at different levels or linking different levels, i.e. as local, local to global, global to local or global. We also describe three distinct approaches-theory-driven, data-driven and model selection-to these questions. We show, with reference to our own research on species across different taxa, how we move between these different levels of description and how these various approaches can be applied to link levels together.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 189 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
Switzerland 2 1%
Netherlands 1 <1%
Portugal 1 <1%
Denmark 1 <1%
United Kingdom 1 <1%
Japan 1 <1%
China 1 <1%
Unknown 177 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 47 25%
Researcher 27 14%
Student > Master 24 13%
Student > Bachelor 23 12%
Professor 9 5%
Other 32 17%
Unknown 27 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 69 37%
Computer Science 15 8%
Physics and Astronomy 13 7%
Mathematics 12 6%
Engineering 9 5%
Other 35 19%
Unknown 36 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 26 March 2024.
All research outputs
#14,184,647
of 24,723,421 outputs
Outputs from Interface Focus
#348
of 606 outputs
Outputs of similar age
#96,752
of 174,139 outputs
Outputs of similar age from Interface Focus
#3
of 8 outputs
Altmetric has tracked 24,723,421 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 606 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.0. This one is in the 41st percentile – i.e., 41% 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 174,139 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.