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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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Mentioned by

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2 tweeters

Citations

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42 Dimensions

Readers on

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140 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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 140 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 4%
Switzerland 2 1%
Denmark 1 <1%
United Kingdom 1 <1%
Portugal 1 <1%
China 1 <1%
Japan 1 <1%
India 1 <1%
Netherlands 1 <1%
Other 0 0%
Unknown 125 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 27%
Researcher 21 15%
Student > Master 18 13%
Student > Bachelor 15 11%
Unspecified 12 9%
Other 35 25%
Unknown 1 <1%
Readers by discipline Count As %
Agricultural and Biological Sciences 59 42%
Unspecified 17 12%
Computer Science 14 10%
Physics and Astronomy 12 9%
Engineering 9 6%
Other 28 20%
Unknown 1 <1%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 20 February 2013.
All research outputs
#8,261,479
of 13,175,533 outputs
Outputs from Interface Focus
#215
of 348 outputs
Outputs of similar age
#54,795
of 98,118 outputs
Outputs of similar age from Interface Focus
#3
of 7 outputs
Altmetric has tracked 13,175,533 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 348 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one is in the 29th percentile – i.e., 29% 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 98,118 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.