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Nearest-neighbour clusters as a novel technique for assessing group associations

Overview of attention for article published in Royal Society Open Science, June 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
11 tweeters

Citations

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1 Dimensions

Readers on

mendeley
19 Mendeley
citeulike
1 CiteULike
Title
Nearest-neighbour clusters as a novel technique for assessing group associations
Published in
Royal Society Open Science, June 2015
DOI 10.1098/rsos.140232
Pubmed ID
Authors

Sean A. Rands

Abstract

When all the individuals in a social group can be easily identified, one of the simplest measures of social interaction that can be recorded is nearest-neighbour identity. Many field studies use sequential scan samples of groups to build up association metrics using these nearest-neighbour identities. Here, I describe a simple technique for identifying clusters of associated individuals within groups that uses nearest-neighbour identity data. Using computer-generated datasets with known associations, I demonstrate that this clustering technique can be used to build data suitable for association metrics, and that it can generate comparable metrics to raw nearest-neighbour data, but with much less initial data. This technique could therefore be of use where it is difficult to generate large datasets. Other situations where the technique would be useful are discussed.

Twitter Demographics

The data shown below were collected from the profiles of 11 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 5%
Unknown 18 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 37%
Researcher 6 32%
Student > Doctoral Student 2 11%
Lecturer > Senior Lecturer 1 5%
Student > Postgraduate 1 5%
Other 2 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 63%
Unspecified 3 16%
Psychology 2 11%
Neuroscience 1 5%
Medicine and Dentistry 1 5%
Other 0 0%

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 January 2015.
All research outputs
#868,102
of 6,232,855 outputs
Outputs from Royal Society Open Science
#280
of 492 outputs
Outputs of similar age
#35,270
of 189,487 outputs
Outputs of similar age from Royal Society Open Science
#2
of 8 outputs
Altmetric has tracked 6,232,855 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 492 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 54.4. This one is in the 42nd percentile – i.e., 42% 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 189,487 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 81% of its contemporaries.
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 6 of them.