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Models in biology: ‘accurate descriptions of our pathetic thinking’

Overview of attention for article published in BMC Biology, April 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#21 of 1,245)
  • High Attention Score compared to outputs of the same age (99th percentile)

Mentioned by

blogs
6 blogs
twitter
218 tweeters
facebook
7 Facebook pages
googleplus
6 Google+ users
f1000
1 research highlight platform

Citations

dimensions_citation
119 Dimensions

Readers on

mendeley
701 Mendeley
citeulike
17 CiteULike
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Title
Models in biology: ‘accurate descriptions of our pathetic thinking’
Published in
BMC Biology, April 2014
DOI 10.1186/1741-7007-12-29
Pubmed ID
Authors

Jeremy Gunawardena

Abstract

In this essay I will sketch some ideas for how to think about models in biology. I will begin by trying to dispel the myth that quantitative modeling is somehow foreign to biology. I will then point out the distinction between forward and reverse modeling and focus thereafter on the former. Instead of going into mathematical technicalities about different varieties of models, I will focus on their logical structure, in terms of assumptions and conclusions. A model is a logical machine for deducing the latter from the former. If the model is correct, then, if you believe its assumptions, you must, as a matter of logic, also believe its conclusions. This leads to consideration of the assumptions underlying models. If these are based on fundamental physical laws, then it may be reasonable to treat the model as 'predictive', in the sense that it is not subject to falsification and we can rely on its conclusions. However, at the molecular level, models are more often derived from phenomenology and guesswork. In this case, the model is a test of its assumptions and must be falsifiable. I will discuss three models from this perspective, each of which yields biological insights, and this will lead to some guidelines for prospective model builders.

Twitter Demographics

The data shown below were collected from the profiles of 218 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 701 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 28 4%
United Kingdom 18 3%
Switzerland 6 <1%
Spain 6 <1%
Portugal 5 <1%
Germany 5 <1%
Mexico 5 <1%
Brazil 4 <1%
Italy 3 <1%
Other 23 3%
Unknown 598 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 214 31%
Researcher 170 24%
Student > Bachelor 69 10%
Student > Master 54 8%
Professor 52 7%
Other 113 16%
Unknown 29 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 313 45%
Biochemistry, Genetics and Molecular Biology 104 15%
Physics and Astronomy 43 6%
Engineering 36 5%
Computer Science 27 4%
Other 125 18%
Unknown 53 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 170. 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 22 February 2020.
All research outputs
#92,527
of 14,389,463 outputs
Outputs from BMC Biology
#21
of 1,245 outputs
Outputs of similar age
#1,243
of 189,764 outputs
Outputs of similar age from BMC Biology
#1
of 1 outputs
Altmetric has tracked 14,389,463 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,245 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.6. This one has done particularly well, scoring higher than 98% 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 189,764 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them