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Big data need big theory too

Overview of attention for article published in Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences, November 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#8 of 2,105)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
2 news outlets
twitter
297 tweeters
facebook
4 Facebook pages
wikipedia
2 Wikipedia pages
googleplus
1 Google+ user

Citations

dimensions_citation
50 Dimensions

Readers on

mendeley
171 Mendeley
citeulike
5 CiteULike
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Title
Big data need big theory too
Published in
Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences, November 2016
DOI 10.1098/rsta.2016.0153
Pubmed ID
Authors

Peter V. Coveney, Edward R. Dougherty, Roger R. Highfield

Abstract

The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales. Here, we point out the weaknesses of pure big data approaches with particular focus on biology and medicine, which fail to provide conceptual accounts for the processes to which they are applied. No matter their 'depth' and the sophistication of data-driven methods, such as artificial neural nets, in the end they merely fit curves to existing data. Not only do these methods invariably require far larger quantities of data than anticipated by big data aficionados in order to produce statistically reliable results, but they can also fail in circumstances beyond the range of the data used to train them because they are not designed to model the structural characteristics of the underlying system. We argue that it is vital to use theory as a guide to experimental design for maximal efficiency of data collection and to produce reliable predictive models and conceptual knowledge. Rather than continuing to fund, pursue and promote 'blind' big data projects with massive budgets, we call for more funding to be allocated to the elucidation of the multiscale and stochastic processes controlling the behaviour of complex systems, including those of life, medicine and healthcare.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'.

Twitter Demographics

The data shown below were collected from the profiles of 297 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 171 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 1%
United States 2 1%
Italy 1 <1%
Luxembourg 1 <1%
United Kingdom 1 <1%
Belgium 1 <1%
Unknown 163 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 38 22%
Student > Ph. D. Student 30 18%
Student > Master 17 10%
Professor 16 9%
Unspecified 16 9%
Other 54 32%
Readers by discipline Count As %
Unspecified 26 15%
Agricultural and Biological Sciences 20 12%
Computer Science 17 10%
Engineering 13 8%
Social Sciences 12 7%
Other 83 49%

Attention Score in Context

This research output has an Altmetric Attention Score of 216. 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 15 September 2019.
All research outputs
#60,258
of 13,533,438 outputs
Outputs from Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences
#8
of 2,105 outputs
Outputs of similar age
#2,733
of 266,666 outputs
Outputs of similar age from Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences
#1
of 44 outputs
Altmetric has tracked 13,533,438 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 2,105 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.8. This one has done particularly well, scoring higher than 99% 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 266,666 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 98% of its contemporaries.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.