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. 2013;8(2):e54847.
doi: 10.1371/journal.pone.0054847. Epub 2013 Feb 11.

Phylomemetic patterns in science evolution--the rise and fall of scientific fields

David Chavalarias  1 , Jean-Philippe Cointet
Affiliations

Phylomemetic patterns in science evolution--the rise and fall of scientific fields

David Chavalarias et al. PLoS One. 2013.

Abstract

We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries, and modeled as lineage relationships between scientific fields. We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they exhibit strong regularities, with clearly identifiable phylomemetic patterns. Some structural properties of the scientific fields - in particular their density -, which are defined independently of the phylomemy reconstruction, are clearly correlated with their status and their fate in the phylomemy (like their age or their short term survival). Within the framework of a quantitative epistemology, this approach raises the question of predictibility for science evolution, and sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Steps contributing towards the reconstruction of a phylomemy.
Figure 2
Figure 2. Inter-temporal fields matching.
Figure 3
Figure 3. Example of a branch featuring branching and merging nodes.
Figure 4
Figure 4. Sample of the phylomemy reconstruction for .
The phylomemetic branches naturally cluster the scientific fields into large areas of research. The branches presented in this figure have been labeled by their most commonly occurring terms (gap junction, extra cellular matrix, etc.). Time flows from left to right (from 1991 to 2010). Color coding has been used to highlight the existence of emerging terms (in red) or recombinations (in yellow) in clusters (cf. the Results section): a term associated with two **stars indicates that it is emerging, whereas one *star indicates that it is a recombination.
Figure 5
Figure 5. Relation between fields density and their age.
A. Variation of the mean density depending on the branch age, for different values of threshold formula image. B. Dependence of the mean density on the fields’ position in the phylomemy. Fields in the phylomemy have a much higher density than ephemeral fields, and their density distribution suggests trends in the “life cycle” of thematic fields: the density grows when a new field is emerging, and decreases when the field starts to be neglected by the community. Error bars represent the 95% confidence interval. Only lower bars are plotted for better visibility.
Figure 6
Figure 6. Relation between the density of fields and their sustainability.
A. Variation of the mean density in the vicinity of emerging nodes and declining nodes. B. Empirical probability of a field being in decline, as a function of the density of the fields belonging to the phylomemetic network. Fields on emerging segments have been excluded from this analysis due to their specific density dynamics. The histogram represents the proportion of fields in each bin of densitity values. Error bars represent the 95% confidence interval.
Figure 7
Figure 7. Variations of the number nodes in each category in function of .
The phylomemetic network undergoes drastic changes in its composition for the studied range of formula image values, where the number of nodes in each category varies up to a factor 10.
Figure 8
Figure 8. Evolution of the density in the vicinity of special events.
A. Variation of density in the vicinity of a branching node. B. Variation of density in the vicinity of a merging node. Error bars represent the 95% confidence interval.
Figure 9
Figure 9. Variation of the rate of emergence (A) and conceptual recombinations (B) in the phylomemy, in the vicinity of a branching node.
We observe a rate of conceptual emergence which is above average and quickly drops at the following period; whereas the proportion of recombinations is below average and quickly increases at the following period. Error bars represent the 95% confidence interval.
Figure 10
Figure 10. Variation of the rate of emergence (A) and conceptual recombinations (B) in the phylomemy, in the vicinity of a merging node.
We observe a peak of conceptual emergence one period before merging, and a rate of conceptual emergence below average at merging nodes; while the rate of recombination is above average at merging nodes and is a monotonous increasing function in the vicinity of merging nodes. Error bars represent the 95% confidence interval.
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References

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Publication types

Grants and funding

This research has been supported by the Paris Île-de-France Complex Systems Institute (ISC-PIF), the Ecole Polytechnique, the Centre National de la Recherche Scientifique (CNRS), the INRA (INRA-SenS) and the ADEME (grant n°1204C0003). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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