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

Chavalarias D, Cointet JP - PLoS ONE (2013)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Complex Systems Institute of Paris Ile-de-France, Paris, France. david.chavalarias@ehess.fr

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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Related in: MedlinePlus

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  values, where the number of nodes in each category varies up to a factor 10.
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pone-0054847-g007: 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 values, where the number of nodes in each category varies up to a factor 10.

Mentions: It is noteworthy to mention that these patterns are very stable for , despite the fact that the phylomemetic network undergoes drastic changes in its composition for this range of values. Indeed, as shown on Fig. 7, the number of nodes in each category can vary up to a factor 10.


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

Chavalarias D, Cointet JP - PLoS ONE (2013)

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  values, where the number of nodes in each category varies up to a factor 10.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3569444&req=5

pone-0054847-g007: 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 values, where the number of nodes in each category varies up to a factor 10.
Mentions: It is noteworthy to mention that these patterns are very stable for , despite the fact that the phylomemetic network undergoes drastic changes in its composition for this range of values. Indeed, as shown on Fig. 7, the number of nodes in each category can vary up to a factor 10.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Complex Systems Institute of Paris Ile-de-France, Paris, France. david.chavalarias@ehess.fr

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.

Show MeSH
Related in: MedlinePlus