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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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Example of a branch featuring branching and merging nodes.
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pone-0054847-g003: Example of a branch featuring branching and merging nodes.

Mentions: Before describing the structure of phylomemetic networks, various definitions need to be given, as summarized in Fig. 3. Connected components of the phylomemetic network are called branches, and generally correspond to large, clearly-cut domains, that is a set of scientific fields that have evolved with a common scientific background, but which can potentially address many different issues. For each branch, the most frequent terms are considered to automatically generate a label and acquire a general description of the issues studied in these large domains (some example are given in Fig. 4).


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

Chavalarias D, Cointet JP - PLoS ONE (2013)

Example of a branch featuring branching and merging nodes.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0054847-g003: Example of a branch featuring branching and merging nodes.
Mentions: Before describing the structure of phylomemetic networks, various definitions need to be given, as summarized in Fig. 3. Connected components of the phylomemetic network are called branches, and generally correspond to large, clearly-cut domains, that is a set of scientific fields that have evolved with a common scientific background, but which can potentially address many different issues. For each branch, the most frequent terms are considered to automatically generate a label and acquire a general description of the issues studied in these large domains (some example are given in Fig. 4).

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