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Quantifying the rise and fall of scientific fields
Science advances by pushing the boundaries of the adjacent possible. While the global scientific enterprise grows at an exponential pace, at the mesoscopic level the exploration and exploitation of research ideas are reflected through the rise and fall of research fields. The empirical literature ha...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223313/ https://www.ncbi.nlm.nih.gov/pubmed/35737658 http://dx.doi.org/10.1371/journal.pone.0270131 |
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author | Singh, Chakresh Kumar Barme, Emma Ward, Robert Tupikina, Liubov Santolini, Marc |
author_facet | Singh, Chakresh Kumar Barme, Emma Ward, Robert Tupikina, Liubov Santolini, Marc |
author_sort | Singh, Chakresh Kumar |
collection | PubMed |
description | Science advances by pushing the boundaries of the adjacent possible. While the global scientific enterprise grows at an exponential pace, at the mesoscopic level the exploration and exploitation of research ideas are reflected through the rise and fall of research fields. The empirical literature has largely studied such dynamics on a case-by-case basis, with a focus on explaining how and why communities of knowledge production evolve. Although fields rise and fall on different temporal and population scales, they are generally argued to pass through a common set of evolutionary stages. To understand the social processes that drive these stages beyond case studies, we need a way to quantify and compare different fields on the same terms. In this paper we develop techniques for identifying common patterns in the evolution of scientific fields and demonstrate their usefulness using 1.5 million preprints from the arXiv repository covering 175 research fields spanning Physics, Mathematics, Computer Science, Quantitative Biology and Quantitative Finance. We show that fields consistently follow a rise and fall pattern captured by a two parameters right-tailed Gumbel temporal distribution. We introduce a field-specific re-scaled time and explore the generic properties shared by articles and authors at the creation, adoption, peak, and decay evolutionary phases. We find that the early phase of a field is characterized by disruptive works mixing of cognitively distant fields written by small teams of interdisciplinary authors, while late phases exhibit the role of specialized, large teams building on the previous works in the field. This method provides foundations to quantitatively explore the generic patterns underlying the evolution of research fields in science, with general implications in innovation studies. |
format | Online Article Text |
id | pubmed-9223313 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-92233132022-06-24 Quantifying the rise and fall of scientific fields Singh, Chakresh Kumar Barme, Emma Ward, Robert Tupikina, Liubov Santolini, Marc PLoS One Research Article Science advances by pushing the boundaries of the adjacent possible. While the global scientific enterprise grows at an exponential pace, at the mesoscopic level the exploration and exploitation of research ideas are reflected through the rise and fall of research fields. The empirical literature has largely studied such dynamics on a case-by-case basis, with a focus on explaining how and why communities of knowledge production evolve. Although fields rise and fall on different temporal and population scales, they are generally argued to pass through a common set of evolutionary stages. To understand the social processes that drive these stages beyond case studies, we need a way to quantify and compare different fields on the same terms. In this paper we develop techniques for identifying common patterns in the evolution of scientific fields and demonstrate their usefulness using 1.5 million preprints from the arXiv repository covering 175 research fields spanning Physics, Mathematics, Computer Science, Quantitative Biology and Quantitative Finance. We show that fields consistently follow a rise and fall pattern captured by a two parameters right-tailed Gumbel temporal distribution. We introduce a field-specific re-scaled time and explore the generic properties shared by articles and authors at the creation, adoption, peak, and decay evolutionary phases. We find that the early phase of a field is characterized by disruptive works mixing of cognitively distant fields written by small teams of interdisciplinary authors, while late phases exhibit the role of specialized, large teams building on the previous works in the field. This method provides foundations to quantitatively explore the generic patterns underlying the evolution of research fields in science, with general implications in innovation studies. Public Library of Science 2022-06-23 /pmc/articles/PMC9223313/ /pubmed/35737658 http://dx.doi.org/10.1371/journal.pone.0270131 Text en © 2022 Singh et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Singh, Chakresh Kumar Barme, Emma Ward, Robert Tupikina, Liubov Santolini, Marc Quantifying the rise and fall of scientific fields |
title | Quantifying the rise and fall of scientific fields |
title_full | Quantifying the rise and fall of scientific fields |
title_fullStr | Quantifying the rise and fall of scientific fields |
title_full_unstemmed | Quantifying the rise and fall of scientific fields |
title_short | Quantifying the rise and fall of scientific fields |
title_sort | quantifying the rise and fall of scientific fields |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223313/ https://www.ncbi.nlm.nih.gov/pubmed/35737658 http://dx.doi.org/10.1371/journal.pone.0270131 |
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