Principles of scientific research team formation and evolution
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Abstract (arXiv)
Research teams are the fundamental social unit of science, and yet there is currently no model that describes their basic property: size. In most fields teams have grown significantly in recent decades. We show that this is partly due to the change in the character of team-size distribution. We explain these changes with a comprehensive yet straightforward model of how teams of different sizes emerge and grow. This model accurately reproduces the evolution of empirical team-size distribution over the period of 50 years. The modeling reveals that there are two modes of knowledge production. The first and more fundamental mode employs relatively small, core teams. Core teams form by a Poisson process and produce a Poisson distribution of team sizes in which larger teams are exceedingly rare. The second mode employs extended teams, which started as core teams, but subsequently accumulated new members proportional to the past productivity of their members. Given time, this mode gives rise to a power-law tail of large teams (10-1000 members), which features in many fields today. Based on this model we construct an analytical functional form that allows the contribution of different modes of authorship to be determined directly from the data and is applicable to any field. The model also offers a solid foundation for studying other social aspects of science, such as productivity and collaboration.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Mar 2014.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 3; 2025: 1; 2026: 1.
7 papers.
- 2024Generating Interesting Scientific Ideas using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaderscited by 21
- 2026EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discoverycited by 2
- 2021A Measure of Research Tastecited by 1
- 2025The Effect of Gender Diversity on Scientific Team Impact: A Team Roles Perspectivecited by 1
- 2021Generalization of graph network inferences in higher-order graphical modelscited by 0
- 2024Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Modelcited by 0
- 2024Collaborative Team Recognition: A Core Plus Extension Structurecited by 0
- 2026EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discovery20 May 2026
- 2025The Effect of Gender Diversity on Scientific Team Impact: A Team Roles Perspective29 Dec 2025
- 2024Collaborative Team Recognition: A Core Plus Extension Structure7 Jun 2024
- 2024Generating Interesting Scientific Ideas using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders27 May 2024
- 2024Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model28 Feb 2024
- 2021Generalization of graph network inferences in higher-order graphical models12 Jul 2021
- 2021A Measure of Research Taste17 May 2021