The study of the structure of science and its evolution, often referred to as ‘science of science,’ has become a pivotal area of research, leading to significant breakthroughs across various fields. This domain leverages statistical and quantitative methods to assess and compare the quality, quantity, diversity, and other characteristics of research entities, such as articles, journals, and authors. These methods have empowered stakeholders to develop and implement policies that enhance the scientific environment. Recent advancements in network science and neural networks have further accelerated discoveries and facilitated the dissemination of scientific knowledge.
Despite these advancements, there remain gaps in understanding the complex interactions within scientific networks, such as citation networks, co-authorship patterns, and the evolution of scientific topics. The availability of massive datasets has opened new avenues for incorporating sophisticated data science and machine learning techniques, yet the full potential of these tools in the science of science field is still being explored.
This Research Topic aims to explore complex network-based approaches and analyses within the field of science of science. The overarching objective is to deepen our understanding of the dynamics of scientific evolution, collaboration, and the growth of research areas. By examining these dynamics, the Research Topic seeks to answer critical questions about the mechanisms driving scientific progress and the factors influencing the success of scientific entities.
To gather further insights into complex network-based approaches and analyses in the field of science of science, we welcome articles addressing, but not limited to, the following themes:
• models of dynamics, including scientific evolution, citations, and collaboration
• multi-agent discovery and mechanistic models for citation dynamics
• analysis of textual features of scientific documents and career trajectories of scholars
• birth, growth, and death of fields of study, and comparison between distinct fields
• temporal series analysis obtained from scientific entities
• representation and analysis of science using time-varying networks
• development and analysis of network science tools
• multilayer and multiplex representations of knowledge
• analysis of high-order structures, such as hypergraphs and simplices
• academic productivity and bibliometric indices
• visualization techniques for scientific analysis
• case studies of specific science of science questions
• querying, storing, and handling large scholarly datasets
• predicting the success of scientific entities and embedding scientific entities.
The study of the structure of science and its evolution, often referred to as ‘science of science,’ has become a pivotal area of research, leading to significant breakthroughs across various fields. This domain leverages statistical and quantitative methods to assess and compare the quality, quantity, diversity, and other characteristics of research entities, such as articles, journals, and authors. These methods have empowered stakeholders to develop and implement policies that enhance the scientific environment. Recent advancements in network science and neural networks have further accelerated discoveries and facilitated the dissemination of scientific knowledge.
Despite these advancements, there remain gaps in understanding the complex interactions within scientific networks, such as citation networks, co-authorship patterns, and the evolution of scientific topics. The availability of massive datasets has opened new avenues for incorporating sophisticated data science and machine learning techniques, yet the full potential of these tools in the science of science field is still being explored.
This Research Topic aims to explore complex network-based approaches and analyses within the field of science of science. The overarching objective is to deepen our understanding of the dynamics of scientific evolution, collaboration, and the growth of research areas. By examining these dynamics, the Research Topic seeks to answer critical questions about the mechanisms driving scientific progress and the factors influencing the success of scientific entities.
To gather further insights into complex network-based approaches and analyses in the field of science of science, we welcome articles addressing, but not limited to, the following themes:
• models of dynamics, including scientific evolution, citations, and collaboration
• multi-agent discovery and mechanistic models for citation dynamics
• analysis of textual features of scientific documents and career trajectories of scholars
• birth, growth, and death of fields of study, and comparison between distinct fields
• temporal series analysis obtained from scientific entities
• representation and analysis of science using time-varying networks
• development and analysis of network science tools
• multilayer and multiplex representations of knowledge
• analysis of high-order structures, such as hypergraphs and simplices
• academic productivity and bibliometric indices
• visualization techniques for scientific analysis
• case studies of specific science of science questions
• querying, storing, and handling large scholarly datasets
• predicting the success of scientific entities and embedding scientific entities.
