Abstract
We propose an original approach to describe the scientific progress in a quantitative way. Using innovative Machine Learning techniques we create a vector representation for the PACS codes and we use them to represent the relative movements of the various domains of Physics in a multi-dimensional space. This methodology unveils about 25 years of scientific trends, enables us to predict innovative couplings of fields, and illustrates how Nobel Prize papers and APS milestones drive the future convergence of previously unrelated fields.
Highlights
Accepted: May 15, 2020Published: June 18, 2020Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; we enable the publication of all of the content of peer review and author responses alongside final, published articles
Scientific progress [1] has been already investigated from multiple points of view [2], that range from the study of scientific careers and the evolution of single scientific fields to the mutual impacts between science and society
In this paper we use the APS database of physics articles to build a multi-dimensional space to investigate the relative motion of scientific fields, as defined by the PACS codes
Summary
Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; we enable the publication of all of the content of peer review and author responses alongside final, published articles. Scientific progress [1] has been already investigated from multiple points of view [2], that range from the study of scientific careers and the evolution of single scientific fields to the mutual impacts between science and society This latter issue is greatly influenced by the availability of prediction models. While in [21] this approach was introduced and used to forecast new combinations of the technological codes to make prediction on the future patenting activity, here we aim to quantitatively measure scientific trends in the Physics literature by looking at the dynamics PACS codes This enables us to predict new combinations of fields and to assess the impact of extra-ordinary contributions such as Nobel Prize papers and APS milestones. We discuss in more detail the database and the methodology we used to build our representation of PACS from the data
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