Exploring Scientific Exchange in Agricultural Meteorology with Network Analysis

TL;DR

This paper uses temporal network analysis to study the evolution and key members of the International Meteorological Organization's agricultural commission from 1913 to 1947, revealing insights into scientific community dynamics and historical impacts.

Contribution

It applies temporal network analysis to historical scientific data, demonstrating how this method uncovers community evolution and influential actors over time.

Findings

Identified key members and their influence within the commission.

Showed how historical events affected the organization's structure.

Highlighted the strengths and limitations of temporal networks in historical analysis.

Abstract

Network analysis is becoming increasingly relevant in the historical investigation of scientific communities and their knowledge circulation process, because it offers the opportunity to explore and visualize connections amog scientific actors on a scale qualitatively different from traditional historical methods. Temporal networks are especially suitable for this task, as they allow to investigate the evolution of scientific communities over time. In this paper we will rely on the analytical tools provided by temporal networks to examine the technical comission on agriculture (1913 - 1947) established by the International Meteorological Organization (IMO). By using the membership data available on this commission, we will investigate how this scientific community evolved over the decades, who were its key members, which national groups were represented, and how historical events, such as the two world wars, impacted on the work of this organization. This will give us an insight into the knowledge circulation process of this scientific body, as the IMO was an international organization based on voluntary cooperation and its work was first and foremost the immediate consequence of the interaction amog its members. In our paper we will rely on centrality measures (eigenvector, joint, and conditional centrality) to understand the structure of the comission's network, and we will constantly point out the strengths and weaknesses of temporal networks in the analysis of historical data.