Automatic extraction of academic collaborations in neuroimaging

Our ability to quantitatively study large-scale social and behavioural phenomena such as peer influence and confirmation bias within scientific circles rest on quality and relevant data. Yet the compilation of specific coauthorship databases are often restricted to certain well-defined fields of study or publication resources, limiting the extent and depth by which investigations can be performed. Ultimately, we aim to understand how the social construct and its underlying dynamics influence the trajectories of scientific endeavors. This work is motivated by an interest in observing social patterns, monitoring their evolution, and possibly understanding the emergence and spreading of ideas and their biases in the neuroimaging community; central themes to deciphering facts from opinions. However, before being able to fully investigate and address these fundamental and inherently complex questions, we need to address the extraction and validation of data. The goal of this project was to leverage publicly available information on Google Scholar (GS) to automatically extract coauthorship networks. <br>The tool can be accessed through a public website. The site is constructed using a set of openly accessible libraries allowing the display of coauthorship networks as interactive graphs. Visitors can peruse a set of pre-computed networks extracted using custom Python scripts designed to crawl GS based on a set of predefined constraints (e.g. search topic, publication journal). The proposed interface offers seamless manipulation to keep interaction straightforward and easy to use. The simplicity of the design aims to reach a maximum number of users, assuming a minimal level of technical knowledge.