An analysis of the abstracts presented at the annual meetings of the Society for Neuroscience from 2001 to 2006
J. M. Lin, J. W. Bohland, P. Andrews, G. Burns, C. B. Allen, P. P., Mitra

TL;DR
This study analyzed abstracts from the Society for Neuroscience meetings (2001-2006) using NLP and data visualization to reveal research trends, community structure, and demographic patterns in neuroscience.
Contribution
It provides a comprehensive, data-driven overview of neuroscience research dynamics, demographics, and community structure over six years, using advanced data processing techniques.
Findings
High geographical concentration in northeastern US
60% of authors appeared only once in six years
Growth in behavioral neuroscience, decline in cellular/molecular neuroscience
Abstract
We extracted and processed abstract data from the SFN annual meeting abstracts during the period 2001-2006, using techniques and software from natural language processing, database management, and data visualization and analysis. An important first step in the process was the application of data cleaning and disambiguation methods to construct a unified database, since the data were too noisy to be of full utility in the raw form initially available. The resulting co-author graph in 2006, for example, had 39,645 nodes (with an estimated 6% error rate in our disambiguation of similar author names) and 13,979 abstracts, with an average of 1.5 abstracts per author, 4.3 authors per abstract, and 5.96 collaborators per author (including all authors on shared abstracts). Recent work in related areas has focused on reputational indices such as highly cited papers or scientists and journal…
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