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Neurotree: A Collaborative, Graphical Database of the Academic Genealogy of Neuroscience

Overview of attention for article published in PLOS ONE, October 2012
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Title
Neurotree: A Collaborative, Graphical Database of the Academic Genealogy of Neuroscience
Published in
PLOS ONE, October 2012
DOI 10.1371/journal.pone.0046608
Pubmed ID
Authors

Stephen V. David, Benjamin Y. Hayden

Abstract

Neurotree is an online database that documents the lineage of academic mentorship in neuroscience. Modeled on the tree format typically used to describe biological genealogies, the Neurotree web site provides a concise summary of the intellectual history of neuroscience and relationships between individuals in the current neuroscience community. The contents of the database are entirely crowd-sourced: any internet user can add information about researchers and the connections between them. As of July 2012, Neurotree has collected information from 10,000 users about 35,000 researchers and 50,000 mentor relationships, and continues to grow. The present report serves to highlight the utility of Neurotree as a resource for academic research and to summarize some basic analysis of its data. The tree structure of the database permits a variety of graphical analyses. We find that the connectivity and graphical distance between researchers entered into Neurotree early has stabilized and thus appears to be mostly complete. The connectivity of more recent entries continues to mature. A ranking of researcher fecundity based on their mentorship reveals a sustained period of influential researchers from 1850-1950, with the most influential individuals active at the later end of that period. Finally, a clustering analysis reveals that some subfields of neuroscience are reflected in tightly interconnected mentor-trainee groups.

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The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
United Kingdom 1 1%
Singapore 1 1%
Unknown 65 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 29%
Student > Ph. D. Student 12 17%
Student > Master 6 9%
Professor > Associate Professor 5 7%
Student > Postgraduate 4 6%
Other 10 14%
Unknown 12 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 13%
Computer Science 8 12%
Medicine and Dentistry 6 9%
Psychology 5 7%
Social Sciences 4 6%
Other 21 30%
Unknown 16 23%