A detailed open access model of the PubMed literature.

Scientific Data
Kevin W BoyackRichard Klavans

Abstract

Portfolio analysis is a fundamental practice of organizational leadership and is a necessary precursor of strategic planning. Successful application requires a highly detailed model of research options. We have constructed a model, the first of its kind, that accurately characterizes these options for the biomedical literature. The model comprises over 18 million PubMed documents from 1996-2019. Document relatedness was measured using a hybrid citation analysis + text similarity approach. The resulting 606.6 million document-to-document links were used to create 28,743 document clusters and an associated visual map. Clusters are characterized using metadata (e.g., phrases, MeSH) and over 20 indicators (e.g., funding, patent activity). The map and cluster-level data are embedded in Tableau to provide an interactive model enabling in-depth exploration of a research portfolio. Two example usage cases are provided, one to identify specific research opportunities related to coronavirus, and the second to identify research strengths of a large cohort of African American and Native American researchers at the University of Michigan Medical School.

References

Nov 1, 2007·BMC Bioinformatics·Jimmy Lin, W John Wilbur
Jul 19, 2012·PloS One·Katy BörnerKevin W Boyack
Mar 28, 2019·Scientific Reports·V A TraagN J van Eck
Oct 11, 2019·PLoS Biology·B Ian HutchinsGeorge M Santangelo
Oct 11, 2019·PLoS Biology·B Ian HutchinsGeorge M Santangelo
Oct 22, 2019·Science Advances·Travis A HoppeGeorge M Santangelo
Jun 28, 2020·Scientific Data·Jian XuYing Ding
Sep 16, 2020·PloS One·Richard KlavansDewey A Murdick

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Citations

Mar 2, 2021·PLoS Biology·Stylianos SerghiouJohn P A Ioannidis

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Methods Mentioned

BETA
four hybrids

Software Mentioned

SciVal
Tableau
Tableau Reader
METHDISC
Star Metrics
Scopus
PHRASE
Figshare
MESH
iCite2

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