Data-driven unification of 72 biomedical publication types and study designs into a hierarchical rubric
By
Smalheiser, Neil R, Menke, Joe D, Holt, Arthur W
Summary
This article presents a data-driven approach to unifying 72 biomedical publication types and study designs (PTs) into a single rubric and hierarchy. The researchers computed pairwise similarities of each PT against all others to form a similarity matrix, then performed hierarchical clustering to categorize them. Spearman correlations among PT pairs ranged from strongly negative to strongly positive (−0.732 to +0.997), with a mean of 0.176. The analysis yielded 13 clusters of PTs and 5 broader categories, providing a unified framework for classifying biomedical research types.
Source
bskyData-driven unification of 72 biomedical publication types and study designs into a hierarchical rubricacademic.oup.comKey quotes
· 4 pulledOur goal is to unify the 72 biomedical publication types and study designs (collectively, PTs) into a single rubric and hierarchy.
This is carried out in a data-driven manner by computing pairwise similarities of each PT against all others to form a similarity matrix.
Spearman correlations among PT pairs ranged from strongly negative to strongly positive (−0.732 to +0.997), with a mean of 0.176.
Overall, we obtained 13 clusters of PTs and 5 broader categories.
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