F1000Prime: an analysis of discipline-specific reader data from Mendeley

Abstract

We have used the F1000Prime recommended paper set (n= 114,582 biomedical papers) to inquire the number of Mendeley readers per (sub-) discipline via the Mendeley Application Programming Interface (API). Although the (sub-) discipline of Mendeley readers is self-assigned and not mandatory, we find that a large share (99.9%) of readers at Mendeley does share their (sub-) discipline. As expected, we find most readers of F1000Prime recommended papers work in the disciplines of biology and medicine. A network analysis reveals strong connections between the disciplines of engineering, chemistry, physics, biology, and medicine.