  We are faced with an unprecedented production in scholarly publications
worldwide. Stakeholders in the digital libraries posit that the document-based
publishing paradigm has reached the limits of adequacy. Instead, structured,
machine-interpretable, fine-grained scholarly knowledge publishing as Knowledge
Graphs (KG) is strongly advocated. In this work, we develop and analyze a
large-scale structured dataset of STEM articles across 10 different
disciplines, viz. Agriculture, Astronomy, Biology, Chemistry, Computer Science,
Earth Science, Engineering, Material Science, Mathematics, and Medicine. Our
analysis is defined over a large-scale corpus comprising 60K abstracts
structured as four scientific entities process, method, material, and data.
Thus our study presents, for the first-time, an analysis of a large-scale
multidisciplinary corpus under the construct of four named entity labels that
are specifically defined and selected to be domain-independent as opposed to
domain-specific. The work is then inadvertently a feasibility test of
characterizing multidisciplinary science with domain-independent concepts.
Further, to summarize the distinct facets of scientific knowledge per concept
per discipline, a set of word cloud visualizations are offered. The
STEM-NER-60k corpus, created in this work, comprises over 1M extracted entities
from 60k STEM articles obtained from a major publishing platform and is
publicly released https://github.com/jd-coderepos/stem-ner-60k.
