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.

本文旨在通过针对 10 个不同学科的 STEM 论文提供的一个大规模结构数据集分析研究，分析一个大型多学科语料库的可行性，进而为科学知识的各个方面提供总结。

STEM科学的过程、方法、材料和数据命名实体概述