Structure of the Global Nanoscience and Nanotechnology Research Literature

Abstract

Text mining was used to extract technical intelligence from the open source global nanotechnology and nanoscience research literature. An extensive nanotechnology/ nanoscience-focused query was applied to the Science Citation Index/ Social Science Citation Index (SCI/ SSCI) databases. The nanotechnology/ nanoscience research literature technical structure (taxonomy) was obtained using computational linguistics, document clustering, and factor analysis. The nanotechnology/ nanoscience research literature infrastructure (prolific authors, key journals/ institutions/ countries, most cited authors/ journals/ documents) for each of the clusters generated by the document clustering algorithm was obtained using bibliometrics. Another novel addition was the use of phrase auto-correlation maps to show technical thrust areas based on phrase co-occurrence in Abstracts, and the use of phrase-phrase cross-correlation maps to show technical thrust areas based on phrase relations due to the sharing of common co-occurring phrases. The use of factor matrices quantified further the strength of the linkages among institutions and among countries, and validated the copublishing networks shown graphically on the maps. The ~400 most cited nanotechnology papers since 1991 were grouped, and their characteristics generated. Whereas the main analysis provided technical thrusts of all nanotechnology papers retrieved, analysis of the most cited papers allowed their unique characteristics to be displayed.

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No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 25 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.