TechMiner: Analysis of bibliographic datasets using Python

Abstract

Bibliometric analyses and tech-mining studies are assuming a crucial role in research, covering the need to synthetize current knowledge; however, they can be difficult to conduct due to several analyses employ different software tools, some of them available only under commercial licenses, and in some cases, requiring advanced programming skills for their use. This article introduces a graphical Python-based application that performs bibliometric and statistical analyses of data sets extracted from Scopus. TechMiner allows the user to run basic task as data cleaning and author disambiguation and advanced analyses as keyword clustering or citation analysis.

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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 — 11 citations exist upstream.

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