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SoftwareX
Article . 2023 . Peer-reviewed
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SoftwareX
Article . 2023
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https://dx.doi.org/10.60692/qx...
Other literature type . 2023
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Other literature type . 2023
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TechMiner: Analysis of bibliographic datasets using Python

TechMiner: تحليل مجموعات البيانات الببليوغرافية باستخدام بايثون
Authors: Juan D. Velásquez;

TechMiner: Analysis of bibliographic datasets using Python

Abstract

Les analyses bibliométriques et les études minières technologiques assument un rôle crucial dans la recherche, couvrant la nécessité de synthétiser les connaissances actuelles ; cependant, elles peuvent être difficiles à mener en raison de plusieurs analyses utilisant différents outils logiciels, dont certains ne sont disponibles que sous licences commerciales et, dans certains cas, nécessitent des compétences avancées en programmation pour leur utilisation. Cet article présente une application graphique basée sur Python qui effectue des analyses bibliométriques et statistiques des ensembles de données extraits de Scopus. TechMiner permet à l'utilisateur d'exécuter des tâches de base comme le nettoyage des données et la désambiguïsation des auteurs et des analyses avancées comme le regroupement de mots-clés ou l'analyse des citations.

Los análisis bibliométricos y los estudios de minería tecnológica están asumiendo un papel crucial en la investigación, cubriendo la necesidad de sintetizar el conocimiento actual; sin embargo, pueden ser difíciles de realizar debido a que varios análisis emplean diferentes herramientas de software, algunas de ellas disponibles solo bajo licencias comerciales y, en algunos casos, requieren habilidades avanzadas de programación para su uso. Este artículo presenta una aplicación gráfica basada en Python que realiza análisis bibliométricos y estadísticos de conjuntos de datos extraídos de Scopus. TechMiner permite al usuario ejecutar tareas básicas como limpieza de datos y desambiguación de autores y análisis avanzados como agrupación de palabras clave o análisis de citas.

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.

تلعب التحليلات الببليومترية ودراسات التعدين التكنولوجي دورًا حاسمًا في البحث، حيث تغطي الحاجة إلى تجميع المعرفة الحالية ؛ ومع ذلك، قد يكون من الصعب إجراؤها نظرًا لأن العديد من التحليلات تستخدم أدوات برمجية مختلفة، بعضها متاح فقط بموجب تراخيص تجارية، وفي بعض الحالات، تتطلب مهارات برمجة متقدمة لاستخدامها. تقدم هذه المقالة تطبيقًا بيانيًا قائمًا على Python يقوم بإجراء تحليلات ببليومترية وإحصائية لمجموعات البيانات المستخرجة من Scopus. يسمح TechMiner للمستخدم بتشغيل المهمة الأساسية كتنظيف البيانات وتوضيح المؤلف والتحليلات المتقدمة كتجميع الكلمات الرئيسية أو تحليل الاقتباس.

Related Organizations
Keywords

Tech-mining studies, Data Analysis, Bibliometric Analysis and Research Evaluation, MEDLINE, FOS: Political science, Social Sciences, FOS: Law, Decision Sciences, Data science, QA76.75-76.765, Cluster analysis, Artificial Intelligence, Bibliometric Analysis, Machine learning, Scopus, Information retrieval, Computer software, Data mining, Citation, Political science, Science mapping, Systematic literature review, Python (programming language), Scientific Computing, Computer science, Programming language, World Wide Web, Statistical Analysis, Bibliometrics, Systematic mapping study, Computer Science, Physical Sciences, Scientific Computing and Data Analysis with Python, Statistical Computing and Data Analysis in R, Statistics, Probability and Uncertainty, Law, Software, Python

  • BIP!
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    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    6
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Top 10%
gold