An R-tool for comprehensive science mapping analysis. A package for quantitative research in scientometrics and bibliometrics.
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Updated
Jul 2, 2024 - R
An R-tool for comprehensive science mapping analysis. A package for quantitative research in scientometrics and bibliometrics.
LitStudy: Using the power of Python to automate scientific literature analysis from the comfort of a Jupyter notebook
Local relational access to openly-available publication data sets
A Bibliometric and Scientometric Python Library Powered with Artificial Intelligence Tools
A command line tool for querying and modeling citation networks from the Astrophysical Data System (ADS) in a format compatible with Gephi
Wosis - a python package developed to support bibliometric analysis of Web of Science data from querying to visualization.
It's a publication performance analysis of papers published in National Taipei University through 2017 to 2022
A collection of Research Intelligence methods and workflows
📚 BibExplorer: a tool to process curricula, extract article meta-data from various APIs, and calculate bibliometric indicators.
Analyses, R code and data for review of 2009-2019 research on capture-recapture
The Academic Review Tool (ART) is a package for performing academic reviews and bibliometric analyses in Python. It offers capabilities for discovering, retrieving, and analysing academic literature at scale.
Chilean Political Science Impact Ranking
Mapping the Landscape of AI, ML, and DL in Climate Change Research
A web-based annotation tool and frontend for ML-based citation extraction from PDFs based on the AnyStyle library
Research on PubMed and Reproducible Bibliometric Analysis of Pathology Articles
R Shiny app: Research Evaluation of Faculty
Structurizes institution export data from reporting module of Polish Scientific Bibliography (PBN)
Agricultural robots is a repository to explore the use of Github while sharing research about agricultural robotics
Systematic characterization of data-driven research on eczema and atopic dermatitis.
Using publication records collected by Li et al (2018) for Nobel laureates in physics, chemistry, and physiology/medicine from 1900-2016, plus data from scite (scite.ai) on >3 million citation statements to these publications, I attempt to analyze how work of Nobel Prize winners is cited before vs after receiving the Nobel Prize
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