A github repository that gathers different scripts to extract (mannually or via APIs) and then clean bibliometric data.
In this post, you will learn how to extract data from Dimensions website and how to clean them. These data allow you to build bibliographic networks.
In this post, you will learn how to extract data from Scopus website or with Scopus APIs and how to clean the data extracted from Scopus website. These data allow you to build bibliographic networks.
When the temptation is growing in you to try your hand at quantitative methods, the first question is likely to be "but how can I do, and which tools should I learn to use?" I give here some arguments to engage yourself in learning R and then present different tutorials and R packages useful for historians of economics.
I am very pleased to announce the initial release of biblionetwork to CRAN! biblionetwork is designed to build easily and quickly large list of edges for bibliometric networks. You can identify the edges for different types of network (bibliometric coupling or co-citation, or co-authorship networks) and use different methods to calculate the weights of edges.
Building an online interactive platform displaying bibliometric data on a large set of macroeconomic articles. Our goal is to settle the basis for a broad and long-run project on the history of macroeconomics, as well as to bring to historians tools to run quantitative inquiries to support their own research work.
The biblionetwork package provides functions to create fastly bibliometric networks like bibliographic coupling network, co-citation network and co-authorship network.
The networkflow package proposes functions to make it easier and quicker to work on networks. It mainly targets working on bibliometric networks. This package heavily relies on [igraph](https://igraph.org/r/) and [tidygraph](https://tidygraph.data-imaginist.com/index.html), and aims at producing ready-made networks for projecting them using [ggraph](https://ggraph.data-imaginist.com/).