Github Clarkdatalabs Bibliometric Networks Bibliometric Networks

Leo Migdal

-Dec 5, 2025, 7:01 PM

github clarkdatalabs bibliometric networks bibliometric networks

Networks can provide significant measures to identify data driven patterns and dependencies. Though, given a data file it can be difficult to discern how one may approach creating such a network. In this tutorial, we will use a bibliographic data file downloaded from a query search in Scopus to walk through the process of cleaning the data file, writing a python script to parse the... We tried out multiple Python libraries for ease of use and efficiency before landing on this combination. Building a network was more intuitive in NetworkX than iGraph. However, it took several minutes to render our large graph and a interaction was sticky.

Pyvis was easy to build a network with and can be expanded to incorporate more advanced NetworkX functionality with only a couply lines of code. However it still took a long time to render, with slow manipulation. Holoviews, which runs on top of the native Python visualization library Bokeh, enables NetworkX to render quickly, with versitile manipulation. The graphs are produced in HTML and JavaScript for easy integration into webpages. While we originally developed this script in a local notebook, we found that running it through Google's cloud-based Jupyter notebook environment Colaboratory is a smoother option, particularly for nacent coders. We encountered version conflicts between the dependencies when setting up a local notebook environment that were bipassed in Colab.

Colaboratory allows you to use and share Jupyter notebooks from your browser, without having to download, install, or run anything on your own computer. Notebooks can be saved to Google Drive, Github or downloaded locally. This code contains OAuth2 functionality to access data from Google Drive, with a link to instructions for access from Github. A single line of code adapts the script render in Colab. To open the notebook in Colab, click on the notebook from the repository list. GitHub will open a preview, click this icon from the top of the notebook to open directly in Colaboratory.

(If the preview doesn't load, you may have to disable your ad blocker.) Alternatively, you can clone or download this repository and put in Google Drive. Google Drive will recognize the .ipynb notebook file format and give you the option to open in Colaboratory. The Colab file and the Jupyter Notebook file with an example csv can be found in their respective folders in the Github Repository. To run the Jupyter Notebook, it would be best to clone the repositiory and open Jupyter Notebook from your local environment. Networks can provide significant measures to identify data driven patterns and dependencies. Though, given a data file it can be difficult to discern how one may approach creating such a network.

In this tutorial, we will use a bibliographic data file downloaded from a query search in Scopus to walk through the process of cleaning the data file, writing a python script to parse the... We tried out multiple Python libraries for ease of use and efficiency before landing on this combination. Building a network was more intuitive in NetworkX than iGraph. However, it took several minutes to render our large graph and a interaction was sticky. Pyvis was easy to build a network with and can be expanded to incorporate more advanced NetworkX functionality with only a couply lines of code. However it still took a long time to render, with slow manipulation.

Holoviews, which runs on top of the native Python visualization library Bokeh, enables NetworkX to render quickly, with versitile manipulation. The graphs are produced in HTML and JavaScript for easy integration into webpages. While we originally developed this script in a local notebook, we found that running it through Google’s cloud-based Jupyter notebook environment Colaboratory is a smoother option, particularly for nacent coders. We encountered version conflicts between the dependencies when setting up a local notebook environment that were bipassed in Colab. Colaboratory allows you to use and share Jupyter notebooks from your browser, without having to download, install, or run anything on your own computer. Notebooks can be saved to Google Drive, Github or downloaded locally.

This code contains OAuth2 functionality to access data from Google Drive, with a link to instructions for access from Github. A single line of code adapts the script render in Colab. To open the notebook in Colab, click on the notebook from the repository list. GitHub will open a preview, click this icon from the top of the notebook to open directly in Colaboratory. (If the preview doesn’t load, you may have to disable your ad blocker.) Alternatively, you can clone or download this repository and put in Google Drive. Google Drive will recognize the .ipynb notebook file format and give you the option to open in Colaboratory.

The Colab file and the Jupyter Notebook file with an example csv can be found in their respective folders in the Github Repository. To run the Jupyter Notebook, it would be best to clone the repositiory and open Jupyter Notebook from your local environment. There was an error while loading. Please reload this page. Classifying 3-d objects (specifically, buildings) using LIDAR point cloud source data. Geographical data and parallel computing with R and Python

A workshop for network analysis with Cytoscape Using SuperCollider to generate music from web crawling data leaflet map with time filtering abilities Bibliometrics, also known as scientometrics, is the quantitative study of the process of scholarly publication of research achievements. The complex system of scholarly publication reveals different types of networks, mainly citation networks (where links represent bibliographic references) and collaboration networks (in which links correspond to article co-authorships). These networks are analysed in order to capture meaningful properties of the underlying research system, and in particular to determine the influence of bibliometric units like scholars and journals.

Science and social science citation map (generated with Mapequation.org) Collaboration map for the field of network science (generated with Mapequation.org) A central question is: why bibliometric analysis of research performance? Peer review, that is, the evaluation made by expert peers, undoubtedly is an important procedure of quality judgment. But peer review and related expert-based judgments may have serious shortcomings. Subjectivity, i.e., dependence of the outcomes on the choice of individual reviewers, is one of the major problems.

Moreover, peer review is slow and expensive (at least in terms of hours of volunteer work devoted to refereeing). In particular, peer review methodology is practically unfeasible when the number of units to evaluate is consistent, e.g., all papers published by all members of a large research structure. Bibliometric assessment of research performance is based on the following central assumptions: Find items in UIC Library collections, including books, articles, databases and more. Find items on the UIC Library website, including research guides, help articles, events and website pages. CRExplorer.

Cited References Explorer uses data downloaded from Scopus and Web of Science to perform citation analysis over time and is often used to determine influential publications in a subject area. Free to download and use. Publish or Perish. A software program that pulls information from several databases, including Web of Science, Scopus, Google Scholar, Microsoft Academic, and CrossRef. Free to download and use. ScientoPyUI.

An open-source software program allows users to import data downloaded from Scopus and Web of Science to perform a scientific analysis of citations, find the H-Index, and more. Free to download and use. There was an error while loading. Please reload this page. The goal of biblionetwork is to provide functions to create bibliometric networks like bibliographic coupling network, co-citation network and co-authorship network. It identifies edges and calculates the weights according to different methods, depending on the type of networks, the type of nodes, and what you want to analyse.

These functions are optimized to be used on very large dataset. The original function, which uses data.table (Dowle and Srinivasan 2020) and allows the user to find edges and calculate weights for large networks, was developed by François Claveau. The different functions in this package have been developed, from Claveau’s original idea, by Alexandre Truc and Aurélien Goutsmedt. The package is maintained by Aurélien Goutsmedt.[1] You can install the development version from GitHub with: The basic function of the package is the biblio_coupling() function.

This function calculates the number of references that different articles share together, as well as the coupling angle value of edges in a bibliographic coupling network (Sen and Gan 1983). What you need is just a file with entities (documents, authors, universities, etc.) citing references.[2] See the vignette("Using_biblionetwork") for a more in-depth presentation of the package. This example use the data incorporated in the package.

Github Clarkdatalabs Bibliometric Networks Bibliometric Networks

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In This Tutorial, We Will Use A Bibliographic Data File