Hi Rasmus,

You might want to check out the paper linked here, presented recently at IEEE Visualization conference. The authors present a tool called SurVis https://github.com/fabian-beck/survis for analysis of document collections. I don't think they build citation networks, but should be worth checking out.

There is a huge body of work on citation linking and visualization (early work by Chaomei Chen & Katy Borner). Search IEEEXplore for visualization of documents and you might find other relevant papers.

I am building an app for deriving insights from documents and collections of documents. My app is still in development, but I'll be happy to share with you - send me a message.

Best,

Sid

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Article Visual Analysis and Dissemination of Scientific Literature C...

 Thank you! SurVis seems really neat, will have to play around with it a little bit to see if it fits with what I'm aiming to do.

After some Googleing around I found VOSViewer and CitNetExplorer (both from the same developer). These uses WebofScience input and can create visual representations of co-citations and more. Although it cannot use a preexisting bibtex-file since those lack the metadata on inter-citations. Thus you would somehow have to carefully search WoS and manually include and exclude references to match your library. But, those can possibly be really useful into finding key papers within a search topic when you start off writing a thesis or what not!

http://www.vosviewer.com/

http://www.citnetexplorer.nl/

PS: And yes, I would love to try out your app whenever you feel it is ready (and will of course provide beta-tester feedback if you wish)

Rasmus, I don't have an answer but am very interested in this type of software.  I would love to read your paper to see how you implement it.  Would you notify me when it is completed?

Sidharth, I would also be interested in your app and would be willing to help beta-test it.

Distiller. https://distillercer.com/ 

Best. 

Ramiro

Cochrane library can help you find randomised controlled trials of a particular health related topic.

In response to Ian's project:

I had a collection of ~300 academic studies and wanted to explore citation patterns. So I wrote a script in R that identified which studies cited which other studies and returns the results in a spreadsheet that I can load into Gephi as an edges file. I then upload a spreadsheet with my metadata (journal, year, field, etc) as a node file, and I can visualize and analyze the citation network as well as sort / color / change size of nodes based on journal, year, number of citations, disciplinary field, etc. I found Gephi to be very useful, and I support its open source approach to data analysis. I'm not sure if this same approach will work for others, but the R script I used is available here: Code R script for creating a cross-citation network

It requires you to have a folder of the texts in question and a list of their titles.

As an extension of A.R. Siders' answer, I have re-created the R script in Python and added automatic text extraction from PDFs.

The Python script takes as input a Zotero .CSV collection of articles with PDF paths and produces Gephi-formatted CSVs. The code and more information is here: https://github.com/jaks6/citation_map

Like others,i would recommend VOSViewer tool

to visualize articles and conducting a systematic review or meta-analysis.The best part of it is clustering solutions which are quite helpful in visualizing the dense scholar data

Have you looked at NVivo?

https://www.qsrinternational.com/nvivo-qualitative-data-analysis-software/home

My favourite is Vosviewer

You can try R/Python + Gephi/Vosviewer.

R and Python are used for pretreating data and generating graph data.

Gephi/Vosivewer areused for visualisation.

Actually, Python can do it all. But the Gephi/Vosviewer are more friendly-interaction.

I recomend you vos viewer

A.R. Siders I will try your script but I am a R apprentice, could I send you an e-mail with questions in case I didn't do it correctly?

connectedpapers.com

As an extension to A.R. Siders' answer, I updated the R script and included automatic data preparations based on medatada downloaded from Scopus. I also made a few modifications and attached a complete example dataset with R script, Scopus metadata, and expected outputs for reference. See this github repository for more information: https://github.com/jxshen311/R_citation_network_extension

I'm still using the powerfull research and reference manager named Quiqqa (http://www.qiqqa.com/). Unfortunately the software is no longer supported and migrated to an open source project (https://github.com/jimmejardine/qiqqa-open-source). The software automatically find which papers in your library reference other papers in your library. Also, the brainstorming function (https://www.youtube.com/watch?v=6A8C0b03PAQ ) lets you "place" a new document in the literature before you read it allowing you to explore its authors, what else in your library they wrote, who else they cited, and even what tags you or autoTags Qiqqa gave to each of the documents.

I do hope its close to what your looking for. Regards, Igor Chiambretti

You could try with BiblioShiny, which is defined by the authors "Bibliometrix R Package for non-coders"

https://www.bibliometrix.org/

Best,

Use Origin and GraphPad for that.

Try using VOSViewer. It's easy to use and visualize. The only problem is you might have repeated terms if they have a slight change of spellings (eg. Dam, dam, Dams) So you might need to edit the files to produce a better relation.

Use Metawin, if you not familiar then use origin.

Regarding the quality evaluation of systematic review. If the topic within the healthcare domain, you can follow the protocol (http://www.prisma-statement.org/PRISMAStatement/Checklist.aspx). If the topic is outside the healthcare field, you can try the Average Citation Count described in the review paper from Ens (Article Revisiting Collaboration through Mixed Reality: The Evolutio...

Argo Scholar lets you visually explore the literature (200+ million articles) https://poloclub.github.io/argo-scholar/

It's a web app, so there is nothing to install. And you can share your exploration results easily via a URL.

Cheers,

Polo

If you wish to look into certain connections among citations, ResearchRabit may be useful.