citecorp is a new (hit CRAN in late August) R package for working with data from theOpenCitations Corpus (OCC).OpenCitations, run by David Shotton and Silvio Peroni,houses the OCC, an open repository of scholarly citation dataunder the very open CC0 license.

Time for an update to a previous post. For the past few years, I have been using an automated process to track citations to my lab’s work on Google Scholar (details of how to set this up are at the end of this post). Due to the nature of how Google Scholar tracks citations, it means that citations get added (hooray!) but might be removed (booo!). Using a daily scrape of the data it is possible to watch this happening.

I read this recent paper about very highly cited papers and science funding in the UK. The paper itself was not very good, but the dataset which underlies the paper is something to behold, as I’ll explain below. The idea behind the paper was to examine very highly cited papers in biomedicine with a connection to the UK. Have those authors been successful in getting funding from MRC, Wellcome Trust or NIHR?

Citations are a crucial piece of scholarly work. They hold metadata on each scholarly work, including what people were involved, what year the work was published, where it was published, and more. The links between citations facilitate insight into many questions about scholarly work. Citations come in many different formats including BibTex, RIS, JATS, and many more. This is not to be confused with citation styles such as APA vs. MLA and so on.

This post is about a citation analysis that didn’t quite work out. I liked this blackboard project by Manuel Théry looking at the influence of each paper authored by David Pellman’s lab on the future directions of the Pellman lab.

In a previous post I made a little R script to crunch Google Scholar data for a given scientist. The graphics were done in base R and looked a bit ropey. I thought I’d give the code a spring clean – it’s available here. The script is called ggScholar.R (rather than gScholar.R). Feel free to run it and raise an issue or leave a comment if you have some ideas.

I’ve previously written about Google Scholar. Its usefulness and its instability. I just read a post by Jon Tennant on how to harvest Google Scholar data in R and I thought I would use his code as the basis to generate some nice plots based on Google Scholar data. A script for R is below and can be found here. Graphics are base R but do the job. First of all I took it for a spin on my own data.

At present BioStor provides a simple display of an article extracted from BHL. You get the page images, and sometimes a map and an altmetric "donut". But we can do better than this. For example, I'm starting to experiment with displaying a list of literature cited by the article.

In what area of scholarship are repeated replications of always the same experiment every time published and then received with surprise, only to immediately be completely ignored until the next study? Point in case from an area that ought to be relevant to almost every single scientist on the planet: research evaluation.

The other day I was alerted to an interesting evaluation of international citation data. The author, Curt Rice, mentions a particular aspect of the data: In 2000, 25% of Norwegian articles remained uncited in their first four years of life. By 2009, this had fallen to about 15%. This shows that the “bottom” isn’t pulling the average down. In fact, it’s raising it, making more room for the top to pull us even higher.