Let's be clear here, Google Scholar is ill designed for use for systematic reviews . I am not trying to argue otherwise. (Obligatory warning, I am not a real systematic review librarian) But why exactly?

In a previous post, I looked at how Google Scholar ranks co-authors. While I had the data available I wondered whether paper authorship could be used in other ways. A few months back, John Cook posted about using Jaccard index and jazz albums. The idea is to look at the players on two jazz albums and examine the overlap.

On a scientist’s Google Scholar page, there is a list of co-authors in the sidebar. I’ve often wondered how Google determines in what order these co-authors appear. The list of co-authors on a primary author’s page is not exhaustive. It only lists co-authors who also have a Google Scholar profile. They also have to be suggested to the primary author and they need to accept the co-author to list them on the page.

Google Scholar, die Suchmaschine für wissenschaftliche Texte, verfügt über eine neue Funktion. Diese soll bei der Suche nach Open Access-Publikationen unterstützen. Was das Feature kann, wie es in der Wissenschaft ankommt und welche Alternativen es gibt, das hat Alexandra Ketterer recherchiert.

Part of a series on the development of Early Career Researchers in the lab. We spent a session discussing how to create a research profile. This led to a second session on CVs. Here is an outline of what we covered. CVs We talked about different CV formats first of all. We focussed on academic CVs mainly, but we discussed the differences between academic and CVs for jobs outside academia.

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.

Ideas on measuring the "impact" of a natural history collection have been bubbling along, as reflected in recent comments on iPhylo, and some offline discussions I've been having with David Blackburn and Alan Resetar.My focus has been at the specimen-level, with a view to motivation the adoption of persistent specimen-level identifiers so that we can citations of specimens over time (e.g., in publications and databases such as GenBank). Not only

My article describing BioStor — "Extracting scientific articles from a large digital archive: BioStor and the Biodiversity Heritage Library" — has finally seen the light of day in BMC Bioinformatics (doi:10.1186/1471-2105-12-187, the DOI is not working at the moment, give it a little while to go live, meantime you can access the article here).Getting this article published was more work than I expected.

Hot on the heels of Geoffrey Nunberg's essay about the train wreck that is Google books metadata (see my earlier post) comes Google Scholar’s Ghost Authors, Lost Authors, and Other Problems by Péter Jacsó. It's a fairly scathing look at some of the problems with the quality of Google Scholar's metadata.Now, Google Scholar isn't perfect, but it's come to play a key role in a variety of bibliographic tools, such as Mendeley, and Papers.