A quick note: I've been working with an intern to track some research down, but the keywords are slushy and the controlled vocabulary in the databases we're using just hasn't been cutting it. Mostly, we've been able to make some progress by seeing who is citing the articles that seem most relevant, but even that traditional citation-tracing method isn't producing quite as much as I hoped.

But then I happened on a nifty new tool today, the JSTOR Labs Text Analyzer, thanks to the kind of serendipty my Twitter community seems to promote. Basically, you upload a document (something you wrote, a text you're reading, an article PDF, a syllabus, even) and . . . something magical happens. The analyzer finds patterns in the text and looks for similar documents. The words used in the pattern appear on one side. There are sliders for how much you want to emphasize some concepts. There's a collection of keywords roughly sorted by type, and you choose which ones are most relevant to your interests or decide which ones aren't of interest. You can even add your own words. If you want your results to emphasize current content, there's a checkbox for that. Results can be limited to the JSTOR content your library subscribes to, or you can search it all to see what you might want to obtain through interlibrary loan. It only surfaces JSTOR content, but that's a lot of good material.

Maybe it was the nature of the fuzzy, interdisciplinary topic I was trying (and failing) to capture through my usual methods, but this tool surfaced stuff I hadn't previously seen, and it was easy to scroll through and quickly browse and select the most promising results. The interface is clean and intuitive (which, I'm sorry to say, can't be said of library database interfaces generally speaking). I'll be playing with it and will see what my students think of it, but at first glance, all I can say is WOW. This is cool.

Now I need to explore the other projects that Alex Humphreys and the rest of the JSTOR Labs team have been up to. Fascinating stuff.