Google Scholar simplifies my life (again)

Google Scholar has largely replaced PubMed as the literature search engine of choice for my generation. It’s more intuitive, requires less futzing around with keywords, and generally seems to produce better results with less hassle. Today, Google Scholar got a little bit better, adding the ability to search within articles that cite a specific article. Sound confusing? Then let me explain:

When researching a particular gene or reading up on a technique, you’ll eventually stumble upon a great paper. It’s clearly a seminal paper in the field and is perfectly related to what you’re looking for, but it’s a little out of date. Surely, you think, other people have been working on this problem over the last decade!

The old way to find out was to click on the “Cited By” link and then scroll through all the papers that appeared, looking for relevant titles, then skimming their abstracts. (Being able to get even this much information was a major breakthrough not so long ago). On really solid papers, though, that cited-by list might number into the hundreds. Now, Google has made this step easier by allowing you to do full-text search within that list, effectively narrowing your search to a specific lineage of scientific discovery.

In the future, I’d love to see this expanded to second or third-degree neighbors. I’d also like the ability to go the opposite direction, and search within all the papers that a specific paper cites. I can’t gripe too much about lack of features, though. Only a couple of decades ago, people were still following citation trails manually, by pulling the relevant journals off the shelf and making photocopies. It’s amazing that anything got done at all back then.

Lessons for article recommendation services

Today someone proposed the creation of a sub-reddit where scientists could recommend papers to each other. While it’s a nice thought, I can almost guarantee that it’s going to be a failed effort. There are already sites like Faculty of 1000, which try to use panels of experts to recommend good papers. In my experience, they mostly fail at listing things that I want to read.

The main reason such sites are useless is that we scientists are uber-specialized, so what you think is the greatest paper ever will likely have very litle interest for me. It’s not that I don’t want to read about cool discoveries in other fields, it’s just that I don’t have time to. Until they invent the matrix-esque brain-jack for rapid learning, I have to prioritize my time, and my field and my work will always come first.

There are only two systems I’ve found that work well. The first are recommendation systems based on what you’ve read in the past, and what your colleagues are reading. CiteULike, for example, recommends users that have bookmarked similar papers to you, and perusing through their libraries gives me an excellent source of material. The other quality source of recommendations is FriendFeed, where I can subscribe to the feeds of other bioinformaticians with similar interests, and we can swap links to papers and comments about those papers.

Both of these systems are all about building micro-communities, with a focus that you can’t achieve in larger communities like Reddit. In this way, it’s sort of like a decentralized version of departmental journal clubs, or specialized scientific conferences. Any site that ignores the value of creating this type of community is pretty much doomed to failure from the start.

(reposted from my personal blog)