There are tons of social tools for scientists online, and the somewhat lukewarm adoption is a subject of occasional discussion on friendfeed. The general consensus is that the online social tools, in general, which have seen explosive growth are the ones that immediately add value to an existing collection. Some good examples of this are Flickr for pictures and Youtube for video. I think there’s an opportunity to similarly add value to scientists’ existing collections of papers, without requiring any work from them in tagging their collections or anything like that. The application I’m talking about is a curated discovery engine.
There are two basic ways to find information on the web – searches via search engines and content found via recommendation engines. Recommendation engines become increasingly important where the volume of information is high, and there are two basic types of these: human-curated and algorithmic. Last.fm is an example of a algorithmic recommendation system, where artists or tracks are recommended to you based on correlations in “people who like the same things as you also like this” data. Pandora.com is an example of the other kind of recommendation system, where human experts have scored artists and tracks according to various components and this data feeds an algorithm which recommends tracks which score similarly. Having used both, I find Pandora to do a much better job with recommendations. The reason it does a better job is that it’s useful immediately. You can give it one song, and it will immediately use what’s known about that song to queue up similar songs, based on the back-end score of the song by experts. Even the most technology-averse person can type a song in the box and get good music played back to them, with no need to install anything.
Since the reason for the variable degree of success of online social tools for scientists is largely attributed to the lack of participation, I think a great way to pull in participation by scientists would be to offer that kind of value up-front. You give it a paper or set of papers, and it tells you the ones you need to read next, or perhaps the ones you’ve missed. My crazy idea was that a recommendation system for the scientific literature, using expert-scored literature to find relevant related papers, could do for papers what Flickr has done for photos. It would also be exactly the kind of thing one could do without necessarily having to hire a stable of employees. Just look at what Euan did with PLoS comments and results.
Science social bookmarking services such as Mendeley, or perhaps search engines such as NextBio, are perfectly positioned to do something like this for papers, and I think it would truly be the killer app in this space.