Last year, I spent a lot of time exploring the many new social networks for scientists that hit the market. At the time, my conclusions were that most, if not all, failed to offer anything that the scientists I work with were interested in doing; that the networks demanded a significant time commitment; and that scientists (at least biologists) were ignoring them in droves. I haven’t spent a huge amount of time with these networks since then, as there hasn’t been anything particularly new or exciting offered, and participation still seems to be lagging. David Bradley at the Sciencebase blog has done an excellent recent review and it appears that things haven’t changed very much:

“The point of all these various social networking, social media, and other online tools is communication. However, with the majority of scientists feeling perhaps that they are already well-served by their existing ‘meat-space’ networks they just do not see the point. And many of the current offerings do not immediately appear to be of value: there is no ‘killer app’ to draw practicing scientists in…”

Bradley gives what I think are overly generous estimates of use of the sites, given the level of traffic one sees on most. Using sheer numbers of members is always problematic since so many people sign up, take a look around, and never return. Numbers of members actively posting in the last week/month are much more telling, but very few sites are willing to give out such data. Proponents of such networks argue that we’re still in early days and that eventually, membership will grow. Personally, I think that ship has already sailed, at least for the current set of offerings. The issue isn’t a lack of awareness of social networks–who hasn’t heard of Myspace or Facebook?–but instead is a lack of compelling reasons to participate. There was a point, around the launch of the Nature Network, where these sorts of sites received all sorts of attention and coverage, both in the formal science press and in the blogosphere. Most biologists I’ve spoken with who tried them found they weren’t particularly useful. I used to get asked about them all the time. Now, it’s rare that they’re even mentioned. I don’t expect to see mainstream buy-in unless there’s a major shift in what the sites have to offer, some new paradigm that’s more attractive, the “killer app” mentioned above.

There’s also an idea that as Generation “F” (for “Facebook”) scientists enter the lab, they’ll be well-seasoned in using social networks, and will expect to employ them as scientists. That may be true, but the problem is that those early graduate students have the least to offer any social network. They haven’t yet done the research, so they have little by way of results to communicate, they’re unlikely to be in charge of setting up collaborations for the lab and because of their inexperience, have the least practical advice for others. By the time they can offer useful contributions to a network, they are likely to be too busy to spend much time on a network and will probably be indoctrinated into the more traditional ways of networking. It’s something of a paradox in this field, those most likely to participate are the least valuable to other network members.

Most networks seem to make two assumptions that doom them to failure: 1) that networking and communication is a central part of a scientist’s day, and 2) that scientists are willing to openly communicate on a wide scale with their communities. The first is a failure of perspective, those building and promoting social networks are “true-believers“, people whose lives revolve around social networking. While communication of results, networking and building collaboration are important for scientists, they’re somewhat peripheral compared to doing actual research. These are things one does in addition to one’s “real” work, performing experiments and seeking funding is often more important as well. Finding collaborators is at best, a sporadic event, not something done often. Any network that asks for regular participation and priority in time and effort will fail for this reason.

The second issue stems from a fear of prematurely exposing one’s data and a hesitancy to offend anyone who might later be deciding on your grants, reviewing your papers or hiring you or one of your students.  Much data is hard-earned and while a vocal minority promote Open Science, the reality of it is that most researchers are very protective of the fruits of their labors.  They want to feel they’ve fully exploited their data before releasing it to their competitors. And while many scientists respect thoughtful criticism and enjoy debating the meaning of their results, in general, the field has become more and more polite, more and more timid. Where questions asked after a seminar used to try to poke holes in an argument, now those sorts of discussions have moved into private groups. If you want to hear people’s real opinions of a talk, don’t listen to the questions asked in public; instead, head to the bar later that day for a one-on-one discussion.

Scientists tend to have fairly small trusted circles, and opinions (at least negative ones) are only expressed within these small groups.  Your preliminary data is only exposed to your labmates, perhaps to your department or a group of collaborators. It’s unlikely you’ll see truly open communication beyond these sorts of groups (especially from the younger scientists mentioned above) due to fear of committing career suicide. Both are unfortunate, but are parts of the current culture. Any network that hopes to succeed must adapt to the culture of the community, rather than trying to rewrite it.  Networks can work on the level of these smaller groups, and there is certainly some benefit in providing more efficient ways for labmates to keep each other up to date. But by working on this level, one loses the positive effects of scale, the benefits of receiving varied opinions, and advice from beyond one’s circle.

Even if these issues are addressed, mainstream acceptance is not a given. It’s very difficult to replace a system that for most scientists, already works pretty well. Krueger, again, from the article:

“For adoption of new technologies in science, it has to be an order of magnitude more useful than current tools,” says Krueger, “We just don’t have the time to waste learning new tools that only marginally increase our productivity.”