As I mentioned, I spent most of last week and weekend attending two unconferences, BioBarCamp and Scifoo.

By their very nature, unconferences tend not to converge on a single topic; over the past week, I paricipated in discussions whose topics ranged from the importance of database annotation to how mushrooms could save the world to the current technical considerations involved in settling Mars. Nonetheless, even in the anarchic environs of an unconference, self-reinforcing trends arise over the course of the discussions, and themes do emerge (though each participant might perceive different patterns and come away with a completely different report of an event’s most important themes).

For me, the most powerful and important theme emerging from the week was the idea of “open science.” This term refers not to any one initiative or project, but the cloud of concepts that includes open access publication, use of open source solutions (especially for protocols and software), commons-based licensing, and full publication of all raw data (including “failed” experiments). It also incorporates more radical ideas like opening one’s notebook in real time, prepublishing unreviewed results, replacing current models of peer review with annotation and user ratings, and redesigning (or ditching) impact factors. The world implied by these concepts is one of radical sharing, in which credit still goes where credit is due but by dramatically different mechanisms.

Open science isn’t so much “pay it forward” (though there is a bit of that) as an effort to create a (scientific) world in which no one is paying at all, a world in which there’s no incentive to withhold or protect ownership of data. The science fiction writer Iain M. Banks once wrote that “money implies poverty” — indeed, many of the current models of data ownership and publication, and their accompanying “currencies” of proprietorship, prestige and closed-access publication, imply a world in which data is scarce and must be hoarded. But data is not scarce anymore.

Given a suitable set of one-to-one and one-to-many agreements between the stakeholders, then, the benefits of sharing could come to outweigh any conceivable advantage derived from secrecy. Perhaps “open science” could be defined (for the moment) as the quest to design and optimize such agreements, along with the quest to design the best tools and licenses to empower scientists as they move from the status quo into the next system — because (and this is very important) if it is to ever succeed, open science has to work not because of governmental fiat or because a large number of people suddenly start marching in lockstep to an unnatural tune, but because it works better than competing models. Proof of that particular pudding will be entirely in the eating.

During the meetings, I met quite a few people involved in this mission, and I want to mention their organizations and projects here:

  • OpenWetWare, “an effort to promote the sharing of information, know-how, and wisdom among researchers and groups who are working in biology & biological engineering” – including tools for protocol sharing and open notebooks;
  • Epernicus, a social networking site for scientists that automatically connects peers based on institution, history, skills and research focus;
  • JournalFire, “a centralized location for you to share, discuss, and evaluate published journal articles” (still in beta);
  • Science Commons, the scientific wing of the Creative Commons, which “designs strategies and tools for faster, more efficient web-enabled scientific research. We identify unnecessary barriers to research, craft policy guidelines and legal agreements to lower those barriers, and develop technology to make research data and materials easier to find and use.”;
  • Nature Precedings, “a free online service launched in 2007 enabling researchers in the life sciences to rapidly share, discuss and cite preliminary (unpublished) findings”; and
  • UnPubDatabase, a discussion of ways for scientists to rapidly and efficiently publish “negative” results, both to allow re-analysis of data and to prevent the scientific community from following the same blind alley more than once.

Academic scientists aren’t the only ones to potentially benefit, by the way — pharmaceutical companies routinely run the same experiments as one another and often find that expensive trials could be avoided if they’d only had access to data mouldering in a competitor’s vault — so open science can benefit the profit sector as well, and there are already plans underway to make that possible.

I’m enthusiastic about bringing open science into my own project and my own laboratory — indeed, in a fit of post-conference ecstasy I basically put myself on record promising to do so. For reasons that have everything to do with available energy levels, I suspect that full-blown openness is probably easier to accomplish when it’s present from the beginning of a project, so I’m especially eager to put these ideas to the test in a large-scale collaboration that is just getting underway. I have no idea how it will go — I expect to meet resistance, especially to the more radical ideas like open notebooks — but it’s nonetheless an exciting time. Will I be able to convince my collaborators to try out open science approaches? Once implemented, will they work? I don’t know, but I am convinced that it’s a hypothesis worth testing.