simply publish everything that they produce; they engage in strategic maneuvers about who is going to get access to what data and materials under what terms and conditions. Publication is only one move (albeit an extremely important one) in an extremely complex process. The practices used to regulate access to data are quite different in molecular biology, as opposed to high-energy physics, as opposed to a large clinical trial. There are different expectations and rules about control over the flow of data in those settings. As a result, any analysis of how changes in the public domain might affect science must focus on particular research communities, not science as a whole. When I refer to scientists, I am referring to researchers working in molecular biology and other benchtop biomedical fields that exhibit similar cultures.
Brandt-Rauf and I set out to create a theoretical framework and analytic method for comparing data access practices across diverse scientific fields. We concluded that such a framework must treat the category “data” as problematic; that is, one cannot focus on what the scientists themselves in a particular area regard as “data,” as if their notion of data were unambiguous and universal to all fields, but instead to consider the full range of forms of data and heterogeneous resources that researchers produce and use (Hilgartner and Brandt-Rauf, 1994).
In molecular biology, these data and resources include all sorts of written inscriptions (such as sequence data) and biomaterials. They also include instruments, software, techniques, and a variety of “intermediate results.” In the laboratory, these entities are woven together into complicated assemblages. An isolated, single biological material sitting alone in a test tube is a useless thing; to be scientifically meaningful, it must be linked using labels and other inscriptions to the source of the sample and its particular characteristics. Moreover, to use the material, one needs a laboratory equipped with an appropriate configuration of people, techniques, instruments, and so forth. As scientific work proceeds, materials and inscriptions are processed and reprocessed, so these assemblages continuously evolve, producing new data and materials (Latour and Woolgar, 1979). Many of the items found in a laboratory can be found in any laboratory, but some of the items—especially those toward the “leading edges” of these evolving assemblages—are available only in a few places, or perhaps only in one place. These scarce and unique items can convey a significant competitive edge. For example, the laboratory that first develops a useful new technique, the researcher who collects a particularly interesting set of DNA samples, and the creator of a powerful new algorithm all end up controlling strategically important resources. They can enter into negotiations about collaborations and other exchanges from a strong position, owing to the value and scarcity of the resource.
In small-scale biomedical research, with its many independent operators, a dynamic, invisible economy exists below the radar screen of what looking at the published literature reveals. There is a huge range of transactions going on all the time. Scientists have to decide whether to publish a result immediately or delay publication until an even better result is achieved. In many areas, such as gene hunting, several research groups may be racing to reach the same goal, and an early publication from one group may help competing groups to catch up (Hilgartner, 1997). Given such strategic considerations, scientists have to decide whether to publish right away, or to delay publication, or to provide information on a limited basis to specially targeted audiences. Often, they work to negotiate agreements with the heads of other academic laboratories, or perhaps with commercial organizations. Many of these exchanges entail at least temporary restrictions on publication. As scientists work to build collaborations, they seek to avoid arrangements that will cause them to become merely the provider of a “service,” as molecular biologists put it, to another lab without benefiting themselves (Knorr Cetina, 1999). Sometimes researchers provide these services expecting a quid pro quo later. Sometimes they provide them out of the goodness of their hearts. Sometimes they provide them because funding agencies or other policy makers encourage them to do so (Hilgartner, 1998). But complex negotiations, replete with strategic gamesmanship and uncertainty, are routine in small-scale biomedical research.
Having briefly characterized the strategic role of data and the wide variety of transactions that surround data and resources, it is finally time to turn to my main question: What would happen to this area of science if the public