of the individual scientist; people might share research products with their immediate colleagues but, in general, they keep their results in the laboratory. Private science, in this model, occupies a temporal phase in the research process: research products remain within the realm of private science until publication occurs. When a researcher decides to publish findings or to present them at meetings, research results cross the private-public boundary. Similarly, at that point, whatever research-related information is needed to replicate the results also becomes public science. In the public science phase, norms of openness govern the exchange of research-related information so that scientists can validate results and advance the field. Problems of lack of openness in this model occur mainly when people fail to provide research-related information after publication of research results. McCain found little indication that that was a widespread practice.

For defenders of scientific openness, McCain's conclusion might be welcome. But rather than confidently deciding to adjourn our workshop early, we might consider some of the limitations of the gift-exchange perspective. Most important, the perspective gives little consideration to the processes through which scientists decide what entities to move across the private-public boundary and when. To understand the traffic patterns discussed above, it is necessary to look more closely at how, why, and when the entities produced in scientific laboratories cross the private-public divide. In addition, the gift-exchange perspective is based on a rather sharp distinction between public and private science, which is at variance with the many gradations of ''publicness'' that actually occur in scientific practice.


To address those kinds of problems, Sherry Brandt-Rauf and I developed an alternative perspective—the data-stream perspective (Hilgartner and Brandt-Rauf 1994). This framework is informed by a variety of recent social studies of science that emphasize scientific practice and culture, and it is especially amenable to actor-network theory (Callon 1995, Latour 1987). The data-stream perspective conceptualizes data not as isolated objects, but as entities that are embedded in evolving streams of scientific production.

It is important at this point to say something about how Brandt-Rauf and I use the term data. We define it inclusively as the many different entities that scientists produce and use during the process of research. In this usage, data include a wide variety of materials, instruments, techniques, and written inscriptions. Such a broad definition is needed because scientists in every subfield have their own specialized conceptual categories for classifying the resources that they use and produce. Distinctions among data, findings, and results, between samples and materials, and among techniques, software, and instrumentation can become confusing when they cross subfields because the terms are not used uniformly.

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