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DOI: 10.1177/107554709401500401 1994 15: 355Science Communication

Stephen Hilgartner and Sherry I. Brandt-Raufof Access Practices

Data Access, Ownership, and Control: Toward Empirical Studies

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355

Data Access, Ownership, and ControlToward Empirical Studies of Access Practices

STEPHEN HILGARTNER

SHERRY I. BRANDT-RAUFColumbia University

Issues surrounding data access, ownership, and control raise important issues for science policy.The new sociology of science has examined many features of scientific knowledge and practice,but has made only preliminary efforts to study data access. Building on ethnographic studies ofscientific laboratories (and other constructivist work), this article suggests how the new soci-ology of science can study data access empirically. The article develops a perspective based onan analysis of the process of scientific production and the creation, packaging, and exchange ofdata streams. It also provides an example of how constructivist studies can contribute to policyanalysis.

Introduction

In recent years, the issue of access to scientific data has sparked intensedebate. What obligations do the producers of data have to share them withcolleagues and competitors? When are scientific results private property andwhen are they the common property of the scientific community or societyat large? In more general terms, who is obligated to share which data withwhom, when, how, and under what terms and conditions? And how shouldpolicy and law address these problems? From a practical standpoint, thesequestions are significant because data-sharing disputes can waste resources,slow the progress of research, and lead to hostility and mistrust amongscientists. Perhaps more importantly, data access has become a central issue

AuthorsNote: The authors acknowledge the financial support of the National Science Founda-tion, National Center for Human Genome Research, National Institutes of Health, Charles A.Dana Foundation, and Samuel and May Rudin Foundation.

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356 KNOWLEDGE: CREATION, DIFFUSION, UTILIZATION

in defining the purposes and nature of the research system, the relationshipbetween academic research and private enterprise, and the shape of theboundaries separating what is public and what is private in research.

Conflict over data access has occurred in many contexts, including scien-tific priority races, civil litigation,2 and when commercial or national securityinterests are involved (Nelkin 1984). In addition, the proper role of journals,funding agencies, and professional societies in establishing guidelines orrequirements for the sharing or public release of data has provoked contro-versy.3 Access to data has also been a salient concern in debates aboutuniversity-industry relations. Because conflict over access has surroundedmany types of data, it is useful in the context of science policy to define theterm data broadly, to include such entities as biological materials, reagents,novel instrumentation, and other scarce or limited resources.

Scholars have addressed the issues of access, ownership, and control ofdata from several, often overlapping perspectives. One literature, emanatingfrom Mertonian sociology of science, has emphasized the notion that scien-tific findings are communal property, and analyzed such situations as prioritydisputes (Cozzens 1989; Merton [1942] 1973; Zuckerman 1988). A second ap-proach, based in a law and science perspective, has focused on such topics asintellectual property rights in biotechnology and computers (e.g., Eisenberg1987; Samuelson 1989). A third perspective has explored university/industryrelations and the effects of commercial values on the academy (Chubin 1985;Etzkowitz 1983, 1989; Kenney 1986; Krimsky 1991; Nelkin 1984; Nelson1989; Markle and Robin 1985; Weiner 1986). Finally, there is some work thatanalyzes the ethics of data sharing and ownership with an eye towarddeveloping normative frameworks for science policy in this area (Weil andHollander 1989; Weil and Snapper 1989).On the matters of data sharing and access, however, the &dquo;new&dquo; sociology

of science and technology (Star 1988) has been surprisingly silent. Thisrapidly-growing field by now includes a broad range of constructivist,semiotic, and ethnographic perspectives that are exploring many aspects ofscientific practice and culture (Pickering 1992), and so one might expect tofind an abundance of work on data-access practices that draws on theseperspectives. In fact, the literature remains sparse. A recent collection ofessays on data access and ownership (Weil and Snapper 1989) contains noteven a single contribution that draws on ethnographic studies of scientificlaboratories. The new sociology of science has only made preliminary effortsto use the theoretical insights of ethnographic studies of laboratories toanalyze data-access issues. Similarly, there is a dearth of empirical researchthat examines the actual practices of scientists regarding access to data. Littlehas been written on how scientists decide who gets access to which data and


357Hilgartner, Brandt-Rauf / ACCESS PRACTICES

under what terms and conditions. In much of contemporary technoscience(both within and beyond the academy), open publication of data is augmentedby informal exchanges that form the underpinnings of a dynamic &dquo;under-ground economy,&dquo; and play an important role in such processes as the broker-ing of collaborations.4 Understanding this economy is critical both for the-ory and for policy analysis. Nevertheless, constructivists (and like-minded)analysts of science have devoted little attention to the question of howscientists grant and restrict access to data through their day-to-day activities.A few scholars in the field have touched on the subject. Fujimura (1992)

notes the importance of scientists negotiations to obtain biological materials,but her central concern-how scientific work is coordinated among diversesocial worlds-leads her gaze away from access practices per se. Traweek(1988) discusses the culture of negotiations over access to beamtime (andother resources) in the high-energy physics community, but her aim was todevelop a broad anthropological account of this specialty, not to concentrateon access issues. Indeed, with the exception of a useful series of articles thatwe discuss below (Mackenzie, Cambrosio and Keating 1988; Cambrosio,Keating, and Mackenzie 1990; and Mackenzie, Keating and Cambrosio1990), there is an absence of sustained attention to access practices.s

To what can we attribute this relative silence? We believe that the principalreason stems from the historical development of the discipline. The newsociology of science in part defined itself in opposition to Mertonian sociol-ogy of science, a field that had been concerned with exchange relations inscience (although it asks different questions than the ones posed here). Inaddition, the new sociology of sciences initial emphasis on knowledgeconstruction and related epistemological problems may have drawn attentionaway from access issues. Alternatively, some might suggest a more unchari-table interpretation of the neglect of data access: The new sociology ofscience has little to contribute to understanding access issues in particular orto improving policy analysis in general. Within the field of science, technol-ogy, and society, a persistent criticism of constructivist accounts of sciencehas been that they lack relevance to policy. We believe this criticism to be inerror. Below, we argue that the new sociology of science provides a freshpoint of departure for studies of data access, and that such work can contributeto a more profound understanding of data access practices and ultimately toimproved policy analysis in this area. The gaps in the literature are wide, andwe do not aim to close them in a brief discussion article. Our goals are moremodest: (1) to explore how the new sociology of science might address data-access practices; and (2) to suggest its relevance to policy analysis, usingdata-access issues as a case in point.



What can constructivist and ethnographic perspectives contribute to theunderstanding of data access? Constructivism offers a starting point forempirical studies that might ask a variety of questions: How do scientistsconstruct and erase boundaries that determine who gets access to which data,at what time, and under what terms and conditions? How are these boundaryconstruction practices related to the process of data production (e.g., theshifting factual status of data during laboratory activity)? Under what circum-stances do scientists rely on different methods of controlling access? In whatrhetorical terms do scientists criticize and justify access practices and deci-sions ? And how are formal and informal rules and conventions that bear onaccess (including intellectual property law) interpreted and transformed?

These questions promise to be fruitful areas of research for both theoreticaland practical reasons. From the point of view of social theory, issues ofcompetition, cooperation, and ownership are of the utmost importance tounderstanding science as an enterprise and its place in late-twentieth centurysocieties. An adequate sociology of science therefore must consider patternsof resource exchange or the mechanisms and rhetoric used to create, sustain,and undermine social boundaries; to establish ownership; or to engineercollaborations. Similarly, the interaction between scientific practices andthose of commerce and law merit careful empirical study. From a practicalperspective, studies of the questions sketched out above can inform evalu-ation and criticism of present practices and of possible alternatives.

In pursuing this line of research, constructivist research would have to takeinto account a number of features of contemporary scientific practice identi-fied in ethnographic studies of laboratories:

1. In order to understand access practices, constructivist research would have toavoid treating &dquo;data&dquo; in common sense terms and subject data-and theircontent, production, and packaging-to social analysis. Such research wouldhave to pay especially close attention to the way particular inputs and outputsare embedded in evolving streams of scientific production.

2. Constructivist work on access practices would need to consider the complexityof contemporary research networks, and the multiple participants in, andconsumers of, research products.

3. A constructivist perspective cannot restrict its attention to a limited number ofmeans of disseminating data (such as open publication). On the contrary, itwould have to consider the wide menu of available mechanisms for granting,limiting, or denying access to data and the strategic issues and incentivesassociated with each.

4. Such work would need to examine the ways in which access practices interactwith intellectual property law and strategies for commercialization.

Below, we briefly discuss each of these topics in turn and explore how the in-sights of ethnographic work on science are relevant to the issue of data access.



Data Streams

In common discourse about science, the normal course of research is oftendescribed in terms of what one might call the &dquo;produce and publish model&dquo;:first, a scientist produces data (or &dquo;scientific findings&dquo;), which are conceivedof as the output of scientific production; second, he or she disseminates thedata through open publication (or informal communication); and third, thefreely-available data serve as an input for other scientists, who evaluate it,certify it, and build on it. Although the produce and publish model is aprofound oversimplification, it implicitly underlies much discussion of data-access issues, which tends to frame restrictions on access as departures fromthe normal course of science. In contrast, ethnographic studies of scientificlaboratories suggest the need for a process-oriented model that does not treatdata as well-defined, stable entities and that is oriented toward flow andcontinuity (e.g., Knorr-Cetina 1981,1992; Latour 1987; Latour and Woolgar1979; Lynch 1985; see also Collins 1985). Drawing on the ethnographicliterature, we argue that an alternative &dquo;data stream model&dquo; should be em-ployed in the analysis of access practices. Data should be conceptualized notas the end-products of research or even as isolated objects, but as part of anevolving data stream.

These data streams have several important properties. First, they aretypically composed of an extremely heterogeneous collection of entities.Because access issues concern both the products of, and tools for, conductingscientific research, several observers have noted that when considering dataaccess, it is important not to conceive of &dquo;data&dquo; too narrowly (McCain 1991;Weil and Hollander 1989). Scientists use a variety of terms to discuss theinputs to, and outputs of, their work. Their common sense categories fordiscussing these items include a variety of terms: data, findings, and results;samples, materials, and reagents; laboratory techniques, protocols, know-how, and experience; algorithms, software, and instruments. The precisemeanings scientists give these terms vary situationally and the usage acrossdifferent fields is not consistent. Accordingly, we use the term data in a broadmanner, encompassing both inputs and outputs, as a shorthand for &dquo;informa-tion and other resources produced by or needed for scientific work. &dquo; Thisdefinition would include knowledge that is embodied in material form, as inscientific instruments. Data, in this formulation, would include a heteroge-neous mix of entities-not only the results of experiments or surveys, butalso biological materials and other samples, software, laboratory techniques,access to research sites, craft knowledge, and a wide variety of other formsof information and know-how. Depending on the form of data, decisionsabout access entail different strategic and practical considerations. Providing



access to a protein sample is a very different matter from training a colleaguein a novel technique.

Protein crystallography, for example, typically involves many forms ofdata, including: clones for producing protein samples, techniques for crystal-lizing proteins, instruments for producing x-ray or synchrotron radiation,electron density maps, structure factors, preliminary atomic models, algo-rithms for improving those models, atomic coordinates (at various levels ofdetail), and computer-generated pictures of molecular structures. Although itis possible to classify these items into types, using the common sensecategories of results, materials, techniques, instruments, software, and so on,the point is not merely the variety of forms of data, but the complex linkagesamong them. What scientific work produces and what scientific work re-quires are not isolated items from any of these categories, but complexassemblages that weave together items from all (or many) of these categories.Indeed, the limits of the common sense categories are underlined by the factthat it is often difficult to assign an item to a single category. An algorithmused to estimate coordinates in crystallography is in one sense a mathematicaltext; in another sense, an instrument; and in a third sense, a piece of software.

The assemblages that are necessary for scientific production to proceedare composed of heterogeneous networks of information and resources, someof which are completely mundane, like pencils, and some of which are exoticbits of scientific apparatus. All laboratories rest on the sociotechnical infra-structure of the societies in which they operate (Rip 1982). It is impossibleto run a competitive protein crystallography lab without reliable electricityand water supplies, without computers and technicians. Resting on this baseare a variety of instruments and practices that are specific to the research areain question, but are widely available either as commercial products or &dquo;freescientific information&dquo; (Mackenzie, Cambrosio, and Keating 1988). Datastreams also include more specialized entities that are not available throughpublic channels and are typically disseminated through personal contacts.Finally, at the leading edge of a data stream one finds entities that are novelor extremely scarce and are available only through special arrangements, ifat all. Thus, the elements that make up data streams fall along a spectrum,ranging from the most mundane infrastructure to the most novel and scarceproducts of scientific work.6 Strictly speaking, all of these entities are part ofa laboratorys data stream, but in the analysis of access practices, the criticalissues concern things that lie toward the leading edge of the stream.

Another critical dimension of heterogeneity concerns factual status: Someportions of a data stream may be considered well-established, whereas othersmay be of uncertain reliability. At one extreme, some elements of data streamsmay be so uncertain that even the scientists who produced them doubt their



credibility and utility. At the other extreme, some elements may be widelyregarded to be reliable and valuable. In this context, scientists perceptionsof the reliability and value of particular parts of a data stream can play acritical role in access decisions. To complicate these matters further, ethno-graphic research has demonstrated that during the research process, data donot have well-defined or stable meanings (Knorr-Cetina 1981; Latour andWoolgar 1979). Instead, their significance and utility varies, sometimeswildly, as scientists try to separate facts from artifacts, signal from noise.Because data are constantly interpreted and reinterpreted, and because it takestime for preliminary results to be refined and considered reliable, this issueof timing may be a salient component of decision making about access (e.g.,as researchers attempt to determine whether data are &dquo;ready&dquo; to disseminate).

Beyond heterogeneity, another critical characteristic of data streams con-cerns the way they are composed of chains of products. Ethnographic workhas shown that the use of &dquo;inscription devices&dquo; (Latour and Woolgar 1979)is a central feature of laboratory practice. Initially, researchers use primaryinscription devices, such as x-ray film, to record information. Later, scientistsproduce second-, third-, and n-order inscriptions (inscriptions about inscrip-tions) as, for example, they convert markings on x-ray films into numbers,and the numbers into tables, graphs, models, and pictures that are eventuallyincorporated into scientific papers (Latour 1987). Laboratories process ma-terials in a similar manner, extracting purified samples from pieces of tissueand subjecting them to further manipulation. The production of inscriptionsand the manipulation of samples are both hierarchical operations that producea series of products; the process of data production tends to move from rawmaterials, at the upstream end, to increasingly refined materials and inscrip-tions downstream.

The translations and conversions that occur as scientific work proceeds,generally speaking, change not only the information content and materialform of the data, but also the purposes for which they can be used. Becausethese operations alter the utility of the data, they clearly influence accesspractices. But even more fundamentally, the fact that data streams arecomposed of chains of products suggests the advantages of conceiving ofdata streams as continuous phenomena, a move that shifts the level of analysisfrom the individual end-product to the stream as a whole. This shift leads tobroader questions. One no longer asks simply whether access to a particu-lar end-product is provided; one asks which portions of the entire data streamare disseminated, to whom, by what means, and when. Indeed, viewing thestream as a continuous phenomenon undermines the very notion of an&dquo;end-product.&dquo; In principle, any end-point may become a starting point; everyoutput may become an input. One therefore cannot assume that data somehow



arrive on the scene in pre-packaged units that are transferable, sharable, orpublishable, or that there is some discrete point in time at which data should&dquo;naturally&dquo; be transferred. On the contrary, there is always more than oneway of dividing a data stream into portions that may (or may not) bedisseminated. Notions about what can be packaged in a form that can bepublished (or transferred by other means) are set by conventions in particularscientific fields, and these conventions are part of what one must explain ifone is to understand access practices.9

The Complexity of Research Networks

In the previous section, we have seen that data streams are complicatedand evolving assemblages of heterogeneous components that vary in theirmaterial form, information content, reliability, scarcity, novelty, and per-ceived value. The research networks in which data are produced and ex-changed share this complexity. Much discussion of data access has beenframed in terms of relationships between two parties. In the case of informalcommunication, these parties are typically a primary researcher (the source,or producer of the data) and a secondary researcher (the requester, who wantsto obtain access). McCain (1991), for example, uses this source-requesterframe in her study of the dissemination of research-related information ingenetics.~ In the case of publication (or submission to public databases orrepositories), the communications are conceptualized as binary transactionsbetween a researcher (the source) and an abstract collectivity, such as &dquo;thescientific community.&dquo; Framing data access in binary terms, however, iscalled into question by recent social studies of science, which have revealedthe complexity of contemporary research networks.&dquo; The scientist, as Knorr-Cetina (1981, 68-91) puts it, is a &dquo;socially situated reasoner&dquo; who is linkedto many other people and organizations through resource relationships.Access practices are intimately involved in the construction and maintenanceof research networks, so analysis of data-access practices cannot assumetwo-party relationships but must take account of a fuller range of relevantactors.

For one thing, it is inadvisable to assume that the decisionmaker regardingaccess is an individual researcher. A single research project in, for example,the biomolecular sciences may involve many parties, including: a researchteam composed of scientists from several institutions, from several fields ofstudy, with a variety of levels of training, and with varying degrees ofinvolvement in the project; government and corporate sponsors that providefunds; a host university that provides space and equipment; institutions that



provide access to human subjects; and the subjects themselves.2 These ac-tors may have very different goals and differing claims to portions of the datastream. The researchers may differ about the nature and timing of publica-tions and about how best to insure that they receive a good return on theirresearch investments. Corporations and scientist-entrepreneurs may seek tocontrol access to promote financial interests. Government funders and foun-dations may be interested in rapid dissemination to benefit the researchenterprise or to prove that their seed money has bome fruit. Human subjects(or their advocates) may be concerned about confidentiality of data or mayhave their own financial stakes, as in the well-known case of John Moore,who sued for royalties from a biotechnology product created using his cells.l3The countless variations cannot be enumerated here, but clearly in many casesaccess decisions are negotiated within research networks, rather than madeby individuals.

Similarly, one cannot assume that the audiences and markets for dataconsist either of individual requesters or of undifferentiated aggregates orabstract collectivities, such as the &dquo;readership of Celf or the &dquo;specialists inthe field.&dquo; Competing research groups, potential collaborators, and the au-thors of studies with conflicting results may be the most prominent featuresof the social landscape. Other salient audiences may be gatekeepers who con-trol key resources, such as deans, department heads, program officers, andcorporate sponsors. In areas with commercial promise, potential markets forresearch-based products may also be important, as may corporations or ven-ture capitalists who are skilled at translating anticipated markets into real ones.Clearly, when the participants in research consider the issue of access, theyencounter a complex world with a variety of incentives and disincentives.

Access Strategies and Mechanisms

Much discussion of data access uses a model of research that emphasizesopen publication and the process of peer review, replication, and rewardthrough credit for discovery. In university-based research, academic rewardstructures clearly encourage scientists to publish findings in the peer-reviewedliterature. But open publication is by no means the only way to grant access: Data in their many forms are also bartered with other research groups as partof the terms of collaboration, distributed to selected colleagues, patented,transferred by training visitors in novel techniques, provided to limitedgroups of recipients on a confidential basis, bought and sold, &dquo;pre-released&dquo;to corporate sponsors prior to publication, or kept in the lab pending a futuredecision about their disposition. Thus, in addition to open publication, there



Figure 1. Relationship between the availability of a technique and the competitive edgeit conveys. Following initial development, the technique is progressively routi-nized and becomes available to growing numbers of labs. As this occurs, thecomparative advantage it conveys declines.

are many other mechanisms for disseminating portions of the datastream that a research group produces. Some of these mechanisms result inwidespread distribution, and others provide much more limited, targetedaccess-sometimes mediated by intricate inter-, intra-, and extra-laboratorynegotiations.

Decisions and negotiations about access entail a variety of strategic con-siderations. Central to these is the issue of scientific competition: The datathat a research group controls can convey a competitive edge to the extentthat the data are both valuable and in scarce supply. As access becomes morewidespread, the competitive edge declines. In molecular biology, for exam-ple, new techniques frequently undergo a development cycle through whichthey are progressively simplified and made routinized (Figure 1). The mostsuccessful new techniques move from being available in a single laboratory(where they require experienced personnel or even so-called magic hands) tobeing available in standard protocols or even prepackaged kits sold by thescientific supplies industry (Fujimura 1987). Until the technique is routi-nized, laboratories familiar with its use enjoy a comparative advantage thatdeclines with its diffusion.



The competitive edge provided by possessing unique data can be exploitedin a variety of ways. One method is to restrict access and use the data toproduce more (and perhaps more valuable) data, a strategy built aroundscarcity. Another approach is to provide carefully-targeted access: for exam-ple, to use the data as a bargaining chip in negotiations with potential collabo-rators or corporate sponsors, promising access in return for the provision ofother resources. Again, this strategy depends in part on scarcity, whichenhances the value of the data during negotiations. A third approach is toprovide widespread access to the data (e. g. , by publication) in the hopes thatones scientific credibility-the central currency of scientific production(Latour and Woolgar 1979)-will be enhanced as others use, draw on, andcite the data. These methods are not necessarily mutually exclusive; they canbe used in succession, or, if applied to different portions of the data stream,in combination.

As the above discussion suggests, timing is a critical issue in accessstrategy. In part, this is because access control interacts with quality control.Researchers often carefully scrutinize results to determine if they are &dquo;ready&dquo;to publish or whether more experiments are needed to verify accuracy.Premature publication can be embarrassing if the results are later deemedincorrect, but on the other hand, the researcher who delays publication maylose priority or other opportunities to exploit the data. Often the decision isnot merely when to grant access, but how much of the data stream todisseminate. From a strategic perspective, releasing too much too soon mayallow free riders to get ahead. Some researchers may be concerned thatreleasing data too early may aid the ultimate free rider-someone bent onoutright theft of ideas or intellectual property. On the other hand, limitingaccess to the data stream may have its own costs: for example, it may leaveclaims weakly supported and therefore less credible, or it may raise the ireof colleagues.

Another set of strategic issues concern the actual mechanisms involved inrestricting, targeting, or granting access to specific portions of data streams.In each area of research, the cost and strategic implications of distributingparticular items are closely intertwined with the structure of data streams inthat field. Some forms of data may be rapidly and inexpensively distributed.But novel instruments may be impossible to distribute except by bringingpeople to the site, and preparing samples for sharing may be time consumingor costly (Hilgartner forthcoming; McCain 1991; National Academy ofSciences 1985). Moreover, access to some items in a data stream can enableanother laboratory to reproduce a major portion of the chain that constitutesthe stream. Distributing these items, therefore, can be regarded as function-ally equivalent to distributing a large portion of the data stream. In contrast,



some items can be functionally separated from the rest of the data stream, sothey can be distributed without providing implicit access to other items in thechain. As a concrete example, consider DNA samples in molecular biology.If one possesses a viable cell line, one can endlessly replicate the entirecomplement of DNA from that cell line. On the other hand, if one possessesa small sample of purified DNA from that same cell line, the entire comple-ment cannot be replicated. Researchers who provide access to purified DNA(e.g., on a filter) from a cell line but do not distribute the cells themselves,thus retain exclusive control over the cell line-no matter how widely thepurified samples are distributed. 14

As the above discussion suggests, some data items may be of greatstrategic importance owing to their scarcity and the implicit access theyprovide. Comprehending the strategic issues and negotiations involved indata access requires a detailed understanding not simply of competitivepressures in science, but also of the structure of data streams in particularareas of research, the cost of producing or distributing specific data items,and the feasibility of controlling access.

The Legal System

In the extant literature on data access, intellectual property policy andcommercialization have been persistent themes, so we discuss these mattersonly briefly. From the perspective developed here, the critical questionsconcern the manner in which the legal systems categories and contingenciesinteract with access practices. The legal system has not focussed on the issueof access to data generally (Cecil and Griffin 1985), and there have been fewcourt cases directly concerned with data access. Nevertheless, the lawsattention to questions of ownership and control inevitably implicates accessas well.s

Broadly speaking, the law approaches the question of control over theproducts of scientific research from a private property perspective,6 and itoffers several mechanisms for controlling access to data. These includefederal mechanisms like patent and copyright as well as creations of statelaw, such as trade secrets, misappropriation, contract, and conversion. Eachlegal mechanism carries different requirements and is applicable in differentcircumstances or to different types of creations.&dquo; For example, &dquo;writings&dquo;that are original expressions of an idea are protected by copyright law. Un-der patent law (35 U.S.C. Sec. 100[a]), &dquo;any new and useful process, machine,manufacture, or composition of matter... or improvement thereof may bepatented if it meets certain legal requirements (e.g., novelty and non-



obviousness). Each mechanism creates a proprietary category, rooted in adifferent concept of ownership, with different rights and responsibilities. Ingeneral, the laws approach to ownership of data is atomistic; only byplucking items from a data stream and placing them into discrete categoriescan the legal system designate an end-product that may qualify for some typeof protection. Data that are not construed as falling into one of the legalsystems other categories are considered to fall within the public domain, aresidual category of expressions and creations that may be used or reproducedwith impunity.l8 Similarly, the law presupposes an ability to identify &dquo;thecreator&dquo; of a work.

When analyzing how the legal system interacts with access practices, it isuseful to make a crude distinction between areas in which the law is firmlyestablished and areas in which the direction and rate of its expansion areuncertain. In the well-established areas, one can treat the law as offering arelatively stable set of legal mechanisms that the participants in research candraw on to protect data, and the analyst can confine her attention to thequestion of how these mechanisms are used in practice.19 But in sociotech-nical domains where the law is evolving in unpredictable directions (e.g.,intellectual property law about biotechnology), the relationship between legaland scientific practices is more fluid, and this fluidity itself must become afocus of analysis.

In a series of three articles, Cambrosio, Keating, and Mackenzie haveexplored one area in which scientific and legal innovations have closelyinteracted in an environment of legal uncertainty-the commercialization ofmonoclonal antibodies.20 Although the central focus of these authors has notbeen data access per se, their writings represent the most notable contributionof the new sociology of science to the existing literature on data access. Theiranalysis of patenting of monoclonal antibodies documents &dquo;a subtle butsignificant shift in the political economy of science,&dquo; as patents have beenused to appropriate data that previously scientists would have regarded asfalling within the public domain (Mackenzie, Cambrioso, and Keating 1990).They thus demonstrate how the contours of the public domain change withchanging interpretations of the boundaries of proprietary categories. In anarticle on a patent dispute, Cambrosio, Keating, and Mackenzie (1990)examine the rhetoric the parties in the litigation used to contest such issuesas whether an invention was a patentable invention or merely an &dquo;obvious&dquo;development. Taken as a whole, their work shows how in sociotechnicaldomains where the law is uncertain, legal and scientific practices are simul-taneously constructed, in part through their interaction.

Although the law is more directly concerned with ownership of data thanwith data access, analyses of access practices cannot neglect the interplay



between legal and scientific practices. In particular, we need more researchthat pays attention to (1) how participants in research try to employ the lawto attain protection; (2) how they respond to perceived legal contingencies insituations of uncertainty; (3) the dilemmas and strategies created by thedisparity between the laws atomistic approach to ownership and the conti-nuity of data streams and research networks; and (4) the multiple interactionsamong access practices, law, and the political economy of research that canredefine the legal regimes governing areas such as biotechnology.

Conclusion

Above, we have sketched out, briefly and in general terms, the complexityof access practices, and we have suggested how the new sociology of sciencecould approach access issues. Analyzing these practices requires that wespeak not simply of inputs and end-products, but of data streams; not just ofsources and requesters, but of research networks; not merely of open publi-cation, but also of restricted, targeted, and implicit access; not only of scienceand law as separate realms, but also of the processes-taking place at theirexpanding borders-that are defining new technical-legal regimes.

Even to hint at the complexity of data access practices, as we have triedto do above, casts a harsh light on the wide gaps in the literature. But from amore optimistic perspective, having the same gaps shown in stark reliefsuggests the potential of constructivist research to help surmount the limitsof the traditional literature. Concepts such as data streams, that are firmlyrooted in the new sociology of science, point the way toward rich, empiricallygrounded accounts of access practices-accounts that will advance boththeoretical understandings and policy analysis. Indeed, the insights of ethno-graphic studies have already provided the tools to move well beyond thetraditional literature. Moreover, the data stream approach outlined abovesuggests ways to link ethnographic research on laboratory practices withother disciplines (e. g. , through economic analyses of the incentive structuresfor the parties in access negotiations, or critical studies of the law). Thisinterdisciplinary work will have direct relevance to policy.

The strength of constructivism in policy research is its ability to strip awayour collective blinders, to reveal how reality has been made, and to widen therange of social choice. We believe that the study of access practices is an areawhere constructivist studies of science can begin to make immediate, practi-cal contributions to science policy. Ultimately, of course, to move beyond theworld of academic debate, policy-oriented research obviously must be linkedto normative questions: How can disputes about access best be managed?



What data access policies can contribute to research productivity whilepromoting other social goals? Should the public domain be defended againstencroachment by proprietary categories of information? And how, in a worldwhere science is a commodity as well as a calling, can public interests beprotected? Far from being irrelevant to these questions, the new sociology ofscience is in a unique position to enlighten the debate about them.

Notes

1. See Weil and Snapper (1989) for an edited volume on the subject. The issue of datasharing was also the subject of a session at the 1989 meetings of the American Association forthe Advancement of Science (AAAS) and of a National Academy of Sciences (1985) report. Seealso the special issue of Science, Technology, & Human Values on "Secrecy in University-BasedResearch: Who Controls, Who Tells," Spring 1985; and the section on "Who Owns ScientificData?" in the Project on Scientific Fraud (National Council of Lawyers and Scientists 1989).

2. Farnsworth v. Proctor and Gamble (1985) and Vanderbilt Law Review (1984).3. For an example in the field of crystallography, see Barinaga (1989).4. We thank a reviewer of our grant proposal for the "underground economy" metaphor.5. Mackenzie, Cambrosio, and Keating (1988), Mackenzie, Keating and Cambrosio

(1990), and Cambrosio, Keating, and MacKenzie (1990); see also Bowker (1992).6. Another level of heterogeneity in data streams concerns the varied ways in which the

elements of a data stream fit into legal categories of ownership. This issue is discussed byMackenzie, Cambrosio, and Keating (1988), and we return to it below.

7. In addition, the meaning and utility of data is dependent on context. A piece of data canbe reanalyzed in light of other findings or used in conjunction with new techniques, and this cancompletely alter what can be done with it. Similarly, the same data can be used to pursue differentlines of research, some of that may not be foreseeable at the time access decisions are made,especially in fast-moving fields.

8. The same operation may raise or lower utility, depending on the purposes of later users.9. Clearly, considerations that one might classify as "technical" (e.g., what parts of the data

stream are durable, which degrade at room temperature, which are bulky, and so forth) influencethese conventions. But such considerations cannot by themselves explain these conventions.

10. This study, based on Mertonian theory and Hagstroms (1965) notion of gift exchange,used bibliometric and interviewing methods to analyze patterns of acknowledgements ofassistance in published articles and patterns of requests for assistance and response to thoserequests. See Latour and Woolgar (1979) for a critique of Hagstroms model.

11. The new sociology of science has developed several models for conceptualizing thiscomplexity, including actor-network theory (Callon 1986; Latour 1987) and social worlds theory(e.g., Clarke 1990; Fujimura 1987; Star 1988).

12. In such situations the boundary of "the" project may also be unclear.13. Moore v. U. of Cal. Regents (1988).14. This example suggests how the scarcity of specific types of data can result from access

practices as well as influence these practices.15. Previous legal work in this area includes: Bent, Schwaab, Conlin, and Jeffery (1987),

Bartlett and Siena (1983-84), Cecil and Boruch (1988), Cecil and Griffin (1985), Davidson andDeMay (1986), Eisenberg (1987), Ferguson (1981), Fordham Law Review (1984), Friedman



(1986), Harvard Journal of Legislation (1987), Melton (1988), Sieber (1988), and Stanley andStanley (1988).

16. Information that is classified secret owing to concerns about national security is anexception. See Nelkin (1984) and Weil (1988) for discussion of national security issues in dataaccess.

17. For example, researchers in some fields have begun to use the legal contract as a wayof resolving questions of data access at the outset of collaboration. But as with any contract,these instruments will only withstand challenge if they are executed by the appropriate parties,those who have the "right" to the data in the first place. Constructing a contract that willaccomplish what is intendedfor example, to grant limited access to datarequires one todetermine whether the parties to the contract have the power to distribute the data and, perhaps,that these are the only such parties.

18. In general, data that have appeared in articles in the scientific literature may beconsidered part of the public domain, although the articles themselves may be covered bycopyright.

19. Of course, the possibility that stable areas of law will undergo dramatic change mustalways be considered.

20. See note 6.

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STEPHEN HILGARTNER is Assistant Professor in the Center for the Study of Societyand Medicine, Columbia University.

SHERRY I. BRANDT-RA UF is Assistant Professor in the Center for the Study of Societyand Medicine, Columbia University.


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