At one extreme, comparative data reuse can be accomplished with access to publicly available data archives, which promotes greater equity in science. We combine ethnographic, interview, and documentary evidence to present a theoretical framework for a continuum of types of data reuses. We found similar patterns of data reuse within and between consortia, despite considerable variation in research domains, access to data, research methods, and time periods. This article reports the findings of a qualitative meta-analysis of research on two large, long-term, distributed, and interdisciplinary scientific consortia. We have studied scientific data practices over a period of two decades. When scientists seek data from sources beyond their own laboratories and current collaborations, under what conditions do public data suffice? When do scientists pursue interpersonal contact for further expertise about those data and their contexts of origin? How does data reuse vary by research domain, purposes for potential reuse, access to data creators, and time period? Answers to these questions can guide the design of digital archives, policies for data governance, and public policy for open access to data. As a consequence of the expertise involved in their creation, data are difficult to extricate from the context in which they originated (Latour, 1987).Īn important question for the sciences and for public policy is to ask what kinds of data reuse are made possible by access to public data archives and what kinds are not. Scientists and other scholars develop deep expertise in their research domain, methods, and tools, all of which become integral to the data they collect, analyze, interpret, report in publications, and may later deposit in digital archives. While all of these costly public investments are necessary for data reuse, they are not sufficient to ensure that those data are useful for further research, nor that those assets will be reused. To provide open access to research data, stakeholders must build digital archives, populate those archives, and maintain them. Scientific practice and public policy continue to move toward open access to publications, data, software, code, and other research products. Keywords: data, science, reuse, biomedicine, environmental sciences, open science, data practices, science policy Data reuse is a process that occurs within knowledge infrastructures that evolve over time, encompassing expertise, trust, communities, technologies, policies, resources, and institutions. Based on these findings, we theorize the data creators’ advantage, that those who create data have intimate and tacit knowledge that can be used as barter to form collaborations for mutual advantage. Metadata, ontologies, and other forms of curation benefit interpretation for any kind of data reuse. Data integration requires more specialized scientific knowledge and deeper levels of epistemic trust in the knowledge products. Integrative reuse requires contributory expertise, which involves the ability to perform the action, such as reusing data in a new experiment. Comparative data reuse requires interactional expertise, which involves knowing enough about the data to assess their quality and value for a specific comparison such as calibrating an instrument in a lab experiment. We propose a typology of data reuses ranging from comparative to integrative. When they sought others’ data for reanalysis or for combining with their own data, which was relatively rare, most preferred to collaborate with the data creators. By conducting a qualitative meta-analysis of evidence on two long-term, distributed, interdisciplinary consortia, we found that scientists frequently sought data from public collections and from other researchers for comparative purposes such as “ground-truthing” and calibration. We test those assumptions by asking where scientists find reusable data, how they reuse those data, and how they interpret data they did not collect themselves. Open access to data, as a core principle of open science, is predicated on assumptions that scientific data can be reused by other researchers.
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