When considering the “openness” of data it is unsurprising that most conversations focus onthe online environment – how data is collated, moved and recombined for multiple purposes.Nonetheless, it is important to recognize that the movements online are only part of thedata lifecycle. Indeed, considering where and how data are created – namely, the researchsetting – are of key importance to Open Data initiatives. In particular, such insights offer keyunderstandings of how and why scientists engage with in practices of openness, and how datatransitions from personal control to public ownership.This paper examines research settings in low/middle-income countries (LMIC) to betterunderstand how resource limitations influence Open Data buy-in. Using empirical fieldwork inKenyan and South African laboratories it draws attention to some key issues currently overlookedin Open Data discussions. First, that many of the hesitations raised by the scientists aboutsharing data were as much tied to the speed of their research as to any other factor. Thus, itwould seem that the longer it takes for individual scientists to create data, the more hesitantthey are about sharing it. Second, that the pace of research is a multifaceted bind involvingmany different challenges relating to laboratory equipment and infrastructure. Indeed, it isunlikely that one single solution (such as equipment donation) will ameliorate these “binds ofpace”. Third, that these “binds of pace” were used by the scientists to construct “narratives ofexclusion” through which they remove themselves from responsibility for data sharing.Using an adapted model of technology first proposed by Elihu Gerson, the paper then offerskey ways in which these critical “binds of pace” can be addressed in Open Data discourse. Inparticular, it calls for an expanded understanding of laboratory equipment and research speedto include all aspects of the research environment. It also advocates for better engagementwith LMIC scientists regarding these challenges and the adoption of frugal/responsible designprinciples in future Open Data initiatives.
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