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Technology Transfer and True Transformation: Implications for Open Data

机译:技术转让与真正转型:对开放数据的启示

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p class="p1"When considering the “openness” of data it is unsurprising that most conversations focus on the 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 the data lifecycle. Indeed, considering where and how data are created – namely, the research setting – are of key importance to Open Data initiatives. In particular, such insights offer key understandings of how and why scientists engage with in practices of openness, and how data transitions from personal control to public ownership./pp class="p3"This paper examines research settings in low/middle-income countries (LMIC) to better understand how resource limitations influence Open Data buy-in. Using empirical fieldwork in Kenyan and South African laboratories it draws attention to some key issues currently overlooked in Open Data discussions. First, that many of the hesitations raised by the scientists about sharing data were as much tied to the speed of their research as to any other factor. Thus, it would seem that the longer it takes for individual scientists to create data, the more hesitant they are about sharing it. Second, that the pace of research is a multifaceted bind involving many different challenges relating to laboratory equipment and infrastructure. Indeed, it is unlikely that one single solution (such as equipment donation) will ameliorate these “binds of pace”. Third, that these “binds of pace” were used by the scientists to construct “narratives of exclusion” through which they remove themselves from responsibility for data sharing./pp class="p3"Using an adapted model of technology first proposed by Elihu Gerson, the paper then offers key ways in which these critical “binds of pace” can be addressed in Open Data discourse. In particular, it calls for an expanded understanding of laboratory equipment and research speed to include all aspects of the research environment. It also advocates for better engagement with LMIC scientists regarding these challenges and the adoption of frugal/responsible design principles in future Open Data initiatives./p
机译:class =“ p1”>当考虑数据的“开放性”时,大多数对话都集中在在线环境上就不足为奇了–出于多种目的如何整理,移动和重组数据。尽管如此,重要的是要认识到在线移动只是数据生命周期的一部分。实际上,考虑在何处以及如何创建数据(即研究设置)对于开放数据计划至关重要。尤其是,这些见解提供了关于科学家如何以及为什么参与开放实践以及数据如何从个人控制转变为公有制的关键理解。 class =“ p3”>本文研究了低水平的研究环境/中等收入国家(LMIC),以更好地了解资源限制如何影响开放数据购买。利用肯尼亚和南非实验室的实地实地调查,它引起人们对当前在开放数据讨论中忽略的一些关键问题的关注。首先,科学家对共享数据的许多犹豫与他们的研究速度以及其他任何因素都息息相关。因此,似乎每个科学家创建数据花费的时间越长,他们对共享数据的犹豫就越多。其次,研究的步伐是多方面的,涉及与实验室设备和基础设施有关的许多不同挑战。实际上,单一解决方案(例如设备捐赠)不太可能改善这些“步伐”。第三,科学家利用这些“步伐”来构建“排他性叙述”,使他们摆脱对数据共享的责任。 class =“ p3”>使用适应性技术模型该论文首先由Elihu Gerson提出,然后提供了关键方法,可以在开放数据讨论中解决这些关键的“步伐”。特别是,它要求对实验室设备和研究速度有更广泛的了解,以涵盖研究环境的所有方面。它还主张与LMIC科学家就这些挑战进行更好的互动,并在未来的开放数据计划中采用节俭/负责任的设计原则。

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