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What lies beneath?: Knowledge infrastructures in the subseafloor biosphere and beyond

机译:背后是什么?:海底生物圈及其以外的知识基础设施

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We present preliminary findings from a three-year research project comprised of longitudinal qualitative case studies of data practices in four large, distributed, highly multidisciplinary scientific collaborations. This project follows a 2 (times ) 2 research design: two of the collaborations are big science while two are little science, two have completed data collection activities while two are ramping up data collection. This paper is centered on one of these collaborations, a project bringing together scientists to study subseafloor microbial life. This collaboration is little science, characterized by small teams, using small amounts of data, to address specific questions. Our case study employs participant observation in a laboratory, interviews ((n=49) to date) with scientists in the collaboration, and document analysis. We present a data workflow that is typical for many of the scientists working in the observed laboratory. In particular, we show that, although this workflow results in datasets apparently similar in form, nevertheless a large degree of heterogeneity exists across scientists in this laboratory in terms of the methods they employ to produce these datasets—even between scientists working on adjacent benches. To date, most studies of data in little science focus on heterogeneity in terms of the types of data produced: this paper adds another dimension of heterogeneity to existing knowledge about data in little science. This additional dimension makes more complex the task of management and curation of data for subsequent reuse. Furthermore, the nature of the factors that contribute to heterogeneity of methods suggest that this dimension of heterogeneity is a persistent and unavoidable feature of little science.
机译:我们提供了一个为期三年的研究项目的初步发现,该项目包括对四个大型,分布式,高度跨学科的科学合作中的数据实践进行纵向定性案例研究。该项目遵循2(倍)2的研究设计:两个合作是大科学,两个是小科学,两个已经完成了数据收集活动,而两个正在扩大数据收集。本文以其中一项合作为中心,该项目将科学家聚集在一起,研究海底微生物的生活。这种协作不是一门科学,其特点是由小型团队使用少量数据来解决特定问题。我们的案例研究采用了参与者在实验室中的观察,与科学家的访谈(迄今为止,n = 49)和文件分析。我们提供了一个数据工作流,这对于在观察实验室工作的许多科学家来说都是典型的。特别是,我们表明,尽管此工作流程产生的数据集在形式上看似相似,但就其用于生成这些数据集的方法而言,即使是在相邻工作台上的科学家之间,该实验室中的科学家之间仍存在很大程度的异质性。迄今为止,大多数关于小科学的数据研究都围绕产生的数据类型关注异质性:本文将异质性的另一个维度添加到了有关小科学数据的现有知识中。这个额外的维度使管理和整理数据以供后续重用的任务变得更加复杂。此外,影响方法异质性的因素的性质表明,异质性的这一维度是很少科学的一个持久且不可避免的特征。

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