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Data Cleaners for Pristine Datasets: Visibility and Invisibility of Data Processors in Social Science

机译:原始数据集的数据清理器:社会科学中数据处理器的可见性和隐性

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This article investigates the work of processors who curate and "clean" the data sets that researchers submit to data archives for archiving and further dissemination. Based on ethnographic fieldwork conducted at the data processing unit of a major US social science data archive, I investigate how these data processors work, under which status, and how they contribute to data sharing. This article presents two main results. First, it contributes to the study of invisible technicians in science by showing that the same procedures can make technical work invisible outside and visible inside the archive, to allow peer review and quality control. Second, this article contributes to the social study of scientific data sharing, by showing that the organization of data processing directly stems from the conception that the archive promotes of a valid data set-that is, a data set that must look "pristine" at the end of its processing. After critically interrogating this notion of pristineness, I show how it perpetuates a misleading conception of data as "raw" instead of acknowledging the important contribution of data processors to data sharing and social science.
机译:本文研究了处理器的工作,这些处理器负责整理和“清理”研究人员提交给数据档案库的数据集,以进行归档和进一步分发。基于在美国主要社会科学数据档案馆的数据处理部门进行的人种学现场调查,我研究了这些数据处理器如何工作,处于何种状态以及它们如何促进数据共享。本文介绍了两个主要结果。首先,它表明了相同的程序可以使技术工作在存档内部和外部不可见,从而允许同行评审和质量控制,从而对科学研究中的隐形技术人员有所帮助。其次,本文通过显示数据处理的组织直接源于档案库提倡有效数据集(即必须看起来像“原始”的数据集)的概念,为科学数据共享的社会研究做出了贡献。在处理结束时。在对这种原始概念进行了严格的质疑之后,我将展示它如何使误导性的数据概念永久化为“原始”,而不是承认数据处理器对数据共享和社会科学的重要贡献。

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