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Bringing Data Out of the Shadows

机译：将数据带出阴影

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The question of what lurks in the shadows is a perennial one. In the case of data, although bringing data "into the open" is often accepted uncritically as an obvious good, it is important to establish why doing so is important not only for those who produce and share data but also for policymakers (on the "open science" movement more generally, see, for instance, The Royal Society 2012). What count as "quality" data also warrants deeper analysis, because not just any random data are considered important by creators and (re)users. It is notable in the papers in this special issue that definitions both of data and of what count as quality data appear to differ across the relevant communities of practice, within communities as they evolve over time, and between situational contexts (see also Borgman 2012; Leonelli 2016). What makes data count as "data" (and perhaps even what make data visible) is the outcome of negotiations within a discipline, field, or community of practice about underlying concepts and the goals of research, as, for instance, Alison Wylie's paper stresses in the context of archaeological evidence and Linsey McGoey's article explores with regard to data on wealth distribution. Conflicts about these matters can take place between those even in closely related fields or communities of practice, as are detailed in all of the papers in this issue. Hence, it is essential to consider not only how data (or more precisely, a community of practice's views on data) serve to define and delimit the community of practice. It is necessary to go further to examine how data are tied in with the establishment of identity (Hackett 2005) and claims to expertise (a complex and politically plagued issue in science; see, for instance, Hilgartner 2000; Brown 2009), including in negative cases where some entities are not in fact considered to be high-quality data (or even data at all). Communities often distinguish themselves from one another, and mark off their territories, by focusing on weaknesses in the quality of the data created or used by others: consider the examples in Nadine Levin and Sabina Leonelli's paper about valuing biological data. Hence, what sometimes begins as a conflict between practitioners over techniques, methods, and standards in fact can become a marker of differing epistemologies and in turn diverse identity claims (Hackett 2005). In short, the transformation of some data into "shadow data" is a complex process and clearly is not just (or primarily) about the data themselves.
机译：潜伏在阴影中的问题是一个长期存在的问题。以数据为例，尽管通常不加批判地将数据“公开化”为明显的好处，但重要的是要确定为什么这样做不仅对产生和共享数据的人而且对政策制定者也很重要（在“开放科学”运动，例如，参见The Royal Society 2012）。所谓的“质量”数据也值得进行更深入的分析，因为创建者和（重新）用户不仅认为随机数据很重要。在本期特刊中的论文中值得注意的是，数据的定义以及作为质量数据的定义在相关实践社区之间，随着时间推移而发展的社区内部以及在情境之间都似乎有所不同（另见Borgman，2012；莱昂内利（2016）使数据被视为“数据”（甚至可能使数据可见）的是学科，领域或实践社区内有关基础概念和研究目标的谈判结果，例如艾莉森·怀里（Alison Wylie）的论文强调Linsey McGoey在考古证据的背景下探讨了财富分配数据。这些问题之间甚至在密切相关的领域或实践社区之间也可能发生冲突，正如本期所有论文所详述的那样。因此，不仅要考虑数据（或更确切地说是实践界对数据的看法）如何定义和界定实践界，这一点至关重要。有必要进一步研究数据如何与身份的建立联系在一起（Hackett 2005）和对专业知识的要求（科学中一个复杂且政治上困扰的问题；参见，例如Hilgartner 2000； Brown 2009），包括否定情况下，某些实体实际上不被视为高质量数据（甚至根本不是数据）。社区通常通过关注其他人创建或使用的数据质量的弱点来区分自己，并划定自己的领地：考虑一下Nadine Levin和Sabina Leonelli论文中有关评估生物数据的示例。因此，有时起因于实践者之间在技术，方法和标准方面的冲突而开始的事实实际上可能成为不同认识论的标志，进而可能成为不同身份认同的标志（Hackett 2005）。简而言之，将某些数据转换为“影子数据”是一个复杂的过程，显然不仅仅（或主要是）关于数据本身。

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