Prospecting (in) the data sciences
Prospecting (in) the data sciences
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DOI：
10.1177/2053951720906849
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发表时间：
2020-01
影响因子：
8.5
通讯作者：
S. Slota;Andrew Hoffman;David Ribes;G. Bowker
中科院分区：
文献类型：
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作者：
S. Slota;Andrew Hoffman;David Ribes;G. Bowker
数据科学的特点是利用异构数据来解决真实的世界问题。但数据科学没有自己的数据，必须在真实的世界领域中寻找数据。我们将这种对数据的搜索称为“勘探”，并认为勘探的动态在数据科学中是普遍存在的，甚至是数据科学的特征。勘探的目的是使数据，知识，专业知识和实践的世界领域可用和易于处理的数据科学方法和认识论。勘探先于数据合成、分析或可视化，由发现无序或不可访问的数据资源的上游工作构成，然后进行排序并提供可用于计算。通过这项工作，数据科学将自己定位在所有事物的中间，能够参与这个，那个或任何领域，因此勘探是数据科学作为一门通用科学不断形成的关键驱动力。
Data science is characterized by engaging heterogeneous data to tackle real world questions and problems. But data science has no data of its own and must seek it within real world domains. We call this search for data “prospecting” and argue that the dynamics of prospecting are pervasive in, even characteristic of, data science. Prospecting aims to render the data, knowledge, expertise, and practices of worldly domains available and tractable to data science method and epistemology. Prospecting precedes data synthesis, analysis, or visualization, and is constituted by the upstream work of discovering disordered or inaccessible data resources, thereafter to be ordered and rendered available for computation. Through this work, data science positions itself in the middle of all things—capable of engaging this, that, or any domain—and thus prospecting is a key driver of data science’s ongoing formation as a universal(izing) science.