Making Messy Data Work for Conservation
Making Messy Data Work for Conservation
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DOI：
10.1016/j.oneear.2020.04.012
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发表时间：
2020-05-22
期刊：
影响因子：
16.2
通讯作者：
Keane, Aidan
中科院分区：
文献类型：
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作者：
Dobson, A. D. M.;Milner-Gulland, E. J.;Keane, Aidan
环保主义者越来越多地使用非结构化的观测数据，如公民科学记录或护林员巡逻观察，以指导决策。这些数据集通常很大，而且收集起来相对便宜，它们具有巨大的潜力。然而，由此产生的数据通常是“混乱的”，它们的使用可能会产生相当大的成本，其中一些是隐藏的。我们通过解释数据收集者的偏好、技能和激励如何影响其包含的信息质量以及释放其潜力所需的投资，概述了与凌乱数据相关的机会和限制。我们广泛借鉴了各门科学，分解了观察过程的要素，以突出可能的偏见和错误来源，同时强调跨学科合作的重要性。我们提出了一个评估杂乱数据的框架，以指导那些参与这些类型数据集的人，并使它们用于保护和更广泛的可持续性应用。
Conservationists increasingly use unstructured observational data, such as citizen science records or ranger patrol observations, to guide decision making. These datasets are often large and relatively cheap to collect, and they have enormous potential. However, the resulting data are generally "messy,'' and their use can incur considerable costs, some of which are hidden. We present an overview of the opportunities and limitations associated with messy data by explaining how the preferences, skills, and incentives of data collectors affect the quality of the information they contain and the investment required to unlock their potential. Drawing widely from across the sciences, we break down elements of the observation process in order to highlight likely sources of bias and error while emphasizing the importance of cross-disciplinary collaboration. We propose a framework for appraising messy data to guide those engaging with these types of dataset and make them work for conservation and broader sustainability applications.