Exposing the Science in Citizen Science: Fitness to Purpose and Intentional Design
Exposing the Science in Citizen Science: Fitness to Purpose and Intentional Design
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DOI：
10.1093/icb/icy032
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发表时间：
2018-07-01
影响因子：
2.6
通讯作者：
Simmons, Brooke
中科院分区：
文献类型：
--
作者：
Parrish, Julia K.;Burgess, Hillary;Simmons, Brooke
公民科学是一种日益增长的现象。由于每年有数百万人参与，数十亿美元的实物捐赠，这种广泛的、细粒度的数据收集方法应该会在主流科学界和高等教育界获得热情支持。然而，许多学术研究人员表现出明显的偏见，反对使用公民科学作为严格信息的来源。为了让公众参与科学研究，让研究界参与公民科学实践，需要相互理解公认的科学质量标准，以及在与广泛的公众基础合作时项目设计和实施的相应细节。我们定义了一种以科学为基础的类型学，重点是项目交付类型(S)的程度和产生直接对科学和自然资源管理有用的有效科学成果所需的数据/工作的质量。如果项目意图包括对科学的直接贡献，并且公众积极参与，无论是虚拟的还是实际的，我们都会审查质量保证措施(在项目的设计和实施阶段提高数据质量的方法)和质量控制措施(提高科学成果质量的特别方法)。我们认为，如果数据收集简单，质量控制包括算法投票、统计修剪和/或计算建模，则可以通过大量的、主要是一次性的参与来产生高质量的科学。让参与者进行重复的、往往是复杂的抽样的中小型项目可以通过专家领导的培训和精心设计的材料以及通过独立核查来提高质量。这两种方法--大规模简化和谨慎复杂--都能产生更有力的科学成果。
Citizen science is a growing phenomenon. With millions of people involved and billions of in-kind dollars contributed annually, this broad extent, fine grain approach to data collection should be garnering enthusiastic support in the mainstream science and higher education communities. However, many academic researchers demonstrate distinct biases against the use of citizen science as a source of rigorous information. To engage the public in scientific research, and the research community in the practice of citizen science, a mutual understanding is needed of accepted quality standards in science, and the corresponding specifics of project design and implementation when working with a broad public base. We define a science-based typology focused on the degree to which projects deliver the type(s) and quality of data/work needed to produce valid scientific outcomes directly useful in science and natural resource management. Where project intent includes direct contribution to science and the public is actively involved either virtually or handson, we examine the measures of quality assurance (methods to increase data quality during the design and implementation phases of a project) and quality control (post hoc methods to increase the quality of scientific outcomes). We suggest that high quality science can be produced with massive, largely one-off, participation if data collection is simple and quality control includes algorithm voting, statistical pruning, and/or computational modeling. Small to mid-scale projects engaging participants in repeated, often complex, sampling can advance quality through expert-led training and well-designed materials, and through independent verification. Both approaches-simplification at scale and complexity with care-generate more robust science outcomes.