公民科学中的数据质量和验证机制

A. Wiggins, Greg Newman, R. Stevenson, Kevin Crowston
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引用次数: 188

摘要

数据质量是采用公众参与科学研究(PPSR)或“公民科学”方法的研究人员最关心的问题。这种科学合作模式依赖于大量志愿者的贡献,这些志愿者往往是未知的,他们的专业知识各不相同。在对PPSR项目的调查中,我们发现大多数项目采用多种机制来确保数据质量和适当的验证级别。基于作者的直接经验和对调查数据的回顾,我们创建了一个由18种机制组成的框架,这些机制通常被PPSR项目用于确保数据质量,并注意到两类错误来源(协议、参与者)和三个潜在的干预点(参与之前、期间和之后),它们可用于指导项目设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mechanisms for Data Quality and Validation in Citizen Science
Data quality is a primary concern for researchers employing a public participation in scientific research (PPSR) or ``citizen science'' approach. This mode of scientific collaboration relies on contributions from a large, often unknown population of volunteers with variable expertise. In a survey of PPSR projects, we found that most projects employ multiple mechanisms to ensure data quality and appropriate levels of validation. We created a framework of 18 mechanisms commonly employed by PPSR projects for ensuring data quality, based on direct experience of the authors and a review of the survey data, noting two categories of sources of error (protocols, participants) and three potential intervention points (before, during and after participation), which can be used to guide project design.
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