Driven by Curiosity: Interaction Techniques and Incentive Mechanisms for Crowdsourcing Scientific Tasks

科学越来越需要大量的数据；然而，许多涉及数据收集、注释和分析的研究任务还没有完全由计算机自动化。公民科学的理念是让大量的人通过网络贡献和处理科学数据，解决数据的规模、传播和复杂性超出算法分析能力或小团队带宽的问题。这些公民科学平台的成功部分归功于现有的爱好者（例如，观鸟者、业余天文学家），他们已经感兴趣并渴望提供帮助。不幸的是，相反的情况实际上更常见——科学家经常有大多数人不熟悉的研究数据和问题，并且努力将劳动密集型的数据处理任务转化为本质上激励参与者去做的事情。缺乏激励和维持参与的机制是阻碍公民科学系统被广泛采用的一个主要障碍。

Science is increasingly data-intensive; yet, many research tasks involving the collection, annotation and analysis of data are not yet fully automated by computers. The idea of citizen science is to engage massive number of people over the Web to contribute and process scientific data, to tackle problems where the scale, spread and complexity of the data is beyond the analytic capability of algorithms or the bandwidth of small teams.  The success of these citizen science platforms is in part due to an existing population of hobbyists (e.g., birders, amateur astronomers) who are already interested and eager to help. Unfortunately, the reverse scenario is actually more common - often scientists have research data and problems that are unfamiliar to most people, and struggle to transform labor intensive data processing tasks into something that is intrinsically motivating for participants to do. The lack of mechanisms for incentivizing and sustaining participation presents a major roadblock preventing citizen science systems from widespread adoption.