EAGER: Collaborative Research: A Computational Model for Evaluating the Quality of Citizen Science Contributions

EAGER：协作研究：评估公民科学贡献质量的计算模型

• 批准号: 1451079
• 负责人: Mary Lou Maher
• 金额: $ 9.73万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1451079&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Computational; Model

Citizen science is a form of research collaboration that involves members of the public in scientific projects, bringing multiple voices and ideas to problem solving and community participation. Citizen science can be powerful because while specific individuals may lack formal expertise and be limited in their ability to contribute high-quality data and new directions, a crowd of individuals may collectively possess the expertise and creativity necessary for identifying and solving difficult problems. However, a major concern for collecting scientific data from the crowd is the varied quality of the contributed data and their relevance to scientific hypotheses. The PIs will explore the potential for deriving metrics from research on computational creativity to automatically assess the quality of citizen science data as a complement to existing research on human assessment of data quality. The project will also explore how the automated assessment of quality can be incorporated into an agent that makes suggestions to individuals in the crowd about the quality of their data, resulting in a prototype for a computational agent that measures the novelty and value of a cizen science contribution. This project will inform future research in computational agents that learn from and contribute to the crowd in order to address challenges associated with the quality of the data and ideas from crowdsourcing in citizen science. More specifically, the project includes a) development of a model of citizen-science-data quality based on the notion that good contributions are not just reliable and accurate but also novel and surprising; b) an evaluation of the model against citizen-science data that has been labeled by humans for quality; and c) initial studies of the effect of computational quality feedback on the behavior and perception of members of the crowd. Extending quality assessment to include creativity and being able to make such assessments automatically is potentially tranformative for citizen science projects. The PIs will demonstrate their agent-based model of quality in citizen science projects including their own NatureNet project, which involves crowd participants in data collection in nature preserves and also in the design of scientific challenges and interaction experience that facilitate data collection.

公民科学是一种研究合作形式，让公众参与科学项目，为问题解决和社区参与带来多种声音和想法。公民科学之所以强大，是因为虽然特定的个人可能缺乏正式的专业知识，并且贡献高质量数据和新方向的能力受到限制，但一群人可能集体拥有识别和解决难题所需的专业知识和创造力。然而，从人群中收集科学数据的一个主要问题是所提供数据的不同质量及其与科学假设的相关性。 PI 将探索从计算创造力研究中导出指标的潜力，以自动评估公民科学数据的质量，作为对现有数据质量人类评估研究的补充。该项目还将探索如何将自动质量评估纳入到一个代理中，该代理向人群中的个人提供有关数据质量的建议，从而产生一个计算代理的原型，用于衡量公民科学的新颖性和价值贡献。该项目将为计算代理的未来研究提供信息，这些计算代理向人群学习并为人群做出贡献，以解决与公民科学众包数据和想法质量相关的挑战。更具体地说，该项目包括：a) 开发公民科学数据质量模型，其理念是良好的贡献不仅可靠、准确，而且新颖、令人惊讶； b) 根据人类已标记质量的公民科学数据对模型进行评估； c) 计算质量反馈对群体成员的行为和感知的影响的初步研究。将质量评估扩展到包括创造力并能够自动进行此类评估对于公民科学项目来说可能会带来变革。 PI 将在公民科学项目中展示他们基于代理的质量模型，包括他们自己的 NatureNet 项目，该项目涉及人群参与者参与自然保护区的数据收集，以及促进数据收集的科学挑战和互动体验的设计。