EAGER: Reliable Data from Heterogeneous Groups of Citizen Scientists

EAGER：来自不同公民科学家群体的可靠数据

• 批准号: 1644828
• 负责人: Maurizio Porfiri
• 金额: $ 10万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1644828&HistoricalAwards=false
• 关键词: EAGER; Reliable; Data; Heterogeneous; Groups

Citizen science involves the general public in research activities that are conducted in collaboration with professional scientists. Citizens' participation shortens the duration and lowers the costs of certain research activities. A key challenge inhibiting the widespread adoption of citizen science is guaranteeing the reliability of contributions submitted by volunteers. Traditional approaches have relied on redundant distribution of tasks, whereby multiple volunteers are indiscriminately assigned identical tasks. However, most citizen science projects suffer from a scarcity of long term contributors and an abundance of casual, short term volunteers. Drawing inspiration from species across every phylum of life where physical and behavioral heterogeneities are evolutionarily selected, this EArly-concept Grant for Exploratory Research (EAGER) project posits that heterogeneities in citizen scientists will improve the reliability of data gathered. The envisioned paradigm will promote the progress of science, by enabling researchers to quickly gather large quantities of reliable data with minimal changes to existing infrastructure. Outcomes of this project will be mutually beneficial to researchers and society at large: researchers will have more confidence in citizen science and put forward more exciting projects which will contrive to enhance the scientific literacy of the public.This research program seeks to demonstrate a novel methodology to cogently distribute tasks among volunteers based on prior performance, affinity to the project, and technical potential. Specifically, the project hypothesizes that data obtained from subsamples of participants that are highly heterogeneous in terms of individual attributes will lead to more reliable data, thereby enabling a significant reduction in the degree of task redundancy and an improvement in data quality. This hypothesis will be tested within Brooklyn Atlantis, an online citizen science project for monitoring the environmental health of the Gowanus Canal - a highly polluted Superfund site. In Brooklyn Atlantis, citizen scientists identify objects of interest in images taken from the surface of the canal through an aquatic robot. A series of studies will be performed to: i) elucidate the relationship between data reliability and individual attributes; ii) quantify the potential of data fusion to enhance quality and accuracy of contributions; and iii) understand the role of group heterogeneity on data reliability. Rigorous statistics and constrained optimization will drive the implementation of an optimal task allocation engine for use in distributed citizen science applications.

公民科学让公众参与与专业科学家合作进行的研究活动。公民的参与缩短了某些研究活动的持续时间并降低了成本。阻碍公民科学广泛采用的一个关键挑战是保证志愿者提交的贡献的可靠性。传统方法依赖于任务的冗余分配，即多个志愿者被不加区别地分配相同的任务。然而，大多数公民科学项目都缺乏长期贡献者和大量临时、短期志愿者。这个早期概念的探索性研究资助（EAGER）项目从各个生命门类的物种中汲取灵感，这些物种的身体和行为异质性是通过进化选择的，它认为公民科学家的异质性将提高所收集数据的可靠性。设想的范式将使研究人员能够快速收集大量可靠的数据，同时对现有基础设施进行最小的改变，从而促进科学的进步。该项目的成果将为研究人员和整个社会带来互惠互利：研究人员将对公民科学更有信心，并提出更多令人兴奋的项目，从而努力提高公众的科学素养。该研究计划旨在展示一种新颖的方法根据先前的表现、对项目的亲和力和技术潜力，在志愿者之间合理地分配任务。具体来说，该项目假设从个体属性高度异质的参与者子样本中获得的数据将产生更可靠的数据，从而显着降低任务冗余程度并提高数据质量。这一假设将在布鲁克林亚特兰蒂斯进行测试，这是一个在线公民科学项目，用于监测高瓦努斯运河（一个污染严重的超级基金站点）的环境健康。在布鲁克林亚特兰蒂斯，公民科学家通过水生机器人从运河表面拍摄的图像中识别出感兴趣的物体。将进行一系列研究以： i）阐明数据可靠性与个体属性之间的关系； ii) 量化数据融合的潜力，以提高贡献的质量和准确性； iii) 了解群体异质性对数据可靠性的作用。严格的统计和约束优化将推动用于分布式公民科学应用的最佳任务分配引擎的实施。

项目成果

The Influence of Social Information and Self-expertise on Emergent Task Allocation in Virtual Groups

社会信息和自我专业知识对虚拟群体紧急任务分配的影响

• DOI: 10.3389/fevo.2018.00016
• 发表时间: 2018-02
• 期刊: Frontiers in Ecology and Evolution
• 影响因子: 3
• 作者: Nakayama, Shinnosuke;Diner, David;Holland, Jacob G.;Bloch, Guy;Porfiri, Maurizio;Nov, Oded
• 通讯作者: Nov, Oded

Producing knowledge by admitting ignorance: Enhancing data quality through an “I don’t know” option in citizen science

通过承认无知来生产知识：通过公民科学中的“我不知道”选项来提高数据质量

• DOI: 10.1371/journal.pone.0211907
• 发表时间: 2019-02
• 期刊: PLOS ONE
• 影响因子: 3.7
• 作者: Torre, Marina;Nakayama, Shinnosuke;Tolbert, Tyrone J.;Porfiri, Maurizio;Kestler, Hans A.
• 通讯作者: Kestler, Hans A.

Bring them aboard: Rewarding participation in technology-mediated citizen science projects

让他们加入：奖励参与以技术为媒介的公民科学项目

• DOI: 10.1016/j.chb.2018.08.017
• 发表时间: 2018-12
• 期刊: Computers in Human Behavior
• 影响因子: 9.9
• 作者: Cappa, Francesco;Laut, Jeffrey;Porfiri, Maurizio;Giustiniano, Luca
• 通讯作者: Giustiniano, Luca

Matching individual attributes with task types in collaborative citizen science

将个人属性与协作公民科学中的任务类型相匹配

• DOI: 10.7717/peerj-cs.209
• 发表时间: 2019
• 期刊: PeerJ Computer Science
• 影响因子: 3.8
• 作者: Nakayama S;Torre M;Nov O;Porfiri M
• 通讯作者: Porfiri M