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.

公民科学使公众参与与专业科学家合作进行的研究活动。公民的参与缩短了持续时间，并降低了某些研究活动的成本。抑制广泛采用公民科学的主要挑战是保证志愿者提交的捐款的可靠性。传统方法依赖于任务的冗余分布，从而使多个志愿者徒劳地分配了相同的任务。但是，大多数公民科学项目都遭受了长期贡献者的稀缺性和大量休闲，短期志愿者的困扰。从进化中选择了身体和行为异质性的每个生命状况中的物种中的灵感，这项早期概念探索性研究的赠款（急切）提出，公民科学家的异质性将提高收集数据的可靠性。设想的范式将通过使研究人员能够快速收集大量可靠数据，而对现有基础设施的变化很小，这将促进科学的进步。该项目的成果将对研究人员和整个社会互利：研究人员将对公民科学有更多的信心，并提出更令人兴奋的项目，这些项目将努力提高公众的科学素养。本研究计划旨在展示一种新的方法，以证明基于先前绩效的志愿者在志愿者中分配了基于先前的绩效，与项目的亲和力，与项目的亲和力以及技术潜能。具体而言，该项目假设从参与者的子样本中获得的数据在个体属性方面高度异质将导致更可靠的数据，从而显着降低了任务冗余的程度和数据质量的提高。该假设将在布鲁克林亚特兰蒂斯（Brooklyn Atlantis）中进行检验，布鲁克林（Brooklyn Atlantis）是一个在线公民科学项目，用于监测高瓦努斯运河（Gowanus Canal）的环境健康 - 高度污染的超级基金网站。在布鲁克林亚特兰蒂斯（Brooklyn Atlantis），公民科学家通过水生机器人从运河表面捕获的图像中发现了感兴趣的对象。将进行一系列研究以：i）阐明数据可靠性和各个属性之间的关系； ii）量化数据融合的潜力以提高贡献的质量和准确性； iii）了解组异质性在数据可靠性中的作用。严格的统计和受限的优化将推动实施最佳的任务分配引擎，以用于分布式公民科学应用。

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
• 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
• 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

• DOI: 10.1016/j.chb.2018.08.017
• 发表时间: 2018-12-01
• 期刊: COMPUTERS IN HUMAN BEHAVIOR
• 影响因子: 9.9
• 作者: Cappa, Francesco;Laut, Jeffrey;Giustiniano, Luca
• 通讯作者: Giustiniano, Luca