SoCS: Collaborative Research: Focusing Attention to Improve the Performance of Citizen Science Systems: Beautiful Images and Perceptive Observers

SoCS：协作研究：集中注意力提高公民科学系统的性能：美丽的图像和敏锐的观察者

• 批准号: 1211071
• 负责人: Carsten Oesterlund
• 金额: $ 30.51万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1211071&HistoricalAwards=false
• 关键词: SoCS; Collaborative; Research; Focusing; Attention

The goal of this project is to develop a next-generation socio-computational citizen science platform that combines the efforts of human classifiers with those of computational systems to maximize the efficiency with which human attention can be used. Dealing with the flood of digital data that confronts researchers is the fundamental challenge of twenty-first century research. New techniques, tools and strategies for dealing with massive data sets, whether they consist of vast numbers of base-pair DNA sequences or terabytes of data from all-sky astronomical surveys, present an opportunity to establish a new paradigm of scientific discovery, but the task is not easy. In many areas of research, the relentless growth of data sets has led to the adoption of increasingly automated and unsupervised methods of classification. In many cases, this has led to degradation in classification quality, with machine learning and computer vision unable to replicate the successes of human pattern recognition. The growth of citizen science on the web has provided a temporary solution to this problem, demonstrating that it is possible to recruit hundreds of thousands of volunteers to make an authentic contribution to results, boosting human analysis through the collective wisdom of a crowd of classifiers. However, human classifiers alone will not be able to cope with expected flood of data from future scientific instruments. This research will be carried out by a partnership between computer and social scientists, addressing research problems both in automated data analysis and social science through systems implementation, alongside field research and experiments with project participants. The intellectual merit of this project lies in its contribution to advancing knowledge and understanding in multiple domains of science. First, the work will contribute to developing new methods of computational data analysis, initially with analysis of astronomical images, and later extending to additional fields. Second, the project includes social science research to test and apply theories of human motivation and learning in an online context, which can then be applied to a broad range of social-computational problems. By mixing human and computational elements, the planned system has the potential to transform the application of citizen science and its approach to data analysis. This project will advance science while promoting teaching, training and learning. One of the most significant broader impacts for its citizen science activities is enabling a community of hundreds of thousands of volunteers to participate in research, a powerful and rapidly developing form of informal science education. By choosing the relatively generic topic of image classification, beginning with astronomy but not limited to that field of science, the techniques developed under this grant will be of significant value to future investigations in similar research areas, thus enhancing the infrastructure for research and education.

该项目的目的是开发下一代社会计算公民科学平台，该平台将人类分类器与计算系统的努力相结合，以最大程度地利用人类注意力的效率。处理研究人员面临的数字数据泛滥是二十一世纪研究的基本挑战。 用于处理大量数据集的新技术，工具和策略，无论是由大量的基本对DNA序列还是来自全套天文学调查的数据的大量数据组成，为建立新的科学发现范式提供了机会，但任务并不容易。在许多研究领域，数据集的无情增长导致采用了日益自动化和无监督的分类方法。在许多情况下，这导致分类质量的降解，机器学习和计算机视觉无法复制人类模式识别的成功。公民科学在网络上的增长为这个问题提供了临时解决方案，表明有可能招募成千上万的志愿者对结果做出真实贡献，从而通过一群分类者的集体智慧来促进人类的分析。但是，单独的人类分类器将无法应付未来科学仪器的预期数据。这项研究将由计算机与社会科学家之间的伙伴关系进行，通过系统实施以及与项目参与者进行实地研究以及实验，解决自动数据分析和社会科学的研究问题。 该项目的智力优点在于它对在科学多个领域的知识和理解方面的贡献。首先，这项工作将有助于开发新的计算数据分析方法，最初是对天文图像的分析，然后扩展到其他字段的方法。其次，该项目包括在线环境中测试和运用人类动机和学习理论的社会科学研究，然后可以将其应用于广泛的社会兼容问题。通过混合人类和计算元素，计划的系统有可能改变公民科学的应用及其对数据分析的方法。该项目将在促进教学，培训和学习的同时推进科学。对其公民科学活动的最重要影响之一是使成千上万的志愿者的社区参与研究，这是一种强大而快速发展的非正式科学教育形式。 通过选择相对通用的图像分类主题，从天文学开始，但不限于该科学领域，该赠款中开发的技术将对类似研究领域的未来研究具有重要价值，从而增强了研究和教育的基础设施。