Spokes: MEDIUM: NORTHEAST: Collaborative Research: Data Science Foundry: A Collaborative Platform for Computational Social Science

该研究项目将为计算社会科学开发一个协作数据科学平台，称为数据科学铸造厂。目前，大规模数据的收集和管理是一个相对非结构化的过程，数据处理决策是以一种特别的方式做出的。然而，社会已经开始依靠数据驱动的科学来解决与政策相关的问题。开发一个提供结构的协作平台将使社会科学家能够协作并验证彼此的研究。这个项目有可能改变研究的设计方式和数据的处理方式。通过研究设计、程序和验证的协作管理，协作平台将导致对研究的更高程度的信任。该合作平台还将增加可在短时间内完成的研究数量。该平台将作为开源开发，从而促进与社区的互动，并使不同的机构能够安装该程序。该项目将开发一个协作平台，社会科学家可以使用该平台进行协作并验证彼此的研究。调查小组将尝试确定数据驱动社会科学的最佳协作模式，确定自动化如何最大程度地增强研究，并开发明确和隐含的机制，以建立对端到端数据处理管道及其产生的结果的信任。为了帮助平台的发展，研究团队将专注于调查结果的预测，这是计算社会科学中一个特定但广泛适用的问题类型。这类问题涉及到特征工程阶段的大量主观评价和数据转换阶段的大量解释。这些独特的挑战将受益于协作工作流和支持最终结果信任的机制。该项目将汇集三个不同的团队来开发这个平台：计算机科学家开发抽象、api和系统；统计学家在方法和研究设计方面提供帮助；社会科学家帮助定义问题和工作流程并提供用户反馈。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This research project will develop a collaborative data science platform for computational social science called the Data Science Foundry. The collection and management of large-scale data currently is a relatively unstructured process, with data-processing decisions being made in an ad hoc fashion. Society has started to rely on data-driven science to address policy-related questions, however. The development of a collaborative platform that provides structure will allow social scientists to collaborate and validate each other's studies. This project has the potential to transform how studies are designed and how data will be processed. The collaborative platform will result in a higher level of trust in the studies conducted via the collaborative curation of study design, procedures, and validation. The collaborative platform also will increase the number of studies that can be done in a short span of time. The platform will be developed as open-source, thereby facilitating interactions with the community and enabling different institutions to install the program.This project will develop a collaborative platform that social scientists can use to collaborate and validate each other's studies. The investigative team will attempt to identify the best possible collaborative model for data-driven social science, determine how automation can most enhance the studies, and develop explicit and implicit mechanisms to establish trust in end-to-end data processing pipelines and the results they generate. To aid in the platform's development, the research team will focus on the prediction of outcomes from surveys, a specific yet widely applicable type of problem within computational social science. This class of problems involves much subjective assessment during the feature engineering state as well as copious interpretation during the data transformation stage. These unique challenges will benefit both from a collaborative workflow and from mechanisms that enable trust in the eventual results. The project will bring together three distinct teams to develop this platform: computer scientists to develop abstractions, APIs and systems; statisticians to help with methods and study design; and social scientists to help define the problems and workflow and to provide user feedback.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.