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名为数据科学基金会的协作式数据科学平台。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，数据处理决策是以临时方式作出的。然而，社会已经开始依赖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。该项目有可能改变研究的设计方式和数据处理方式。协作平台将通过协作管理研究设计、程序和验证来提高研究的信任度。合作平台还将增加可以在短时间内完成的研究的数量。该平台将以开源形式开发，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来协作和验证彼此的研究。调查团队将试图为数据驱动的社会科学确定可能的最佳协作模式，确定自动化如何最大限度地增强研究，并开发显式和隐式机制，以建立对端到端数据处理管道及其产生的结果的信任。为了帮助该平台的发展，研究团队将专注于对调查结果的预测，这是计算社会科学中一种具体但广泛适用的问题类型。这类问题在特征工程阶段涉及大量的主观评估，在数据转换阶段涉及大量的解释。这些独特的挑战既将受益于协作工作流程，也将受益于能够信任最终结果的机制。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；统计学家帮助方法和研究设计；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NSF的法定使命，并通过使用基金会的智力优势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.