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

辐条：媒介：东北：协作研究：数据科学铸造厂：计算社会科学协作平台

• 批准号: 1760052
• 负责人: Matthew Salganik
• 金额: $ 25万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1760052&HistoricalAwards=false
• 关键词: Spokes; MEDIUM; NORTHEAST; Collaborative; Research

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.

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，数据处理决策是以特定的方式做出的。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合作平台将使社会科学家能够合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的处理方式。协作平台将通过协作管理研究设计、程序和验证，提高对研究的信任度。该合作平台还将增加在短时间内可以完成的研究数量。该平台将以开放源代码的方式开发，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进行协作并验证彼此的研究。研究团队将尝试为数据驱动的社会科学确定最佳的合作模型，确定自动化如何最大限度地增强研究，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。为了帮助平台的开发，研究团队将专注于预测调查结果，这是计算社会科学中一种特定但广泛适用的问题。这类问题在特征工程状态期间涉及很多主观评估，在数据转换阶段涉及大量解释。这些独特的挑战将受益于协作工作流程和机制，使最终结果的信任。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;该奖项反映了NSF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进行评估，被认为值得支持的搜索.

项目成果

Prediction, Machine Learning, and Individual Lives: an Interview with Matthew Salganik

预测、机器学习和个人生活：马修·萨尔加尼克访谈

• DOI: 10.1162/99608f92.eecdfa4e
• 发表时间: 2020
• 期刊: Harvard Data Science Review
• 作者: Salganik, Matthew;Maffeo, Lauren;Rudin, Cynthia
• 通讯作者: Rudin, Cynthia

Integrating explanation and prediction in computational social science

• DOI: 10.1038/s41586-021-03659-0
• 发表时间: 2021-06-30
• 期刊: NATURE
• 影响因子: 64.8
• 作者: Hofman, Jake M.;Watts, Duncan J.;Yarkoni, Tal
• 通讯作者: Yarkoni, Tal

Measuring the predictability of life outcomes with a scientific mass collaboration

• DOI: 10.1073/pnas.1915006117
• 发表时间: 2020-04-14
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Salganik, Matthew J.;Lundberg, Ian;McLanahan, Sara
• 通讯作者: McLanahan, Sara