Spokes: MEDIUM: NORTHEAST: Collaborative Research: Data Science Foundry: A Collaborative Platform for Computational Social Science
Spokes: MEDIUM: NORTHEAST: Collaborative Research: Data Science Foundry: A Collaborative Platform for Computational Social Science
批准号:
1760052
负责人:
Matthew Salganik
金额:
$25.0万
依托单位:
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
项目状态:
已结题
起止时间:
2018-09-01 至 2021-08-31
中文摘要
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英文摘要
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.
期刊论文(8)
专著(0)
科研奖励(0)
会议论文
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
影响因子:
--
作者:
[Salganik, Matthew, Maffeo, Lauren, Rudin, Cynthia]
通讯作者:
Rudin, Cynthia
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
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
Doctoral Dissertation Research: Algorithmic Pretrial Risk Assessments in the Courtroom
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批准号:2001832
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项目类别:Standard Grant
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资助金额:$1.6万
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财政年份:2020
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负责人:Matthew Salganik
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依托单位:
Doctoral Dissertation Research: Reputational Consequences of Scholarships
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批准号:2001853
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项目类别:Standard Grant
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资助金额:$1.58万
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财政年份:2020
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负责人:Matthew Salganik
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依托单位:
海外基金