Workshop on Finding Datasets for Empirical Research in the Social Sciences: Washington, D.C. - November 2019

寻找社会科学实证研究数据集研讨会：华盛顿特区 - 2019 年 11 月

• 批准号: 1940967
• 负责人: julia lane
• 金额: $ 1万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940967&HistoricalAwards=false
• 关键词: Workshop; Finding; Datasets; Empirical; Research

AbstractThis award funds a workshop that will explore how to use methods from computer science to an important issue in the social sciences. The great challenges of our time are human in nature - terrorism, climate change, the use of natural resources, and the nature of work - and require robust social science to understand the sources and consequences. But social science researchers face issues in finding data that can be used for important questions. This is in part because of the difficulty of sharing confidential data and because of the lack of scientific infrastructure that describes how the data can be used and applied to common problems. A major challenge is search and discovery. Many social science data and outputs cannot be easily discovered by other researchers even when deposited in the public domain. A new generation of automated search tools could help researchers discover how data are being used, in what research fields, with what methods, with what code and with what findings. And automation can be used to reward researchers who validate the results and contribute additional information about use, fields, methods, code, and findings. This workshop will bring together an interdisciplinary expert community to develop an agenda for action.This workshop will bring together experts in a project that automates the collection and codification of knowledge from publications and people. The goal is to advance technological approaches to applying text analysis and machine learning techniques on a series of different publication corpora to identify the datasets referenced in each publication and draw out the required elements. the use of data depends critically on knowing how it has been produced and used before: the required elements what do the data measure, what research has been done by what researchers, with what code, and with what results. Acquiring that knowledge has historically been manual and inadequate. The challenge is particularly acute in the case of confidential data on human subjects, since it is impossible to provide fully open access to the source files. The experts who attend this workshop will work to develop an agenda for future efforts to use new technologies to solve this important problem.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.

AbstractThis奖资助一个研讨会，将探讨如何使用从计算机科学的方法，在社会科学的一个重要问题。我们这个时代的巨大挑战本质上是人类的--恐怖主义、气候变化、自然资源的使用和工作的性质--需要强大的社会科学来理解其根源和后果。 但社会科学研究人员在寻找可用于重要问题的数据方面面临问题。部分原因是难以分享机密数据，以及缺乏科学基础设施来说明如何使用数据并将其应用于常见问题。 一个主要的挑战是搜索和发现。许多社会科学数据和产出即使存放在公共领域，也不容易被其他研究人员发现。新一代的自动搜索工具可以帮助研究人员发现数据是如何被使用的，在什么研究领域，用什么方法，用什么代码，用什么发现。自动化可以用来奖励那些验证结果并贡献有关使用、字段、方法、代码和发现的额外信息的研究人员。该讲习班将汇集一个跨学科的专家群体，以制定一项行动议程，并将汇集专家参与一个项目，使从出版物和人员收集和编纂知识的工作自动化。其目标是推进技术方法，将文本分析和机器学习技术应用于一系列不同的出版物语料库，以确定每个出版物中引用的数据集，并提取所需的元素。数据的使用关键取决于了解数据以前是如何产生和使用的：所需的要素，数据衡量什么，哪些研究人员进行了哪些研究，使用什么代码，以及产生了什么结果。 获取这些知识历来是手工的，而且是不充分的。在涉及人类受试者的机密数据的情况下，这一挑战尤其严峻，因为不可能完全开放源文件。参加此次研讨会的专家将致力于制定一个议程，为未来的努力，利用新技术来解决这一重要问题。这个奖项反映了NSF的法定使命，并已被认为是值得支持的，通过评估使用基金会的知识价值和更广泛的影响审查标准。