Creating a Data Quality Control Framework for Producing New Personnel-Based S&E Indicators

创建数据质量控制框架以产生新的基于人员的S

• 批准号: 1917663
• 负责人: Jason Owen-Smith
• 金额: $ 46.2万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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: 2019-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917663&HistoricalAwards=false
• 关键词: Creating; Data; Quality; Control; Framework

Science and Engineering (S&E) research generates substantial returns in terms of human knowledge, social and economic benefits. Nations around the globe compete for scientific and technological leadership through substantial research funding and focused efforts to develop highly trained workforces. To date, efforts to measure and understand national and international trends in S&E and to assess global strengths and weaknesses, have largely relied on the analysis of documents such as patents and publications using big, growing datasets. But this approach too often misses or mistakenly identifies the people and teams who do productive science and engineering work. Robust indicators of the size, composition, collaboration, and mobility of the S&E workforce within and across nations are largely missing from analysis and reporting. These key aspects of the national and international scientific enterprise are poorly captured by data analysis focused on documents and citations. To address this problem, this project develops person level workforce and collaboration measures that could add granularity to comparisons of international S&E competitiveness and lead to new policy insights for S&E workforce training, hiring, and retention for a nation's future. The prerequisite of such person level indicators is that individual researchers who appear in multiple bibliographic datasets are correctly identified and linked. Effective identification and linkage of authors based on their names is daunting because names are often ambiguous. This is particularly the case for Asian names, which poses a significant problem as Asian researchers play an increasingly important role in many fields of research. This project addresses the challenge of systematically and routinely disambiguating names in big bibliographic datasets using a new Automated and Stratified Entity Disambiguation framework. Core datasets for this effort are derived using a new method that relies on multiple data fields and an iterative process to automatically create disambiguated datasets that can be used to train artificial intelligence tools to conduct robust person level analysis. To improve disambiguation accuracy, name instances are stratified into two groups according to name-ethnicity and disambiguated separately to produce optimal models learned on the automatically generated truth data. Based on the disambiguated data, this project develops new person-level S&E indicators that characterize the landscape and trends of the international S&E research workforce across all science and engineering fields. The new big data tools for automatic disambiguation at scale will be documented and released publicly to enable expansion, validation, and reuse by the science community as well as science of science policy researchers.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.

科学和工程研究在人类知识、社会和经济效益方面产生了巨大的回报。地球仪世界各国通过大量研究资金和集中精力培养训练有素的劳动力来争夺科学和技术领导地位。迄今为止，衡量和理解国家和国际科技发展趋势以及评估全球优势和劣势的努力，主要依赖于使用大型不断增长的数据集对专利和出版物等文件进行分析。 但这种方法往往会错过或错误地识别从事生产性科学和工程工作的人员和团队。在分析和报告中，很大程度上缺少关于国家内部和国家之间的S E劳动力的规模、组成、协作和流动性的强有力指标。国家和国际科学事业的这些关键方面，在侧重于文献和引文的数据分析中，得不到很好的捕捉。为了解决这个问题，该项目开发的人的水平的劳动力和协作措施，可以增加粒度的国际S E竞争力的比较，并导致新的政策见解S E劳动力培训，招聘和保留一个国家的未来。这种个人水平指标的前提是，出现在多个书目数据集中的研究人员被正确识别和链接。根据作者的姓名有效识别和联系作者是令人生畏的，因为姓名往往含糊不清。亚洲人的名字尤其如此，这是一个重大问题，因为亚洲研究人员在许多研究领域发挥着越来越重要的作用。该项目解决了使用新的自动化和分层实体消歧框架在大型书目数据集中系统地和常规地消除名称歧义的挑战。这项工作的核心数据集是使用一种新方法获得的，该方法依赖于多个数据字段和一个迭代过程来自动创建消歧数据集，这些数据集可用于训练人工智能工具，以进行强大的个人层面分析。为了提高消歧的准确性，名称实例被分层成两组，根据名称种族和消歧分别产生最佳模型上自动生成的真理数据学习。基于消除歧义的数据，该项目开发了新的个人层面的S E指标，其特征在于跨所有科学和工程领域的国际S E研究队伍的景观和趋势。用于大规模自动消歧的新的大数据工具将被记录并公开发布，以便科学界以及科学政策研究者的科学扩展，验证和重用。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Model Reuse in Machine Learning for Author Name Disambiguation: An Exploration of Transfer Learning

• DOI: 10.1109/access.2020.3031112
• 发表时间: 2020-10
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Jinseok Kim;Jason Owen-Smith

Effect of Chinese characters on machine learning for Chinese author name disambiguation: A counterfactual evaluation

• DOI: 10.1177/01655515211018171
• 发表时间: 2021-05
• 期刊: Journal of Information Science
• 影响因子: 2.4
• 作者: Jinseok Kim;Jenna Kim;Jinmo Kim

ORCID-linked labeled data for evaluating author name disambiguation at scale

• DOI: 10.1007/s11192-020-03826-6
• 发表时间: 2021-02-11
• 期刊: SCIENTOMETRICS
• 影响因子: 3.9
• 作者: Kim, Jinseok;Owen-Smith, Jason
• 通讯作者: Owen-Smith, Jason