EAGER: Collaborative Research: A Benchmark Data Linkage Repository (DLRep)

EAGER：协作研究：基准数据链接存储库 (DLRep)

• 批准号: 1744065
• 负责人: Margaret Levenstein
• 金额: $ 26万
• 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: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1744065&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Benchmark; Data

This EAGER award provides pilot funding for a new kind of software repository that could help data scientists create better ways to combine existing data sets. The goal is to encourage collaboration between scientists who consider data methodology, users and builders of integrated data sets, and data custodians. The result may be new high-quality methods for record linkages and better use of existing data. Common, transparent, and reproducible approaches to linkage methodology and privacy preservation will help data analysts meet the highest scientific standards. The project may decrease the gap between research and practice in data science, resulting in better evidence for U.S. policymakers, businesses, and citizens.The PIs will establish a common benchmarking repository of linkage methodologies, Data Link Repository (DLRep) at the Inter-University Consortium for Political and Social Research (ICPSR) at the University of Michigan. Participants in the repository will be able to add comments and ask questions, allowing for engagement between data custodians, providers, producers, holders and data users, as well as between various groups of data users. In addition data users will be able to upload and share code snippets related to the data, allowing for knowledge sharing and better reproducibility. Data users will be able to link related publications and citations via DOI import or by manually entering citation information. This will provide feedback to data providers and other data users about how the data have been used. The repository will accelerate the development of new record linkage algorithms and evaluation methods by providing access to both methods and relevant data. This community effort will improved the reproducibility of analysis conducted on integrated data. The repository will facilitate comparisons of alternative linkage methodologies when using the same and different data, and move forward the provision of privacy-aware integrated data.

这项急切的奖项为一种新型的软件存储库提供了试点资金，可以帮助数据科学家创建更好的方法来结合现有数据集。目的是鼓励考虑数据方法，用户和集成数据集的用户和数据保管人之间的合作。结果可能是用于记录链接和更好地使用现有数据的新的高质量方法。链接方法和隐私保护的常见，透明和可再现的方法将有助于数据分析师符合最高的科学标准。该项目可能会减少数据科学研究和实践之间的差距，从而为美国决策者，企业和公民提供更好的证据。PIS将在密歇根大学的政治和社会研究（ICPSR）的联合会Inter-Universe Inserversity联合会上建立一个链接方法的共同基准测试库，数据链接库存库（DLREP）。存储库的参与者将能够添加评论并提出问题，从而使数据保管人，提供者，生产商，持有人和数据用户以及各个数据用户之间的参与。另外，数据用户将能够上传和共享与数据相关的代码段，从而可以进行知识共享和更好的可重复性。数据用户将能够通过DOI导入或手动输入引用信息来链接相关的出版物和引用。这将向数据提供商和其他数据用户提供有关如何使用数据的反馈。存储库将通过提供对方法和相关数据的访问来加速新的记录链接算法和评估方法的开发。这种社区努力将改善对综合数据进行的分析的可重复性。使用相同的数据和不同的数据时，存储库将促进替代链接方法的比较，并继续提供隐私感知的集成数据。