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

EAGER奖为一种新型软件存储库提供试点资金，这种存储库可以帮助数据科学家创建更好的方法来组合现有数据集。目标是鼓励考虑数据方法学的科学家、集成数据集的用户和构建者以及数据保管者之间的合作。其结果可能是新的高质量记录联系方法和更好地利用现有数据。通用、透明、可重复的链接方法和隐私保护方法将帮助数据分析师达到最高的科学标准。该项目可能会缩小数据科学研究与实践之间的差距，为美国政策制定者、企业和公民提供更好的证据。这些pi将在密歇根大学的校际政治和社会研究联盟（ICPSR）建立一个共同的链接方法基准库，即数据链接库（DLRep）。存储库中的参与者将能够添加评论和提出问题，从而允许数据保管人、提供者、生产者、持有人和数据用户之间以及不同数据用户组之间的参与。此外，数据用户将能够上传和共享与数据相关的代码片段，从而实现知识共享和更好的再现性。数据用户将能够通过DOI导入或手动输入引文信息链接相关出版物和引文。这将向数据提供者和其他数据用户提供关于数据使用情况的反馈。该储存库将通过提供对方法和有关数据的访问，加速新的记录链接算法和评价方法的发展。这个社区的努力将提高对综合数据进行分析的再现性。当使用相同和不同的数据时，存储库将促进对备选链接方法的比较，并推动提供具有隐私意识的集成数据。

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.