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

这一热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组合现有的数据集。其目标是鼓励考虑数据方法论的科学家、综合数据集的用户和构建者以及数据保管者之间的合作。其结果可能是记录联系和更好地利用现有数据的新的高质量方法。链接方法和隐私保护的通用、透明和可重复的方法将帮助数据分析人员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。PI将在密歇根大学的大学间政治和社会研究联盟(ICPSR)建立一个通用的链接方法基准存储库--数据链接库(DLRep)。储存库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、持有者和数据使用者之间以及不同的数据使用者群体之间的互动。此外，数据用户将能够上传和共享与数据有关的代码片段，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来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有关数据使用情况的反馈。该储存库将通过提供对方法和相关数据的访问，加快开发新的记录链接算法和评价方法。社区的这一努力将提高对综合数据进行分析的可重复性。当使用相同和不同的数据时，该储存库将促进对替代联系方法的比较，并推动提供具有隐私意识的综合数据。

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.