Transforming Data Discovery Through Behavior Modeling and Recommendation

通过行为建模和推荐转变数据发现

• 批准号: 2121789
• 负责人: Libby Hemphill
• 金额: $ 36.29万
• 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: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2121789&HistoricalAwards=false
• 关键词: Transforming; Data; Discovery; Through; Behavior

Social scientists are encouraged to share their data so that their work can be evaluated, replicated, and extended. Open science movements and public funding for science, especially, have increased the number and breadth of datasets that scientists share for reuse and inspection. However, sharing data does not guarantee that it will be found and reused. Search technologies have been enhanced by recommender systems, but they have not been widely applied to research data. A better understanding of how researchers search for existing data is needed in order to design systems to recommend relevant data to researchers. This study will determine if redesigning data search systems to include recommended results can help social scientists discover datasets to reuse effectively. The results of this project will help data archives ensure returns on our national investments in scientific data by increasing data reuse and will promote scientific progress by connecting researchers with relevant data.The project will a) develop a model of human information behavior that explains how social scientists currently search for data, b) design a prototype data recommender system, and c) evaluate the model and system through field experiments. How data search compares to other information behaviors such as general search is not clear, and this project will explain how data search unfolds. The project also determines whether recommendation systems, popular in fields such as book and movie recommendations, can also work for data and increase their reuse.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.

鼓励社会科学家分享他们的数据，以便对他们的工作进行评估、复制和扩展。尤其是开放科学运动和对科学的公共资助，增加了科学家共享的数据集的数量和广度，以供重复使用和检查。然而，共享数据并不能保证它会被找到和重复使用。推荐系统已经增强了搜索技术，但它们还没有广泛应用于研究数据。为了设计向研究人员推荐相关数据的系统，需要更好地理解研究人员如何搜索现有数据。这项研究将确定重新设计数据搜索系统以包括推荐的结果是否可以帮助社会科学家发现可有效重复使用的数据集。该项目的成果将帮助数据档案通过增加数据重用来确保我国在科学数据方面的投资回报，并将通过将研究人员与相关数据联系起来来促进科学进步。该项目将a)开发一个解释社会科学家目前如何搜索数据的人类信息行为模型，b)设计一个数据推荐系统原型，c)通过现场实验对该模型和系统进行评估。数据搜索与其他信息行为(如普通搜索)相比如何比较尚不清楚，这个项目将解释数据搜索是如何展开的。该项目还决定了在书籍和电影推荐等领域广受欢迎的推荐系统是否也能为数据工作并增加它们的重复使用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization

DataChat：为数据集搜索和可视化构建对话代理原型

• DOI: 10.1002/pra2.820
• 发表时间: 2023
• 期刊: Proceedings of the Association for Information Science and Technology
• 作者: Fan, Lizhou;Lafia, Sara;Li, Lingyao;Yang, Fangyuan;Hemphill, Libby
• 通讯作者: Hemphill, Libby

Direct, Orienting, and Scenic Paths: How Users Navigate Search in a Research Data Archive

直接、定向和风景路径：用户如何在研究数据档案中导航搜索

• DOI: 10.1145/3576840.3578275
• 发表时间: 2023
• 期刊: Proceedings of the ACM SIGIR Conference On Human Information Interaction and Retrieval
• 作者: Lafia, Sara;Million, A.J.;Hemphill, Libby
• 通讯作者: Hemphill, Libby