Transforming Data Discovery Through Behavior Modeling and Recommendation

我们鼓励社会科学家分享他们的数据，以便评估、复制和扩展他们的工作。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以供重复使用和检查的数据集的数量和广度。然而，共享数据并不能保证它会被找到和重用。推荐系统增强了搜索技术，但尚未广泛应用于研究数据。需要更好地了解研究人员如何搜索现有数据，以便设计系统向研究人员推荐相关数据。这项研究将确定重新设计数据搜索系统以包含推荐结果是否可以帮助社会科学家发现可有效重用的数据集。该项目的成果将帮助数据档案馆通过增加数据重用来确保我们国家在科学数据方面的投资获得回报，并将通过将研究人员与相关数据联系起来促进科学进步。该项目将a）开发人类信息行为模型，解释社会科学家目前如何搜索数据，b）设计原型数据推荐系统，c）通过现场实验评估模型和系统。数据搜索与一般搜索等其他信息行为相比如何尚不清楚，本项目将解释数据搜索如何展开。该项目还确定了在书籍和电影推荐等领域流行的推荐系统是否也可以用于数据并增加其重用。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

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.