Transforming Data Discovery Through Behavior Modeling and Recommendation

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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.