Libraries' Data Grant Program awards our first winner

Libraries' Data Grant Program awards our first winner

April 6, 2021

The University of Arizona Libraries are happy to announce the winner of our first Data Grant Program.

We invited university-affiliated researchers across campus to apply to the program to purchase commercially available datasets that would support their work. The data could be numerical, geospatial, textual, visual, or audio.

The winning grant applicant─Qianhui Li─is a Ph.D. candidate from the School of Government and Public Policy. As a result, we purchased a dataset from Illumis that provides lobbying registration data by state.

Faculty, students, and staff frequently use our A-Z Databases among many other online library resources. “Our goal is to build our data collection at the libraries and make it even more accessible to the University community,” said Chris Kollen, Data Curation Librarian and a member of the data grant program committee. 

In its first year, however, the data grant program is already making a difference for one scholar. Li is using the lobbying registration data to conduct a quality quantitative analysis and complete her doctoral degree as scheduled.

“I am so thankful to the libraries’ data grant program for supporting my project,” said Li. “I gain easy access to data on federal and state-level lobbying registrations without worrying about the financial issues and business communication challenges during the phase of data collection and procurement.” 

The dataset is available for one year in ReDATA, the University of Arizona data repository that the Libraries' Research Engagement department (formerly the Office of Digital Innovation & Stewardship) launched in 2020. 

“I was thrilled to receive this grant," said Li. "Not only does it support my own project, but it will be available to other scholars who are interested in similar topics or find the data useful to their successful completion of research or degrees.” 

Questions? Learn more about the Data Grant Program.