This is the second in a series of articles reporting on a study of researcher data management practices and data services at the University of Vermont. This article reports on the second quantitative phase of an exploratory sequential mixed methods research design focused on researcher data management practices and related institutional support and services. The study aims to understand data management activities and challenges of faculty at the University of Vermont (UVM), a higher research activity Research University, in order to develop appropriate research data services (RDS). Data was collected via a survey, built on themes from the initial qualitative data analysis from the first phase of this study. The survey was distributed to a nonrandom census sample of full-time UVM faculty and researchers (P=1,190); from this population, a total of 319 participants completed the survey for a 26.8% response rate. The survey collected information on five dimensions of data management: data management activities; data management plans; data management challenges; data management support; and attitudes and behaviors towards data management planning. Frequencies, cross tabulations, and chi-square tests of independence were calculated using demographic variables including gender, rank, college, and discipline. Results from the analysis provide a snapshot of research data management activities at UVM, including types of data collected, use of metadata, short- and long-term storage of data, and data sharing practices. The survey identified key challenges to data management, including data description (metadata) and sharing data with others; this latter challenge is particular impacted by confidentiality issues and lack of time, personnel, and infrastructure to make data available. Faculty also provided insight to RDS that they think UVM should support, as well as RDS they were personally interested in. Data from this study will be integrated with data from the first qualitative phase of the research project and analyzed for meta-inferences to help determine future research data services at UVM.