Dr. Claibourn is the Director of Research Data Services and the Social, Natural, and Engineering Sciences at the University of Virginia Library where she leads a team of statistical and computational consultants, data curation and data discovery librarians, research software specialists, and subject librarians. She previously served as the first Associate Director UVA’s Data Science Institute, working across grounds to identify, coordinate, and build services and infrastructure for data-intensive research.
Prior to joining the UVA Library and the Data Science Institute, Dr. Claibourn was a faculty member in the Department of Politics at UVA (2004-2011) and at the University of Oklahoma (2001-2004). Her publications include Presidential Campaigns and Presidential Accountability (University of Illinois Press 2011) as well articles in The Journal of Politics, Political Research Quarterly, Political Communication, Political Behavior, and Legislative Studies Quarterly. Her research on the impact of electoral campaigns has been funded by the National Science Foundation. Her current research extends her work on presidential campaigns and accountability to extract, compare, and present features (e.g., tone, topic, ideology) from multiple and heterogeneous news sources representing presidential politics.
Dr. Claibourn has taught graduate courses on probability and statistical theory, linear modeling, time series analysis, maximum likelihood analysis, and multilevel modeling. She served as an invited instructor at the Inter-University Consortium for Political and Social Research’s Summer Program on Quantitative Methods of Social Research (2002-2005) where she taught advanced maximum likelihood and mixed-effects modeling. She has worked as an applied statistician in the Demographics & Workforce group at the Weldon Cooper Center for Public Service (2011-2013) where she developed population projections and used spatial analysis, time series modeling, and mixed effects approaches for understanding demographic change. She continues to teach data analysis and statistical methods to graduate students, with a current emphasis on text analysis.