Abstract: As scientific research becomes more data intensive, the demand to share and preserve research data is growing. To respond to these demands, archives need to develop evidence-based data curation efforts that maximize scholarly impact. I investigate research data discovery and reuse in order to understand the impact of data curation decisions and identify opportunities for interdisciplinary collaboration. In this talk, I will describe computational approaches I have developed to analyze data curation work and to spatially organize interdisciplinary knowledge. My first project leverages text classification to analyze curatorial workflows at a large data archive and provides insights into the impacts of data curation decisions at scale. My second project organizes topics extracted from research products into science maps, providing a spatial decision support framework for an interdisciplinary research institute. Together, these projects provide a foundation for studying how curation decisions influence data reuse and respond to the evolving needs of diverse research communities.

Bio: Sara Lafia is a Research Fellow at ICPSR at the University of Michigan. She holds a Ph.D. in Geography from UC Santa Barbara. Her research interests include data curation, digital archives, civic data, and geographic information systems (GIS). In her work, she considers issues of research data findability, accessibility, interoperability, and reuse (FAIR) for a wide range of users. She develops computational approaches to study the impact of data curation on data reuse, scholarship, and knowledge production. Her work has been published in multidisciplinary, internationally recognized venues including IEEE eScience, Patterns, Conference on Spatial Information Theory, International Conference on GIScience, and Transactions in GIS. She also serves as an Earth Science Information Partners (ESIP) Community Fellow in the areas of data discovery and semantic technologies.