To find out the best ways to share and archive research data, Libby Hemphill studies the impact of different "curatorial actions" on the reuse of data archived by the Inter-university Consortium for Political and Social Research.

The proposed work will allow students to more easily seek help from their peers in the context of programming courses.

UMSI's activities in the project include designing, developing, and testing a suite of tools for live streaming moderators. These tools will focus on helping moderators dynamically manage their emotional and cognitive capabilities

Libby Hemphill will research and design new technologies to augment online moderator performance.

This Growing Convergence Research project aims to develop algorithms and tools to better use social media data and other new forms of publicly available text data to advance understanding of human behavior and society.

The project addresses bottlenecks in scientific and engineering research workforce development by developing and offering three instructional activities: (a) a project-based course in which students work directly with Michigan communities to design cyberinfrastructure tools, and (b) two massive, open online courses (MOOCs) in which students learn the fundamentals of data science for work in the public sector.

Libby Hemphill and Andrea Thomer investigate how curatorial actions impact the use of digital collections.

This study examines social determinants of health (SDOH) screening practices in a variety of clinical settings through an evidence review, interviews with patients and clinicians, observations, and development of a prototype tool for SDOH screening. We anticipate that findings will benefit create generalizable knowledge about standardization, its impact on health care providers and patients, and the design of screening tools to to reflect the unique characteristics of SDOH data.

This research examines a series of sociotechnical experiments in the American Midwest that invest in the promise of entrepreneurial agency and data-driven solutions to address complex economic and societal challenges. Drawing from ethnographic research and co-design, this project examines both the challenges and opportunities of such regional experimentation, working closely with the most vulnerable groups of the population who are often harmed by rather than benefitting from the experimental interventions currently unfolding.

The project uses state-of-the-art machine learning approaches to automatically detect new terms and phrases being used by white supremacists online.