MIDAS advances cross-cutting research in data science theory, methodology and applications, and its positive impact on our society. We offer research funding, facilitate ideas development, foster collaboration, enable access to important datasets, and provide grant proposal support.
Funding
MIDAS research funding supports high-impact and innovative research in data science theory, methodology, applications in all research domains, and encourages research with significant and positive societal impact. During Phase I of funding support (2015-2018), the MIDAS Challenge Initiative provided $10 million funding for projects in data-intensive transportation research, health science, social science, and learning analytics. MIDAS also launched the Data Science for Music program with funding for projects that apply data science methods to the study of music theory, composition, performance and audience participation.
MIDAS is currently seeking applications for the next round of funding: Propelling Original Data Science (PODS).
Learn more about each of the research hubs
Learning Analytics
Improving Education Through Data
Health Science
Advancing the Spectrum of Biomedical Research
Social Science
Leading the Evolution of Social Research
Music
Connecting Data Science and Music
MIDAS provides support for U-M investigators to improve the data science component of grant proposals in many different ways, ranging from a simple description of MIDAS facilities and resources to substantial collaborative research.
Our first discussion board, “Data Science Methods in Python”, is open to all University of Michigan members and welcomes questions at all levels. Users of the group as well as consultants at CSCAR will respond to questions. Questions not addressed here can be addressed through in-person consultation sessions with CSCAR.
MIDAS manages and promotes the use of large datasets that could be of value to many U-M data scientists.
MIDAS facilitates research idea development and collaboration through formal and informal interactions with U-M data scientists. We help researchers identify collaborators. We also organize research working groups for faculty and research staff based on research themes and funding opportunities. The themes cut across traditional disciplines and foster interaction between theorists and application scientists. We encourage researchers to propose themes.
