MIDAS coordinates efforts to bring attention to important research topics that cut across traditional disciplines, foster interaction between theorists and application scientists, enable innovative ideas and new collaboration, and elevate the quality of data science research across U-M campuses. We particularly encourage researcher-initiated working groups and workshops to catalyze interdisciplinary research ideas and teams. The goals of such activities can be one or more of the following:
- Developing novel research themes that have the potential of putting U-M as a national leader in these areas;
- Developing research ideas that will become major grant proposals;
- Developing interdisciplinary teams.
The activities can be journal clubs that help a diverse range of researchers understand each other’s fields and formulate collaboration ideas, research chalk talks, grant writing sessions, or others that fit with the goals. The activities should tend to be recurrent because it takes time to build ideas and teams. MIDAS can provide a small amount of funding, help coordinate the activities, and connect researchers from all U-M units with diverse background and expertise.
If you would like to submit an idea or have questions, please email email@example.com.
Examples of working groups:
- Data Integration. Data integration is an essential component of data science research in almost all research areas that use heterogeneous data varying in format, dimensionality, quality and granularity. The examples are endless: multi-omics data in biological research; the integration of patient longitudinal data, lab data, sensor data and other types of diagnosis and self-report; environmental monitoring based on statistical data, image data and geospatial data; the integration of social media and other web-based data with traditional survey data… The working group discusses challenges such as idiosyncratic integration methods, missing data, bias and coverage, consistency and quality control issues.
- Data Science for Music. This group attract researchers with diverse background who would like to venture into this highly interdisciplinary research area. We define “data science for music” broadly, including computer music; computational music theory; Big Data analytics in music education, audience engagement and marketing; music in healthcare, advocacy and other settings; and any other research at the intersection of data science and music.
- Mobile Sensor Analytics. Mobile sensors are taking on an increasing presence in our lives, from wearable devices for physiological and cognitive monitoring, sensors in vehicles for kinematic, environment and driver behavior monitoring, to environmental monitoring and epidemiological tracking. Our group discuss theory and application in mobile sensor analytics, including real-time data collection, streaming data analysis, active on-line learning, mobile sensor networks, and energy efficient mobile computing.
- Teaching Data Science. Our working group is an interdisciplinary platform that fosters ideas and collaboration in the development of data science teaching methods and materials. We discuss issues such as: How do we teach data science to students with various levels of preparation? How do we build data science modules to incorporate into existing domain science courses? How do we raise awareness of ethics and social responsibility in data science teaching? How do we teach data science to independent researchers?
- Trustworthy Data Science. This broad theme includes research and its application on data security, privacy, data fairness, validity, and sensible applications to policy. Our group welcomes methodologists as well as researchers in any research area who take these issues into consideration.
Past working groups linked with funding opportunities:
- Foundation Funding and Social Science
- NSF Big Data Spokes
- NSF BIGDATA
- NSF Resource Implementations for Data Intensive Research in the Social, Behavioral and Economic Sciences
- NSF Secure and Trustworthy Cyberspace