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 integration is increasingly critical in biological research; clinical research benefits greatly from the integration of patient longitudinal data, lab data, sensor data and other types of diagnosis and self-report; environmental monitoring often needs the integration of statistical data, image data and geospatial data; social science research, including education, political science and economics, increasingly integrates social media and other web-based data with traditional survey data… All the applications encounter similar data science challenges, including idiosyncratic integration methods, missing data, bias and coverage, consistency and quality control issues. Our working group welcomes researchers with interest in data integration methodology and its application in any scientific domain. The Michigan Institute for Data Science (MIDAS) continues to convene a research working group on data integration to create a forum that will foster new ideas and collaborations.
- Chalk talks
- Yang Chen (Assistant Professor, Dept. Statistics) will talk about her experience on data integration and some statistical methodology, and seek interests in collaboration.
- Jamie Estill (staff scientist, HITS) will describe at a high level the capabilities and strength of data virtualization for data integration, using medical research examples, and discuss with the group how data virtualization can facilitate their research.
- Open discussion on ideas and collaboration.
For questions, please contact Jing Liu, MIDAS Senior Scientist and Industry Partnership Leader (firstname.lastname@example.org; 734-764-2750). Please share this announcement with your colleagues who might be interested.