Smart data collection for CryoEM |
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| Affiliation: | 1. New York Structural Biology Center, New York, NY, USA;2. University of Washington, Institute for Protein Design, Seattle, WA, USA;3. National Institute of Environmental Health Sciences, NIH, Durham, NC, USA;4. Laboratory for Molecular Biology, Medical Research Council, Cambridge, England;5. California Institute of Technology, Pasadena, CA, USA;6. Northwestern University, Evanston, IL, USA;7. MIT-IBM Watson AI Lab, Cambridge, MA, USA;8. ThermoFisher Scientific, Eindhoven, The Netherlands;9. SLAC National Accelerator Laboratory, Menlo Park, CA, USA;10. University of California at San Francisco, San Francisco, CA, USA;11. Pacific Northwest CryoEM Center, Portland, OR, USA;12. University of Michigan, Ann Arbor, MI, USA;13. Structura Biotechnology, Toronto, Canada;14. New York University School of Medicine, New York, NY, USA;15. Biocomputing Unit, Natl. Center of Biotechnology, CSIC, Madrid, Spain;p. Florida State University, Tallahassee, FL, USA;q. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA |
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| Abstract: | This report provides an overview of the discussions, presentations, and consensus thinking from the Workshop on Smart Data Collection for CryoEM held at the New York Structural Biology Center on April 6–7, 2022. The goal of the workshop was to address next generation data collection strategies that integrate machine learning and real-time processing into the workflow to reduce or eliminate the need for operator intervention. |
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