SEP 21, 2016 07:30 AM PDT
Using Data to Drive Automated Screening and Effective Reporting
SPONSORED BY: Beckman Coulter Life Sciences
CONTINUING EDUCATION (CME/CE/CEU) CREDITS: P.A.C.E. CE | Florida CE
6 20 2146

Speakers:
  • Staff Applications Scientist, Beckman Coulter Life Sciences
    Biography
      Mike Kowalski is a Staff Applications Scientist in the Sample Preparation and Applied Markets group of Beckman Coulter Life Sciences. He received his Ph.D. in Microbiology and Molecular Genetics from Harvard University. Prior to joining Beckman, Mike was a postdoctoral fellow at the Novartis Institutes for Biomedical Research, where he used automation to screen for novel regulators of stem cell pluripotency and differentiation. Since joining Beckman, Mike has developed automated applications in the areas of cell culture, cellular analysis, and mass spectrometry.
    • Staff Systems Engineer, Beckman Coulter Life Sciences
      Biography
        Tim Sherrill is a Staff Systems Engineer in the Integrated Solutions group of Beckman Coulter Life Sciences. He received his degree in Computer Science from Rose-Hulman Institute of Technology. Tim has spent his career with Beckman Coulter in the areas of software research and development, custom engineering services, field project management, and life science automation. His projects have focused in the areas of automated data processing and last year, a new version of Beckman Coulter's data management product, DART.

      Abstract:
      The most important job of automated biology is to produce results that moves your research forward. If automation only executes a program of pre-determined set of steps, the researcher is left to update programs (even for simple changes) and process data with copy and paste.  This webinar will use a screen for miRNAs that regulate cancer cell viability and chemosensitivity to demonstrate how to make an effective data management plan. A data management plan uses input data to identify incoming samples, process data to drive automation, and analytical information to power further analysis inquiries. 

      Learning Objectives:
      •    The data-management workflow: Input, Process, Analyze
      •    Data-workflow hierarchy: Runs, Plates, Wells
      •    Data-management plan essentials
      •    See a data-management plan in action on an actual screening campaign!

      Show Resources
      Loading Comments...