Skip to main content
Explore URMC

URMC CTSI

menu
TBS Graduate Student Receives Young Investigator Award

TBS Graduate Student Receives Young Investigator Award

TBS student Elizabeth “Libby” Saionz recently received an Optical Society of America Young Investigator Award for presenting her research showing that starting visual training sooner rather than later can help stroke patients regain more vision, more quickly.

New Guidelines for Human Research: Common Rule and NIH Confidentiality Updates

New Guidelines for Human Research: Common Rule and NIH Confidentiality Updates

Recent updates to the Common Rule, a set of federal regulations for the ethical conduct of research with human subjects, and the NIH Certificate of Confidentiality policy aim to reduce regulatory burden on researchers while ensuring protection of research subjects. Learn what this means for you.

Team Science at URMC: Using Social Network Analysis to Visualize Research Collaborations

Team Science at URMC: Using Social Network Analysis to Visualize Research Collaborations

UR CTSI is collecting data from URMC faculty regarding their collaborations within the medical center. An email will be sent to all URMC faculty on Tuesday, October 3 with a link to an online survey. The data will help us understand how the research environment at URMC is evolving over time.

UR CTSI Demystifies Translational Science with New Online Course

UR CTSI Demystifies Translational Science with New Online Course

The University of Rochester Clinical and Translational Science Institute has developed one of the first massive open online courses (MOOC) focused on translational science. Anyone interested in learning more about translational science can audit the course for free.

Studying Healthcare as a Network: Does the Analysis Match the Question?

Studying Healthcare as a Network: Does the Analysis Match the Question?

Studying the healthcare system as a network can help researchers uncover patterns of opioid over-prescription or predict how a virus outbreak might overtax a healthcare network. But researchers from the University of Rochester Medical Center caution that the type of algorithms used to make these discoveries could provide misleading results.