Data And Computational Science Series - Year Of Open Research Series: Sharing Data - Open MRI, A Big Data Case Study (Workshop - Main Campus Libraries) #13302

Import to your Calendar

With the upcoming changes to the NIH Data Management and Sharing Policy, researchers funded by the NIH are facing major changes to the dissemination of research results.  Data Sharing is now a major component of the output.  Big Data and Sensitive Data present very real challenges and researchers will have questions.  What infrastructure and resources support this level of sharing and still protect study participants?  What does a reasonable workflow look like?  What are UC specific considerations to ensure compliancy with funders requirements?  In this session, UC researchers will hear from an NIH scientist who is facing these same challenges and an neuroscientist turned data management librarian who currents supports open research initiatives including sharing MRI data as to how they will face these challenges and what their workflows are.  After the presentations, there will be ample time for what promises to be a vibrant and challenging discussion on the issues and potential solutions.

The in person venue can hold 39 people. There is also a virtual option as needed.  For questions about the session, please contact Amy Koshoffer

Course Details

Monday, February 6, 2023
From 2:00 PM to 3:30 PM
Univ. of Cincinnati-Medical Science Bldg
Amy Koshoffer
(513) 556-1310
231 Albert Sabin Way
Cincinnati, Ohio 45267
United States of America
E005HA (Lucas Board Room, Winkler Center in the Health Sciences Library) (Virtual options are available) 231 Albert Sabin Way Cincinnati, Ohio 45229

Featured Presenters will be:

Adam Thomas PhD - Director of the Data Science and Sharing Team, National Institute of Health

John Borghi PhD - Former Manager, Research and Instruction, School of Medicine - Lane Medical Library, Standford University and soon to be member of Office of the Senior Associate Dean of Research and Stanford's CTSA.


Meeting ID: 923 1793 8422

Passcode: 593074