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Bay Area Open Science Group - Developing and using open-source software tools: from neuroinformatics to biomechanics Online
This month, the Bay Area Open Science Group will be joined by Sydney Covitz, a Bioengineering PhD Student at Stanford. Sydney graduated from Swarthmore College in 2020 with a double major in Computer Science and English Literature. She is currently working in the Neuromuscular Biomechanics Lab (NMBL), led by PI Scott Delp, using an open-source video-based application the lab developed to analyze upper extremity function in neuromuscular clinic patients. Between college and graduate school, Sydney spent three years as a Senior Software Developer in the University of Pennsylvania's Lifespan Brain Institute where she collaborated with informatics teams around the world to develop software packages and workflows for reproducibly analyzing large neuroimaging datasets. Her talk will touch on her experience in neuroinformatics software development and data management as well as her current work using open-source biomechanics tools for clinical analysis.
The Bay Area Open Science Group is a growing community for Bay Area academics and researchers interested in incorporating open science into their research, teaching, and learning. Targeting students, faculty, and staff at UCSF, Berkeley, and Stanford, the goal of the community is to increase awareness of and engagement with all things open science, including open access articles, open research data, open source software, and open educational resources. Through this work the group hopes to connect researchers with tools they can use to make the products and process of science more equitable and reproducible.
Register for this event via Zoom
Related LibGuide: Reproducible Data Management by Ariel Deardorff
- Date:
- Tuesday, May 20 2025
- Time:
- 3:00pm - 4:00pm
- Time Zone:
- Pacific Time - US & Canada (change)
- Campus:
- Online
- Online:
- This is an online event.
- Event URL:
- https://ucsf.zoom.us/meeting/register/2wSn4HoCSami01zdmjzHoA
- Categories:
- Data Science Scholarly Communication