The Eisenberg Family Depression Center publishes open source tools, automations, and research workflows designed to accelerate mental health discoveries while remaining highly reusable across any research discipline. By reducing technical barriers and promoting interdisciplinary collaboration, we help researchers solve complex data problems without starting from zero.
What we build
Get involved
Open source works best when students, professional developers, and researchers can learn from one another and build on shared solutions.
Automated sleep data cleanup and processing to harmonize Fitbit data obtained via Fitabase with self-reported sleep diary entries sent via SMS-to-Email. [10.6084/m9.figshare.25669173.v1]
Efficient pre-processing, cleaning, and visualization of Ecological Momentary Assessment (EMA) survey data in R to enable high-quality, real-time behavioral insights. [DOI: 10.5281/zenodo.17982075]
Standard code repo template that provides common features, README sections, and U-M copyright notices to help new repositories comply with U-M guidelines.
Infinite tokens and zero cloud fees: code off the grid with a containerized local AI stack for developers and researchers, automatically tuned to your GPU’s exact VRAM specs.
Standard code repo template that provides common features, README sections, and U-M copyright notices to help new repositories comply with U-M guidelines.
This organization has no public members. You must be a member to see who’s a part of this organization.
Top languages
Loading…
Uh oh!
There was an error while loading. Please reload this page.
Most used topics
Loading…
Uh oh!
There was an error while loading. Please reload this page.
You are visitor 01402503 — GeezerVison does not store the pages you look up. Best viewed with Netscape Navigator 4.7 or Internet Explorer 6 at 800×600. No cookies required.
