SYNAPSE-Synthetic Healthcare Data Platform for Data Science
Providing skills development and mentored research projects using synthetic healthcare data generated by MDClone — advancing health data science across Africa.
SYNAPSE is an NIH-funded project that equips researchers across Africa with hands-on expertise in synthetic healthcare data — enabling rigorous, privacy-preserving research on the MDClone ADAMS platform.
A partnership between the University of Rwanda, AIMS, Washington University in St. Louis, and MDClone, with four DS-I Africa programs participating.
About the ProjectComprehensive training and research using synthetic healthcare data
Faculty trained in MDClone software through short beginner/advanced courses with hands-on experience and peer mentoring from expert users at WUSTL.
Immersive, semester-long research experiences using synthetic healthcare data. Trainees paired with faculty mentors toward scientific publication.
Continuous evaluation of training and research projects. Feedback from trainees and faculty used to improve the program and ensure quality outcomes.
Workshops, hackathons, and immersive research experiences — building data science capacity across Africa
Comprehensive workshop introducing participants to synthetic healthcare data, its applications, and the MDClone ADAMS platform. The training provided hands-on experience with synthetic data generation and analysis, covering foundational concepts in privacy-preserving research methods.
Workshop presented at the 3rd Annual DS-I Africa Scientific Meeting in Kigali. Objectives included discussing DS-I Africa training programs (U2R and UE5 funded), introducing synthetic data for health research, and exploring additional training opportunities in Rwanda.
Intensive training for faculty (n=6) and first trainee cohort (n=8) on MDClone ADAMS Platform. The hackathon training was conducted alongside DS-I Africa 3rd annual networking meeting in Kigali.
A glimpse into our training workshops, panel discussions, and networking events
Whether you are a prospective trainee, potential collaborator, or researcher interested in synthetic health data — we would love to hear from you.