Live Learn Innovate Foundation
Research Data Collection Partnership
Ethical, secure data infrastructure for longitudinal research
The Live Learn Innovate Foundation (LLIF) is a 501(c)(3) nonprofit that provides privacy-first data infrastructure to support responsible research involving human participants.
We help research teams collect accurate, participant-consented quantitative data while reducing long-term data stewardship and compliance burden.
LLIF offers:
- A secure nonprofit-governed data foundation
- Participant-controlled data access and consent
- Long-term data retention without resale incentives
- Infrastructure designed to support longitudinal studies
Researchers may use LLIF as:
- A secure backend data repository, or
- A data ingestion layer integrated with their own tools
When appropriate, research teams may choose to allow participants to use independent participant-facing tools that connect to LLIF infrastructure.
These tools can support:
- Health and activity data syncing (e.g., Apple Health, Google Fit, Fitbit)
- Habit and event logging
- Longitudinal, real-world data capture
Important: LLIF does not operate research studies, analyze results, or act as a sponsor. Researchers retain full study ownership and responsibility.
LLIF was established as a nonprofit specifically to reduce data misuse risk.
Our governance model supports:
- Participant opt-in consent
- Clear data access boundaries
- No resale or secondary commercial exploitation
- Infrastructure aligned with IRB and ethical research principles
Participant data remains protected as a donor-restricted asset under nonprofit governance.
We work with:
- Academic researchers
- Universities and research institutions
- Nonprofit research organizations
- Public-interest studies requiring long-term data stewardship
Each partnership is reviewed to ensure mission alignment and ethical standards.
LLIF provides data infrastructure only.
We do not:
- Sponsor studies
- Recruit participants
- Sell or monetize research data
- Provide medical advice or analysis