Prepare a local research environment
Contribute source expertise, feasibility review, local implementation, quality validation, governance, and contextual interpretation.
- Data owner or local PI
- Analyst and data engineer
- Governance or privacy lead
Home / Join, FAQ & contact
Join the network
Build readiness through collaboration
IMPRESIVE brings together data partners, local analysts, clinical and methods experts, and decision-makers to make distributed studies feasible, reproducible, and interpretable across settings.
Collaboration route
Start a collaboration through PHDc
PHDc is the organizational bridge for multinational operations, methods and programming, quality control, integrated reporting, and talent development.
- Multinational operations
- Methods & programming
- Quality control
- Integrated reporting
- Talent development
Three pathways
Where could you contribute?
Develop reproducible methods
Help translate questions into protocols, definitions, QA logic, analysis modules, and transportability assessments.
- Clinical or methods researcher
- Pharmacoepidemiologist
- Statistician or informatician
Connect evidence to use
Clarify clinical, regulatory, policy, or post-marketing needs and the evidence standards required for responsible use.
- Healthcare or policy organization
- Regulatory or safety stakeholder
- Industry research partner
Frequently asked questions
An international multi-database study-preparedness platform. It aligns data feasibility, methods, collaboration, and interpretation before and during distributed research.
No. Person-level source data remain under local control. Approved local teams run common programs and return only outputs permitted by local governance.
No. The programme has used both OMOP and Sentinel common data models. The route must fit the study question, source data, secure environment, and required terminology handling.
No. Readiness depends on the question and includes required variables, follow-up, method implementation, local governance, and interpretability.
Not in the initial public release. Thesis results first need approved, standardized, visualization-ready data; member and proposal systems require separate product development.
Teams examine data and implementation first, then interpret remaining differences through population, coding, coverage, healthcare practice, heterogeneity, and transportability.